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 |
|---|---|---|---|---|
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_co... | 77 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import ... | 77 | 1 |
import numpy as np
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : List[Any] ):
SCREAMING_SNAKE_CASE = (0, 0)
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = 0
SCREAMIN... | 698 |
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_filename,
... | 698 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import ... | 19 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class UpperCAmelCase__ ( nn.Module ):
__snake_case : int
__snake_case : ... | 206 | 0 |
"""simple docstring"""
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def __lowerCAmelCase( __UpperCAmelCase ,__UpperCAmelCase ):
"""simple docstring"""
... | 283 | """simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ... | 283 | 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_retribert import RetriBertTokenizer
A_ = logging.get_logger(__name__)... | 391 |
"""simple docstring"""
from manim import *
class __lowerCAmelCase ( UpperCAmelCase ):
'''simple docstring'''
def UpperCamelCase__ ( self: int ):
UpperCamelCase_ =Rectangle(height=0.5 , width=0.5 )
UpperCamelCase_ =Rectangle(... | 391 | 1 |
import math
from collections.abc import Callable
def snake_case( __magic_name__ , __magic_name__ , __magic_name__ ) -> float:
'''simple docstring'''
lowercase : float = xa
lowercase : float ... | 708 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _A :
def __init__( self : int ) -> Any:
"""simple docstring"""
lowercase : List[Any] = ''''''
lowercas... | 596 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
A = logging.get_logger(__name__) # pylint: disable=invalid-name
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simpl... | 187 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
lowe... | 645 | 0 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basi... | 720 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, requi... | 519 | 0 |
from sklearn.metrics import fa_score
import datasets
snake_case : Optional[int] = '\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'
snake_case : Dict = '\nArgs:\n ... | 605 |
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_tokenization_commo... | 605 | 1 |
"""simple docstring"""
import re
import string
import numpy as np
import datasets
lowerCamelCase_ : Any = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
lowerCamelCase_ : Tuple ... | 302 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_... | 302 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( SCREAMING_SNAKE_CASE__: int, SCREAMING_SNAKE_CASE__: int ) -> list:
"""simple docstring"""
__a = word.split()
def justify(SCREAMING_SNAKE_CASE__: Optional[int], SCREAMING_SNAKE_CASE... | 448 |
from __future__ import annotations
def UpperCAmelCase__ ( __snake_case , __snake_case ) -> bool:
_A = get_failure_array(__snake_case )
# 2) Step through text searching for pattern
_A , _A = 0, 0 # index into text, pattern
while i < len(__snake... | 317 | 0 |
from ....utils import logging
__a : int = logging.get_logger(__name__)
class _UpperCamelCase ( _UpperCAmelCase ):
"""simple docstring"""
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=None , lowerCAmelCase__=20_48 ) -> Optional[An... | 522 | 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,
require_torch,
)
from .... | 522 | 1 |
"""simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
f... | 293 | '''simple docstring'''
from __future__ import annotations
from typing import Any
def snake_case_ ( __snake_case : list[Any]) -> None:
create_state_space_tree(__snake_case , [] , 0)
def snake_case_ ( __snake_case : list[Any] , __snake_case : list... | 274 | 0 |
"""simple docstring"""
def __lowerCamelCase ( lowerCAmelCase__ ):
if not grid or not grid[0]:
raise TypeError('The grid does not contain the appropriate information' )
for cell_n in range(1 ,len(grid[0] ) ):
grid[0][cell_n] += grid[0]... | 554 |
"""simple docstring"""
print((lambda quine: quine % quine)('''print((lambda quine: quine %% quine)(%r))'''))
| 554 | 1 |
from collections import defaultdict
def lowercase ( __A : int ) -> int:
'''simple docstring'''
snake_case : Union[str, Any] = 1
snake_case : str = True
for v in tree[start]:
if v not in visited:
ret += dfs(__A )
... | 36 |
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 TFModelTesterMixin, ids_tensor, random_... | 333 | 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() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotA... | 12 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. 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.org/licenses/LICENSE-2.0
... | 12 | 1 |
import torch
from diffusers import DiffusionPipeline
class __A ( snake_case__ ):
'''simple docstring'''
def __init__( self , _snake_case , _snake_case ):
super().__init__()
self.register_modules(unet=_snake_case , scheduler=_snake_case )
d... | 424 | snake_case = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLab... | 424 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowercase_ ( _lowercase ) -> list[int]: # This function is recursive
'''simple docstring'''
lowerCamelCase_ : int = len(__snake_case )
# If the array contains only one element, we return it (it's the st... | 712 |
'''simple docstring'''
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
__lowercase : Union[str, Any] = logging.get_... | 357 | 0 |
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
__UpperCAmelCase = logging.getLogger(__name__)
@dataclass
class __lowercase... | 65 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchF... | 65 | 1 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCAmelCase ( SCREAMING_SNAKE_CASE_ ):
__SCREAMING_SNAKE_CASE : Optional[int] = ["image_processor", "tokenizer"]
__SCREAMING_SNA... | 183 |
"""simple docstring"""
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class ... | 183 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TF... | 78 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def __a ( ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase_= {
"""repo_name""": ["""test_repo1""", """test_repo2""", """test... | 593 | 0 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class lowerCAmelCase_ :
@property
def __a ( self ):
return s... | 677 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> Optional[Any]:
_lowercase : Tuple ... | 677 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokeni... | 96 |
"""simple docstring"""
from random import randint
from tempfile import TemporaryFile
import numpy as np
def a ( __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Optional[int] ) -> int:
... | 96 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
snake_case = {
"""configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}
try:
if... | 706 |
'''simple docstring'''
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
snake_case = logging.getLogger(__name__)
snake_case = ... | 568 | 0 |
"""simple docstring"""
import mpmath # for roots of unity
import numpy as np
class lowercase__ :
def __init__( self , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None) -> Tuple:
# Input as list
_lowerCamelCase : Any = list(poly_a or [0]... | 88 |
"""simple docstring"""
def UpperCamelCase ( _A , _A ) -> int:
lowercase : int = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
lowercase : List[Any] = n - k
# Calculate C(n,k)
for i in range(_A ... | 264 | 0 |
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
def __init__( self :Any , __lowercase :Dict ):
__lowerCamelCase : List[Any] =arr.split(''',''' )
def __lowercase ( self :int ):
__lowerCamelCas... | 703 |
"""simple docstring"""
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
_UpperCamelCase = logging.get_logger(__name__... | 363 | 0 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class SCREAMING_SNAKE_CASE_ ( _UpperCamelC... | 279 |
# Copyright 2023 The HuggingFace 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.org/licenses/LICENSE-2.0
#
# Unless req... | 279 | 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 UpperCAmelCase_ ( unittest.TestCase ):
"""... | 578 |
import os
def __UpperCAmelCase ( ) -> Union[str, Any]:
"""simple docstring"""
with open(os.path.dirname(__a ) + '''/grid.txt''' ) as f:
_a : str = [] # noqa: E741
for _ in range(20 ):
l.append([int(__a ) for x in f.read... | 578 | 1 |
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 = {
'''facebook/data2vec-text-base''': '''... | 579 |
"""simple docstring"""
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class snake_case_ :
"""simple docstring"""
def __init__( self , ... | 260 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case__ , snake_case__ ):
'''simple docstring'''
A : Optional[Any] = ''''''
for i in table:
res += inp[i - 1]
return res
def lowerCAmelCase_ ( snake_case__ ):
... | 343 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
if len(snake_case__ ) <= 1:
return [tuple(snake_case__ )]
A : Dict = []
def generate(snake_case__ , snake_case__ ):
if k == 1:
... | 343 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testin... | 271 |
"""simple docstring"""
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from ... | 674 | 0 |
'''simple docstring'''
from __future__ import annotations
def a ( UpperCamelCase_ : list[int] , UpperCamelCase_ : list[int] , UpperCamelCase_ : list[int] , UpperCamelCase_ : list[list[str]] , UpperCamelCase_ : int , ) ... | 712 |
'''simple docstring'''
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def a ( UpperCamelCase_ : str , UpperCamelCase_ : List[Any] , UpperCam... | 581 | 0 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 1_0 ) -> str:
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) or n < 0:
raise ValueError("Invalid input" )
A__ = 1_0**n
A__ = ... | 514 |
from datetime import datetime
import requests
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str ) -> bytes:
'''simple docstring'''
A__ = "https://downloadgram.net/wp-json/wppress/video-downloader/video?url="
A__ = requests.get(base_url + ... | 514 | 1 |
'''simple docstring'''
def lowerCAmelCase ( UpperCamelCase__ : int = 1_0 , UpperCamelCase__ : int = 2_2 ):
"""simple docstring"""
__UpperCAmelCase = range(1 , UpperCamelCase__ )
__UpperCAmelCase = range(1 , UpperCamelCase__ )
r... | 654 | '''simple docstring'''
def lowerCAmelCase ( UpperCamelCase__ : int ):
"""simple docstring"""
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
__UpperCAmelCase = f"""Input value of [number={number}] must be an integer"""
raise TypeError(UpperCamelCa... | 654 | 1 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependenc... | 76 |
"""simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( lowercase_ : int ,... | 674 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__SCREAMING_SNAKE_CASE = logging.get_logger(__name_... | 709 |
from sklearn.metrics import recall_score
import datasets
__SCREAMING_SNAKE_CASE = """
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is th... | 17 | 0 |
'''simple docstring'''
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speec... | 26 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase : Dict ={"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
t... | 316 | 0 |
"""simple docstring"""
from __future__ import annotations
def UpperCamelCase ( _A , _A , _A , _A ) -> List[Any]: # noqa: E741
while r - l > 1:
lowercase : List[str] = (l + r) // 2
if v[m] >= key:
lowercase ... | 348 |
"""simple docstring"""
from __future__ import annotations
_lowerCAmelCase = [True] * 1_00_00_01
_lowerCAmelCase = 2
while i * i <= 1_00_00_00:
if seive[i]:
for j in range(i * i, 1_00_00_01, i):
_lowerCAmelCase = False
i += 1
def UpperCamelCase ... | 348 | 1 |
'''simple docstring'''
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# pytho... | 489 |
'''simple docstring'''
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,
Dist... | 489 | 1 |
'''simple docstring'''
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTeste... | 464 |
'''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def UpperCAmelCase ( A : Union[str, Any] , A : Optional[int] ... | 464 | 1 |
def lowercase_ (A : int , A : Optional[Any] , A : Union[str, Any] ):
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(__A ) )
def lowercase_ (A : List[str] , A : Tuple ... | 478 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class lowercase__ ( __SCRE... | 475 | 0 |
'''simple docstring'''
def _snake_case ( lowercase ) -> Dict:
for i in range(0 , lowercase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(""" """ , end="""""" )
for _ in range(0 , i + 1 ): # printing stars
... | 715 |
'''simple docstring'''
import qiskit
def _snake_case ( lowercase , lowercase ) -> qiskit.result.counts.Counts:
__a : Any = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
__a : str ... | 697 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
... | 459 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
_UpperCamelCase = 'docs/source/en/_toctree.yml'
def a_ ( _lowerCAmelCase ) -> Any:
__lowerCamelCase : Optional[int] = defaultdict(_lowerCAmelCase )
__lowerCam... | 459 | 1 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
req... | 713 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def SCREAMING_SNAKE_CASE( __UpperCamelCase ) -> int:
a__ : List[Any] = prime_factors(__UpperCamelCase )
if is_square_free(__UpperCamelCase ):
return -1 if len(__UpperCamelCa... | 207 | 0 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def lowercase ( SCREAMING_SNAKE_CASE ) -> np.ndarray:
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def lowercase ... | 205 |
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
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREA... | 205 | 1 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import... | 700 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase = {
"""configuration_mask2former""": [
"""MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Mask2FormerConf... | 454 | 0 |
'''simple docstring'''
import os
import pytest
from attr import dataclass
lowercase_ = """us-east-1""" # defaults region
@dataclass
class a_ :
'''simple docstring'''
UpperCamelCase = 42
UpperCamelCase = '''arn:aws:iam::558105141721:role/sagemake... | 314 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def lowerCamelCase ( __lowerCamelCase : str , __lowerCamelCase : str ) ->str | Literal[False]:
_SCREAMING_SNAKE_CASE = list(__lowerCamel... | 314 | 1 |
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
... | 709 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import... | 306 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
loggin... | 130 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def __a ( ) -> int:
'''simple docstring'''
UpperCAmelCase_, UpperCAmelCase_= 9, 14 # noqa: F841
UpperCAmelCase_= [
[0, 1, 4],
[0, 7, 8],
... | 593 | 0 |
import os
import sys
import unittest
lowercase_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, r... | 586 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_uti... | 586 | 1 |
def A ( lowercase__ : int , lowercase__ : float , lowercase__ : float ) -> float:
return round(float(moles / volume ) * nfactor )
def A ( lowercase__ : float , lowercase__ : float , lowercase__ : float ) -> float:
return round(float((moles * 0.0821 * temperature) / (... | 45 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
sk... | 612 | 0 |
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE__ = Lock()
def lowerCamelCase ( _snake_case : Any ,_snake_case : List[str] ... | 539 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
SCREAMING_SNAKE_CASE__ ... | 539 | 1 |
import logging
import os
from .state import PartialState
class A ( logging.LoggerAdapter ):
@staticmethod
def lowercase_ (__UpperCAmelCase : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase__ = PartialSta... | 486 | import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = '▁'
Up... | 486 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set... | 403 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax... | 403 | 1 |
'''simple docstring'''
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowercase : Optional[Any] = (DDIMParallelScheduler,)
__lowercase : List... | 365 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCAmelCase : Any = {
'''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConf... | 107 | 0 |
"""simple docstring"""
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[Any] = logging.get_logger(__name__)
# TODO Update this
__A : Tuple = {
"facebook/es... | 708 |
"""simple docstring"""
import operator
def lowercase ( _SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : bool = False , _SCREAMING_SNAKE_CASE : list | None = None ):
'''simple docstring'''
_UpperCAmelCase = ... | 95 | 0 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def lowerCAmelCase_ ( __a ) -> float:
"""simple docstring"""
return np.dot(__a , __a )
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''... | 59 |
from __future__ import annotations
from math import pi
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_):
"""simple docstring"""
if (inductance, frequency, reactance).count(0) != 1:
raise ValueError("""One and only one argument must be 0""")
if... | 648 | 0 |
from __future__ import annotations
UpperCamelCase_ = []
def A ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> bool:
'''simple docstring'''
for i in range(len(__UpperCAmelCase ) ):
if board[row][i] == 1:
... | 705 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = ... | 561 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
class __lowercase ( __snake_case ):
UpperCamelCase = '''timm_backbone'''
def __init__( self : Optional[int] , ... | 377 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) ->Optional[Any]:
UpperCAmelCase = ("""dense.weight""", """attention.self.query"... | 377 | 1 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
UpperCamelCase__ = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
UpperCamelCase__ = [ord(letter) for letter in string.ascii_lowercase... | 707 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import D... | 640 | 0 |
'''simple docstring'''
a_ = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
a_ = ['a', 'b', 'c', 'd', 'e']
def _a( UpperCamelCase__ : str, UpperCamelCase__ : str, UpperCamelCase__ : Dict ):
'''simple doc... | 296 |
'''simple docstring'''
from __future__ import annotations
import requests
def _a( UpperCamelCase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple =f"https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pret... | 296 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import Counter
from random import random
class UpperCAmelCase:
"""simple docstring"""
def __init__( self ) -> Tuple:
"""simple docstring"""
... | 720 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
... | 298 | 0 |
'''simple docstring'''
from __future__ import annotations
def _a ( _lowerCamelCase , _lowerCamelCase ) -> bool:
"""simple docstring"""
if len(_snake_case ) == 0:
return False
__snake_case : int = len(_sna... | 26 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class lowerCamelCase__ ( _A):
"""simple ... | 2 | 0 |
'''simple docstring'''
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
lowercase__ = 6378137.0
lowercase__ = 6356752.314245
lowercase__ = 6_37_81_37
def __snake_case ( lowercase : float , lowe... | 420 |
'''simple docstring'''
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def __snake_case ( lowercase : float , lowercase : float , lowercase : bool = False ):
if radian_mode... | 420 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"junn... | 28 |
'''simple docstring'''
from torch import nn
def __lowercase (_lowercase ) -> Union[str, Any]:
"""simple docstring"""
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
el... | 150 | 0 |
def _UpperCamelCase (a__ :int , a__ :int ):
"""simple docstring"""
return 1 if input_a == input_a else 0
def _UpperCamelCase ():
"""simple docstring"""
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ... | 721 |
from typing import Any
def _UpperCamelCase (a__ :list ):
"""simple docstring"""
if not input_list:
return []
UpperCamelCase__ = [input_list.count(a__ ) for value in input_list]
UpperCamelCase__ = max(a__ ) #... | 548 | 0 |
'''simple docstring'''
def a_ ( UpperCamelCase_ ):
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 452 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def a_ ( UpperCamelCase_ ):
create_state_space_tree(UpperCamelCase_ , [] , 0 )
def a_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
if index == len(Upper... | 452 | 1 |
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 OptionalDependencyNotA... | 37 |
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ):
if upper_limit < 0:
raise ValueError("Limit for the Catalan sequence must be ≥ 0" )
lowercase__ = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
lowercase__ = 1
if upper_limit > 0:
lowercase__ = 1
... | 37 | 1 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Optional[Any] = """"""
__lowerCAmelCase : str = (
... | 188 |
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,
resize,
to_channel_dimension_format,
)
... | 188 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import... | 498 |
"""simple docstring"""
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def _lowerCAmelCase ( UpperCAmelCase__ : Tuple, UpperCAmelCase__ : Union[str, Any]=None ) ->Tuple... | 498 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
"""sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
# See all ViT MSN models at ht... | 77 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import M... | 505 | 0 |
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case__ ( U... | 709 |
from __future__ import annotations
from math import ceil, floor, sqrt
def lowercase__( A = 2_0_0_0_0_0_0 ):
snake_case__ : list[int] = [0]
snake_case__ : int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_numbers.append... | 303 | 0 |
"""simple docstring"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def UpperCAmelCase__ (snake_case__ : BertModel , snake_case__ : str , snake_case__ : str ):
"""simple docstrin... | 609 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
A_ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlat... | 609 | 1 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .... | 153 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : str ,lowerCAmelCase_ : str ) -> bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] =get_failure_array(lowerCAmelCase_ )
... | 153 | 1 |
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()
... | 408 |
'''simple docstring'''
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 RealmToken... | 365 | 0 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class lowercase :
_a = field(
default="codeparrot/codeparrot",metadata={"help": "Model name or path of model to be trained."} )
_a = field(
default="./",metadata={"help":... | 54 |
from __future__ import annotations
def lowerCAmelCase_ ( snake_case_,snake_case_ = None ):
_A : Tuple = word_bank or []
# create a table
_A : int = len(snake_case_ ) + 1
_A : list[list[list[str]]] = []
for _ in range(snake_case_ ):... | 54 | 1 |
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> int:
if not isinstance(__snake_case , __snake_case ):
raise ValueError("""multiplicative_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""multiplicative_persistence()... | 108 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
__magic_name__ = "https://www.indeed.co.in/jobs?q=mobile+app+development&l="
def _lowerCAmelCase ( UpperCamelCase_ = "mumbai" ):
__SCREAMI... | 155 | 0 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def A (__lowerCamelCase :Union[str, Any] ):
_lowerCAmelCase = [
"""encoder.version""",
"""decoder.version""",
"""model.... | 162 |
'''simple docstring'''
def A (__lowerCamelCase :str , __lowerCamelCase :str ):
assert x is not None
assert y is not None
_lowerCAmelCase = len(__lowerCamelCase )
_lowerCAmelCase = len(__lowerCamelCase )
# declaring the array for storing the dp values
... | 162 | 1 |
'''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_warmu... | 653 |
"""simple docstring"""
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
A_ : str =logging.get_logger(__name__)
A_ : Any ="""... | 650 | 0 |
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
c... | 706 |
from __future__ import annotations
from collections.abc import Generator
def SCREAMING_SNAKE_CASE__ ( ) -> Generator[int, None, None]:
_lowercase = {}
_lowercase = 2
while True:
_lowercase = factor_map.pop(snake_case__ , snake_case__ ... | 535 | 0 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__magic_name__ : Tuple =logging.get_logger(__name__)
__magic_name__ : Optional[int] ={
"vocab_file": "voc... | 664 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ : Tuple = logging.get_logger(__name__)
lowerCamelCase__ : Any =... | 698 | 0 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
_A : Optional[int] = numpy.array([0, 0])
_A : Union[str, Any] = numpy.array([0.5, 0.8_66_02_54])
_A : List[Any] = numpy.array([1,... | 705 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects imp... | 189 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_av... | 566 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_avai... | 566 | 1 |
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
Au... | 703 |
'''simple docstring'''
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
_SCREAMING_SNAKE_CASE = WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])
... | 56 | 0 |
'''simple docstring'''
__lowerCAmelCase = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
i... | 358 |
'''simple docstring'''
def __lowerCamelCase ( ) -> Union[str, Any]:
_a : Optional[Any] = []
_a : List[str] = 1
while len(lowerCAmelCase_ ) < 1E6:
constant.append(str(lowerCAmelCase_ ) )
i += 1
_a : Optional[Any] = ''.join(lowerCAmelCase_ )
return ... | 358 | 1 |
'''simple docstring'''
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
__A : int = ... | 267 |
'''simple docstring'''
import torch
from transformers import AutoModel
class __UpperCamelCase ( torch.nn.Module ):
def __init__( self :Union[str, Any] ,_UpperCamelCase :Tuple="sayef/fsner-bert-base-uncased" ):
super(_UpperCamelCase ,self ).__init__()
sn... | 267 | 1 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomS... | 321 | import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'BridgeTower/bridgetower-base': 'https://huggingface.co/BridgeTower/bridgetower-base/blob/main/conf... | 321 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class snake_... | 374 |
"""simple docstring"""
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_s... | 374 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokeniz... | 661 |
"""simple docstring"""
from __future__ import annotations
from math import ceil, floor, sqrt
def lowerCAmelCase_( lowercase_ : int = 2_00_00_00 ) -> int:
_lowerCamelCase = [0]
_lowerCamelCase = 42
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ... | 661 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import Autoencoder... | 709 |
'''simple docstring'''
import random
def A_ ( snake_case , snake_case , snake_case = False ):
SCREAMING_SNAKE_CASE:dict = {i: [] for i in range(snake_case )}
# if probability is greater or equal than 1, then generate a complete graph
if probability >... | 465 | 0 |
def _lowercase ( __lowerCamelCase : int ) -> "list[int]":
'''simple docstring'''
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
UpperCamelCase__ : Optional[int] = [0] * (upper_limit + 1)
# ... | 344 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE : str = [
"""word_embeddings... | 344 | 1 |
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.models.b... | 719 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_d... | 308 | 0 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
SCREAMING_SNAKE_CASE_ = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must b... | 300 |
import os
def __SCREAMING_SNAKE_CASE ( ) -> int:
with open(os.path.dirname(lowerCAmelCase ) + "/grid.txt" ) as f:
_UpperCAmelCase : Optional[Any] = [] # noqa: E741
for _ in range(20 ):
l.append([int(lowerCAmelCase ) for x in f.readline().split(... | 300 | 1 |
'''simple docstring'''
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
lowercase__ : Tuple = len(UpperCAmelCase )
lowercase__ : Union[str, Any] = [[0] * n for i in range(UpperCAmelCase )]
for i in range(UpperCAmelCase ):
lowercas... | 428 | '''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__a: Optional[Any] = logging.get_logger(__name__)
__a: str =... | 428 | 1 |
import mpmath # for roots of unity
import numpy as np
class lowercase :
def __init__( self : List[str] , _lowercase : Optional[int]=None , _lowercase : Optional[int]=None ):
# Input as list
SCREAMING_SNAKE_CASE__ : List[str] ... | 35 |
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class __lowerCAmelCase ( a , unittest.TestCase ):
"""simple docstring"""
_SCREAMING_... | 283 | 0 |
'''simple docstring'''
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
_UpperCamelCase = collecti... | 211 |
'''simple docstring'''
import argparse
import datetime
def _lowerCAmelCase( UpperCAmelCase_ : str ) -> str:
lowerCAmelCase__ = {
"""0""": """Sunday""",
"""1""": """Monday""",
"""2""": """Tuesday""",
"""3""": """Wedne... | 211 | 1 |
'''simple docstring'''
# Lint as: python3
import itertools
import os
import re
a_ : Optional[Any] = re.compile(R"""([A-Z]+)([A-Z][a-z])""")
a_ : List[str] = re.compile(R"""([a-z\d])([A-Z])""")
a_ : Optional[Any] = re.compile(R"""(?<!_)_(?!_)""")
a_ : Optional[int] =... | 676 |
'''simple docstring'''
from __future__ import annotations
def a_ ( __snake_case : int ) -> list[int]:
"""simple docstring"""
lowerCamelCase_ =[True] * limit
lowerCamelCase_ =False
lowerCamelCase_ =False
lowerCamelCase_ =True
for i i... | 676 | 1 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import... | 380 |
print((lambda quine: quine % quine)('print((lambda quine: quine %% quine)(%r))'))
| 380 | 1 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class _lowerCAmelCase ( a ):
"""simple docstring"""
__magic_name__ :Tuple = """WhisperFeatureExtractor"""
__magic_name__ :Dict = """WhisperTokenizer"""
def __init__( ... | 93 | '''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class _a ( __a ):
"""simple docstring"""
A_ = (DDPMParallelScheduler,)
def lowerCamelCase__ ( self : Optional[Any] , **... | 451 | 0 |
from __future__ import annotations
def A__ ( lowerCamelCase , lowerCamelCase ) -> list[list[int]]:
UpperCamelCase_: list[list[int]] = []
UpperCamelCase_: list[int] = []
UpperCamelCase_: int = 0
UpperCamelCase_: Any = sum(lo... | 670 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> Union[str, Any]:
# Initialise PyTorc... | 670 | 1 |
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