code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.... | 116 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_model... | 285 | 0 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
__UpperCAmelCase = TypeVar("KT")
__UpperCAmelCase = TypeVar("VT")
class UpperCamelCase__ ( Generic[KT, VT] ):
"""simple docstring"""
def __init__( self , ... | 597 |
import socket
def A__ ( ):
SCREAMING_SNAKE_CASE_ = socket.socket(socket.AF_INET, socket.SOCK_STREAM )
SCREAMING_SNAKE_CASE_ = socket.gethostname()
SCREAMING_SNAKE_CASE_ = 1_23_12
sock.connect((host, port) )
sock.send(B'''Hello server!''' )
with open('''Received_file''', ... | 597 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def lowerCAmelCase_ ( _lowerCamelCase: Optional[int] , _lowerCamelCase: str , _lowerCamelCase: str , _lowerCamel... | 578 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _UpperCamelCase ( lowerCamelCase__ ):
'''simple docstring'''
_A : int = (DDPMScheduler,)
def UpperCamelCase__ ( self : U... | 578 | 1 |
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_available, is_vision_availab... | 707 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import Fla... | 188 | 0 |
"""simple docstring"""
def A ( __snake_case: str ) -> str:
"""simple docstring"""
if collection == []:
return []
# get some information about the collection
__magic_name__ = len(lowerCAmelCase__ )
__magi... | 545 |
'''simple docstring'''
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
... | 75 | 0 |
'''simple docstring'''
import os
import sys
import unittest
A : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_... | 163 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMi... | 163 | 1 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
_lowerCamelCase : int = collections.namedtuple("""_Datasets""", [... | 87 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
snake_case__ = logging.getLogger(__n... | 583 | 0 |
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,
ge... | 94 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Tuple = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available():
raise OptionalD... | 94 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __Upp... | 366 |
'''simple docstring'''
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 lowerCamelCase( SCREAMING_SNAKE_CASE_ ) -> str:
A_ = int(SCREAMING_SNA... | 366 | 1 |
def A_ ( _lowerCAmelCase : Optional[int] = 10_00 ):
"""simple docstring"""
_a , _a = 1, 1
_a = []
for i in range(1, n + 1 ):
_a = prev_numerator + 2 * prev_denominator
_a = prev_numerator + prev_denomina... | 720 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : int = 10**12 ):
"""simple docstring"""
_a = 1
_a = 0
_a = 1
_a = 1
while numerator <= 2 * min_total - 1:
prev_numerator += 2 * numerator
numerator += ... | 285 | 0 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
snake_case : List[Any] = 1_00
snake_case : Any = set(range(3, NUM_PRIMES, 2))
primes.add(2)
snake_case : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime no... | 605 |
def SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case ) -> int:
def count_of_possible_combinations(snake_case ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for i... | 375 | 0 |
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
__a = logging.getLogger()
def a ( snake_case__: ... | 705 |
def a ( snake_case__: int ):
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
raise ValueError('''check_bouncy() accepts only integer arguments''' )
lowercase_ = str(snake_case__ )
lowercase_ = ... | 409 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase ={
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ConditionalD... | 208 | from __future__ import annotations
import bisect
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] , lowerCAmelCase_: int , lowerCAmelCase_: int = 0 , lowerCAmelCase_: int = -1 ):
if hi < 0:
snake_case_ : Any = len(lowerCA... | 666 | 0 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils impor... | 718 |
'''simple docstring'''
def UpperCamelCase_ ( A__ : int ):
'''simple docstring'''
assert isinstance(A__ , A__ ), f'The input value of [n={number}] is not an integer'
if number == 1:
return 2
elif number < 1:
lowerCAmelCase_ ... | 398 | 0 |
from __future__ import annotations
from math import pi, sqrt
def snake_case_ ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) -> tuple:
if inductance <= 0:
raise ValueError("Inductance cannot be 0 or negative" )
elif capacitance <= 0:
... | 397 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase( snake_case_ ):
"""simp... | 397 | 1 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCamelCase ( _A , _A , _A ):
# Initialise PyTorch model
lowerCAmelCase_ = TaConfi... | 325 |
class A :
def __init__( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = name
lowerCAmelCase_ = value
lowerCAmelCase_ = weight
def __repr__( self ):
... | 325 | 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, Patchi... | 605 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
... | 605 | 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... | 713 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
if not isinstance(snake_case__ , snake_case__ ) or number < 0:
raise ValueError("""Input must be a non-negative integer""" )
_snake_case : Dict = 0
... | 28 | 0 |
'''simple docstring'''
from math import factorial
def A_( A : int = 20):
UpperCamelCase = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
UpperCamelCase = n // 2
return int(factorial(A) / (factori... | 3 | '''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ : Any = {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
try:
... | 390 | 0 |
'''simple docstring'''
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowercase__ = re.compile(R"\b(a|an|the)\b", re.UNICODE)
lowercase__ = None
def __UpperCamelCase ( ) -> Union[str, Any]:
'''simple... | 276 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ = {
"configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
"feature_extraction_mctct": ["MCTCTFeatureExtractor"],
... | 276 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {'''configuration_fnet''': ['''FNET_PRETRAINED_CONFI... | 247 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
'''facebook/con... | 247 | 1 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class _UpperCamelCase :
def __init__( self :Optional[Any] , lowerCamelCase :Collection[float] | None = None ) -> List[Any]:
if components is No... | 715 |
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 ( _lowerCAmelCase : Any , _lowerCAmelCase : List[st... | 364 | 0 |
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
_lowerCamelCase : str = datasets.logging.get_logger(__name__)
_lowerCamelCase : List[Any] = "\\n@inproceedings{bleurt,\n title={BLEURT: Learning Robust Metrics for Text Generat... | 429 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : List[str] = logging.get_logger(__name__)
_lowerCamelCase : Optional[int] = {}
class __snake_case (_a ):
lowerCAmelCase__ = "llama"
lowerCAmelCase__ = [... | 429 | 1 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization... | 715 |
from sklearn.metrics import mean_squared_error
import datasets
__a : Union[str, Any] = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blond... | 559 | 0 |
def __UpperCamelCase ( _lowerCAmelCase ):
"""simple docstring"""
try:
UpperCAmelCase = float(_lowerCAmelCase )
except ValueError:
raise ValueError("Please enter a valid number" )
UpperCAmelCase = decimal - int(_lowerCAmelCase )
if fractional_par... | 333 |
from __future__ import annotations
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
UpperCAmelCase = []
create_all_state(1 , _lowerCAmelCase , _lowerCAmelCase , [] , _lowerCAmelCase )
return result
def __UpperC... | 333 | 1 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
... | 719 | """simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
S... | 104 | 0 |
'''simple docstring'''
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
UpperCamelCase_ = logging.getLogger(__name__)
if is_torch_... | 92 |
def __a ( SCREAMING_SNAKE_CASE ) -> list:
'''simple docstring'''
if len(SCREAMING_SNAKE_CASE ) < 2:
return collection
def circle_sort_util(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> bool:
__UpperCAmelCase ... | 303 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def __a ( A__ , A__ , A__ , A__ , A__ ) -> int:
if depth < 0:
raise ValueError("Depth cannot be less than 0" )
if len(A__ ) == 0:
raise ValueError("Scores cannot be empty" )
... | 159 |
'''simple docstring'''
import torch
from torch import nn
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE :... | 159 | 1 |
'''simple docstring'''
import os
def UpperCamelCase ( ) -> str:
'''simple docstring'''
with open(os.path.dirname(lowercase_ ) + '''/grid.txt''' ) as f:
lowercase =[] # noqa: E741
for _ in range(2_0 ):
l.append([int(lowercase_ ) for x in f.readline().split()] )
lowe... | 72 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
_lowerCamelCase ={
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bilinear""": PIL.Image.Resampling.BILI... | 681 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
snake_case_ : Optional[Any] = {
"configuration_squeezebert": [
"SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SqueezeBertConfig",
"Squ... | 253 |
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | 253 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/config.json',
'xln... | 348 |
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def __UpperCamelCase ( lowercase... | 600 | 0 |
import logging
from transformers.configuration_utils import PretrainedConfig
_lowerCamelCase : Union[str, Any] = logging.getLogger(__name__)
class __snake_case (_a ):
lowerCAmelCase__ = "masked_bert"
def __init__( self : Union[str, Any] , _UpperCAmelC... | 196 |
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_available
from ...test_config... | 196 | 1 |
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
fro... | 60 |
import tensorflow as tf
from ...tf_utils import shape_list
class __lowerCAmelCase ( tf.keras.layers.Layer ):
def __init__(self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=1 , __magic_name__=False , **__magic_name__ ... | 60 | 1 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
... | 714 |
"""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
_snake_case = WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])
def sna... | 659 | 0 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE__ ( metaclass=_UpperCAmelCase ):
A_ : int = ['torch', 'transformers', 'onnx']
def __init__(self : Tuple , *a__ : List[Any] , **a__ : Optional[int] ):
... | 592 |
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable... | 592 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a: int = logging.get_logger(__name__)
_a: Optional[int] = {
"""BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-base/blob... | 704 |
from sklearn.metrics import mean_squared_error
import datasets
_a: Any = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.... | 268 | 0 |
import argparse
import gc
import json
import os
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... | 276 |
"""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,
StableDiffusionXLImga... | 594 | 0 |
from __future__ import annotations
A : Optional[int] = 8.988e9 # units = N * m^s * C^-2
def __lowerCamelCase ( __a :float , __a :float , __a :float , __a :float ) -> dict[str, float]:
"""simple docstring... | 247 |
def __lowerCamelCase ( __a :int ) -> list[int]:
"""simple docstring"""
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
A__ = [True] * (num + 1)
A__ = 2
while p * p <= num:
if primes[p]... | 247 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Optional[int] , a_ : int , a_ : int , a_ : float = 0 ):
"""simple docstri... | 69 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
snake_case_ : List[str] = logging.get_logger(__name__)
class lowercase__ ( snake_case_ ):
'''simple docstring'''
def ... | 212 | 0 |
def A ( snake_case__ : Optional[Any] ) -> int:
'''simple docstring'''
__snake_case = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def A ( snake_case__ : List[Any] = 100 ) -> int:
'''simple docstring'... | 712 |
def A ( snake_case__ : int ) -> bool:
'''simple docstring'''
if p < 2:
raise ValueError('p should not be less than 2!' )
elif p == 2:
return True
__snake_case = 4
__snake_case = (1 << p) - 1
for _ in range(p - 2 ):
__snake_cas... | 676 | 0 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, requ... | 274 | '''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def snake_case_ ( __snake_case : float , __snake_case : float , __snake_case : int) -> float:
lowerCAmelCase_ = x
lowerCAmelCase_ = y
for step in range(__snake_case... | 274 | 1 |
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_pegasus import DEFAULTS, task_specific_par... | 708 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''tanreinama/GPTSAN-2.8B-spout_is_uniform''': (
'''https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/conf... | 505 | 0 |
'''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,
)
lowercase__ : ... | 8 |
'''simple docstring'''
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from trans... | 28 | 0 |
"""simple docstring"""
from collections.abc import Iterable
from typing import Any
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__(self , lowerCAmelCase__ = None ):
'''simple docstring'''
_UpperCamelCase : Dict ... | 717 |
"""simple docstring"""
import numpy as np
def __lowerCAmelCase ( __lowerCAmelCase : np.ndarray ) -> np.ndarray:
return 1 / (1 + np.exp(-vector ))
def __lowerCAmelCase ( __lowerCAmelCase : np.ndarray ) -> np.ndarray:
return vector * s... | 239 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_... | 29 |
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors i... | 470 | 0 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def lowercase ( SCREAMING_SNAKE_CASE__ : int = 8 ) -> str:
_snake_case : List[Any] = ascii_letters + digits + punctuation
return ... | 198 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also sa... | 198 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
... | 119 |
'''simple docstring'''
from __future__ import annotations
def __UpperCamelCase ( lowercase__ : float, lowercase__ : float, lowercase__ : float ):
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError('One and on... | 119 | 1 |
"""simple docstring"""
from manim import *
class __snake_case ( __lowerCAmelCase ):
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: str = Rectangle(height=0.5 , width=0.5)
a__: int = Rectangle(hei... | 217 | """simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sent... | 217 | 1 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class __SCREAMING_SNAKE_CASE (__A ):
"""simple docstring"""
_a : List[Any] = '''MCTCTFeatureExtractor'''
_a : List[Any] = '''A... | 536 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
__lowerCAmelCase ... | 536 | 1 |
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 Bac... | 707 |
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , a : int ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = size
SCREAMING_SNAKE_CASE : Union[str, Any] = [0] ... | 193 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import TypedDict
class A_ ( _a ):
lowerCAmelCase__ = 42
lowerCAmelCase__ = 42
def lowerCamelCase_( _lowerCamelCase ) -> list[str]:
'''simple docstring'''
if no... | 46 |
'''simple docstring'''
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_availab... | 459 | 0 |
'''simple docstring'''
def _UpperCamelCase ( lowerCAmelCase__: Optional[Any] = 100 ) -> int:
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 0
for i in range(1 ,n + 1 ):
sum_of_squares += i**2
sum_of_ints +... | 712 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class snake_case ( datasets.BeamBasedBuilder ):
... | 238 | 0 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCAmelCase__: Optional[int] = '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blond... | 345 |
'''simple docstring'''
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images... | 460 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
tr... | 521 |
'''simple docstring'''
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE... | 521 | 1 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : int ) -> List[Any]:
return str(lowerCamelCase_ ) == str(lowerCamelCase_ )[::-1]
def snake_case_ ( _lowerCAmelCase : int ) -> Union[str, Any]:
return int(lowerCamelCase_ ) + int(str... | 127 |
'''simple docstring'''
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_d... | 502 | 0 |
'''simple docstring'''
import numpy as np
UpperCamelCase__: Any = [
["a", "b", "c", "d", "e"],
["f", "g", "h", "i", "k"],
["l", "m", "n", "o", "p"],
["q", "r", "s", "t", "u"],
["v", "w", "x", "y", "z"],
]
class SCREAMING_SNAKE_CASE:
... | 528 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__: Optional[Any] = logging.get_logger(__name__)
UpperCamelCase__: Tuple = {
"huggingface/t... | 528 | 1 |
SCREAMING_SNAKE_CASE :Union[str, Any] = {str(digit): digit**5 for digit in range(10)}
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(SCREAMING_SNAKE_CASE_ ) )
d... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCamelCase_ = _modexpt(SCREAMING_SNAKE_CASE_ , ... | 628 | 1 |
"""simple docstring"""
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
lowercase_ : int ... | 717 |
"""simple docstring"""
from collections.abc import Iterable
from typing import Any
class UpperCamelCase :
def __init__( self , snake_case__ = None ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : List[str] = value
_SCREAMING_SNAKE_CASE ... | 295 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class lowerCamelCase :
def __init__( self : Optional[Any] , __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : float = 0 ... | 196 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
snake_case = datasets.load_iris()
snake_case = np.array(data["""data"""])
snake_case = np.array(data["""target"""])
snake_case = data["""... | 62 | 0 |
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
pass
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
pass
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Any ):
__snake_case : int = [
... | 708 | from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
def __lt__( self : Tuple , _lowerCAmelCase : Optional[int] ):
ret... | 390 | 0 |
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, SingleSente... | 79 |
'''simple docstring'''
from __future__ import annotations
def snake_case ( a_ : list[int] , a_ : list[int] , a_ : list[int] , a_ : list[list[str]] , a_ : int , ) -> None:
"""simple docstring"""
UpperCamelCase_ : List[Any]... | 208 | 0 |
class _lowerCAmelCase :
def __init__( self : int , __snake_case : int , __snake_case : List[Any]=None , __snake_case : List[str]=None ):
lowerCamelCase :Union[str, Any] = data
lowerCamelCase :str = previous
... | 710 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
if not is_torch_available(... | 49 | 0 |
def a_ ( lowerCAmelCase_ : str ):
if len(lowerCAmelCase_ ) <= 1:
return [tuple(lowerCAmelCase_ )]
__lowerCAmelCase = []
def generate(lowerCAmelCase_ : int, lowerCAmelCase_ : List[str] ):
if k == 1:
res.append(tuple(a... | 53 | 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
if is_torch_available():
import torch
if is_vision_available... | 192 | 0 |
_lowercase : int = [0, 2, 4, 6, 8]
_lowercase : int = [1, 3, 5, 7, 9]
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
... | 720 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
_lowercase = Lock()
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__):
... | 683 | 0 |
"""simple docstring"""
from __future__ import annotations
__snake_case = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
class _lowerCAmelCase :
def __init__( self ... | 178 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : Union[str, Any] = logging.get_logger(__name__)
snake_case__ : Tuple = {
'microsoft/unispeech-sat-base-100h-libri-ft': (
'https://hugging... | 278 | 0 |
'''simple docstring'''
import os
import sys
import unittest
UpperCamelCase__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_dummies # noqa: E402
from check_dummies import create_dummy_f... | 312 |
'''simple docstring'''
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git... | 312 | 1 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def lowerCamelCase_ ( __UpperCamelCase ):
A_ , A_ = analyze_text(_SCREAMING_SNAKE_CASE )
A_ = list(''' ''' + ascii_lowercase )
# wha... | 141 |
'''simple docstring'''
import argparse
import json
from tqdm import tqdm
def _snake_case ( ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--src_path... | 433 | 0 |
def UpperCamelCase_ ( a_ = 1000 ) ->int:
A , A =1, 1
A =[]
for i in range(1 , n + 1 ):
A =prev_numerator + 2 * prev_denominator
A =prev_numerator + prev_denominator
if len(str(a_ ) ) > len(str(a_ ) ):
result.append(a_ )
A =numerator
A =denom... | 689 |
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.get_logger(__name__)
__a ... | 689 | 1 |
import os
import pytest
from attr import dataclass
_snake_case = '''us-east-1''' # defaults region
@dataclass
class lowerCAmelCase_ :
"""simple docstring"""
UpperCAmelCase__ = 42
UpperCAmelCase__ = "arn:aws:iam::558105141721:role/sagemaker_executio... | 383 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import M... | 343 | 0 |
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
import pyarrow as pa
... | 701 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from... | 601 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
... | 284 |
'''simple docstring'''
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
_UpperCamelCase : Optional[Any] = "\\n@inproceedings{snover-etal-2006-study,\n title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n author ... | 284 | 1 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
snake_case_ : Optional[int] = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership f... | 169 |
from ..utils import DummyObject, requires_backends
class __snake_case ( metaclass=a ):
UpperCAmelCase__ : List[str] = ['''torch''', '''torchsde''']
def __init__( self : Optional[Any] , *_snake_case : Tuple , **_snake_case : List[Any]... | 169 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
A = logging.get_logger(__name__)
A = {
"Visual-Attention-Network/van-base": (
"https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
),
}
class lowercase__ ( ... | 475 |
import math
import unittest
def __UpperCAmelCase ( __A ) -> bool:
'''simple docstring'''
assert isinstance(__A , __A ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
... | 475 | 1 |
"""simple docstring"""
def lowercase__ ( lowerCAmelCase : list[list[int]] , lowerCAmelCase : int , lowerCAmelCase : int , lowerCAmelCase : list[int] ) -> bool:
"""simple docstring"""
if graph[path[curr_ind - 1]][next_ver] == 0:
... | 183 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def lowercase__ ( lowerCAmelCase : list[float] ) -> Dict:
"""simple docstring"""
return np.maximum(0 , lowerCAmelCase )
if __name__ == "__main__":
print(np.arr... | 183 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
lowercase : Tuple = log... | 302 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def A_ ( A__ , A__ , A__ , A__ , A__ = None , A__ = None , A__ = None , ) -> List[Any]:
if config_... | 302 | 1 |
lowerCAmelCase__ = [
(1_0_0_0, """M"""),
(9_0_0, """CM"""),
(5_0_0, """D"""),
(4_0_0, """CD"""),
(1_0_0, """C"""),
(9_0, """XC"""),
(5_0, """L"""),
(4_0, """XL"""),
(1_0, """X"""),
(9, """IX"""),
(5, """V"""),
(4, """IV"""),
(1, """I"""),
]
def lo... | 648 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""bert-base-uncased""": """https://huggingface.co... | 648 | 1 |
"""simple docstring"""
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('''.''')
def A__ ( A__ ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = ... | 426 |
"""simple docstring"""
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def A__ ( A__ , A__ , **A__ ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase = AutoConfig.from_pretrained(A__ , **A__ )
_UpperC... | 426 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : int = logging.get_logger(__name__)
__UpperCamelCase : List[Any] = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-... | 704 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__UpperCamelCase : Optional[Any] = {
'''configuration_convnext''': ['''CONVNEXT... | 417 | 0 |
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common import ConfigTester... | 136 |
def lowerCAmelCase_ ( _lowercase : list , _lowercase : int , _lowercase : int = 0 , _lowercase : int = 0) -> int:
"""simple docstring"""
a__ : str = right or len(_lowercase) - 1
if left > right:
return -1
elif list_dat... | 136 | 1 |
'''simple docstring'''
def A__ ( A : str , A : str):
'''simple docstring'''
if not (isinstance(A , A) and isinstance(A , A)):
raise ValueError("longest_common_substring() takes two strings for inputs")
UpperCamelCase : Optional[int] ... | 435 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
lowerCAmelCase_ = (
'4S 3H 2C 7S 5H',
'9D 8H 2C 6S 7H',
'2D 6D 9D TH 7D',
'TC 8C 2S JH 6C',
'JH 8S TH AH QH',
'TS KS 5S 9S AC',
'KD 6S 9... | 435 | 1 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
... | 63 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a : Optional[int] = logging.get_logger(__name__)
class a ( lowercase__ ):
... | 63 | 1 |
from collections import defaultdict
def lowercase__( A , A ):
snake_case__ : Dict = first_str.lower().strip()
snake_case__ : int = second_str.lower().strip()
# Remove whitespace
snake_case__ : str = first_str.replace('... | 303 |
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowerCamelCase : Dict = {
'facebook/maskformer-swin-base-ade': (
'htt... | 303 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_f... | 628 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def lowerCA... | 628 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"""google/pix2struct-textcaps-base""": (
"... | 715 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
SCREAMING_SNAKE_CASE__ = get_logger(__name__)
class __lowerCamelCase ( enum.Enum ):
"""simple docstring"""
lowerCAmelCase__ =... | 601 | 0 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
__lowerCAmelCase : Tuple = '''\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Che... | 58 |
'''simple docstring'''
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__SCREAMING_SNAKE_CASE = 4
__SCREAMING_SNAKE_CASE = 3
class low... | 688 | 0 |
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCamelCase ( __snake_case , unittest.TestCase ):
... | 546 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def _lowerCAmelCase ( UpperCamelCase__: Union[str, Any] , UpperCamelCase__: Optional[int] , UpperCamelCase__: Tuple , UpperCamelCase__: Any=5 ) -> Optional[Any]:
"""s... | 546 | 1 |
'''simple docstring'''
def a ( _UpperCAmelCase = 1_0 , _UpperCAmelCase = 1_0_0_0 , _UpperCAmelCase = True ) -> int:
"""simple docstring"""
assert (
isinstance(_UpperCAmelCase , _UpperCAmelCase )
and isinstance(_UpperCAmelCase ,... | 697 |
'''simple docstring'''
from __future__ import annotations
def a ( _UpperCAmelCase ) -> bool:
"""simple docstring"""
a_ = len(_UpperCAmelCase )
# We need to create solution object to save path.
a_ = [[0 for _ in range(_UpperCAmelCase )] for _ in r... | 697 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
a__ : List[Any] =l... | 434 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
@staticmethod
@abstractmethod
def _lowerCamelCase ( __A : ArgumentParser ):
raise NotImplementedError()
@abst... | 434 | 1 |
"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteSch... | 299 |
"""simple docstring"""
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_mo... | 299 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCamelCase = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SwiftFormerConfig""... | 717 |
'''simple docstring'''
def _A ( _lowerCAmelCase ):
"""simple docstring"""
if number > 0:
raise ValueError('input must be a negative integer' )
__lowercase =len(bin(_lowerCAmelCase )[3:] )
__lowercase =bin(abs(_lowerCAmelCase ... | 454 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/m... | 186 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Debe... | 665 | 0 |
'''simple docstring'''
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
SCREAMING_SNAKE_CASE_ : List[str] = logging.getLogger(__name__)... | 702 | '''simple docstring'''
def UpperCamelCase__ ( _lowercase : List[str] ) -> Union[str, Any]:
__UpperCAmelCase: int = []
__UpperCAmelCase: List[Any] = []
__UpperCAmelCase: List[Any] = {
"""^""": 3,
"""*""": 2,
"""/""": 2,
"""%""": 2,
"""+"""... | 466 | 0 |
from collections.abc import Sequence
def UpperCamelCase ( _A : Sequence[int] | None = None )-> int:
"""simple docstring"""
if nums is None or not nums:
raise ValueError("Input sequence should not be empty" )
A__ = nums[0]
for i in range(1 ... | 491 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
... | 491 | 1 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class _A ( _UpperCAmelCase ):
"""simple docstring"""
UpperCamelCase_ : Dict = '''WhisperFeatureExtractor'''
UpperCamelCase_ : Tuple = '''WhisperTokenizer'''
d... | 93 | """simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import requir... | 93 | 1 |
def lowercase ( _lowerCAmelCase ): # noqa: E741
UpperCAmelCase__ = len(_lowercase )
UpperCAmelCase__ = 0
UpperCAmelCase__ = [0] * n
UpperCAmelCase__ = [False] * n
UpperCAmelCase__ = [False] * n
def dfs(_lowerCAmelCase , _lowerCAmelCase , ... | 392 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'google/bit-50': 'https://huggi... | 520 | 0 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
lowerCamelCase_ = TypeVar('''T''')
class __A( Generic[T] ):
"""simple docstring"""
def __i... | 86 |
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_up... | 86 | 1 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tok... | 308 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
__UpperCAmelCase = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIV... | 308 | 1 |
"""simple docstring"""
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __snake_case ( _lowercase):
@require_torch
def SCREAMING_SNAKE_CASE ( self : Tuple... | 598 |
"""simple docstring"""
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
lowerCAmelCase__ = False
class __snake_case ( unittest.TestCase):
d... | 598 | 1 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_avai... | 129 |
"""simple docstring"""
from importlib import import_module
from .logging import get_logger
__magic_name__ = get_logger(__name__)
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Dict , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : int=None ... | 129 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
snake_case_ : List[Any] = {
"configuration_clip... | 705 |
# Imports
import numpy as np
class __lowerCamelCase :
def __init__( self , __snake_case=None , __snake_case=None , __snake_case=None , __snake_case=None , __snake_case=None ) -> Union[str, Any]:
"""simple docstring"""
... | 166 | 0 |
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