code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from torch import nn
def __snake_case ( UpperCAmelCase_ : Union[str, Any] ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F'''Unsu... | 55 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def a__ ( ) -> tuple[list[int], int]:
UpperCAmelCase : str = [randint(-1_000 , 1_000 ) for i in range(10 )]
UpperCAmelCase : Any = randint(-5_... | 336 | 0 |
from math import ceil
def UpperCamelCase_( _snake_case : int , _snake_case : Any ):
"""simple docstring"""
__a =list(range(0 , _snake_case ) )
__a =[item for sublist in list(device_map.values() ) for item in sublis... | 308 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Any = logging.get_logger(__name__)
_lowerCAmelCase : int = {
"caidas/swin2sr-classicalsr-x2-64": (
"https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/... | 308 | 1 |
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_ ( UpperCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
a__ : ... | 43 | import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available,... | 43 | 1 |
'''simple docstring'''
import sys
import turtle
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ) -> tuple[float, float]:
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCa... | 129 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> List[Any]:
if "cls_token" in name:
... | 129 | 1 |
def _a ( SCREAMING_SNAKE_CASE_ : list[int] , SCREAMING_SNAKE_CASE_ : int ):
__lowerCAmelCase = len(SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, ... | 92 |
def _a ( SCREAMING_SNAKE_CASE_ : int = 1_00_00_00 ):
__lowerCAmelCase = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , SCREAMING_S... | 92 | 1 |
'''simple docstring'''
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import Acce... | 311 |
'''simple docstring'''
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
lowercase : Optional[int] = [
'kernels/rwkv/wkv_cuda.cu',
'kernels/rwkv/wkv_op.cpp',
'kernels/deformable_detr/ms_deform_attn.h',
'kernels/... | 311 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__A = logging... | 10 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils... | 280 | 0 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoT... | 366 |
"""simple docstring"""
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def _lowerCAmelCas... | 181 | 0 |
from math import factorial
def snake_case( __magic_name__ = 1_00 ) -> int:
'''simple docstring'''
return sum(int(__magic_name__ ) for x in str(factorial(__magic_name__ ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter th... | 308 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase_ = {
'configuration_mask2former': [
'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Mask2FormerConfig',
],
}
tr... | 308 | 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, r... | 357 | """simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import ... | 154 | 0 |
def lowerCAmelCase__ ( lowerCamelCase_ : list):
'''simple docstring'''
lowerCAmelCase__ : int = False
while is_sorted is False: # Until all the indices are traversed keep looping
lowerCAmelCase__ : Dict = True
for i in range(0 ... | 129 |
from jiwer import compute_measures
import datasets
__snake_case : Dict ='\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation mea... | 129 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ ... | 358 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
lowerCamelCase_ : int = logging.get_logger(__name__)
class __A ( _SCREAMING_... | 215 | 0 |
'''simple docstring'''
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class UpperCamelCase__ ( lowercase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = "M-CLIP"
def __init__( self , snake_case=1_0_2_4 , ... | 311 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_com... | 311 | 1 |
'''simple docstring'''
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,
Pi... | 136 |
'''simple docstring'''
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,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
... | 136 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( lowercase_ , lowercase_ ):
UpperCAmelCase = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
UpperCAmelCase = n - k
# Calculate C(n,k)
for i in ran... | 78 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ ) -> int:
UpperCAmelCase__ : Tuple = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def a__ ( lowerCAmelCase__ = 1_00 ) -> int:
Up... | 181 | 0 |
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_... | 363 |
def SCREAMING_SNAKE_CASE_ ( __A : list ) -> list:
"""simple docstring"""
a_ : int = len(__A )
for _ in range(__A ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
... | 120 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
A__ : Dict =logging.get_logger(__name__)
class UpperCAmelCase ( snake_case_ ):
def __init__( self : Optional[Any] ... | 70 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _SCREAMING_SNAKE_CASE ( lowerCAmelCase__):
_UpperCamelCase:Optional[int] = ["image_processor", "tokenizer"]
_UpperCamelCase:Tuple = "ChineseCLIPImageProce... | 154 | 0 |
"""simple docstring"""
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
_A = logging.get_logger(__name__)... | 212 |
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class _lowerCamelCase ( unittest.TestCase ):
def _lowerCAmelCase ( self : Dict ) -> None:
"""simple d... | 212 | 1 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
lowercase : Union[str, Any] = 3
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
print('''Gene... | 3 |
'''simple docstring'''
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = False )-> list[float]:
'''simple docstrin... | 215 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
_SCREAMING_SNAKE_CASE = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
_SCREAMING_S... | 357 | import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.util... | 165 | 0 |
"""simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
UpperCAmelCase : Union[str, Any] = re.compile(r"\b(a|an|the)\b", re.UNICODE)
UpperCAmelCase : Optional[Any] = None
def _SCREAMING_... | 136 |
"""simple docstring"""
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
UpperCAmelCase : Union[str, Any] = "__DUMMY_TRANSFORMERS_USER__"
UpperCAmelCase : Dict = "Dummy... | 136 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
A : Dict = {
'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRAINED_CONFIG_... | 33 |
import logging
from transformers.configuration_utils import PretrainedConfig
A : Union[str, Any] = logging.getLogger(__name__)
class __A( a ):
snake_case_ = '''masked_bert'''
def __init__( self , _snake_case=30_522 , _snake_case=768 , ... | 33 | 1 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ... | 10 |
'''simple docstring'''
from sklearn.metrics import recall_score
import datasets
__A : Dict = "\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is... | 120 | 0 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A( unittest.TestCas... | 208 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for... | 208 | 1 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> str:
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod() | 212 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=__magic_name__ )
class A__ ( __magic_name__ ):
lowercase = field(default='audio-cl... | 212 | 1 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__snake_case :Tuple = logging.get_logger(__name__)
__snake_case ... | 131 |
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
def get_matched_characters(_UpperCAmelCase , _UpperCAmelCase ) -> str:
__a = []
__a = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
... | 131 | 1 |
import unittest
import numpy as np
from transformers import BertConfig, 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():
from transformers.mod... | 92 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class lowerCamelCase :
lowerCamelCase__ : Optional[str] = field(
default='codeparrot/codeparrot' ,metadata={'help': 'Model name or path of model to... | 165 | 0 |
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def snake_case_ ( *snake_case , snake_case = None , snake_case=True , snake_case=2 ) -> List[Any]:
from .. import __version__
... | 288 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ... | 288 | 1 |
"""simple docstring"""
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def lowercase ( ):
lowercase_ : Union[str, Any] = {
'''repo_name''': ['''test_repo1''', ''... | 33 |
"""simple docstring"""
def lowercase ( __snake_case : int ):
if not isinstance(__snake_case , __snake_case ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
divisor for di... | 33 | 1 |
_snake_case = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def _A ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ):
# Return True if there is node that has ... | 363 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
... | 201 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileNetVaConfig
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_confi... | 208 |
'''simple docstring'''
from collections.abc import Sequence
def a_ ( _lowerCAmelCase ,_lowerCAmelCase ) -> float:
return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase ) )
def a_ ( _lowerCAmelCase ,_lowerCAmelCas... | 208 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase = {
'''configuration_mgp_str''': ['''MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MgpstrConfig'''],
'''processing_mgp_str''': ['''Mgp... | 370 | """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 Mvp... | 312 | 0 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
lowerCamelCase = logging.get_logger(__name_... | 131 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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_common impor... | 131 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_mod... | 371 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImage... | 108 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def _UpperCAmelCase ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : bool , __lowerCamelCase : list[int] , __lowerCamelCase : float ) -> int... | 288 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'... | 288 | 1 |
from __future__ import annotations
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Dict = list(range(len(_UpperCAmelCase ) ) )
SCREAMING_SNAKE_CASE_: Any = [v / w for v, w in zip(_UpperCAmelCase , _... | 356 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class __lowercase ( tf.keras.optimizers.schedules.LearningRateSchedule ):
"... | 127 | 0 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
UpperCAmelCase__ = logging.get_logger(__name__)
class __lowerCAmelCase ( __lowerCamelCase ):
def __init__( self : Dict , *A : Optional[int] , **A : List[str]) -> ... | 339 |
def lowerCAmelCase_ ( __UpperCAmelCase: int ) -> bool:
return str(__UpperCAmelCase ) == str(__UpperCAmelCase )[::-1]
def lowerCAmelCase_ ( __UpperCAmelCase: int ) -> int:
return int(__UpperCAmelCase ) + int(str(__UpperCAmelCase )[::-... | 201 | 0 |
'''simple docstring'''
_SCREAMING_SNAKE_CASE = "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,
i... | 368 |
'''simple docstring'''
_SCREAMING_SNAKE_CASE = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
_SCREAMING_SNAKE_CASE = ["a", "b", "c", "d", "e"]
def __lowerCamelCase ( __lowerCAmelCase : List[Any] , __lowerCAmelCase : str , __... | 3 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase : str = {'configuration_beit': ['BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BeitConfig', 'BeitOnnxConfi... | 280 |
import cva
import numpy as np
class _a :
"""simple docstring"""
def __init__( self : Any , UpperCAmelCase : float , UpperCAmelCase : int ):
if k in (0.04, 0.06):
A_ = k
A_ ... | 312 | 0 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
snake_case__ = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to... | 4 |
'''simple docstring'''
from collections.abc import Sequence
def snake_case__ ( lowerCamelCase__ : Sequence[float] , lowerCamelCase__ : bool = False ) -> float:
if not arr:
return 0
A_ : Union[str, Any] = 0 if allow_empt... | 4 | 1 |
"""simple docstring"""
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, ... | 84 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ ... | 108 | 0 |
'''simple docstring'''
import re
import string
import numpy as np
import datasets
_lowercase = """
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""
_lowercase = """
Args:
predictions: List of ... | 229 |
'''simple docstring'''
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusion... | 229 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase : Optional[Any] = logging.get_logger(__name__)
lowercase : List[str] = {
"facebook/xlm... | 20 |
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached... | 127 | 0 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformer... | 358 |
class SCREAMING_SNAKE_CASE__ :
def __init__(self : str , a__ : list ):
"""simple docstring"""
__snake_case = set_counts
__snake_case = max(a__ )
__snake_case = len(a__ )
... | 238 | 0 |
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 __snake_case ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE ( self ):
... | 20 |
'''simple docstring'''
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, s... | 3 | 0 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils... | 367 |
def lowerCamelCase_ ( _a : int , _a : list[int] , _a : int ):
'''simple docstring'''
def count_of_possible_combinations(_a : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_... | 59 | 0 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__snake_case =200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to wors... | 4 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class UpperCAmelCase_ :
def __init__( self : Optional[int] , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : ... | 4 | 1 |
from itertools import permutations
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : tuple ):
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
__UpperCamelCase =[7, ... | 358 |
from __future__ import annotations
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ):
if b == 0:
return (1, 0)
((__UpperCamelCase) , (__UpperCamelCase)) =extended_euclid(SCREAMING_SNAKE_CASE__ , a % ... | 117 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def UpperCamelCase_ ( snake_case_ : list , snake_case_ : list ) -> list:
'''simple docstring'''
if len(snake_case_ ) != 2 or len(a[0] ) != 2 or ... | 229 | '''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging i... | 229 | 1 |
'''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... | 359 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def a_ ( _UpperCAmelCase : List[Any] ) -> ... | 0 | 0 |
"""simple docstring"""
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRo... | 46 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class ... | 238 | 0 |
# flake8: noqa
# Lint as: python3
__magic_name__: Optional[int] = [
"VerificationMode",
"Version",
"disable_progress_bar",
"enable_progress_bar",
"is_progress_bar_enabled",
"experimental",
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable... | 363 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxM... | 138 | 0 |
from collections.abc import Sequence
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
return sum(c * (x**i) for i, c in enumerate(snake_case ) )
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
_l... | 82 |
def UpperCamelCase ( __lowerCamelCase : str , __lowerCamelCase : int ):
snake_case : list[list[str]] = [[] for _ in range(__lowerCamelCase )]
snake_case : int = key - 1
if key <= 0:
raise ValueError("Height o... | 59 | 0 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ : Dict = logging.get_logger(__name__)
lowerCamelCase_ : int = {
"""kakaobrain/align-ba... | 362 | from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificat... | 197 | 0 |
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils imp... | 99 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
snake_case__ : Dict = logging.get_logger(__name__)
snake_case__ : Optional[Any] ... | 117 | 0 |
'''simple docstring'''
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
_A : str =logging.get_logger(__name__)
class _lowercase :
a = None
@experimental
def SCREAMING_SNAKE_CASE_ (U... | 359 |
'''simple docstring'''
from math import asin, atan, cos, radians, sin, sqrt, tan
_A : List[str] =637_8137.0
_A : Dict =635_6752.31_4245
_A : int =6_378_137
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ... | 129 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase_ : Optional[Any] = {
'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/mai... | 32 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def _a ( a :List[Any] ) -> Optional[int]:
a = []
... | 0 | 0 |
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
return "\n".join(
F"""{number} * {i} = {number * i}""" for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(... | 366 |
"""simple docstring"""
from __future__ import annotations
import time
_snake_case = list[tuple[int, int]]
_snake_case = [
[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],
[0, 0, 1... | 324 | 0 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int ):
if len(_UpperCAmelCase ) <= 1:
return lst
A__ = 1
while i < len(_UpperCAmelCase ):
if lst[i - 1] <= lst[i]:
i += 1
else:
... | 335 |
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_proces... | 138 | 0 |
def __lowercase ( _UpperCamelCase ) ->list:
"""simple docstring"""
lowercase : Tuple = int(_UpperCamelCase )
if n_element < 1:
lowercase : Any = ValueError('''a should be a positive number''' )
raise m... | 173 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Di... | 173 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a = {
'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
'configuration_data2vec_text': [
'D... | 30 | """simple docstring"""
from ..utils import DummyObject, requires_backends
class _A ( metaclass=lowerCAmelCase ):
snake_case__ : Optional[int] = ['torch', 'torchsde']
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ):
... | 197 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
lowerCamelCase_ : Dict = False
... | 215 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class __A ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
__lowerCAmelCase = "WhisperFeatureExtractor"
__lowerCAmelCase = "WhisperTokenizer"
def __init__( self ... | 215 | 1 |
'''simple docstring'''
def a_ ( lowerCamelCase : int = 1000 ):
lowerCAmelCase = 1, 1
lowerCAmelCase = []
for i in range(1 , n + 1 ):
lowerCAmelCase = prev_numerator + 2 * prev_denominator
lowerCAmelCase = prev_numerator + prev_denominator
... | 4 |
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def lowerCAmelCase__ ( lowerCamelCase_ : Optional[Any]): # picklable for multiprocessi... | 129 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'shi... | 364 |
"""simple docstring"""
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, t... | 133 | 0 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
lowerCAmelCase_ = _symb... | 8 |
'''simple docstring'''
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_avai... | 324 | 0 |
def __lowerCamelCase ( __magic_name__ : int ):
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 367 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)... | 42 | 0 |
"""simple docstring"""
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
... | 173 |
"""simple docstring"""
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test... | 173 | 1 |
import os
def UpperCamelCase ( __lowerCamelCase : int ):
snake_case : List[str] = len(grid[0] )
snake_case : List[str] = len(__lowerCamelCase )
snake_case : Dict = 0
snake_case : Any = 0
... | 10 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase = {
"""configuration_pix2struct""": [
"""PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Pix2StructConfig""",
"""Pix... | 10 | 1 |
'''simple docstring'''
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 i... | 215 |
'''simple docstring'''
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 Patchi... | 215 | 1 |
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def lowercase( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = None ) -> str:
'''simple docstring'''
if version.parse(hfh.__version__ ).release < versio... | 165 | from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def lowercase( UpperCamelCase_ = True , *UpperCamelCase_ , **UpperCamelCase_ ) -> int:
'''simple docstring'''
if not is_tqdm_available():
... | 165 | 1 |
'''simple docstring'''
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class snake_case ( unittest.TestCase ):
... | 55 |
lowercase_ : Optional[int] = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]
def __SCREAMING_SNAKE_CASE ( snake_case_ ):
'''simple docstring'''
_UpperCAmelCase = 0
while number:
# Increased Speed Slightly by checking eve... | 133 | 0 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set... | 369 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
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_backbone_common import BackboneTest... | 297 | 0 |
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...ut... | 308 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class __UpperCAmelCase ( tf.keras.layers.Layer ):
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=1 , ... | 42 | 0 |
'''simple docstring'''
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='session' )
def lowerCamelCase__ ... | 360 |
'''simple docstring'''
lowercase =[0, 2, 4, 6, 8]
lowercase =[1, 3, 5, 7, 9]
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : list[int] , __lowerCamelCase : int ):
'''simple docstring'''
i... | 242 | 0 |
import os
def lowerCAmelCase_ ( __a ) -> Dict:
"""simple docstring"""
lowerCamelCase__: Union[str, Any] =len(grid[0] )
lowerCamelCase__: List[Any] =len(__a )
lowerCamelCase__: str =0
lowerCamelCase__: Tuple =0
lowerCamelCase__: ... | 10 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=__SCREAMING_SNAKE_CASE )
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'... | 10 | 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 require_vision
fro... | 357 |
"""simple docstring"""
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__magic_name__ = {
"sample_size": 32,
"in_channels": 3,
"out_channels": 3,
"layers_per_block": 2,
"num_class_embed... | 255 | 0 |
"""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 FeatureExtractionMix... | 165 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowerCamelCase (nn.Module ):
lowerCamelCase__ : int
lowerCame... | 165 | 1 |
"""simple docstring"""
import argparse
import os
import re
__UpperCamelCase : List[str] = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
__UpperCamelCase : int = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__Uppe... | 361 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCamelCase : Any = {
'''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETR... | 309 | 0 |
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=False ) -> Optional[Any]:
if isinstance(_A , _A ) and isinstance(_A , _A ):
__lowerCamelCase : List[Any] = len(set_a.intersection(_A ) )
if alternative_union:
__lowerCamelCas... | 73 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_... | 297 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ : Tuple ={'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotA... | 118 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase__ : str ={'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}
t... | 118 | 1 |
def _a ( SCREAMING_SNAKE_CASE_ : str = "The quick brown fox jumps over the lazy dog" , ):
__lowerCAmelCase = set()
# Replace all the whitespace in our sentence
__lowerCAmelCase = input_str.replace(" " , "" )
for alpha in input... | 92 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
... | 242 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__magic_name__: Union[str, Any] = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_... | 366 |
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():
from .tokenization_xlnet import... | 138 | 0 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils impo... | 298 |
"""simple docstring"""
import math
def lowercase__ ( _UpperCAmelCase = 1_00 ) -> int:
'''simple docstring'''
lowercase : List[str] = sum(i * i for i in range(1 , n + 1 ) )
lowercase : Dict = int(math.pow(sum(range(1 ... | 255 | 0 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
lowerCAmelCase: List[Any] = object()
# For specifying empty leaf dict `{}`
lowerCAmelCase: int ... | 362 |
'''simple docstring'''
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
Character... | 96 | 0 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartTokenize... | 240 |
'''simple docstring'''
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_... | 309 | 0 |
"""simple docstring"""
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _a ( *_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=2 ) -> Dict:
from .. import __version... | 233 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE ) -> list[int]:
snake_case_ = len(_SCREAMING_SNAKE_CASE )
for i in range(_SCREAMING_SNAKE_CASE ):
for j in range(i + 1 , _SCREAMING_SNAKE_CASE ):
if numbers[j] < numbers[i]:
... | 233 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...ut... | 118 | import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
A : Optional[int] = l... | 118 | 1 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jn... | 93 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class _a ( unittest.TestCase ):... | 93 | 1 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN,... | 156 |
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
logging.se... | 138 | 0 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
lowerCAmelCase__ : Dict = word.split()
def justify(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
lowerCAmelCase__ : ... | 357 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked bef... | 307 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
fro... | 99 |
"""simple docstring"""
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case ( lowercase__ , lowercase__ ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(lowercase__ , ... | 96 | 0 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def __snake_case ( _UpperCAmelCase ):
__a = int(_UpperCAmelCase )
__a , __a , ... | 131 |
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
def get_matched_characters(_UpperCAmelCase , _UpperCAmelCase ) -> str:
__a = []
__a = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
... | 131 | 1 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
lowerCamelCase : int = logging.get_logger(__name__)
class lowerCAmelCase ( __a ):
'''simple docstring'''
def __init__( self : List[str] , *__a : List... | 233 |
lowerCamelCase : Tuple = {'''a''': ['''c''', '''b'''], '''b''': ['''d''', '''e'''], '''c''': [], '''d''': [], '''e''': []}
lowerCamelCase : int = ['''a''', '''b''', '''c''', '''d''', '''e''']
def snake_case_ ( lowerCAmelCase_ : Optional[int] , low... | 233 | 1 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def A (__lowerCamelCase :Optional[int] ):
_lowerCAmelCase = FileLock(str(tmpdir / """foo.lock""" ) )
_lowerCAmelCase = FileLock(str(tmpdir / """foo.lock""" ) ... | 229 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import Fl... | 229 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
... | 93 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_lowercase : str = lo... | 93 | 1 |
'''simple docstring'''
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def A (__lowerCamelCase :List[Any] ):
_lowerCAmelCase = [
"decoder.version",
"decoder.output_projection.weight",
... | 363 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""YituTech/conv-bert-base""": """https://hug... | 229 | 0 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int ) -> int:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ) or number < 0:
raise ValueError("Input must be a non-negative integer" )
_UpperCAmel... | 22 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Dict = {
"""configuration_jukebox""": [
"""JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""JukeboxConfig""",
"""Jukeb... | 307 | 0 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, 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_... | 270 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import... | 270 | 1 |
import argparse
import os
import re
lowerCamelCase = '''src/transformers'''
# Pattern that looks at the indentation in a line.
lowerCamelCase = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
lowerCamelCase = re.compile(R'''^\s*"([^"]+)":''')
... | 131 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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_common impor... | 131 | 1 |
import argparse
UpperCamelCase = '''docs/source/_static/js/custom.js'''
def __lowerCamelCase ( snake_case__ ) -> int:
"""simple docstring"""
with open(__lowerCAmelCase ,encoding="""utf-8""" ,newline="""\n""" ) as f:
_SCREAM... | 353 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_... | 125 | 0 |
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