code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import... | 271 |
'''simple docstring'''
from __future__ import annotations
from functools import lru_cache
from math import ceil
_a : Optional[Any] = 100
_a : Dict = set(range(3, NUM_PRIMES, 2))
primes.add(2)
_a : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not i... | 56 | 0 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class SCREAMING_SNAKE_CASE ( snake_case , snake_case ):
"""s... | 62 |
__A = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__A = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = True
_A = []
for neighbour in graph[... | 62 | 1 |
'''simple docstring'''
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def lowerCAmelCase (__A):
"... | 11 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
a_ = {
'configuration_speecht5': [
'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIV... | 417 | 0 |
'''simple docstring'''
def a ( UpperCamelCase_ : int ) -> int:
if n == 1 or not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
return 0
elif n == 2:
return 1
else:
snake_case__ =[0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - ... | 581 |
'''simple docstring'''
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def a ( ) ... | 581 | 1 |
"""simple docstring"""
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class _lowerCAmelCase :
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> str:
'''simple docstring'''
if d... | 178 |
'''simple docstring'''
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _lowerCAmelCase ( lowercase ) -> Optional[int]:
if not is_accelerate_available():
return method
__lowerCAmelCase =... | 689 | 0 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
e... | 266 |
'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class a :
"""simple docstring"""
def __init__( self : Union[str, Any] , snake_case : List[Any] , snake_case : int , snake_cas... | 266 | 1 |
def UpperCamelCase_( lowerCamelCase_ ) -> Tuple:
_lowercase : Tuple = 0
_lowercase : Any = len(lowerCamelCase_ )
for i in range(n - 1 ):
for j in range(i + 1 , lowerCamelCase_ ):
if arr[i] > arr[j]:
... | 89 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
ex... | 112 | 0 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environm... | 711 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root ... | 516 | 0 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .schedu... | 688 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteSc... | 688 | 1 |
'''simple docstring'''
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils impo... | 721 |
from __future__ import annotations
def lowerCAmelCase_ ( __UpperCAmelCase: str , __UpperCAmelCase: str ) -> bool:
UpperCamelCase__ : List[str] = get_failure_array(__UpperCAmelCase )
# 2) Step through text searching for pattern
UpperCam... | 369 | 0 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simpl... | 215 |
'''simple docstring'''
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
__snake_case : str = pd.read_csv('sample_data.csv', header=None)
... | 215 | 1 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class _a ( unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAK... | 592 |
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"split_dict" , [
SplitDict(),
SplitDict({"train": SplitInfo(name="train" , num_bytes=1337 , num_examples=42 , dataset_name="my_dataset" )} ),
Spli... | 592 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.or... | 8 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified... | 661 | 0 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def A ( _lowercase ):
SCREAMING_SNAKE_CASE : Optional[Any] ... | 705 | import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import Bit... | 34 | 0 |
import math
import tensorflow as tf
from packaging import version
def UpperCamelCase__( UpperCamelCase__ : Optional[int] )->int:
A__ = tf.convert_to_tensor(UpperCamelCase__ )
A__ = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype )... | 190 |
import itertools
import math
def UpperCamelCase__( UpperCamelCase__ : int )->bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numb... | 190 | 1 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_... | 720 |
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Sequence[float] , _lowerCamelCase : int , _lowerCamelCase : int) ... | 94 | 0 |
"""simple docstring"""
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqd... | 83 |
"""simple docstring"""
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
... | 552 | 0 |
def A__ ( lowercase: list, lowercase: list, lowercase: int, lowercase: int, lowercase: int ) -> int:
if index == number_of_items:
return 0
A : Any =0
A : Optional[Any] =0
A : Dict =knapsa... | 661 | from typing import List
from .keymap import KEYMAP, get_character
def A__ ( lowercase: str ) -> List[str]:
def decorator(lowercase: int ):
A : Tuple =getattr(lowercase, 'handle_key', [] )
handle += [key]
setat... | 661 | 1 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
i... | 59 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimensi... | 3 | 0 |
'''simple docstring'''
def _lowerCamelCase (__lowerCamelCase : int = 100 ) -> int:
a__ = set()
a__ = 0
a__ = n + 1 # maximum limit
for a in range(2 , __lowerCamelCase ):
for b in range(2 , __lowerCamelCase ):
a__ ... | 289 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
lowerCAmelCase_ : Optional[int] = "examples/"
lowerCAmelCase_ : Optional[int] = {
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_ve... | 289 | 1 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
lowerCamelCase_ = 4
lowerCamelCase_ = 3
class __a ( snake_case__ ):
"""simple docstring"""... | 151 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import List, Optional
class lowerCAmelCase_ ( snake_case__ ):
"""simple docstring"""
def __init__( self : List[Any] ):
'''simple docstring'''
self.test()
def __a ( se... | 582 | 0 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCamelCase_ ( unittest.TestCa... | 413 |
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class UpperCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def lowercase__ (... | 413 | 1 |
'''simple docstring'''
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class A ( _a ):
def __init__( self : Union[str, Any] , low... | 22 |
def _lowercase ( __SCREAMING_SNAKE_CASE ) -> str:
return " ".join(
''.join(word[::-1] ) if len(__SCREAMING_SNAKE_CASE ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('''Hey wol... | 410 | 0 |
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_... | 103 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
req... | 103 | 1 |
'''simple docstring'''
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,... | 78 |
def __snake_case ( ) -> int:
return 1
def __snake_case ( lowerCAmelCase_ ) -> int:
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def __snake_case ( lowerCAmelCase_ ) -> int:
return 0 if x < 0 else five_pence(x - 5 ) +... | 100 | 0 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
A_ : Tuple = 1
A_ : str = 2
while i * i <= n:
A_ : Optional[Any] = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n > 1:
n_divisors... | 708 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase = {
"""configuration_bigbird_pegasus""": [
"""BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BigBirdPegasusConfig""",
"""BigBirdPega... | 152 | 0 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
_lowerCamelCase : Dict = ge... | 686 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCame... | 686 | 1 |
'''simple docstring'''
import operator as op
def snake_case__ ( _A: Optional[Any] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase = []
lowerCAmelCase = lambda _A , _A : int(x / y ) # noqa: E731 integer division operation
low... | 605 | '''simple docstring'''
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class a__( lowerCAmelCase__ ):
'''simple docstring'''
... | 605 | 1 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
depr... | 60 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import... | 219 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCAmelCase ( lowercase_ , ... | 271 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
_... | 271 | 1 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class a ( unittest.TestCase ):
'''simple d... | 144 |
# using dfs for finding eulerian path traversal
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : List[Any] , __UpperCamelCase : int , __UpperCamelCase : List[str] , __UpperCamelCase : List[str]=None ) -> Optional[Any]:
UpperCAmelCase_ = (path or [])... | 144 | 1 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase ( A__ , A__ , A__ ):
# Initialise PyTorch model
lowercase__ = RemBertConfig... | 642 |
import math
import sys
def _lowerCAmelCase ( A__ ):
lowercase__ = ''
try:
with open(A__ , 'rb' ) as binary_file:
lowercase__ = binary_file.read()
for dat in data:
lowercase__ = F'''{dat:08b}'''
r... | 642 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : Any ={
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
if not is_t... | 364 | import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _a ( lowercase__ : int = 3 ):
'''simple docstring'''
if isinstance(lowercase__ , lowercase__ ):
raise TypeError('number of qubits... | 85 | 0 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils impor... | 707 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def _a (__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if len(__SCREAMING_SNAKE_CASE ) != 32:
raise ValueError('''Input must be of length 32''' )
_UpperCamelCase =b''''''
for i in [3, ... | 271 | 0 |
def lowerCAmelCase_ ( A_):
UpperCamelCase__: List[Any] = [0] * len(A_)
UpperCamelCase__: Any = []
UpperCamelCase__: str = []
UpperCamelCase__: Any = 0
for values in graph.values():
for i in values:
indegree[i] += 1
... | 380 |
import math
def lowerCAmelCase_ ( A_ ,A_):
UpperCamelCase__: Dict = len(A_)
UpperCamelCase__: Optional[Any] = int(math.floor(math.sqrt(A_)))
UpperCamelCase__: Union[str, Any] = 0
while arr[min(A_ ,A_) - 1] < x:
UpperCamelCase__: Any... | 380 | 1 |
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ) -> bool:
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = f'Input value of [number={number}] must be an integer'
raise TypeError(SCREAMING_SNAKE_CASE )
if numbe... | 311 |
from string import ascii_lowercase, ascii_uppercase
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ) -> str:
if not sentence:
return ""
SCREAMING_SNAKE_CASE_ : int = dict(zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
return lower_to_upper.get(sentence[0]... | 311 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
A_ = (3, 9, -11, 0, 7, 5, 1, -1)
A_ = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class __lowerCamelCase :
a__: int
a__: Node | No... | 29 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data... | 677 | 0 |
from random import shuffle
import tensorflow as tf
from numpy import array
def _lowerCAmelCase ( __lowerCamelCase : Optional[int] , __lowerCamelCase : Optional[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = int(__lowerCamelCase )... | 447 |
from collections.abc import Generator
from math import sin
def _lowerCAmelCase ( __lowerCamelCase : bytes ):
"""simple docstring"""
if len(__lowerCamelCase ) != 32:
raise ValueError("Input must be of length 32" )
__SCREAMING_SNAKE_CASE : Union[str, Any] ... | 447 | 1 |
'''simple docstring'''
from typing import List
import numpy as np
def A__ ( UpperCAmelCase_ ):
_UpperCamelCase : Any = {key: len(UpperCAmelCase_ ) for key, value in gen_kwargs.items() if isinstance(UpperCAmelCase_ , UpperCAmelCase_ )}
if len(set(lists_lengt... | 195 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
snake_case_ : Tuple = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CON... | 195 | 1 |
'''simple docstring'''
from itertools import product
def SCREAMING_SNAKE_CASE__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
lowerCAmelCase_ : Tuple =sides_number
lowerCAmelCase_ : Tuple =max_face_number * dice_number
lowerCAmelCa... | 305 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}
clas... | 305 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : List[str] = {
'''configuration_clap''': [
'''CLAP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ClapAudioConfig''',
'... | 543 |
"""simple docstring"""
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__SCREAMING_SNAKE_CASE : Optional[int] = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translatio... | 661 | 0 |
'''simple docstring'''
import os
from collections.abc import Iterator
def _lowerCAmelCase( UpperCAmelCase_ : str = "." ) -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(_lowerCamelCase ):
lowerCAmelCase__ = [d for d in dir_... | 706 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def _lowerCAmelCase( UpperCAmelCase_ : List[Any] ) -> List[str]:
lowerCAmelCase__ = ... | 211 | 0 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table ... | 24 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
a = logging.get_logger(__name__)
a = OrderedDict(
[
# Ba... | 518 | 0 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBer... | 704 |
lowerCamelCase_ = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]
def __magic_name__ ( __a : int ):
'''simple docstring'''
UpperCamelCase__ = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum... | 86 | 0 |
from ..utils import DummyObject, requires_backends
class __a( metaclass=_a ):
"""simple docstring"""
lowerCAmelCase = ['''flax''', '''transformers''']
def __init__( self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ) -> List[Any]:
requires_backen... | 30 | from math import pow, sqrt
def snake_case (*__lowercase ) -> bool:
'''simple docstring'''
_snake_case : str = len(__lowercase ) > 0 and all(value > 0.0 for value in values )
return result
def snake_case (__lowercase , __lowercase ) -> float | ValueError:
... | 670 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation im... | 710 | """simple docstring"""
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def __a ( A , A , A = "x" , A = 10**-10 , A = 1 , ):
'''simple docstring'''
lowercase__ = symbols(A )
lowercase__ = ... | 668 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging
logg... | 106 |
"""simple docstring"""
from math import factorial
UpperCAmelCase : Tuple = {str(d): factorial(d) for d in range(10)}
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> int:
'''simple docstring'''
return sum(DIGIT_FACTORIAL[d] for d in str(__lowerCAmelCa... | 567 | 0 |
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> Optional[int]:
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__UpperCamelCase ,n - 1 ,__UpperCamelCase ) * a) % mod
els... | 384 |
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from tr... | 384 | 1 |
def __a ( A__ : int ):
if not isinstance(A__ , A__ ):
SCREAMING_SNAKE_CASE = F"Input value of [number={number}] must be an integer"
raise TypeError(A__ )
if number < 0:
return False
SCREAMING_SNAKE_CASE = number * numb... | 16 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase ( a_ ):
"""simple docstring"""
A__ : str = ['image_processor', 'tokenizer']
A__ : Dict = 'CLIPImageProcessor... | 683 | 0 |
from __future__ import annotations
class __a :
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> Optional[Any]:
'''simple docstring'''
lowercase__ , lowercase__: Dict = text, pattern
lower... | 335 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, loggi... | 335 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase__ : Union[str, Any] = {
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 48 |
from collections.abc import Sequence
def __lowerCAmelCase ( _UpperCamelCase : Sequence[int] | None = None ) -> int:
'''simple docstring'''
if nums is None or not nums:
raise ValueError('Input sequence should not be empty' )
SCREAMING_SNAKE_CASE = nums[0]
for i in r... | 439 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescal... | 710 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
... | 506 | 0 |
'''simple docstring'''
def lowerCAmelCase (__A = 4_000_000):
"""simple docstring"""
_a = []
_a , _a = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(__A)
_a , _a = b, a + b
return s... | 11 |
"""simple docstring"""
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, i... | 572 | 0 |
"""simple docstring"""
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers imp... | 237 | """simple docstring"""
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
lowerCamelCase : Tuple =TypeVar('''KT''')
lowerCamelCase : Dict =TypeVar('''VT''')
class __snake_case( Generic[KT, VT] ):
... | 237 | 1 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
_lowerCamelCase = pytest.mark.integration
@pytest.mark.parametrize("""path""" , ["""paw... | 6 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokeni... | 632 | 0 |
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if i... | 705 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Optional[Any] = {
'configuration_blip_2': [
'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Blip2Config',
'Blip2QFormerConfig',
... | 94 | 0 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase__ ( A__ ):
__lowerCamelCase... | 306 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLFor... | 306 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
... | 410 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer... | 410 | 1 |
import qiskit
def __SCREAMING_SNAKE_CASE ( a__ : int = 2 ) -> Optional[int]:
__A : str = qubits
# Using Aer's simulator
__A : List[Any] = qiskit.Aer.get_backend("""aer_simulator""" )
# Creating a Quantum Circuit acting on the q register
__A : Tuple ... | 17 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase : Optional[Any] = {
'configuration_falcon': ['FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FalconConfig'],
}
tr... | 3 | 0 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def _A ( ):
a__ : Optional[Any] = 9
a__ : Dict = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5... | 629 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Any = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available():
raise Optional... | 629 | 1 |
_lowerCAmelCase : List[Any] = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/h... | 454 |
from math import sqrt
def __snake_case ( _lowerCAmelCase : int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
retur... | 454 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tenso... | 713 |
def __snake_case ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(_UpperCamelCase ) )
def __snake_case ( _UpperCamelCase , _UpperCamelCase ... | 346 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'robert... | 523 | '''simple docstring'''
class a :
"""simple docstring"""
def __init__( self , snake_case_ , snake_case_ , snake_case_ ):
'''simple docstring'''
__UpperCAmelCase: List[Any] = None
__UpperCAmelCase: Tuple = None
__UpperCAmelCase: L... | 523 | 1 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_comm... | 633 |
import logging
from transformers import PretrainedConfig
lowercase__ :int = logging.getLogger(__name__)
lowercase__ :Dict = {
"bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json",
}
class... | 633 | 1 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a :
def __init__( self : Optional[int] ) -> str:
lowerCamelCase_ = ''
lowerCamelCase_ = ''
... | 549 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the datas... | 549 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@d... | 600 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase: str = logging.get_logger(__name__)
UpperCAmelCase: Optional[Any] = {
"""facebook/wav2vec2-base-960h""": """https://huggi... | 600 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( lowerCAmelCase__ ):
lowerCamelCase_ = str(lowerCAmelCase__ )
return n == n[::-1]
def lowercase ( lowerCAmelCase__ = 1_000_000 ):
lowerCamelCase_ = 0
for i in range(1 ,lowerCAmelCase... | 29 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _lowerCamelCase ( UpperCamelCase_ ):
"""simple docstring"... | 285 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase : str = {'''configuration_yolos''': ['''YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''YolosConfig''', '''YolosOnnxConfig''']}
try:
if n... | 714 | import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin... | 316 | 0 |
"""simple docstring"""
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCAmelCase__ = '''src/transformers'''
# This is to make sure... | 83 |
def lowerCamelCase_ ( UpperCamelCase_ , UpperCamelCase_ ):
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
_a : str = str(bin(UpperCamelCase_ ) )[2:] # remove the leading "0b"
_a : Dict = ... | 471 | 0 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
... | 717 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_... | 116 | 0 |
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
_lowerCAmelCase = False
try:
_lowerCAmelCase = _is_... | 569 |
import os
from math import logaa
def _lowerCAmelCase ( _lowerCAmelCase = "base_exp.txt" ):
'''simple docstring'''
A_ : float = 0
A_ : int = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(_lowerCAmelCase ) ,_lowerCAmelCase ) ) ):
A... | 569 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
a = logging.get_logger(__name__)
a = ... | 716 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from a... | 529 | 0 |
import math
import sys
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : List[str] = ''''''
try:
with open(_lowercase , '''rb''' ) as binary_file:
UpperCAmelCase_ : Dict = binary_file.read()
fo... | 30 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCAmelCase ( unittest.TestCase ):
... | 119 | 0 |
def __lowerCamelCase ( snake_case__ ,snake_case__ ,snake_case__ ) -> int:
"""simple docstring"""
if len(snake_case__ ) != len(snake_case__ ):
raise ValueError("""The length of profit and weight must be same.""" )
if max_weight <= 0:
... | 569 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer im... | 569 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_input... | 24 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.t... | 655 | 0 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
SCREAMING_SNAKE_CASE_ = pytest.mark.integration
@pytest.mark.parametrize("path", ["paws", "csv"] )
def ... | 709 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_toke... | 370 | 0 |
'''simple docstring'''
import math
def _SCREAMING_SNAKE_CASE ( __snake_case : int ):
_A = []
_A = 2
_A = int(math.sqrt(__snake_case ) ) # Size of every segment
_A = [True] * (end + 1)
_A = []
while start <= end:
i... | 107 |
'''simple docstring'''
def a_ ( lowerCamelCase : Optional[Any] ):
stooge(lowerCamelCase , 0 , len(lowerCamelCase ) - 1 )
return arr
def a_ ( lowerCamelCase : Optional[int] , lowerCamelCase : Union[str, ... | 133 | 0 |
'''simple docstring'''
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lo... | 706 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
lowercase =logging.get_logger(__name__)
lowercase ={
'post_extract_proj': 'feature_projection... | 331 | 0 |
"""simple docstring"""
def _A (__a , __a , __a = 0 , __a = 0 ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = right or len(__a ) - 1
if left > right:
return -1
elif list_data[left]... | 512 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelFo... | 512 | 1 |
"""simple docstring"""
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class... | 713 |
"""simple docstring"""
from manim import *
class __a ( lowerCAmelCase__ ):
def snake_case_ ( self ):
_lowerCamelCase = Rectangle(height=0.5 , width=0.5 )
_lowerCamelCase = Rectangle(height=0.46 , width=0.46 )... | 222 | 0 |
from collections import defaultdict
from math import gcd
def A_ ( _UpperCAmelCase = 1_50_00_00 ):
SCREAMING_SNAKE_CASE_: defaultdict = defaultdict(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_: Any = 2
while 2 * euclid_m * (euclid_m + 1) <= limit... | 671 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampli... | 671 | 1 |
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifie... | 710 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case = {
"configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
... | 413 | 0 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __a ( _snake_case ... | 109 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation i... | 29 | 0 |
import numpy as np
from PIL import Image
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : np.ndarray , __UpperCamelCase : int , __UpperCamelCase : int ) -> np.ndarray:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = np.array(... | 379 | import os
__lowerCamelCase : Union[str, Any] = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : str ) -> int:
"""simple docstring"""
SCREAMI... | 379 | 1 |
from math import ceil, sqrt
def lowerCamelCase_ ( __UpperCamelCase = 1_00_00_00 ):
A_ = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
A_ = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else... | 141 |
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils impor... | 141 | 1 |
"""simple docstring"""
from typing import Any
class __UpperCAmelCase :
'''simple docstring'''
def __init__( self , _A ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE =data
_SCREAMING_SNAKE_CASE =None
class __UpperCAmelCase :
'''simple docstring'''
... | 705 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMode... | 165 | 0 |
'''simple docstring'''
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
_A: Optional[int] = get_logger(__name__)
_A: List[Any] = r"""
Args:
input_ids (... | 126 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class UpperCAmelCase ( UpperCAmelCase_ ... | 126 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMix... | 244 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A__ : Any = logging.get_logger(__name__)
A__ : Union[str, Any] = {
'SenseTime/deformable-detr': 'https://hugging... | 244 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 16 |
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.ut... | 612 | 0 |
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class _lowerCamelCase( _a ):
lowercase_ : Union[str, Any] ... | 713 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class _lowerCamelCase( _a ):
@require_torch
def UpperCamelCase ( self) -> int:
"""simple docst... | 354 | 0 |
"""simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class SCREAMING_SNAKE_CASE_ ( ctypes.Structure ):
"""simple docstring"""
__lowercase : Any ... | 155 |
"""simple docstring"""
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__magic_name__ = ... | 155 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_t... | 710 |
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfAr... | 349 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ = {
'''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRA... | 60 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
_lowerCAmelCase = (7_2_0, 1_2_8_0) # Height, Width
_lowerCAmelCase = (0.4, 0.6) # if height or width lower than this scale, drop ... | 161 | 0 |
def __UpperCamelCase ( _A : str ) ->list:
"""simple docstring"""
if n_term == "":
return []
lowerCamelCase_ =[]
for temp in range(int(_A ) ):
series.append(f'1/{temp + 1}' if series else """1""" )
return series
if... | 75 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__A : int = {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
'albert-large-v1': 'https://huggingface.co/albert-lar... | 75 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : List[str] = {
'''configu... | 128 |
'''simple docstring'''
from math import factorial
def lowerCAmelCase__ ( lowerCamelCase : int ,lowerCamelCase : int ,lowerCamelCase : float ):
if successes > trials:
raise ValueError('successes must be lower or equal to trials' )
if trials < 0 or ... | 128 | 1 |
'''simple docstring'''
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of... | 710 |
def snake_case (UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
lowerCamelCase__ = 0
lowerCamelCase__ = len(UpperCamelCase )
for i in range(n - 1 ):
for j in range(i + 1 , UpperCamelCase ):
if arr[i] > arr[j]:
num_invers... | 235 | 0 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
requ... | 21 |
import string
def a__ ( A_ ):
'''simple docstring'''
__magic_name__ = """"""
for i in sequence:
__magic_name__ = ord(A_ )
if 65 <= extract <= 90:
output += chr(155 - extract )
elif 97 <= extract <= 122:
output += chr(219 - ... | 529 | 0 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
_a = [0] * len(__A)
_a = []
_a = []
_a = 0
for values in graph.values():
for i in values:
indegree[i] += 1
... | 705 |
'''simple docstring'''
from __future__ import annotations
import math
def lowerCAmelCase (__A , __A , __A , __A , __A):
"""simple docstring"""
if depth < 0:
raise ValueError('''Depth cannot be less than 0''')
if not scores:
raise V... | 352 | 0 |
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
loggi... | 455 |
def lowerCAmelCase ( UpperCamelCase__ : int , UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
return round(float(moles / volume ) * nfactor )
def lowerCAmelCase ( UpperCamelCase__ : float ... | 202 | 0 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _lowercase ( yaml.SafeLoader ):
"""simple docstring"""
def UpperCAmelCase_ ( self : Any , UpperCamelCas... | 296 | """simple docstring"""
class _lowercase :
"""simple docstring"""
def __init__( self : Tuple , UpperCamelCase__ : Any ) -> int:
'''simple docstring'''
__UpperCamelCase =arr.split(''',''' )
... | 296 | 1 |
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