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 colorsys
from PIL import Image # type: ignore
def lowerCamelCase__ ( a__ , a__ , a__) -> float:
"""simple docstring"""
_snake_case : Tuple = x
_snake_case : List[Any] = y
for step in range(lowerCAme... | 517 | """simple docstring"""
def UpperCAmelCase__ ( lowerCAmelCase__ :int = 5_0 ) -> int:
'''simple docstring'''
lowercase = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , ... | 359 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCAmelCase ( __lowerCAmelCase , unittest.TestCase ):
'''simple docstrin... | 716 |
'''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 datase... | 27 | 0 |
"""simple docstring"""
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
... | 465 |
def _lowerCAmelCase ( __lowerCAmelCase = 200 ) -> int:
"""simple docstring"""
snake_case__ : Optional[int] = [1, 2, 5, 10, 20, 50, 100, 200]
snake_case__ : List[Any] = [0] * (pence + 1)
snake_case__ : str = 1 # base case: 1 w... | 252 | 0 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STA... | 504 |
import torch
def _lowerCAmelCase ():
if torch.cuda.is_available():
UpperCamelCase_ = torch.cuda.device_count()
else:
UpperCamelCase_ = 0
print(f"""Successfully ran on {num_gpus} GPUs""")
if __name__ == "__main__":
main()
| 504 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.mo... | 438 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
_lowerCAmelCase : Tuple = {
"""uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve... | 438 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
... | 705 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ = {
"""configuration_bigbird_pegasus""": [
"""BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BigBirdPegasusConfig""",
... | 370 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a__ : List[str] = {"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxC... | 589 |
"""simple docstring"""
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Any = logging.get_logger(__name__)
a__ : Tuple = {
"""facebook/encodec_24khz""": """https... | 589 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase = {
'''configuration_mobilevit''': ['''MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''... | 721 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, requ... | 291 | 0 |
from __future__ import annotations
def __UpperCamelCase (lowerCAmelCase : list, lowerCAmelCase : int, lowerCAmelCase : int, lowerCAmelCase : int ) -> list:
A = []
A , A = input_list[low:mid], input_list[mid : high + 1]
while left... | 699 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequenceCl... | 699 | 1 |
def A_ ( _lowerCAmelCase ) -> list[int]:
UpperCamelCase : Optional[int] = [0 for i in range(len(_lowerCAmelCase ) )]
# initialize interval's left pointer and right pointer
UpperCamelCase , UpperCamelCase : Optional[Any] = 0, 0
for i in range(1 , len... | 38 |
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
__lowerCamelCase : Dict = logging.get_logger(__name__)
__lo... | 38 | 1 |
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
a = logging.get_logger(__name__)
@add_end_docstring... | 412 |
def UpperCAmelCase_ ( UpperCAmelCase__ = "The quick brown fox jumps over the lazy dog" , ):
lowercase_ = set()
# Replace all the whitespace in our sentence
lowercase_ = input_str.replace(""" """ , """""" )
for alpha in input_str:
if "a" <= alph... | 412 | 1 |
'''simple docstring'''
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if... | 718 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ (_a : str , _a : list[str] | None = None ):
UpperCAmelCase = word_bank or []
# create a table
UpperCAmelCase = len(_a ) + 1
UpperCAmelCase = []
for _ in range(_a ... | 358 | 0 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import ... | 19 |
'''simple docstring'''
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def A__ ( UpperCAmelCase_ ):
if "model" in orig_key:
_UpperCamelCase : List[Any] = orig_key.replace('model.' , '' )
if "norm1" in orig_key:
... | 195 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
_A = logging.get_logger(__name__)
class __UpperCAmelCase ( snake_case__ ):
"""simple docstring"""
def __init__( self : Dict , ... | 228 |
"""simple docstring"""
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_A = "."
# Internal TensorFlow ops tha... | 228 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_lowerCamelCase = logging.get_logger(__name__)
class _snake_case (__SCREAMING_SNAKE_CASE):
def __init__( self ,*_snake_case ,**_snak... | 71 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ..... | 222 | 0 |
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
from tran... | 102 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_... | 102 | 1 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __lowercase( _UpperCAmelCase ):
'''simple docstring'''
@staticmethod
@abstractmethod
def snake_case_ ( __a ):
raise NotImplementedError()
@abstractme... | 594 |
'''simple docstring'''
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_learn... | 421 | 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 _UpperCamelCase ( ... | 708 |
'''simple docstring'''
import qiskit
def _A ( A ,A ) -> qiskit.result.counts.Counts:
lowercase : Tuple = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit acting on the q register
lowercase : List[Any] = qiskit.QuantumCircuit(A ... | 425 | 0 |
import json
import sys
def UpperCamelCase ( snake_case__ : Optional[Any] , snake_case__ : Dict ) -> Dict:
with open(snake_case__ , encoding='utf-8' ) as f:
UpperCamelCase : Optional[Any] = json.load(snake_case__ )
UpperCamelCase ... | 40 |
'''simple docstring'''
import re
def __UpperCAmelCase (lowercase__ ) -> bool:
'''simple docstring'''
a_ = re.compile(
r"^(?:0|94|\+94|0{2}94)" r"7(0|1|2|4|5|6|7|8)" r"(-| |)" r"\d{7}$" )
return bool(re.search(lowercase__ ,lowercase__ )... | 685 | 0 |
def UpperCAmelCase__ ( _A , _A = 0 ):
"""simple docstring"""
a_ = length or len(_lowerCamelCase )
a_ = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
a_ = list_data[i + 1], list_data[i]
a_ = Tru... | 717 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class __lowercase ( enum.Enum ... | 143 | 0 |
'''simple docstring'''
import numpy
# List of input, output pairs
__UpperCAmelCase = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
__UpperCAmelCase = (((515, 22, 13), 555), ((61, 35, 49), 15... | 90 |
'''simple docstring'''
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers... | 541 | 0 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A_ ... | 565 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = [[] for _ in range(__SCREAMING_SNAKE_CASE )]
lowercase = key - 1
if key <= 0:
raise ValueError('Height of grid can\'t be 0 or negative' )
if key == 1 or len(__SCREAM... | 565 | 1 |
def a_ ( __lowercase : int = 4_000_000 ) -> List[Any]:
_snake_case = [0, 1]
_snake_case = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
_snake_case = 0
for j in ... | 686 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch... | 134 | 0 |
from bisect import bisect
from itertools import accumulate
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
UpperCAmelCase = sorted(zip(_lowerCAmelCase , _lowerCAmelCase ) , key=lambda _lowe... | 718 |
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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowerC... | 405 | 0 |
def __lowerCamelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
if digit_amount > 0:
return round(number - int(lowerCAmelCase_ ) , lowerCAmelCase_ )
return number - int(lowerCAmelCase_ )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
... | 496 |
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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_M... | 283 | 0 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, 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... | 701 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A__ : Any = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}
try:
... | 671 | 0 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoCon... | 609 |
"""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,
r... | 609 | 1 |
"""simple docstring"""
def lowercase (_snake_case ,_snake_case ) -> Any:
'''simple docstring'''
__UpperCamelCase = 0
__UpperCamelCase = len(lowerCAmelCase_ ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if so... | 707 |
"""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 require_vision
from ... | 228 | 0 |
from __future__ import annotations
import math
import random
from typing import Any
class __A :
def __init__( self : Dict ):
lowerCAmelCase : list[Any] = []
lowerCAmelCase : int = 0
lowerCAmelCase : int ... | 343 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
def wrapper(*_UpperCAmelCase, **_UpperCAmelCa... | 343 | 1 |
from ...configuration_utils import PretrainedConfig
class A__ ( __snake_case ):
'''simple docstring'''
snake_case__ = """bert-generation"""
def __init__( self : Dict , _SCREAMING_SNAKE_CASE : Optional[int]=5_03... | 410 |
__magic_name__ : List[str] = tuple[float, float, float]
__magic_name__ : Optional[int] = tuple[float, float, float]
def lowercase__ ( _UpperCamelCase , _UpperCamelCase) -> Vectorad:
"""simple docstring"""
UpperCamelC... | 410 | 1 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
... | 469 |
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
__lowerCamelCase = []
for temp in range(int(UpperCamelCase__ ) ):
series.append(F"""1/{temp + 1}""" if series else '1'... | 469 | 1 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMSch... | 615 |
"""simple docstring"""
import unittest
from knapsack import knapsack as k
class A__ ( unittest.TestCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self: Optional[int]) -> List[Any]:
"""simple docstring"""
... | 615 | 1 |
'''simple docstring'''
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class __A ( A ):
'''simple docstring'''
... | 11 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def _a ( UpperCAmelCase ) -> Any:
"""simple docstring"""
return getitem, k
def _a ( UpperCAmelCase , UpperCAmelCase ) -> Union[s... | 315 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
cl... | 720 |
"""simple docstring"""
from __future__ import annotations
A_ = 10
def _lowerCAmelCase ( UpperCAmelCase__ : list[int] ) ->list[int]:
A__ : Any = 1
A__ : Optional[int] = max(UpperCAmelCase__ )
while placement <= max_digit:
... | 498 | 0 |
'''simple docstring'''
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : int=7 ):
UpperCAmelCase = None
if token is not None:
Up... | 447 |
'''simple docstring'''
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 lowercase_ ( unittest.TestCase ):
'''simple docstring'''
... | 447 | 1 |
import math
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = len(UpperCamelCase__ )
snake_case_ = int(math.floor(math.sqrt(UpperCamelCase__ ) ) )
snake_case_ ... | 721 |
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
_UpperCAmelCase : Any = logging.get_logger(__name__)
class lowercase ( lowercase_ ):
def __init__( self , *snake_case , **snake_case ):
... | 108 | 0 |
from __future__ import annotations
from fractions import Fraction
def UpperCamelCase_( _A :int , _A :int )-> bool:
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def UpperCamelCase_( _A :int )-> list[str]:
UpperCamelCase__ ... | 551 |
def UpperCamelCase_( _A :Union[str, Any] )-> List[str]:
UpperCamelCase__ = [0] * len(_A )
UpperCamelCase__ = []
UpperCamelCase__ = []
UpperCamelCase__ = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for... | 551 | 1 |
"""simple docstring"""
from __future__ import annotations
def A__ ( _UpperCAmelCase : list[int] , _UpperCAmelCase : list[int] , _UpperCAmelCase : int ) -> tuple[float, list[float]]:
'''simple docstring'''
snake_case__ : Any = list(range(len... | 711 |
"""simple docstring"""
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
... | 150 | 0 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
_snake_case : Optional[Any] = False
class a (unittest.TestCase ):
"""simple docstrin... | 81 |
from __future__ import annotations
from typing import Any
def lowerCAmelCase_ ( __lowerCamelCase ):
create_state_space_tree(__lowerCamelCase , [] , 0 )
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )... | 81 | 1 |
import unittest
from knapsack import knapsack as k
class a_( unittest.TestCase ):
"""simple docstring"""
def __UpperCamelCase ( self : str) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = [0]
... | 259 |
import argparse
from collections import defaultdict
import yaml
__UpperCAmelCase = "docs/source/en/_toctree.yml"
def A_ ( lowercase_ ) ->Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = defaultdict(lowercase_ )
for doc in model_doc:
counts[doc["... | 259 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_commo... | 634 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Tuple = {
'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
'tokenization_luke': ['Luk... | 634 | 1 |
'''simple docstring'''
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_at... | 39 |
'''simple docstring'''
import re
def __lowerCAmelCase ( lowerCamelCase : str ):
'''simple docstring'''
__lowerCAmelCase = re.compile(
r"^(?:0|94|\+94|0{2}94)" r"7(0|1|2|4|5|6|7|8)" r"(-| |)" r"\d{7}$" )
return bool(re.search(lowerCamelCase , lowerC... | 39 | 1 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modelin... | 694 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_UpperCamelCase : str = {
"configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeo... | 284 | 0 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE ) -> bool:
snake_case_ = [int(_SCREAMING_SNAKE_CASE ) for i in ip_va_address.split(""".""" ) if i.isdigit()]
return len(_SCREAMING_SNAKE_CASE ) == 4 and all(0 <= int(_SCREAMING_SNAKE_CASE ) <= 254 for o... | 709 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
if index == number_of_items:
return 0
snake_case_ = 0
snake_case_ ... | 2 | 0 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_swi... | 0 |
from __future__ import annotations
from math import pow, sqrt
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("One and only one argume... | 623 | 0 |
"""simple docstring"""
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self ):
_lowerCamelCase : List[Any] = 0
_lowerCamelCase : int = 0
_lowerCamelCase : Any = {}
def A_ ... | 492 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretra... | 492 | 1 |
"""simple docstring"""
from __future__ import annotations
class lowercase__ :
'''simple docstring'''
def __init__( self : List[str] , _UpperCAmelCase : int ) -> None:
'''simple docstring'''
... | 82 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
class _UpperCAmelCase :
def __init__( self , lowercase_ ) -> None:
UpperCAmelCase = value
UpperCAmelCase = None
... | 373 | 0 |
'''simple docstring'''
from collections.abc import Sequence
def a ( __a , __a ) -> float:
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(__a ) )
def a ( __a , __a ) -> float:
'''simple docstring'''
... | 280 |
'''simple docstring'''
import numpy as np
def a ( __a , __a , __a , __a , __a ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ :Tuple = int(np.ceil((x_end - xa) / h ) )
UpperCamelCase__ :Optional... | 280 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeli... | 24 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class UpperCAmelCase ( nn.Module ):
def __init__( self : int , __lowerCamelCase : int = 1_6 , __lowerCamelCase ... | 467 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
... | 644 |
'''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 __a (lowerCamelCase , ... | 644 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase__ :List[Any] = {
"""configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}
try:
if not is_torch_av... | 150 |
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def __lowercase () -> str:
"""simple docstring"""
import os as original_os
from os import path as original_path
from os import rename as original_rename
fr... | 150 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import Seque... | 720 | import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
... | 647 | 0 |
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...t... | 548 |
from __future__ import annotations
def A__ ( lowerCamelCase ) -> bool:
UpperCamelCase_: Optional[int] = len(lowerCamelCase )
# We need to create solution object to save path.
UpperCamelCase_: List[str] = [[0 for _ in range(lowerCamelCase )] for _ i... | 548 | 1 |
'''simple docstring'''
import argparse
import collections
import os
import re
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
# python utils/check_table.py
A__ : Union[str, Any] ... | 721 |
'''simple docstring'''
import random
def a_ ( _UpperCAmelCase : list ,_UpperCAmelCase : List[Any] ) -> tuple:
__snake_case , __snake_case , __snake_case : int = [], [], []
for element in data:
if element... | 124 | 0 |
def UpperCamelCase ( ) -> int:
'''simple docstring'''
return 1
def UpperCamelCase ( _a ) -> int:
'''simple docstring'''
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def UpperCamelCase ( _a ... | 257 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_... | 257 | 1 |
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,
)
_lowerCamelCase : Tuple = ... | 704 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowerCamelCase (__lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase_ = ["image_processor", "tokenizer"]
UpperCA... | 157 | 0 |
from copy import deepcopy
class lowercase_ :
def __init__( self , lowercase_ = None , lowercase_ = None) -> None:
if arr is None and size is not None:
a__ =size
a__ =[0] * size
elif arr is not None:
... | 20 |
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 (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.set_... | 20 | 1 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
... | 707 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTeste... | 199 | 0 |
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 import *... | 454 |
from math import sqrt
def lowerCAmelCase_ ( __lowerCamelCase = 1_0_0_0_0_0_0 ):
__snake_case : int = 0
__snake_case : int = 0
__snake_case : int
while num_cuboids <= limit:
max_cuboid_size += 1
... | 81 | 0 |
"""simple docstring"""
from __future__ import annotations
class _lowercase :
"""simple docstring"""
def __init__( self : str , UpperCamelCase__ : Optional[int]=None ) -> List[Any]:
'''simple docstring'''
... | 705 | """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... | 296 | 0 |
"""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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_feature... | 95 |
'''simple docstring'''
from string import ascii_lowercase, ascii_uppercase
def __lowerCamelCase ( A__ ) -> str:
"""simple docstring"""
if not sentence:
return ""
UpperCamelCase = dict(zip(A__ , A__ ) ... | 430 | 0 |
"""simple docstring"""
import os
def UpperCAmelCase ( ):
"""simple docstring"""
A__ = os.path.dirname(os.path.realpath(UpperCamelCase__ ) )
A__ = os.path.join(UpperCamelCase__ , 'triangle.txt' )
w... | 536 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_avai... | 536 | 1 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.uti... | 155 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCamelCase_ = 100 ):
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "... | 155 | 1 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel... | 113 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from... | 113 | 1 |
import argparse
import datetime
def a (lowerCAmelCase__ ):
__a = {
"""0""": """Sunday""",
"""1""": """Monday""",
"""2""": """Tuesday""",
"""3""": """Wednesday""",
"""4""": """Thursday""",
"""5""": """Friday""",
"""6""": """Saturday"""... | 99 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase = 10_00 )-> int:
__UpperCAmelCase = 2**power
__UpperCAmelCase = 0
while n:
__UpperCAmelCase , __UpperCAmelCase = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).... | 126 | 0 |
import gc
import threading
import time
import psutil
import torch
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self )-> List[Any]:
'''simple docstring'''
__UpperCamelCase = psutil.Process()
__Upp... | 451 |
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
import transformers
from transformers import (
... | 451 | 1 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_t... | 6 |
'''simple docstring'''
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def a__ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {}
__SCREAM... | 627 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
__SCREAMING_SNAKE_CASE : Dict ... | 705 |
from __future__ import annotations
import bisect
def UpperCamelCase__ ( lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ = 0 ,lowerCAmelCase__ = -1 ):
if hi < 0:
lowercase = len(lowerCAmelCase__ )
while lo < hi:
lowercase = lo + (hi - lo) //... | 72 | 0 |
'''simple docstring'''
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
fr... | 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 inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_... | 443 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def SCREAMING_SNAKE_CASE_ ( __A :... | 443 | 1 |
"""simple docstring"""
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class __lowercase ( __lowerCamelCase ):
snake_case_ = """"""
snake_case_ ... | 65 |
"""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 dataset
from sklearn.preprocessi... | 690 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, re... | 172 |
'''simple docstring'''
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class _A ( UpperCamelCase ):
'''simple ... | 172 | 1 |
"""simple docstring"""
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def _lowerCamelCase ( lowerCamelCase__ : List[Any] , lowerCamelCase__ : str , lowerCamelCase__ : ... | 200 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {
'configuration_informer': [
'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InformerConfig... | 200 | 1 |
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
lowerCamelCase : Union[str, Any] = '.'
if __name__ == "__main__":
lowerCamelCase : int = os.path.join(REPO_PATH, 'utils/documentation_t... | 708 |
import inspect
import unittest
class __lowercase (unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase ( self ) -> List[Any]:
try:
import diffusers # noqa: F401
except ImportError:
assert False
def UpperC... | 684 | 0 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class a__ ( _lowercase ):
__magic_name__ : U... | 507 |
'''simple docstring'''
from __future__ import annotations
def __lowercase (_SCREAMING_SNAKE_CASE :list[int] ):
if not nums:
return 0
SCREAMING_SNAKE_CASE : Tuple = nums[0]
SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for num in nums[1:]:
SC... | 507 | 1 |
SCREAMING_SNAKE_CASE__ = 8.314_462 # Unit - J mol-1 K-1
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('Invalid inputs. Enter positive value.' )
return moles * kelvin * UNIVE... | 52 |
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.p... | 52 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
A : Tuple = {
'''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
'''susnato/ernie-m-la... | 128 |
'''simple docstring'''
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
A : List[Any] = False
try:
A : ... | 128 | 1 |
import math
from collections import defaultdict
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 KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def _lowerCAmelCase ... | 708 |
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 | 0 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils impo... | 168 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
UpperCAmelCase__ : int = logging.get_logg... | 313 | 0 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
a_ = datasets.load_iris()
a_ = np.array(data['data'])
a_ = np.array(data['target'])
a_ = data['target_names']
a_ , ... | 707 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
a_ = list[list[float | int]]
def _a( UpperCamelCase__ : Matrix, UpperCamelCase__ : Matrix ):
'''simple docstring'''
SCREAMING_SNAKE... | 665 | 0 |
'''simple docstring'''
UpperCAmelCase : List[Any] = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def a__ ( ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = input("""Enter message: """ )
__SCREAMING_SNAKE_CASE = input("""Enter key [alphanumeric]: """ )
__SCREAMING_S... | 627 |
"""simple docstring"""
from math import pi, sqrt, tan
def __UpperCAmelCase ( __UpperCamelCase ):
if side_length < 0:
raise ValueError('''surface_area_cube() only accepts non-negative values''' )
return 6 * side_length**2
def __UpperCAmelCase ... | 76 | 0 |
'''simple docstring'''
def _snake_case ( A ) -> List[str]:
if p < 2:
raise ValueError('''p should not be less than 2!''' )
elif p == 2:
return True
lowerCAmelCase__ = 4
lowerCAmelCase__ = (1 << p) - 1
for _ in range(p - 2 ):
lowerCAmel... | 707 |
'''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docs... | 98 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
return credit_card_number.startswith(("""34""", """35""", """37""", """4""", """5""", """6""") )
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase ... | 277 | """simple docstring"""
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attenti... | 277 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase = {
'''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}
try:
if not is_torch_available():
rai... | 718 |
import math
UpperCamelCase = 1_0
UpperCamelCase = 7
UpperCamelCase = BALLS_PER_COLOUR * NUM_COLOURS
def _a ( lowerCamelCase__ = 20 ) -> str:
lowerCamelCase_ : List[str] = math.comb(lowerCamelCase__ , lowerCamelCase__ )
lowerCamelCase_ ... | 144 | 0 |
'''simple docstring'''
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class UpperCAmelCase_ (_UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase : Optional[int] = 'EncodecFeatureE... | 13 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def _snake_case ( A , A , A , A , A ) -> np.array:
lowerCAmelCase__ = int(np.ceil((x_end - xa) / step_size ) )
lowerCAmelCase__ ... | 90 | 0 |
import warnings
warnings.warn(
"""memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: """
"""`from accelerate import find_executable_batch_size` to avoid this warning.""",
FutureWarning,
)
| 703 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between che... | 25 | 0 |
'''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
from ...utils.backbone_utils import BackboneConfigMixin, ge... | 596 |
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
lowerCamelCase_ = logging.getLogger()
def lowerCamelCase ( a_ ... | 318 | 0 |
'''simple docstring'''
import requests
UpperCamelCase__ : Dict = '''https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey='''
def __UpperCamelCase( _A : List[str] ):
'''simple docstring'''
# fetching a list of articles in json format
UpperCAmelCase__ : Any ... | 711 | '''simple docstring'''
def __UpperCamelCase( _A : str , _A : str ):
'''simple docstring'''
UpperCAmelCase__ : int = len(_A )
UpperCAmelCase__ : int = len(_A )
UpperCAmelCase__ : int = (
first_str_length if first_str_length >... | 496 | 0 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn
from ... | 686 |
"""simple docstring"""
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class lowerCamelCase__ ( lowerCamelCase_ ):
@require_torch
def lowerCamelCase_ ... | 134 | 0 |
"""simple docstring"""
from queue import PriorityQueue
from typing import Any
import numpy as np
def UpperCAmelCase ( a_, a_, a_, a_, a_, a_, a_, a_, a_, ):
'''simple docstring'''
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
lowerCamelCase : ... | 133 |
"""simple docstring"""
import os
import pytest
from attr import dataclass
_A = 'us-east-1' # defaults region
@dataclass
class _lowercase :
lowercase_ = 42
lowercase_ = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
lowercase_ = {
'task_name... | 133 | 1 |
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import Fl... | 637 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a : Optional[Any] = logging.get_logger(__name__)
__a : Dict = {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/c... | 637 | 1 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDi... | 488 |
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():
... | 488 | 1 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_... | 108 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
"tiiua... | 18 | 0 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_t... | 446 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ : str = {"""processing_layoutxlm""": ["""LayoutXL... | 446 | 1 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _UpperCamelCase (a_ ):
snake_case_ = (IPNDMScheduler,)
snake_case_ = (("""num_inference_steps""", 50),)
def __UpperCAmelCase ( self ... | 367 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _UpperCamelCase (a_ ):
snake_case_ = (PNDMScheduler,)
snake_case_ = (("""num_inference_steps""", 50),)
def __UpperCAmelCase ( self ,... | 367 | 1 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __UpperCamelCase ( ):
A_ : str = HfArgumentParser(snake_case__ )
A_ : str = parser.parse_args_into_dataclasses()[0]
A_ : Any = TensorFlowBen... | 480 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_A : Optional[int] = ["""image_processor""", """tokenizer"""]
_A ... | 480 | 1 |
"""simple docstring"""
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
if TY... | 260 |
"""simple docstring"""
# 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 ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffus... | 260 | 1 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__UpperCamelCase : Dict = 0
__UpperCamelCase : Any = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
... | 712 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
fr... | 372 | 0 |
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 :int
__snake_case :float = 0.... | 80 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
_A = logging.getLogger(__name__)
class A ( __UpperCAmelCase ):
def __init__( self, UpperCamelCase__=-1 ):
... | 431 | 0 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__A = (720, 1280) # Height, Width
__A = (0.4, 0.6) # if height or width lower than this scale, drop it.
__A = 1 / 100
__A = ''''''
__A = ''''''
_... | 366 | """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_funnel import FunnelTokenizer
__A = logging.get_logger(__name__)
__A = {'''vocab... | 366 | 1 |
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