code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE :int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Any = {
'''naver-clova-ix/donut-base''': '''https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.j... | 159 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=_a )
class _SCREAMING_SNAKE_CASE ( _a ):
snake_case__ : str = field(default="""automatic-s... | 38 | 0 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
a_ = logging.get_logger(__name__)
a_ = OrderedDict(
[
# Base model mapping... | 175 |
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 OptionalDependencyNotAvailable:
from ...utils.du... | 38 | 0 |
import tensorflow as tf
from ...tf_utils import shape_list
class lowercase__ ( tf.keras.layers.Layer ):
def __init__( self : Tuple , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Dict , UpperCAmelCase_ : List[Any] , UpperCAmelCa... | 176 |
import re
import string
import numpy as np
import datasets
UpperCAmelCase_ : Dict = '''
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
'''
UpperCAmelCase_ : Any = '''
Args:
... | 38 | 0 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def SCREAMING_SNAKE_CASE__ ( lowerca... | 124 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKIN... | 38 | 0 |
"""simple docstring"""
def _A ( lowercase = 10_00 ):
"""simple docstring"""
a =2**power
a =str(lowercase )
a =list(lowercase )
a =0
for i in list_num:
sum_of_num += int(lowercase )
return sum_of_num
if __name__... | 81 |
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,
StableDiffusionXLImgaImgPipeline,
UNet... | 38 | 0 |
"""simple docstring"""
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
lowercase__ = datasets.uti... | 241 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase_ : List[str] = logging.get_logger(__name__)
UpperCAmelCase_ : int = {
'''CarlCochet/trajectory-transformer-halfcheetah-medium-v2''': (
'''https://huggingface.co/CarlCoche... | 38 | 0 |
'''simple docstring'''
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
_lowerCamelCase : Dict = logging.get_logger(__name__)
class __UpperCAmelCase :
'''simple docstring'''
__lowerCAmelCase ... | 258 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : int = 3 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if isinstance(__magic_name__ , _... | 38 | 0 |
'''simple docstring'''
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCamelCase__ ( _a , unittest.TestCase ):
"""simple docstring"""
... | 323 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, re... | 38 | 0 |
def lowercase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : int ) -> list[str]:
return [sentence[i : i + ngram_size] for i in range(len(SCREAMING_SNAKE_CASE__ ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 317 |
# 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 ap... | 38 | 0 |
'''simple docstring'''
def lowercase__ ( __lowercase : int , __lowercase : list ) -> int:
"""simple docstring"""
_enforce_args(__lowercase , __lowercase )
if n == 0:
return 0
__UpperCamelCase = float('-inf' )
... | 53 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : str = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP... | 38 | 0 |
"""simple docstring"""
import math
a_ = 1_0
a_ = 7
a_ = BALLS_PER_COLOUR * NUM_COLOURS
def __UpperCAmelCase ( __UpperCamelCase = 20 ):
__lowercase : str = math.comb(__UpperCamelCase , __UpperCamelCase )
__lowe... | 249 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils im... | 38 | 0 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
fr... | 159 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
UpperCAmelCase_ : int = '''\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wa... | 38 | 0 |
from ..utils import DummyObject, requires_backends
class _lowercase ( metaclass=_a ):
lowercase = ["""flax"""]
def __init__( self : Optional[Any] , *snake_case : str , **snake_case : Tuple ) -> str:
"""simple docstring"""
requires_bac... | 175 |
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 tor... | 38 | 0 |
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.set_verbosity_info()
def _lowercase ( ... | 176 |
from collections.abc import Generator
from math import sin
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : bytes ) -> bytes:
"""simple docstring"""
if len(__magic_name__ ) != 32:
raise ValueError("""Input must be of length 32""" )
UpperCamelCase :int =... | 38 | 0 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to ... | 124 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class _SCREAMING_SNAKE_CASE ( _a ):
def __init__( self : List[Any] , __lowerCamelCase : Callable , __low... | 38 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __A ( ... | 81 |
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
from accelerate import... | 38 | 0 |
"""simple docstring"""
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase ) -> float:
"""simple docstring"""
def get_matched_characters(__UpperCamelCase , __UpperCamelCase ) -> str:
lowerCAmelCase_ : List[Any] = []
lowerC... | 241 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _SCREAMING_SNAKE_CASE ( _a , unittest.TestCase ):
snake_case__ : Optional[Any] ... | 38 | 0 |
'''simple docstring'''
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configurati... | 258 |
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
# import classes
from unilm.wavlm.Wav... | 38 | 0 |
'''simple docstring'''
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
__UpperCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
def... | 323 |
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
UpperCAmelCase_ : Any = logging.get_logger(__name__)
class _SCREAMI... | 38 | 0 |
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 lowercase ( SCREAMIN... | 317 |
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : list[list[int]] , __magic_name__ : int , __magic_name__ : int , __magic_name__ : list[int] ) -> bool:
"""simple docstring"""
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that nex... | 38 | 0 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)
def lowerc... | 53 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tenso... | 38 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import ... | 249 |
# 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 ... | 38 | 0 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def _lowerCAmelCase ( lowerCAmelCase_ :int )->bool:
'''simple docstring'''
snake_case_ = int(number**0.5 )
return number == sq * sq
def _lowerC... | 159 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=_a )
class _SCREAMING_SNAKE_CASE ( _a ):
snake_case__ : str = field(default="""automatic-s... | 38 | 0 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
a_ = [
# tf -> hf
('''/''', '''.'''),
('''layer_''', '''layers.'''),
('''kernel''', '''weight'''),
... | 175 |
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 OptionalDependencyNotAvailable:
from ...utils.du... | 38 | 0 |
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> int:
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
SCREAMING_SNAKE_CASE__ = _modexpt(UpperCamelCase_ , exponent // 2 , UpperCamelCase_ ) % modulo_... | 176 |
import re
import string
import numpy as np
import datasets
UpperCAmelCase_ : Dict = '''
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
'''
UpperCAmelCase_ : Any = '''
Args:
... | 38 | 0 |
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> bool:
return sum(i for i in range(1 ,number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('Program to check whether a number is a Perfect number or not...')
lowerCamelCase : Opti... | 124 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKIN... | 38 | 0 |
"""simple docstring"""
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoder... | 81 |
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,
StableDiffusionXLImgaImgPipeline,
UNet... | 38 | 0 |
"""simple docstring"""
def __lowerCamelCase ( __UpperCamelCase = 1000 ) -> int:
"""simple docstring"""
return sum(e for e in range(3 , __UpperCamelCase ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 241 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase_ : List[str] = logging.get_logger(__name__)
UpperCAmelCase_ : int = {
'''CarlCochet/trajectory-transformer-halfcheetah-medium-v2''': (
'''https://huggingface.co/CarlCoche... | 38 | 0 |
'''simple docstring'''
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class __UpperCAmelCase ( _a ):
'''simple docstring'''
__lowerCAmelCase = (DDIMParallelScheduler,)
__lowerCAmelCase = (("""eta... | 258 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : int = 3 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if isinstance(__magic_name__ , _... | 38 | 0 |
'''simple docstring'''
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__UpperCAmelCase = mode... | 323 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, re... | 38 | 0 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
a__ = logging.get_logger(__name__)
class snake_case ( _a ):
'''simple docstring'''
def __init__( self : List[Any] , *lower... | 317 |
# 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 ap... | 38 | 0 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transfor... | 53 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : str = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP... | 38 | 0 |
"""simple docstring"""
from collections import deque
from .hash_table import HashTable
class UpperCAmelCase_ ( _a ):
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> List[Any]:
super().__init__(*__lowerCamelCase , **__lowerCamel... | 249 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils im... | 38 | 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
impo... | 159 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
UpperCAmelCase_ : int = '''\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wa... | 38 | 0 |
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 FlaxModel... | 175 |
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 tor... | 38 | 0 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class lowercase__ ( enum.Enum ):
A__ : ... | 176 |
from collections.abc import Generator
from math import sin
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : bytes ) -> bytes:
"""simple docstring"""
if len(__magic_name__ ) != 32:
raise ValueError("""Input must be of length 32""" )
UpperCamelCase :int =... | 38 | 0 |
import sys
import turtle
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> tuple[float, float]:
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ,lowercase ,) -> None:
my_pen.... | 124 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class _SCREAMING_SNAKE_CASE ( _a ):
def __init__( self : List[Any] , __lowerCamelCase : Callable , __low... | 38 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ : str = logging.get_logger(__name__)
lowerCamelCase_ : int = {
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-... | 81 |
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
from accelerate import... | 38 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin... | 241 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _SCREAMING_SNAKE_CASE ( _a , unittest.TestCase ):
snake_case__ : Optional[Any] ... | 38 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_featur... | 258 |
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
# import classes
from unilm.wavlm.Wav... | 38 | 0 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
def __A ( lowerCamelCase_ ):
"""simple docstring"... | 323 |
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
UpperCAmelCase_ : Any = logging.get_logger(__name__)
class _SCREAMI... | 38 | 0 |
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from acceler... | 317 |
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : list[list[int]] , __magic_name__ : int , __magic_name__ : int , __magic_name__ : list[int] ) -> bool:
"""simple docstring"""
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that nex... | 38 | 0 |
'''simple docstring'''
def lowercase__ ( __lowercase : list[list[int]] , __lowercase : int , __lowercase : int , __lowercase : list[int] ) -> bool:
"""simple docstring"""
if graph[path[curr_ind - 1]][next_ver] == 0:
return F... | 53 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tenso... | 38 | 0 |
"""simple docstring"""
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def __UpperCAmelCase ( __UpperCamelCase = 3 ):
if isinstance(__UpperCamelCase , __UpperCamelCas... | 249 |
# 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 ... | 38 | 0 |
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing i... | 159 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=_a )
class _SCREAMING_SNAKE_CASE ( _a ):
snake_case__ : str = field(default="""automatic-s... | 38 | 0 |
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def __lowercase ( lowerCamelCase : int ):
UpperCamelCase_ : Tuple = FileLock(str(tmpdir / 'foo.lock' ) )
UpperCamelCase_ : Tuple = FileLock(str(tmpdir / 'foo.lock' ) )
UpperC... | 175 |
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 OptionalDependencyNotAvailable:
from ...utils.du... | 38 | 0 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transf... | 176 |
import re
import string
import numpy as np
import datasets
UpperCAmelCase_ : Dict = '''
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
'''
UpperCAmelCase_ : Any = '''
Args:
... | 38 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils impo... | 124 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKIN... | 38 | 0 |
"""simple docstring"""
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_... | 81 |
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,
StableDiffusionXLImgaImgPipeline,
UNet... | 38 | 0 |
"""simple docstring"""
from math import factorial
def __lowerCamelCase ( __UpperCamelCase = 100 ) -> int:
"""simple docstring"""
return sum(int(__UpperCamelCase ) for x in str(factorial(__UpperCamelCase ) ) )
if __name__ == "__main__":
print(solution(... | 241 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase_ : List[str] = logging.get_logger(__name__)
UpperCAmelCase_ : int = {
'''CarlCochet/trajectory-transformer-halfcheetah-medium-v2''': (
'''https://huggingface.co/CarlCoche... | 38 | 0 |
'''simple docstring'''
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
_lowerCamelCase : Dict = logging.get_logger(__name__)
_lowerCamelCase : Dict = ... | 258 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : int = 3 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if isinstance(__magic_name__ , _... | 38 | 0 |
'''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_image_inputs
if is_torc... | 323 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, re... | 38 | 0 |
import socket
def lowercase ( ) -> List[Any]:
_snake_case : Tuple = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
_snake_case : Union[str, Any] = socket.gethostname()
_snake_case : Optional[int] = 12_312
sock.connect((host, port) )
... | 317 |
# 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 ap... | 38 | 0 |
'''simple docstring'''
import numpy as np
def lowercase__ ( __lowercase : np.array ) -> np.array:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 53 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : str = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP... | 38 | 0 |
"""simple docstring"""
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_... | 249 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils im... | 38 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import loggi... | 159 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
UpperCAmelCase_ : int = '''\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wa... | 38 | 0 |
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTes... | 175 |
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 tor... | 38 | 0 |
from __future__ import annotations
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> list:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = input_list[low:mid], input_list[mid : high ... | 176 |
from collections.abc import Generator
from math import sin
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : bytes ) -> bytes:
"""simple docstring"""
if len(__magic_name__ ) != 32:
raise ValueError("""Input must be of length 32""" )
UpperCamelCase :int =... | 38 | 0 |
from __future__ import annotations
from math import pi
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ) -> dict[str, float]:
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if ... | 124 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class _SCREAMING_SNAKE_CASE ( _a ):
def __init__( self : List[Any] , __lowerCamelCase : Callable , __low... | 38 | 0 |
"""simple docstring"""
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
lowerCamelCase_ : Any = 0b101_100_... | 81 |
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
from accelerate import... | 38 | 0 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_... | 39 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __A ( __lowerCAmelCase )-> str:
"""simple docstring"""
return "".join(sorted(__lowerCAmelCase ) )
def __A ( __lowerCAmelCase )-> list[str]:
... | 39 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, requ... | 39 |
from __future__ import annotations
def __A ( __lowerCAmelCase )-> list[int]:
"""simple docstring"""
_UpperCAmelCase = 2
_UpperCAmelCase = []
while i * i <= n:
if n % i:
i += 1
else:
... | 39 | 1 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
_a = '''https://www.indeed.co.in/jobs?q=mobile+app+development&l='''
def __A ( __lowerCAmelCase = "mumbai" )-> Generator[tuple[str, str], None, None]:
... | 39 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __A ( )-> tuple[list[int], int]:
"""simple docstring"""
_UpperCAmelCase = [randint(-1_000 , 1_000 ) for i in range(10 )... | 39 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def __A ( __lowerCAmelCase )-> Dict:
... | 39 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.traini... | 39 | 1 |
# Copyright 2022 The HuggingFace Team and The OpenBMB 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/LIC... | 39 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_a = get_tests_dir('''fixtures/spiece.model'''... | 39 | 1 |
from collections.abc import Generator
from math import sin
def __A ( __lowerCAmelCase )-> bytes:
"""simple docstring"""
if len(__lowerCAmelCase ) != 32:
raise ValueError('Input must be of length 32' )
_UpperCAmelCase = b''
... | 39 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
_a = lo... | 39 | 1 |
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from .... | 39 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'''distilbert-base-uncased''': '''https://huggingface.co/... | 39 | 1 |
import numpy as np
_a = [
['''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'''],
]
... | 39 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_to... | 39 | 1 |
from ... import PretrainedConfig
_a = {
'''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}
class __lowerCamelCase ( snake_case__):
"""simple docstring"""
UpperCamelCase__ = NEZH... | 39 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self ):
"""simple docstring"""
_UpperCAmelCase = {} # Mapping from char to TrieNode
_UpperCAmelCase = False
def UpperCamelCase ( s... | 39 | 1 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class __lowerCamelCase :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = None
UpperCamelCase__ = ... | 39 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, requ... | 39 | 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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resi... | 39 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
_a = get_logger(__name__)
class __lowerCamelCase ( enum.Enum):
"""simple docstring"""
UpperCamelCase__ = ... | 39 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set... | 39 |
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_backbon... | 39 | 1 |
from __future__ import annotations
def __A ( __lowerCAmelCase , __lowerCAmelCase )-> list[tuple[int, int]]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = position
_UpperCAmelCase = [
(y + 1, x + 2),
... | 39 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCamelCase ( snake_case__ , unittest.TestCase):
"""simple... | 39 | 1 |
def __A ( __lowerCAmelCase = 10**12 )-> int:
"""simple docstring"""
_UpperCAmelCase = 1
_UpperCAmelCase = 0
_UpperCAmelCase = 1
_UpperCAmelCase = 1
while numerator <= 2 * min_total - 1:
... | 39 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''... | 39 | 1 |
from functools import reduce
_a = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''6689... | 39 |
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
_a = logging.get_logger(__name__)
def __A ( ... | 39 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig''',
'''... | 39 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set... | 39 | 1 |
import numpy as np
def __A ( __lowerCAmelCase )-> np.array:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 39 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def __A ( )-> Tuple:
"""simple docstring"""
raise RuntimeError('CUDA out of me... | 39 | 1 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __A ( )-> tuple[list[int], int]:
"""simple docstring"""
_UpperCAmelCase = [randint(-1_000 , 1_000 ) for i in range(10 )... | 39 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...tes... | 39 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_a = {
'''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],
}
try:
if not is_... | 39 |
def __A ( __lowerCAmelCase )-> list:
"""simple docstring"""
if len(__lowerCAmelCase ) < 2:
return collection
def circle_sort_util(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> bool:
_UpperCAmelCase = False
... | 39 | 1 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class __lowerCamelCase ( unittest.TestCase):
"""simple docstring"""
def UpperCamelCase ( self ):
"""simple docstring"""
_Up... | 39 |
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 __lowerCamelCase ( snake_case__):
... | 39 | 1 |
from ...configuration_utils import PretrainedConfig
class __lowerCamelCase ( snake_case__):
"""simple docstring"""
UpperCamelCase__ = "bert-generation"
def __init__( self , UpperCAmelCase=5_0358 , UpperCAmelCase=1024 ,... | 39 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self , UpperCAmelCase = "" , UpperCAmelCase = False ):
"""simple docstring"""
_UpperCAmelCase = {}
# A node will be a leaf if the tree contai... | 39 | 1 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
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
from ..models.auto.modeling_t... | 39 |
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transfor... | 39 | 1 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __A ( )-> str:
"""simple docstring"""
with offline(OfflineSimu... | 39 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __A ( __lowerCAmelCase )-> str:
"""simple docstring"""
return "".join(sorted(__lowerCAmelCase ) )
def __A ( __lowerCAmelCase )-> list[str]:
... | 39 | 1 |
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 __lowerCamelCase ( snake_case__):
... | 39 |
from __future__ import annotations
def __A ( __lowerCAmelCase )-> list[int]:
"""simple docstring"""
_UpperCAmelCase = 2
_UpperCAmelCase = []
while i * i <= n:
if n % i:
i += 1
else:
... | 39 | 1 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docst... | 39 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __A ( )-> tuple[list[int], int]:
"""simple docstring"""
_UpperCAmelCase = [randint(-1_000 , 1_000 ) for i in range(10 )... | 39 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a = {
'''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHI... | 39 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.traini... | 39 | 1 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_bloc... | 39 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_a = get_tests_dir('''fixtures/spiece.model'''... | 39 | 1 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@req... | 39 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
_a = lo... | 39 | 1 |
from string import ascii_uppercase
_a = {str(ord(c) - 55): c for c in ascii_uppercase}
def __A ( __lowerCAmelCase , __lowerCAmelCase )-> str:
"""simple docstring"""
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError('in... | 39 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'''distilbert-base-uncased''': '''https://huggingface.co/... | 39 | 1 |
def __A ( __lowerCAmelCase )-> str:
"""simple docstring"""
_UpperCAmelCase = ''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def __A ( ... | 39 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_to... | 39 | 1 |
def __A ( __lowerCAmelCase )-> Dict:
"""simple docstring"""
_UpperCAmelCase = [0] * len(__lowerCAmelCase )
_UpperCAmelCase = []
_UpperCAmelCase = []
_UpperCAmelCase = 0
for values in graph.values()... | 39 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self ):
"""simple docstring"""
_UpperCAmelCase = {} # Mapping from char to TrieNode
_UpperCAmelCase = False
def UpperCamelCase ( s... | 39 | 1 |
import math
import sys
def __A ( __lowerCAmelCase )-> str:
"""simple docstring"""
_UpperCAmelCase = ''
try:
with open(__lowerCAmelCase , 'rb' ) as binary_file:
_UpperCAmelCase = binary_file.read()
... | 39 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, requ... | 39 | 1 |
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
_a = {
... | 39 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
_a = get_logger(__name__)
class __lowerCamelCase ( enum.Enum):
"""simple docstring"""
UpperCamelCase__ = ... | 39 | 1 |
_a = {}
def __A ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> int:
"""simple docstring"""
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
# we have a prize... | 39 |
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_backbon... | 39 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput... | 39 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCamelCase ( snake_case__ , unittest.TestCase):
"""simple... | 39 | 1 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'''The `image_to_image.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionImg2ImgPipeline` instead.'''
)
| 39 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''... | 39 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_a = {
'''configuration_mobilevit''': ['''MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MobileViTC... | 39 |
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
_a = logging.get_logger(__name__)
def __A ( ... | 39 | 1 |
from __future__ import annotations
from typing import Any
class __lowerCamelCase ( snake_case__):
"""simple docstring"""
pass
class __lowerCamelCase :
"""simple docstring"""
def __init__( self , UpperCAmelCase... | 39 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set... | 39 | 1 |
from __future__ import annotations
def __A ( __lowerCAmelCase , __lowerCAmelCase )-> Any:
"""simple docstring"""
print(F"""Vertex\tShortest Distance from vertex {src}""" )
for i, d in enumerate(__lowerCAmelCase ):
print(F"""{i}\t\t{d}""" )
... | 39 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def __A ( )-> Tuple:
"""simple docstring"""
raise RuntimeError('CUDA out of me... | 39 | 1 |
import comet # From: unbabel-comet
import torch
import datasets
_a = datasets.logging.get_logger(__name__)
_a = '''\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},
title = {Unbabel\'s Participat... | 39 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...tes... | 39 | 1 |
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