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 ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailabl... | 198 | '''simple docstring'''
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class UpperCAmelCase ( pl.LightningModule ):
'''simple docstring'''
def __init__( self , __lowe... | 198 | 1 |
from __future__ import annotations
from math import pi, sqrt
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
if inductance <= 0:
raise ValueError('Inductance cannot be 0 or negative')
elif capacitance <= 0:
raise ValueError('Capacitance cannot be 0 or negative')... | 327 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decode... | 327 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
__lowercase = logging.ge... | 40 |
"""simple docstring"""
__lowercase = {
"""Pillow""": """Pillow<10.0.0""",
"""accelerate""": """accelerate>=0.20.3""",
"""av""": """av==9.2.0""",
"""beautifulsoup4""": """beautifulsoup4""",
"""black""": """black~=23.1""",
"""codecarbon""": """codecarbon==1.... | 40 | 1 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class A ( pl.LightningModule ):
def __init__(self , lowerCAmelCase ):
super().__init__()
__lowercase= mo... | 369 |
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,
TFAutoModelForSequenceC... | 304 | 0 |
'''simple docstring'''
from __future__ import annotations
lowercase__ : Any = list[list[int]]
# assigning initial values to the grid
lowercase__ : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0,... | 324 |
'''simple docstring'''
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
lowercase__ : Any = logging.get_logger(__name__)
class __lowerCAmelCase :
"""simple docstring"""
_snake_case : ... | 324 | 1 |
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class _lowerCamelCase ( UpperCamelCase ):
"""simpl... | 65 |
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_available,
)
from . import BaseT... | 65 | 1 |
"""simple docstring"""
class __A :
def __init__( self ):
_lowerCAmelCase : List[Any] = """"""
_lowerCAmelCase : Optional[int] = """"""
_lowerCAmelCase : List[str] = []
def __A ( self , a__ ... | 44 |
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
from datasets.utils.py_uti... | 328 | 0 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
UpperCAmelCase : Tuple = [
# (stable-diffusion, HF Diffusers)
("time_embed.0.weight", "time_embedding.linear_1.weight"),... | 66 |
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("""socket.socket""" )
@patch("""builtins.open""" )
def __lowerCamelCase ( lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : Dict ):
'''simple docstring'''
low... | 66 | 1 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformer... | 77 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIONA... | 122 | 0 |
"""simple docstring"""
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
__snake_case : Optional[Any] = HUGGINGFACE_HUB_CACHE
__snake_case : str = 'config.json'
__snake_case : Tuple = 'diffusion_pyto... | 58 |
"""simple docstring"""
from ....utils import logging
__snake_case : Optional[Any] = logging.get_logger(__name__)
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self: str , _SCREAMING_SNAKE_CA... | 58 | 1 |
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class _A :
def __init__( self : Any , __SCREAMING_SNAKE_CASE : Union[str, Any]):
'''simple do... | 49 |
import os
import numpy
import onnx
def A_ ( a , a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = a.name
SCREAMING_SNAKE_CASE_ : Dict = b.name
SCREAMING_SNAKE_CASE_ : Optional[int] = ''
SCREAMING_S... | 253 | 0 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCOND... | 148 |
from __future__ import annotations
class __lowerCAmelCase :
def __init__( self , lowerCAmelCase__ ) -> str:
'''simple docstring'''
a__ : int =TypeError(
"Matrices must be formed from a list of ze... | 148 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
CommonSchedulerState,
FlaxKarr... | 269 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 33 | 0 |
"""simple docstring"""
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class __A (snake_case_):
... | 350 |
"""simple docstring"""
import argparse
import json
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
fro... | 233 | 0 |
'''simple docstring'''
import os
import time
import numpy as np
import onnxruntime as ort
lowerCamelCase : List[str] = '''1'''
lowerCamelCase : List[str] = '''0'''
lowerCamelCase : Optional[Any] = '''1'''
lowerCamelCase : List[Any] = ort.SessionOptions... | 2 |
def lowerCAmelCase_ ( A_ ,A_):
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(A_ ,int(b / 2)) * actual_power(A_ ,int(b / 2))
else:
return a * actual_power(A_ ,int(b / 2)) * actual_power(A_ ,int(b / 2))
def lowerCAmelCase_ ... | 149 | 0 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, pre... | 212 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=a_ )
class _lowerCamelCase ( a_ ):
_lowerCamelCase :str = field(d... | 212 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
# See all XGLM models at ... | 64 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
_A : int = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that develop... | 202 | 0 |
def lowerCamelCase_ ( _a , _a ):
"""simple docstring"""
lowerCAmelCase__ : str = [0 for i in range(r + 1 )]
# nc0 = 1
lowerCAmelCase__ : str = 1
for i in range(1 , n + 1 ):
# to compute current row from previous... | 211 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def lowerCamelCase_ ( _a , _a ):
"""simple docstring"""
lowerCAmelCase__ : List[... | 211 | 1 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
a_ :str = "2.13.1"
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse("3.7"):
raise ImportWarning... | 277 |
def lowerCAmelCase_ ( snake_case_ ):
if n_term == "":
return []
_A : list = []
for temp in range(int(snake_case_ ) ):
series.append(f'''1/{temp + 1}''' if series else """1""" )
return series
if __name__ == "__main__":
_sna... | 26 | 0 |
"""simple docstring"""
from __future__ import annotations
A: Optional[int] = list[tuple[int, int]]
A: str = [
[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, 0, 0, 0, 0],
[1, 0, 1, 0... | 76 |
"""simple docstring"""
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def _snake_case ( UpperCamelCase : np.ndarray ):
return input_array.reshape((input_array.size, 1) )
def _snake_cas... | 76 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Any:
'''simple docstring'''
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(__lowerCAmelCase , int(b / 2 ) ) * actual_power(__l... | 136 |
"""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 | 1 |
"""simple docstring"""
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_avai... | 368 |
"""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,
XLMRober... | 230 | 0 |
"""simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_peg... | 335 |
"""simple docstring"""
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_devi... | 335 | 1 |
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 enable_full_determin... | 350 |
from bisect import bisect
from itertools import accumulate
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A_ : List[Any] = sorted(zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) , ke... | 65 | 0 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def lowerCamelCase__ ( __lowerCAmelCase : ndarray ):
"""simple docstring"""
return np.dot(__lowerCAmelCase , __lowerCAmelCase )
class _lowerCAmelCase :
... | 231 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .... | 231 | 1 |
'''simple docstring'''
from math import pi, sqrt
def lowercase_ ( lowerCAmelCase__ : float ):
"""simple docstring"""
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
elif num - int(l... | 16 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_UpperCamelCase = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_... | 16 | 1 |
from ...configuration_utils import PretrainedConfig
lowercase__ : Any = {
'''google/tapas-base-finetuned-sqa''': (
'''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-wtq''': (
'''https://huggingface.co/googl... | 338 | 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
lowercase__ : List[Any] = logging.get_logger(__name__)
lowercase__... | 338 | 1 |
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformers.utils import log... | 22 |
def A(__a: Tuple ):
lowerCAmelCase_ = len(__a )
while cur > 1:
# Find the maximum number in arr
lowerCAmelCase_ = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
lowerCAmelCase_ = arr[mi::-1] + arr[mi + 1 : len(__a )]
# Reve... | 22 | 1 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
snake_case : Optional[Any] = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
a... | 94 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( UpperCAmelCase_ : Unio... | 94 | 1 |
"""simple docstring"""
import operator as op
UpperCAmelCase : List[Any] = "scaler.pt"
UpperCAmelCase : List[str] = "pytorch_model"
UpperCAmelCase : int = "random_states"
UpperCAmelCase : Tuple = "optimizer"
UpperCAmelCase : Dict = "scheduler"
UpperCAm... | 313 |
"""simple docstring"""
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
'''simple docstring'''
lowercase_ = 0
if start < ... | 313 | 1 |
'''simple docstring'''
import sys
from collections import defaultdict
class __a :
def __init__( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = []
def UpperCAme... | 125 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class a__ :
"""simple docstring"""
def __init__( self : Tuple , UpperCAmelCase__ : Optional[int] ) ->str:
"""simple docstring"""
... | 245 | 0 |
"""simple docstring"""
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__lowerCAmelCa... | 32 | """simple docstring"""
import enum
import shutil
import sys
__lowerCAmelCase , __lowerCAmelCase : List[str] =shutil.get_terminal_size()
__lowerCAmelCase : Union[str, Any] ={"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}
class ... | 32 | 1 |
"""simple docstring"""
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimens... | 256 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
_UpperCAmelCase : D... | 285 | 0 |
import datasets
from .evaluate import evaluate
lowerCAmelCase : List[Any] = "\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNL... | 354 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 127 | 0 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def a_ ( lowerCamelCase ):
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output , args.con... | 98 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_di... | 131 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
"microsof... | 369 |
"""simple docstring"""
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism... | 108 | 0 |
'''simple docstring'''
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
... | 206 |
"""simple docstring"""
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
_a = logging.get_logger(__name__)
... | 194 | 0 |
"""simple docstring"""
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
UpperCAmelCase__ = logging.get_logger(__name__)
class lowerCAmelCase__ :
... | 40 |
"""simple docstring"""
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, r... | 40 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
_UpperCAmelCase : Optional[int] ... | 174 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
Stabl... | 174 | 1 |
def _SCREAMING_SNAKE_CASE ( lowercase : List[Any] , lowercase : int ):
'''simple docstring'''
lowerCamelCase_ = [1]
for i in range(2 , lowercase ):
factorials.append(factorials[-1] * i )
assert ... | 208 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
lowerCamelCase : Tuple = {
"transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
... | 208 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class SCREAMING_SNAKE_CASE__ :
__lowerCAmelCase : List[str]
__l... | 109 |
'''simple docstring'''
from __future__ import annotations
A_ = list[list[int]]
# assigning initial values to the grid
A_ = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3,... | 139 | 0 |
from collections.abc import Callable
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> float:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = a
SCREAMING_SNAKE_CASE__ = b
if function(UpperCamelCase_ ) =... | 169 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=_UpperCAmelCase )
class lowercase__ ( _UpperCAmelCase ):
A__ : str =field(default="""audio-classific... | 169 | 1 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _snake_case ( ):
A__ = HfArgumentParser(UpperCAmelCase_ )
A__ = parser.parse_args_into_dataclasses()[0]
A__ = Tensor... | 335 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : list[list[int]] , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : set ):
A__ , A__ = len(UpperCAmelCase_ ), len(grid[0] )
if (
min(Upper... | 335 | 1 |
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_si... | 189 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class a ( UpperCAmelCase ):
_lowercase = ["image_proc... | 189 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 13 |
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 UpperCamelCase (lowercase_: str ) -> Dict:
A__ : int = int(lowercase_ )
A__ , ... | 192 | 0 |
def _lowerCamelCase( lowercase__ ) -> list[list]:
'''simple docstring'''
__lowercase= current_set.copy()
for row_index, row in enumerate(lowercase__ ):
__lowercase= row[0]
for column_index, column in enumerate(lowercase__ ):
if magnitude == 0:
_... | 304 |
from __future__ import annotations
from collections.abc import Callable
def _lowerCamelCase( lowercase__ , lowercase__ , lowercase__ , lowercase__ = 1_0_0 , ) -> float:
'''simple docstring'''
__lowercase= x_start
__lowercase= fnc(lowercase... | 304 | 1 |
import numpy as np
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase = 1E-12, __lowerCamelCase = 1_00, ):
assert np.shape(__lowerCamelCase )[0] == np.shape(__lowerCamelCase )[1]
# Ensure proper dimensionality.
assert np.shape(__lowerCamelCase )[0] == np.shape(__lowerCam... | 299 |
import math
import random
def A__ ( __lowerCamelCase, __lowerCamelCase = False ):
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
__UpperCAmelCase = 0.02
def A__ ( __lowerCamelCase, __lowerCamelCase ):
SCREAMING_SNA... | 299 | 1 |
import math
class lowercase_ :
def __init__( self , lowercase_=0 ): # a graph with Node 0,1,...,N-1
_snake_case : Any = n
_snake_case : Union[str, Any] = [
[math.inf for j in range(0 , _snake_case )] for... | 361 | from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
__SCREAMING_SNAKE_CASE : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__SCREAMING_SNAKE_CASE : list[int] = [ord(letter) for le... | 284 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def UpperCamelCase_ ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> str | Literal[False]:
"""simple docstring"""
_UpperCAmelCase ... | 31 | '''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_avai... | 31 | 1 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def lowerCamelCase__ ( snake_case_ : dict ) ... | 238 |
from __future__ import annotations
snake_case_ = [True] * 1000001
snake_case_ = 2
while i * i <= 1000000:
if seive[i]:
for j in range(i * i, 1000001, i):
snake_case_ = False
i += 1
def lowerCamelCase__ ( s... | 238 | 1 |
"""simple docstring"""
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# ... | 202 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
_A : int = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that develop... | 202 | 1 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
print('\nThe shortest path matrix using Floyd Warshall algorithm\n' )
for i in range(SCREAMING_SNAKE_CASE_ ):
for j in range(SCREAMING_SNAKE_CASE_ ):
if dist[i][j] != float('inf... | 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 itertools import product
def a( A : int , A : int ) -> list[int]:
"""simple docstring"""
a = sides_number
a = max_face_number * dice_number
a = [0] * (max_total + 1)
a = 1
a ... | 227 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase: Union[str, Any] = {
"configuration_bridgetower": [
"BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BridgeTowerConfig",
"BridgeT... | 227 | 1 |
"""simple docstring"""
from __future__ import annotations
import requests
__lowerCAmelCase : List[Any] =set(
"""approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicke... | 370 | """simple docstring"""
def UpperCAmelCase__ ( lowerCAmelCase__ :list[list] ) -> list[list]:
'''simple docstring'''
lowercase = current_set.copy()
for row_index, row in enumerate(lowerCAmelCase__ ):
lowercase = row[0]... | 32 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/mai... | 16 |
"""simple docstring"""
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class __A ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase ( s... | 16 | 1 |
import os
_lowerCamelCase : List[Any] = {"I": 1, "V": 5, "X": 1_0, "L": 5_0, "C": 1_0_0, "D": 5_0_0, "M": 1_0_0_0}
def _UpperCAmelCase (UpperCamelCase_ : str ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = 0
_lowerCAmelCase : Dict =... | 159 |
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def _UpperCAmelCase (UpperCamelCase_ : Sequence[float] , UpperCamelCase_ : int , UpperCamelCase_ : int ):
'''simple docstring'''
... | 159 | 1 |
"""simple docstring"""
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
__SCREAMING_SNAKE_CASE =WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])
def lowercase__( __SCR... | 213 |
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def UpperCamelCase (lowercase_: int , lowercase_: Dict , lowercase_: Tuple ) -> ... | 192 | 0 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
_UpperCAmelCase : str = input("""Enter image url: """).strip()
print(F'''Downloading image from {url} ...''')
_UpperCAmelCase : Tuple = BeautifulSoup(requests.get(url... | 200 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase... | 200 | 1 |
"""simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE ) ->Any:
assert column_title.isupper()
a__: Dict = 0
a__: int = len(A__ ) - 1
a__: int = 0
while index >= 0:
a__: Tuple = (ord(column_title[index] ) - 64) * pow(26 , A__ )
a... | 290 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokeniz... | 104 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeat... | 312 | """simple docstring"""
from __future__ import annotations
import math
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> int:
if depth < 0:
raise ValueError('Depth cannot be less than 0' )
if len(Uppe... | 312 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 210 | import qiskit
def UpperCAmelCase ( lowercase , lowercase ):
"""simple docstring"""
__lowercase = qiskit.Aer.get_backend('''aer_simulator''' )
# Create a Quantum Circuit acting on the q register
__lowercase = qiskit.QuantumCircuit(lowercas... | 210 | 1 |
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate mo... | 330 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
... | 330 | 1 |
"""simple docstring"""
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import ... | 96 |
"""simple docstring"""
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
_A : Optional[int] = logging.getLogger(__name__)
class a__ ( a_ ):
def _... | 202 | 0 |
import os
import sys
_A = os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTokenizer,
... | 352 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCamelCase__ ( a__ : Dict ) -> List[Any]:
UpperCamelCase_ = {}
UpperCamelCase_ ... | 261 | 0 |
from ..utils import DummyObject, requires_backends
class __lowercase ( metaclass=UpperCAmelCase_ ):
"""simple docstring"""
_UpperCAmelCase : int = ['''speech''']
def __init__( self : Optional[int] , *lowerCAmelCase__ : int , **lowerCAmelCase_... | 13 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : Optional[int] = {"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}
try:
if not is_torch_available():
rais... | 13 | 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.modeling_tf_... | 1 |
"""simple docstring"""
import os
from math import logaa
def _snake_case ( lowercase__ : str = "base_exp.txt" ) -> int:
'''simple docstring'''
lowerCAmelCase_ :float = 0
lowerCAmelCase_ :Union[str, Any] = 0
for i, line ... | 1 | 1 |
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self , UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__=None ) -> Tuple:
lowerCamelCase : Tuple = data
lowerCamelCase : str = previous... | 48 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_bas... | 169 | 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 .tok... | 359 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {
'''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}
try:
if... | 244 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : int = logging.get_logger(__name__)
a__ : Optional[int] = {
'''huggingface/time-series-transformer-tou... | 161 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
... | 329 | 0 |
"""simple docstring"""
import math
def snake_case ( A__ ):
UpperCAmelCase_ : Union[str, Any] = 0
UpperCAmelCase_ : Tuple = 0
while num > 0:
UpperCAmelCase_ : int = num % 8
UpperCAmelCase_ : List[str] = octal +... | 253 |
"""simple docstring"""
def snake_case ( A__ ):
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(A__ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('''doctest''').testmod()
| 253 | 1 |
'''simple docstring'''
import qiskit
def UpperCamelCase( UpperCAmelCase_ = 2 ):
UpperCAmelCase : Union[str, Any] = qubits
# Using Aer's simulator
UpperCAmelCase : List[str] = qiskit.Aer.get_backend('aer_simulator' )
# Creating a Quantum Circuit actin... | 151 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowercase__ = {"processing_layoutxlm": ["Layou... | 151 | 1 |
'''simple docstring'''
from typing import Any
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : list , _UpperCamelCase : list , _UpperCamelCase : dict , _UpperCamelCase : dict , _UpperCamelCase : dict , ) -> list:
'''s... | 31 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
cl... | 31 | 1 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/... | 122 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_tor... | 122 | 1 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class a :
__lowerCAmelCase : List[str]
__lowerCAmelCase : Opti... | 44 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A__ = logging.ge... | 44 | 1 |
"""simple docstring"""
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 : Dict ... | 291 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
lowercase : Dict = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to wors... | 42 | 0 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__A : Tuple = logging.get_logge... | 352 |
'''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, to... | 8 | 0 |
"""simple docstring"""
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
f... | 256 | """simple docstring"""
def lowercase ( a__ : float , a__ : float ) -> float:
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
return (bulk_modulus / density) **... | 256 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"""sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/m... | 361 |
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 SCREAMING_SNAKE_CASE_ ( snake_case_ ... | 88 | 0 |
"""simple docstring"""
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
__lowerCamelCase = re.compile(R"^(?P<major>\d+)" R"\.(?P<minor>\d+)" R"\.(?P<patch>\d+)$")
@total_ordering
@dat... | 221 | """simple docstring"""
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def UpperCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
... | 221 | 1 |
'''simple docstring'''
from __future__ import annotations
import os
from typing import Any
import requests
UpperCAmelCase : List[Any] = 'https://api.github.com'
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
UpperCAmelCase : Dict ... | 331 |
'''simple docstring'''
class lowerCAmelCase__ : # Public class to implement a graph
"""simple docstring"""
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : list[list[bool]] ) -> None:
"""simple... | 331 | 1 |
"""simple docstring"""
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 i... | 255 |
"""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 | 1 |
"""simple docstring"""
import requests
def __UpperCAmelCase ( snake_case_ : str , snake_case_ : str ) -> None:
"""simple docstring"""
_lowerCAmelCase = {"""Content-Type""": """application/json"""}
_lowerCAmelCase = requests... | 317 |
"""simple docstring"""
from __future__ import annotations
class __lowerCamelCase :
def __init__(self , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase = text, pattern
_lowerCAmelCase , _low... | 317 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ = {"""configuration_opt""": ["""OPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OPTCon... | 303 |
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_collator... | 303 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_availab... | 125 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
UpperCamelCase = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2... | 125 | 1 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import Backb... | 253 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__magic_name__)
class _A ( __magic_name__):
# `task` is not a ClassVar since we want it to be part of the `asdic... | 253 | 1 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
__lowercase = datasets.utils.logging.get_logger(__name__)
@dataclass
class lowerCamelCase_ ( datasets.... | 351 | # Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | 105 | 0 |
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
UpperCamelCase__ = '%20'.join(argv[1:]) if len(argv) > 1 else quote(str(input('Search: ')))
... | 65 | import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
Up... | 65 | 1 |
"""simple docstring"""
import argparse
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... | 362 |
"""simple docstring"""
import os
from math import logaa
def _snake_case ( lowercase__ : str = "base_exp.txt" ) -> int:
'''simple docstring'''
lowerCAmelCase_ :float = 0
lowerCAmelCase_ :Union[str, Any] = 0
for i, line ... | 1 | 0 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers... | 36 |
from math import loga
def lowerCamelCase__ ( snake_case_ : int ) -> int:
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(snake_case_ , snake_case_ ):
raise TypeError('''Input value mus... | 24 | 0 |
'''simple docstring'''
def a__ ( lowercase : int = 10, lowercase : int = 1000, lowercase : bool = True ) -> int:
"""simple docstring"""
assert (
isinstance(lowercase, lowercase )
and isinstance(lowercase, lowercase )
and isinstance(lowercas... | 287 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, 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()... | 287 | 1 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepar... | 44 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 331 | 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 a_ ( _lowerCAmelCase , unittest.TestCase ):
... | 147 |
'''simple docstring'''
import numpy as np
def UpperCAmelCase_ ( __lowerCamelCase : str ,__lowerCamelCase : Optional[Any] ,__lowerCamelCase : int ,__lowerCamelCase : List[str] ,__lowerCamelCase : List[Any] ):
lowercase_ :Dict = int(np.ceil((x_end - xa) ... | 147 | 1 |
'''simple docstring'''
from ....utils import logging
lowerCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
def __init__( self : Tuple , __a : int , ... | 63 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase_ : List[str] = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/m... | 63 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {}
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :List[Any] = ... | 1 |
"""simple docstring"""
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextMode... | 1 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=A_ )
class UpperCAmelCase__ ( A_ ):
"""simple docstring"""
UpperCAmelCase__ : str = field(default="lan... | 62 |
from importlib import import_module
from .logging import get_logger
__lowerCAmelCase : str =get_logger(__name__)
class _lowercase :
'''simple docstring'''
def __init__( self :List[Any] , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :str=None ) -> int:
... | 9 | 0 |
import datasets
from .evaluate import evaluate
lowerCamelCase__ : Dict = '\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNLP... | 210 |
import gc
import threading
import time
import psutil
import torch
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Optional[Any] ):
SCREAMING_SNAKE_CASE_ = psutil.Process()
SCREAMING_SNAKE_CASE_ = False
def ... | 210 | 1 |
"""simple docstring"""
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
_A : Optional[int] = """\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing ... | 202 |
"""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_... | 202 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Dict = logging.get_logger(__name__)
lowercase : Union[str, Any] = {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}
class lowerCamelCase__ ( _... | 369 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int , _lowerCamelCase : int) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0) == 0)
def _SCREAMING_SNAKE_CASE ( ) -> None:
'''simple docstring'''
... | 151 | 0 |
"""simple docstring"""
def A_ ( _lowercase ):
'''simple docstring'''
if not isinstance(_lowercase, _lowercase ):
raise TypeError("""Input value must be an 'int' type""" )
snake_case_ :Any = 0
while number:
position += 1
number >>... | 66 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case = {
'''configuration_squeezebert''': [
'''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SqueezeBertConfig''',... | 283 | 0 |
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ):
lowercase__ = []
if len(SCREAMING_SNAKE_CASE_ ) == 1:
return [nums.copy()]
for _ in range(len(SCREAMING_SNAKE_CASE_ ) ):
lowercase__ = nums.pop(0 )
lowercase__ = permute(SCREAMING_SNAKE_CA... | 224 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}
class _snake_case ( lowercase__):
Uppe... | 224 | 1 |
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