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 |
|---|---|---|---|---|
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
UpperCAmelCase : Dict ... | 239 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _A( yaml.SafeLoader ):
"""simple docstring"""
def UpperCAmelCase_ ( self , _A ):
__A : Optional[int] = [self.constructed_objects[key_node]... | 239 | 1 |
'''simple docstring'''
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
UpperCAmelCase = 'sshl... | 344 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def _snake_c... | 344 | 1 |
"""simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()... | 77 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
A = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive co... | 77 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> tuple[int, int]:
if b == 0:
return (1, 0)
((__lowerCamelCase) , (__lowerCamelCase)) : Optional[Any] = extended_euclid(lowerCamelCase__ , a... | 337 |
a ="""0.18.2"""
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_note_seq... | 337 | 1 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
lowercase : Optional[int] = "\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={W... | 327 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 327 | 1 |
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@requi... | 5 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tok... | 5 | 1 |
"""simple docstring"""
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if... | 470 |
"""simple docstring"""
def A_ ( lowercase ) -> str:
"""simple docstring"""
UpperCAmelCase_ : Any = 0
# if input_string is "aba" than new_input_string become "a|b|a"
UpperCAmelCase_ : Union[str, Any] = """"""
UpperCAmel... | 470 | 1 |
"""simple docstring"""
import math
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float ):
'''simple docstring'''
if initial_intensity < 0:
raise ValueError('''The val... | 363 |
"""simple docstring"""
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import Squad... | 363 | 1 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_pr... | 513 |
import math
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 SchedulerMixin, SchedulerOutput
class __A( __lowerCamelCase , __lowerCamelCase ):
"""simple docstrin... | 513 | 1 |
'''simple docstring'''
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = OrderedDi... | 702 | '''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
UpperCamelCase_ = loggin... | 320 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_spee... | 467 |
"""simple docstring"""
def UpperCamelCase ( _A ) -> int:
lowercase : Dict = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def UpperCamelCase ( _A = 100 ) -> int:
lowercase : Union[str, An... | 264 | 0 |
"""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,
resize,
to... | 147 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class _UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def _UpperCAmelCase ( self : Optional[int]... | 147 | 1 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ): # noqa: E741
A_ : Any = len(SCREAMING_SNAKE_CASE )
A_ : Optional[Any] = 0
A_ : int = [0] * n
A_ : Tuple = [False] * n
A_ : Union[str, Any] = [False] * n
def dfs(SCR... | 590 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host> --k... | 590 | 1 |
"""simple docstring"""
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWER... | 711 | """simple docstring"""
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowercase : List[str] = logging.get_logger(__name__)
__lowercase : Union[str, ... | 93 | 0 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between chec... | 295 |
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 import FlaxGenerationTesterMixi... | 295 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
f... | 612 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase = {
"""configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
... | 612 | 1 |
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_dif... | 403 | import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase (UpperCAmelCase__ : int , UpperCAmelCase__ : Optional[Any] , UpperCAmelCa... | 403 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
__lowerCamelCase : Dict = {
"""microsoft/swinv2-tiny-patch4-window8-256""": (
"""https://huggingface.co/microsoft/swinv2-tiny-patc... | 38 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__lowerCamelCase : Dict = logging.get_logger(__name__)
__lo... | 38 | 1 |
"""simple docstring"""
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils im... | 118 |
from manim import *
class _A ( _lowerCamelCase ):
def __a ( self : Dict ) -> Any:
"""simple docstring"""
lowercase : Tuple = Rectangle(height=0.5 , width=0.5 )
lowercase : str = ... | 217 | 0 |
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common imp... | 712 |
def _A ( __snake_case :int ) -> bool:
"""simple docstring"""
if not isinstance(__snake_case , __snake_case ):
raise ValueError("check_bouncy() accepts only integer arguments" )
__SCREAMING_SNAKE_CASE = str(__snake_case )
__SCRE... | 214 | 0 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowerCamelCase_ = pytest.mark.integration
@pytest.mark.parametrize("path... | 330 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
_lowerCamelCase = pytest.mark.integration
@pytest.mark... | 71 | 0 |
'''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
lowercase : Optional[int] = '\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluati... | 705 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
lowercase : Tuple = '\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burn... | 343 | 0 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__magic_name__ = datasets.load_iris()
__magic_name__ = np.array(data['data'])
__magic_name__ = np.array(data['target'])
__magic_n... | 665 |
'''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 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _lowerCamelCase ( metaclass=a_ ):
_lowerCamelCase :Dict = ["keras_nlp"]
def __init__( self : List[str] , *UpperCamelCase : Any , **UpperCamelCase : str ) -> Tuple:
... | 716 |
"""simple docstring"""
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
_A = logging.getLo... | 507 | 0 |
import argparse
import os
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_task_guides.py
SCREAMING_SNAKE_CASE : int = "src/transformers"
SCREAMING_SNAKE_CASE : Opt... | 89 |
'''simple docstring'''
from __future__ import annotations
import os
from collections.abc import Mapping
A__ : Optional[Any] = tuple[int, int]
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ... | 13 | 0 |
from __future__ import annotations
from typing import TypedDict
class __magic_name__ ( lowerCAmelCase ):
UpperCAmelCase =4_2
UpperCAmelCase =4_2
def lowerCamelCase__ ( __lowerCamelCase : str ):
'''simple docstring'''
if not isinsta... | 712 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
i... | 331 | 0 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_... | 55 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def a__ ( UpperCamelCase_ : Tuple ):
UpperCAmelCase__ :Dict = [
'''encoder.version''',
'''decoder.version'''... | 467 | 0 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class lowerCAmelCase_ ( __snake_case ):
"""simple do... | 719 |
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def _a ( ) -> Union[str, Any]:
... | 567 | 0 |
"""simple docstring"""
def _UpperCamelCase ( A ):
UpperCamelCase_ =[]
for data in source_data:
for i, el in enumerate(snake_case__ ):
if len(snake_case__ ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(snake_case__ ... | 391 |
from __future__ import annotations
from collections.abc import Callable
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = 1_00 , ):
lowerCAmelCase_ : Any = x_start
lowerCAmelCase_ : Optional[Any] = fnc(snake_case_... | 659 | 0 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install... | 95 |
"""simple docstring"""
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def lowercase ( _SCREAMING_SNAKE_... | 95 | 1 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def lowercase__( __SCREAMING_SNAKE_CASE : int ):
lowercase_ : Dict = [
'encoder.version',
'decoder.version',
... | 425 |
"""simple docstring"""
import itertools
import math
def UpperCAmelCase ( a__ ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1,... | 553 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
"""facebook/s2t-small-librispeech-asr""": (
"""https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"""
),
# See all Spe... | 198 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def lowercase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> Optional[Any]:
# vision encoder
if "img_encoder.pos_embed" in name... | 198 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import BitConfig
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 i... | 442 |
'''simple docstring'''
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class SCREAMING_SNAKE_CASE ( tf.k... | 442 | 1 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> list[int]:
'''simple docstring'''
lowercase_ = [True] * limit
lowercase_ = False
lowercase_ = False
lowercase_ = True
... | 100 |
"""simple docstring"""
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common impo... | 100 | 1 |
'''simple docstring'''
class snake_case :
"""simple docstring"""
def __init__( self : Any , __A : list ):
__UpperCamelCase = set_counts
__UpperCamelCase = max(A_ )
__UpperCamelCase = len(A_ )
__UpperCamelCase = [1] * ... | 399 | """simple docstring"""
import warnings
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
__lowercase : Union[str, Any] = logging.ge... | 564 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_A = {
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
'tokenization_mvp': ['MvpToke... | 719 |
"""simple docstring"""
from copy import deepcopy
class _lowercase :
def __init__( self , UpperCAmelCase_ = None , UpperCAmelCase_ = None ) -> None:
if arr is None and size is not None:
lowerCamelCase : Any = size
lowerCamelCase : Op... | 133 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_availab... | 280 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ : Tuple = {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONF... | 280 | 1 |
from math import pi, sqrt
def lowerCAmelCase_ ( lowerCamelCase ):
if num <= 0:
raise ValueError("""math domain error""" )
if num > 1_7_1.5:
raise OverflowError("""math range error""" )
elif num - int(lowerCamelCase ) not in (0, 0.5):
raise NotImpl... | 367 |
UpperCAmelCase_ : int = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
... | 367 | 1 |
'''simple docstring'''
from manim import *
class lowercase_ ( __snake_case ):
"""simple docstring"""
def lowerCAmelCase_ ( self : Any ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = Rectangle(height=0.5 , width=0.5 )
_SC... | 418 | from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__) # pylint: disable=invalid-name
def snake_case (... | 670 | 0 |
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
from transformers.utils ... | 720 |
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 lowercase ( datasets.BeamBasedBuilder ):
"""simple docstring"""
def _UpperC... | 235 | 0 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
def __magic_name__ ( lowercase , lowercase ):
SCREAMING_S... | 409 |
"""simple docstring"""
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
... | 409 | 1 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
a_ :Dict = {
'tiny.en': 'https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b2... | 250 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICAT... | 250 | 1 |
from math import sqrt
def __snake_case ( lowerCAmelCase_ ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
r... | 100 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_tor... | 276 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_comm... | 211 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
_UpperCamelCase = """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""
_UpperCamelCase = ... | 211 | 1 |
'''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,
)
SCREAMING_SNAKE_CASE_ ... | 523 | '''simple docstring'''
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
SCREAMING_SNAKE_CASE_ = 'scheduler_config.json'
class a ... | 523 | 1 |
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = []
__lowercase = []
__lowercase = {
'''^''': 3,
'''*''': 2,
'''/''': 2,
'''%''': 2,
'''+''': 1,
'''-''': 1,
} # Priority of each operator
__lower... | 714 |
from ..utils import DummyObject, requires_backends
class lowerCamelCase_ ( metaclass=lowerCAmelCase__ ):
'''simple docstring'''
__UpperCAmelCase = ["speech"]
def __init__( self , *snake_case_ , **snake_case_ ) -> List[str]:
... | 527 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
fro... | 467 |
lowerCAmelCase__ = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]
def _UpperCAmelCase (UpperCamelCase__ : int ):
_A : Optional[int] = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
... | 503 | 0 |
'''simple docstring'''
def _snake_case ( _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError("""multiplicative_persistence() only accepts integral values""" ... | 344 |
'''simple docstring'''
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common imp... | 344 | 1 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : str = logging.get_logger(__name__)
__lowercase : int = {
"google/pix2struct-textcaps-base": (
"h... | 564 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowercase : List[Any] = {
"configuration_convnext": ["CONVNEXT_PRETRAINED_CO... | 564 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tens... | 301 |
'''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
__a = logging.get_logger(__name__)
__a = "T5Config"
def __snake_case( ... | 301 | 1 |
from math import pow, sqrt
def UpperCamelCase ( *snake_case__):
lowerCAmelCase_ : Any = len(snake_case__) > 0 and all(value > 0.0 for value in values)
return result
def UpperCamelCase ( snake_case__ , snake_case__):
return (
round(sqrt(molar_mass_a /... | 659 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
_lowercase = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''... | 659 | 1 |
'''simple docstring'''
def snake_case ( snake_case : list , snake_case : int , snake_case : int = 0 , snake_case : int = 0 ) -> int:
"""simple docstring"""
lowerCAmelCase = right or len(snake_case ) - 1
if left > right:
return -1
elif ... | 514 |
'''simple docstring'''
_UpperCamelCase : Optional[int] = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/tra... | 514 | 1 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
SCREAMING_SNAKE_CASE__ = '''\
@inproceedings{popovic-2015-chrf,
title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
author = "Popovi{\'c}, Maja",
booktitle = "Proceedings of... | 9 |
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 A ( __UpperCamelCase ) -> Op... | 9 | 1 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__lowerCamelCase = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__lowerCamelCase = [file for file in filepaths if fi... | 707 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json''',
}
class snake_cas... | 455 | 0 |
"""simple docstring"""
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
A_ : str =2_9_9_7_9_2_4_5_8
# Symbols
A_ , A_ , A_ , A_ : Dict =symbols("""ct x y z""")
def SCREAMING_SNAKE_CASE_ ( snake_case : ... | 650 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : float , snake_case : float )-> float:
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
... | 650 | 1 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property... | 217 | """simple docstring"""
import math
def __a ( ) ->None:
a__: int = input('Enter message: ' )
a__: List[str] = int(input(F'Enter key [2-{len(_SCREAMING_SNAKE_CASE ) - 1}]: ' ) )
a__: int = input('Encryption/Decryption [e/d]: ' )
if mode.... | 217 | 1 |
"""simple docstring"""
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, InputFeatur... | 163 |
"""simple docstring"""
def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
__snake_case = str(bin(SCREAMIN... | 163 | 1 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_... | 714 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
__A : Optional[int] = logging.get_... | 698 | 0 |
"""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 UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_C... | 682 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a__ : Tuple = {
'''configuration_rag''': ['''RagConfig'''],
'''retrieval_rag''': ['''RagRetriever'''],
'''tokenization_... | 682 | 1 |
'''simple docstring'''
def UpperCamelCase ( a ) -> List[str]:
'''simple docstring'''
stooge(a , 0 , len(a ) - 1 )
return arr
def UpperCamelCase ( a , a , a ) -> Dict:
'''simple docstring'''
if i >= h:
return
# If first elem... | 245 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def UpperCamelCase ( a ) -> Optional[int]:
'''simple docstring'''
return ConvertCommand(
args.model_type , args.tf_checkpoint , arg... | 245 | 1 |
from __future__ import annotations
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ):
if len(__UpperCamelCase ) <= 1 or n <= 1:
return
insert_next(__UpperCamelCase ,n - 1 )
rec_insertion_sort(__UpperCamelCase ,n - 1 )
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SN... | 113 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_util... | 65 | 0 |
'''simple docstring'''
def a_ ( __UpperCAmelCase = 10_00 ) -> int:
"""simple docstring"""
snake_case: Union[str, Any] =2**power
snake_case: Any =str(__snake_case )
snake_case: Tuple =list(__snake_case ... | 713 |
'''simple docstring'''
def a_ ( __UpperCAmelCase , __UpperCAmelCase ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
snake_case: Optional[Any] =str(bin... | 347 | 0 |
'''simple docstring'''
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class a__ ( _lowercase, ... | 507 |
'''simple docstring'''
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_ = ... | 507 | 1 |
from __future__ import annotations
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase = None ):
__snake_case : List[Any] = word_bank or []
# create a table
__snake_case : int = len(__lowerCamelCase ) + 1
__snake_cas... | 706 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_snake_case : List[Any] = "\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={Wan... | 203 | 0 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class a ( a__ , unittest.TestCas... | 4 |
'''simple docstring'''
import math
def a__ ( a__ ):
"""simple docstring"""
return math.sqrt(a__ ) * math.sqrt(a__ ) == num
def a__ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = n
while left <= ... | 627 | 0 |
'''simple docstring'''
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
UpperCAmelCase_ : int = logging.getLogger(__name__)
class a ( lowerCAmelCase__ ):
'''simple d... | 712 |
'''simple docstring'''
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch('socket.socket' )
@patch('builtins.open' )
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
_a : List[str] = Mock()
_a : str ... | 424 | 0 |
import logging
import os
from .state import PartialState
class __SCREAMING_SNAKE_CASE( logging.LoggerAdapter ):
@staticmethod
def lowerCAmelCase_ ( UpperCamelCase: Union[str, Any] ) -> Optional[Any]:
snake_case__ = PartialState()
... | 328 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
snake_case_ : Union[str, Any] = {
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
't... | 195 | 0 |
"""simple docstring"""
import colorsys
from PIL import Image # type: ignore
def A_ ( _lowercase, _lowercase, _lowercase ):
'''simple docstring'''
snake_case_ :Optional[Any] = x
snake_case_ :Union[str, Any] = y
for step in range(UpperCAmelCase... | 714 |
"""simple docstring"""
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipelin... | 310 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
fro... | 296 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a_ = {
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
'tokenization_tra... | 296 | 1 |
'''simple docstring'''
# using dfs for finding eulerian path traversal
def __lowerCamelCase ( _UpperCamelCase : Tuple , _UpperCamelCase : Dict , _UpperCamelCase : Optional[Any] , _UpperCamelCase : List[str]=None ):
'''simple docstring'''
UpperCAmelCa... | 43 | '''simple docstring'''
import collections
import os
import re
from pathlib import Path
lowercase__ : List[Any] = "src/transformers"
# Matches is_xxx_available()
lowercase__ : Optional[Any] = re.compile(R"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
lowerc... | 43 | 1 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
a =object()
# For specifying empty leaf dict `{}`
a =object()
def SCREAMING_SNAKE_CASE__ ( lowerCame... | 652 |
# 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, randn_te... | 652 | 1 |
import baseaa
def UpperCamelCase__ ( _A: str ):
'''simple docstring'''
return baseaa.baaencode(string.encode("""utf-8""" ) )
def UpperCamelCase__ ( _A: bytes ):
'''simple docstring'''
return baseaa.baadecod... | 571 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional... | 571 | 1 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Unio... | 690 |
"""simple docstring"""
def __snake_case ( UpperCamelCase__ ) -> list[int]:
"""simple docstring"""
A = [0 for i in range(len(UpperCamelCase__ ) )]
# initialize interval's left pointer and right pointer
A , A = 0, 0
for i in ran... | 690 | 1 |
'''simple docstring'''
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 ...... | 710 |
'''simple docstring'''
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def __lowercase (_SCREAMING_SNAKE_CASE :List[Any] ):
return x + 2
class a__ ( unittest.TestCase ):
def lowercase__ ... | 355 | 0 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.... | 17 |
"""simple docstring"""
def lowercase__ ( lowerCamelCase, lowerCamelCase ):
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 621 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a_ : Tuple = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not is_torch_ava... | 716 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
a_ : int = logging.get_logger(__... | 484 | 0 |
from math import factorial
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
if successes > trials:
raise ValueError('''successes must be lower or equal to trials''' )
if trials < 0 or successes < 0:
raise ValueError('''the fu... | 285 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _lowerCamelCase ( UpperCamelCase_ ):
"""simple docstring"... | 285 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class __SCREAMING_SNAKE_CASE :
snake_case_ = 42
snake_case_ = 42
... | 665 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
a_ = logging.get_logger(__name__)
a_... | 665 | 1 |
'''simple docstring'''
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def __init__( self ... | 347 |
'''simple docstring'''
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def _a ( __lowerCAmelCase : Union[dict, list, tuple, torc... | 347 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],... | 718 |
"""simple docstring"""
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."
)
| 659 | 0 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTe... | 639 |
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = hex_num.strip()
if not hex_num:
raise ValueError('''No value was passed to the function''' )
__lowercase = hex_num[0] == '''-'''
if is_negative:
__lowercase = hex_num[1:]
tr... | 639 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class lowercase_ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple... | 721 |
"""simple docstring"""
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_C... | 505 | 0 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
a = Lock()
def _SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ... | 518 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges ... | 518 | 1 |
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 transformers import CLIPModel, CLIPTokenizerFast
from... | 705 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTest... | 622 | 0 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
SCREAMING_SNAKE_CASE__ : Any = TypeVar("""T""")
SCREAMING_SNAKE_CASE__ : List[Any] = Union[List[T], Tuple[T, ...]]
SCREAMING_SNAKE_CASE__ : int = Union[T, List[T], Dict[str, T]]
SCREAMING_SN... | 79 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
_lowerCamelCase =logging.get_logger(__name__)
class A__ :
def __init__( self , __magic_name__ ... | 681 | 0 |
'''simple docstring'''
from __future__ import annotations
_lowercase : Optional[Any] = tuple[int, int, int]
_lowercase : List[Any] = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
_lowercase : List[str] = "ABCDEFGHIJKLMNOPQRSTUVWX... | 30 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : Optional[Any] = {
"configuration_swiftformer": [
"SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SwiftFormerConf... | 30 | 1 |
"""simple docstring"""
# 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/LICEN... | 93 | """simple docstring"""
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class a ... | 277 | 0 |
'''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 FlaxModelTesterMixin, floats_ten... | 415 | '''simple docstring'''
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import... | 415 | 1 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
lowercase__ ={
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''num_class_embeds''': 1000,... | 521 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( A_ ):
for i in range(len(A_ ) - 1 , 0 , -1 ):
lowerCAmelCase__ : Optional[Any] = False
for j in range(A_ , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
lowerCAmelCase__ ,lowerCAmelCase__ : ... | 450 | 0 |
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""": """htt... | 701 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ = {
"""configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
"""tokenization_luke""": ["""LukeTokenizer"""],
}
try:
... | 541 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __SCREAMING_SNAKE_CASE ... | 672 |
'''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 __SCREAMING_SNAKE_CASE ( datasets.BeamBasedBuilder ):
'''simple d... | 672 | 1 |
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
from transformers... | 26 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before to... | 26 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCAmelCase__ :Optional[int] = {
'''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],
}
try:... | 618 |
def lowerCAmelCase__ ( a__: list ) -> list:
'''simple docstring'''
if len(a__ ) < 2:
return collection
def circle_sort_util(a__: list , a__: int , a__: int ) -> bool:
_UpperCAmelCase = Fa... | 618 | 1 |
"""simple docstring"""
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils impo... | 616 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | 616 | 1 |
'''simple docstring'''
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
UpperCamelCase_ = ["""small""", """medium""", """large"""]
UpperCamelCase_ = """lm_head.decoder.weight"""
UpperCamelCase_ = """lm_head.weight"""
def ... | 92 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {
'''configuration_electra''': ['''ELECTRA_PRETRAINE... | 657 | 0 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def lowerCamelCase__ ( A__ : Any ):
'''simple docstring'''
__lowerCamelCase = args.pruning_method
__lowerCamelCase = args... | 711 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
'bert-base-uncased': 'https://hugging... | 80 | 0 |
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def SCREAMING_SNAKE_CASE ( ) -> None:
"""simple docstring"""
assert or_gate(0 , 0 ) == 0
... | 87 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class _a (unittest.TestCase , __magic_nam... | 456 | 0 |
"""simple docstring"""
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def A_ ( __UpperCamelCase : np.ndarray , __UpperCamelCase : np.ndarray , __UpperCamelCase : np.ndarray , __UpperCamelCase : int , __... | 396 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _lowerCAmelCase ( __snake_case ):
__lowerCAmelCase : Union[str, Any] = ['''image_processor''', '''tokenizer''']
__lowerCAmel... | 396 | 1 |
from sklearn.metrics import matthews_corrcoef
import datasets
_lowerCAmelCase : List[str] = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It t... | 454 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__a : List[str] = "▁"
__a : int = {"vocab_file": "spiece.model"}
__a : A... | 637 | 0 |
def _lowerCamelCase( lowerCAmelCase__ : Tuple = 6008_5147_5143 ):
'''simple docstring'''
try:
SCREAMING_SNAKE_CASE_ : List[Any] = int(_lowercase )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to i... | 712 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class __a :
'''simple docstring'''
UpperCAmelCase__ : Optional[Union[str, Path]] = None
UpperCAmelCase__ : bool = False
... | 97 | 0 |
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