code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
# 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 re... | 315 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
__UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
class UpperCAmelCase_ ( _a):
'''simple docstring'''
def __init__( self , ... | 315 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__UpperCAmelCase : Optional[int] = ""
__UpperCAmelCase : Union[str, Any] = ""
__UpperCAmelCase : Optional[int] = ""
__UpperCAmelCase : Any = 1 # (0 is vertical, 1 is horizontal)
de... | 315 |
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
if is_torch_avail... | 315 | 1 |
# using dfs for finding eulerian path traversal
def a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Tuple=None ):
"""simple docstring"""
... | 315 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def a ( SCREAMING_SNAKE_CASE_ : dict ):
... | 315 | 1 |
import qiskit
def a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
UpperCamelCase : List[str] = qiskit.Aer.get_backend('''aer_simulator''' )
UpperCamelCase : An... | 315 |
__UpperCAmelCase : str = "\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/transformers.git\n"
__UpperCAmelCase : Dict ... | 315 | 1 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class UpperCAmelCase_ ( _a):
'''simple docstring'''
__UpperCamelCase : int = ["image_processor", "tokenizer"]
__UpperCamelCase : List[st... | 315 |
import collections
import os
import re
from pathlib import Path
__UpperCAmelCase : List[str] = "src/transformers"
# Matches is_xxx_available()
__UpperCAmelCase : int = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
__UpperCAmelCase : Optional[int... | 315 | 1 |
from manim import *
class UpperCAmelCase_ ( _a):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
UpperCamelCase : Optional[int] = Rectangle(height=0.5 , width=0.5 )
... | 315 |
def a ( SCREAMING_SNAKE_CASE_ : str = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
UpperCamelCase : Any = set()
# Replace all the whitespace in our sentence
UpperCamelCase : Unio... | 315 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase : List[Any] = logging.get_logger(__name__)
__... | 315 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
__UpperCAmelCase : Union[str, Any] = logging.getL... | 315 | 1 |
import numpy
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase : Tuple = input_array
... | 315 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase : Tuple = logging.get_logger(__name__)
__UpperCAmelCase : Union[str, Any] = {
"kssteven/i... | 315 | 1 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class UpperCAmelCase_ ( nn.Module):
'''simple docstring'''
__UpperCamelCase : int
__U... | 315 |
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 : int = logging.get_logger(__name__)
class ... | 315 | 1 |
from scipy.stats import spearmanr
import datasets
__UpperCAmelCase : Dict = "\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive co... | 315 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_r... | 315 | 1 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
return "".join(sorted(SCREAMING_SNAKE_CASE_ ) )
def a ( SCREAMIN... | 315 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class UpperCAmelCase_ ( _a):
'''simple docstring'''
__UpperCamelCase : int = ["image_processor", "tokenizer"]
__UpperCamelCase : List[st... | 315 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
__UpperCAmelCase : Union[str, Any] = {
"configuration_speecht5": [
"SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 315 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless require... | 315 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase : Optional[int] = {
"configuration_mobilebert": [
"MOBILEBERT_PRETRAINED_CONF... | 315 |
def a ( SCREAMING_SNAKE_CASE_ : int = 5_0 ):
"""simple docstring"""
UpperCamelCase : List[str] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , ro... | 315 | 1 |
__UpperCAmelCase : int = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, Arr... | 315 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class UpperC... | 315 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase : Union[str, Any] = {
"configuration_blenderbot": [
"... | 315 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
return "".join(sorted(SCREAMING_SNAKE_CASE_ ) )
def a ( SCREAMIN... | 315 | 1 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCAmelCase : Any = {
"microsoft/xprophetnet-large-wiki100-cased": (
"https://h... | 315 |
def a ( SCREAMING_SNAKE_CASE_ : list[list[float]] ):
"""simple docstring"""
UpperCamelCase : list[list[float]] = []
for data in source_data:
for i, el in enumerate(SCREAMING_SNAKE_CASE_ ):
i... | 315 | 1 |
def a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
UpperCamelCase : int = len(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = len(SCREAMING_SNAKE_... | 315 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__UpperCAmelCase : Optional[int] = ""
__UpperCAmelCase : Union[str, Any] = ""
__UpperCAmelCase : Optional[int] = ""
__UpperCAmelCase : Any = 1 # (0 is vertical, 1 is horizontal)
de... | 315 | 1 |
__UpperCAmelCase : Dict = "Tobias Carryer"
from time import time
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=int(time() ) ): ... | 315 |
import qiskit
def a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
UpperCamelCase : List[str] = qiskit.Aer.get_backend('''aer_simulator''' )
UpperCamelCase : An... | 315 | 1 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
__UpperCAmelCase : Any = {
"config": [
"EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
"OnnxConfig",
"OnnxConfigWithPast",
"OnnxSeq2SeqConfigWithPast",
"PatchingSpec",
],
"convert": ["exp... | 315 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__UpperCAmelCase : str = logging.get_logger(__name__)
def a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional... | 315 | 1 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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_tensor
from ...test_pipeli... | 315 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def a ( ):
"""simple docstring"""
UpperCamelCase : Any = ArgumentParser('''Diffusers CLI tool''' , usage='''diffusers-cli <command> [<args>]''' )
... | 315 | 1 |
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
if is_torch_avail... | 315 |
def a ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
return "".join(chr(ord(SCREAMING_SNAKE_CASE_ ) - 3_2 ) if '''a''' <= char <= '''z''' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
t... | 315 | 1 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def ... | 315 |
import math
def a ( SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) and (
number >= 0
), "'number' must been an int and positive"
if 1... | 315 | 1 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def a ( SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Union[str, Any] ... | 315 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
__UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
class UpperCAmelCase_ ( _a):
'''simple docstring'''
def __init__( self , ... | 315 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
__UpperCAmelCase : List[Any] = logging.get_logger(__name... | 315 |
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
if is_torch_avail... | 315 | 1 |
# Algorithm for the pigeonhole sorting
def a ( SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase : Optional[int] = min(SCREAMING_SNAKE_CASE_ ) # min() finds the minimum value
UpperCamelC... | 315 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def a ( SCREAMING_SNAKE_CASE_ : dict ):
... | 315 | 1 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONA... | 315 |
__UpperCAmelCase : str = "\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/transformers.git\n"
__UpperCAmelCase : Dict ... | 315 | 1 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
__UpperCAmelCase : int = {
... | 315 |
import collections
import os
import re
from pathlib import Path
__UpperCAmelCase : List[str] = "src/transformers"
# Matches is_xxx_available()
__UpperCAmelCase : int = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
__UpperCAmelCase : Optional[int... | 315 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase : int = logging.get_logger(__name__)
__UpperCAmelCase : Dict = {
"sayakpaul/vit-msn-base": "https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json",
# See all ViT MSN ... | 315 |
def a ( SCREAMING_SNAKE_CASE_ : str = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
UpperCamelCase : Any = set()
# Replace all the whitespace in our sentence
UpperCamelCase : Unio... | 315 | 1 |
from __future__ import annotations
def a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
UpperCamelCase : list[list[int]] = []
create_all_state(1 , SCREAMING_SNAK... | 315 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
__UpperCAmelCase : Union[str, Any] = logging.getL... | 315 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
... | 315 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase : Tuple = logging.get_logger(__name__)
__UpperCAmelCase : Union[str, Any] = {
"kssteven/i... | 315 | 1 |
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
... | 315 |
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 : int = logging.get_logger(__name__)
class ... | 315 | 1 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
__UpperCAmelCase : int = "\\n@misc{chen2021evaluating,\n title={Evaluating ... | 315 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_r... | 315 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@req... | 315 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class UpperCAmelCase_ ( _a):
'''simple docstring'''
__UpperCamelCase : int = ["image_processor", "tokenizer"]
__UpperCamelCase : List[st... | 315 | 1 |
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import Bert... | 315 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless require... | 315 | 1 |
import operator as op
__UpperCAmelCase : List[str] = "scaler.pt"
__UpperCAmelCase : Any = "pytorch_model"
__UpperCAmelCase : Any = "random_states"
__UpperCAmelCase : List[Any] = "optimizer"
__UpperCAmelCase : Optional[int] = "scheduler"
__UpperCAmelCase : str = ... | 315 |
def a ( SCREAMING_SNAKE_CASE_ : int = 5_0 ):
"""simple docstring"""
UpperCamelCase : List[str] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , ro... | 315 | 1 |
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils im... | 315 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class UpperC... | 315 | 1 |
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 tokeni... | 315 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
return "".join(sorted(SCREAMING_SNAKE_CASE_ ) )
def a ( SCREAMIN... | 315 | 1 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeatur... | 315 |
def a ( SCREAMING_SNAKE_CASE_ : list[list[float]] ):
"""simple docstring"""
UpperCamelCase : list[list[float]] = []
for data in source_data:
for i, el in enumerate(SCREAMING_SNAKE_CASE_ ):
i... | 315 | 1 |
import math
class UpperCAmelCase_ :
'''simple docstring'''
def _lowercase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase : Tuple = 0.0
... | 315 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__UpperCAmelCase : Optional[int] = ""
__UpperCAmelCase : Union[str, Any] = ""
__UpperCAmelCase : Optional[int] = ""
__UpperCAmelCase : Any = 1 # (0 is vertical, 1 is horizontal)
de... | 315 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import lo... | 315 |
import qiskit
def a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
UpperCamelCase : List[str] = qiskit.Aer.get_backend('''aer_simulator''' )
UpperCamelCase : An... | 315 | 1 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMa... | 315 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__UpperCAmelCase : str = logging.get_logger(__name__)
def a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional... | 315 | 1 |
from collections.abc import Sequence
def a ( SCREAMING_SNAKE_CASE_ : Sequence[float] , SCREAMING_SNAKE_CASE_ : float ):
"""simple docstring"""
return sum(c * (x**i) for i, c in enumerate(SCREAMING_SNAKE_CASE_ ) )
def ... | 315 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def a ( ):
"""simple docstring"""
UpperCamelCase : Any = ArgumentParser('''Diffusers CLI tool''' , usage='''diffusers-cli <command> [<args>]''' )
... | 315 | 1 |
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase : List[Any] = n
UpperCamelCase : Tuple = [None]... | 315 |
def a ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
return "".join(chr(ord(SCREAMING_SNAKE_CASE_ ) - 3_2 ) if '''a''' <= char <= '''z''' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
t... | 315 | 1 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
__UpperCAmelCase : List[str] = [
# tf -> hf
("/", "."),
("layer_", "layers."),
("kernel"... | 315 |
import math
def a ( SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) and (
number >= 0
), "'number' must been an int and positive"
if 1... | 315 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
__UpperCAmelCase : Any = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except Optiona... | 315 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
__UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
class UpperCAmelCase_ ( _a):
'''simple docstring'''
def __init__( self , ... | 315 | 1 |
__UpperCAmelCase : str = "\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/transformers.git\n"
__UpperCAmelCase : Dict ... | 315 |
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
if is_torch_avail... | 315 | 1 |
from ... import PretrainedConfig
__UpperCAmelCase : Any = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class UpperCAmelCase_ ( _a):
'''simple docstring'''
__UpperCamelCase : Optional[int] = N... | 315 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def a ( SCREAMING_SNAKE_CASE_ : dict ):
... | 315 | 1 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .datac... | 315 |
__UpperCAmelCase : str = "\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/transformers.git\n"
__UpperCAmelCase : Dict ... | 315 | 1 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ... | 315 |
import collections
import os
import re
from pathlib import Path
__UpperCAmelCase : List[str] = "src/transformers"
# Matches is_xxx_available()
__UpperCAmelCase : int = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
__UpperCAmelCase : Optional[int... | 315 | 1 |
def a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 |
def a ( SCREAMING_SNAKE_CASE_ : str = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
UpperCamelCase : Any = set()
# Replace all the whitespace in our sentence
UpperCamelCase : Unio... | 315 | 1 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, rand... | 315 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
__UpperCAmelCase : Union[str, Any] = logging.getL... | 315 | 1 |
def a ( SCREAMING_SNAKE_CASE_ : int = 5_0 ):
"""simple docstring"""
UpperCamelCase : List[str] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , ro... | 315 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase : Tuple = logging.get_logger(__name__)
__UpperCAmelCase : Union[str, Any] = {
"kssteven/i... | 315 | 1 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderToke... | 315 |
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 : int = logging.get_logger(__name__)
class ... | 315 | 1 |
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 UpperCAmelCase_ ( enum.E... | 315 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_r... | 315 | 1 |
def a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
while b:
UpperCamelCase , UpperCamelCase : str = b, a % b
return a
def a ( SCREAMING_SNA... | 315 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class UpperCAmelCase_ ( _a):
'''simple docstring'''
__UpperCamelCase : int = ["image_processor", "tokenizer"]
__UpperCamelCase : List[st... | 315 | 1 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker... | 315 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless require... | 315 | 1 |
import socket
def a ( ):
"""simple docstring"""
UpperCamelCase : Optional[int] = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
UpperCamelCase : Optional[int] = socket.gethostname()
UpperCame... | 315 |
def a ( SCREAMING_SNAKE_CASE_ : int = 5_0 ):
"""simple docstring"""
UpperCamelCase : List[str] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , ro... | 315 | 1 |
# 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 re... | 315 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class UpperC... | 315 | 1 |
from __future__ import annotations
def a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
UpperCamelCase : List[Any] = get_failure_array(SCREAMING_SNAKE_CASE_ )
# 2) St... | 315 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
return "".join(sorted(SCREAMING_SNAKE_CASE_ ) )
def a ( SCREAMIN... | 315 | 1 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
... | 315 |
def a ( SCREAMING_SNAKE_CASE_ : list[list[float]] ):
"""simple docstring"""
UpperCamelCase : list[list[float]] = []
for data in source_data:
for i, el in enumerate(SCREAMING_SNAKE_CASE_ ):
i... | 315 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentenc... | 315 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__UpperCAmelCase : Optional[int] = ""
__UpperCAmelCase : Union[str, Any] = ""
__UpperCAmelCase : Optional[int] = ""
__UpperCAmelCase : Any = 1 # (0 is vertical, 1 is horizontal)
de... | 315 | 1 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase : Tuple = logging.get_logger(__name__)
__UpperCAmelCase : Any = {
"huggingface/time-series-transformer-tourism-monthly": (
"https://hug... | 315 |
import qiskit
def a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
UpperCamelCase : List[str] = qiskit.Aer.get_backend('''aer_simulator''' )
UpperCamelCase : An... | 315 | 1 |
def a ( SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
if num < 0:
return False
UpperCamelCase : int = num
UpperCamelCase : int = 0
while num > 0:
UpperCamelCa... | 315 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__UpperCAmelCase : str = logging.get_logger(__name__)
def a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional... | 315 | 1 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(_a), "Tatoeba directo... | 315 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def a ( ):
"""simple docstring"""
UpperCamelCase : Any = ArgumentParser('''Diffusers CLI tool''' , usage='''diffusers-cli <command> [<args>]''' )
... | 315 | 1 |
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
__UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCAmelCase : Op... | 315 |
def a ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
return "".join(chr(ord(SCREAMING_SNAKE_CASE_ ) - 3_2 ) if '''a''' <= char <= '''z''' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
t... | 315 | 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_verbosi... | 315 |
import math
def a ( SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) and (
number >= 0
), "'number' must been an int and positive"
if 1... | 315 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
class UpperCAmelCase_ ( _a):
'''simple docstring'''
__UpperCamelCase : str = "timm_backbone"
... | 315 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
__UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
class UpperCAmelCase_ ( _a):
'''simple docstring'''
def __init__( self , ... | 315 | 1 |
import string
import numpy
def a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
return b if a == 0 else greatest_common_divisor(b % a , SCREAMING_SNAKE_CASE_ )
class Up... | 315 |
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
if is_torch_avail... | 315 | 1 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPip... | 315 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def a ( SCREAMING_SNAKE_CASE_ : dict ):
... | 315 | 1 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
__UpperCAmelCase : Union[str, Any] = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
__UpperCAmelCase : Optional[Any] = typing.Union[np.floataa, int, float] # ... | 315 |
__UpperCAmelCase : str = "\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/transformers.git\n"
__UpperCAmelCase : Dict ... | 315 | 1 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
__UpperCAmelCase : List[str] = (3, 9, -11, 0, 7, 5, 1, -1)
__UpperCAmelCase : List[str] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class UpperCAmelCase_ :
'''... | 315 |
import collections
import os
import re
from pathlib import Path
__UpperCAmelCase : List[str] = "src/transformers"
# Matches is_xxx_available()
__UpperCAmelCase : int = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
__UpperCAmelCase : Optional[int... | 315 | 1 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
"""simple d... | 315 |
def a ( SCREAMING_SNAKE_CASE_ : str = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
UpperCamelCase : Any = set()
# Replace all the whitespace in our sentence
UpperCamelCase : Unio... | 315 | 1 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCAmelCa... | 315 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
__UpperCAmelCase : Union[str, Any] = logging.getL... | 315 | 1 |
import numpy as np
import datasets
__UpperCAmelCase : Union[str, Any] = "\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt wa... | 315 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase : Tuple = logging.get_logger(__name__)
__UpperCAmelCase : Union[str, Any] = {
"kssteven/i... | 315 | 1 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__UpperCAmelCase : Union[str, Any] = ... | 315 |
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 : int = logging.get_logger(__name__)
class ... | 315 | 1 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_r... | 315 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_r... | 315 | 1 |
from collections import deque
from math import floor
from random import random
from time import time
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self ):
"""simple docstring"""
UpperCamelCase : Un... | 315 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class UpperCAmelCase_ ( _a):
'''simple docstring'''
__UpperCamelCase : int = ["image_processor", "tokenizer"]
__UpperCamelCase : List[st... | 315 | 1 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
fr... | 315 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless require... | 315 | 1 |
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
__UpperCAmelCase : Tuple = logging.getLogger(__name__)
__UpperCAmelCase : List[An... | 315 |
def a ( SCREAMING_SNAKE_CASE_ : int = 5_0 ):
"""simple docstring"""
UpperCamelCase : List[str] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , ro... | 315 | 1 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def a ( SCREAMING_SNAKE_CASE_ :... | 315 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class UpperC... | 315 | 1 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
__UpperCAmelCase : int = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}
try:
... | 315 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
return "".join(sorted(SCREAMING_SNAKE_CASE_ ) )
def a ( SCREAMIN... | 315 | 1 |
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
__UpperCAmelCase : Any = logging.get_logger(__name__)
__UpperCAmelCase : List[str] ... | 315 |
def a ( SCREAMING_SNAKE_CASE_ : list[list[float]] ):
"""simple docstring"""
UpperCamelCase : list[list[float]] = []
for data in source_data:
for i, el in enumerate(SCREAMING_SNAKE_CASE_ ):
i... | 315 | 1 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
f... | 315 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__UpperCAmelCase : Optional[int] = ""
__UpperCAmelCase : Union[str, Any] = ""
__UpperCAmelCase : Optional[int] = ""
__UpperCAmelCase : Any = 1 # (0 is vertical, 1 is horizontal)
de... | 315 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless require... | 315 |
import qiskit
def a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
UpperCamelCase : List[str] = qiskit.Aer.get_backend('''aer_simulator''' )
UpperCamelCase : An... | 315 | 1 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@req... | 315 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__UpperCAmelCase : str = logging.get_logger(__name__)
def a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional... | 315 | 1 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
__UpperCAmelCase : Any = TypeVar("T")
class UpperCAmelCase_ ( Generic[T]):
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_C... | 315 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def a ( ):
"""simple docstring"""
UpperCamelCase : Any = ArgumentParser('''Diffusers CLI tool''' , usage='''diffusers-cli <command> [<args>]''' )
... | 315 | 1 |
def a ( SCREAMING_SNAKE_CASE_ : list[list[float]] ):
"""simple docstring"""
UpperCamelCase : list[list[float]] = []
for data in source_data:
for i, el in enumerate(SCREAMING_SNAKE_CASE_ ):
i... | 315 |
def a ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
return "".join(chr(ord(SCREAMING_SNAKE_CASE_ ) - 3_2 ) if '''a''' <= char <= '''z''' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
t... | 315 | 1 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def a ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
UpperCamelCase , UpperCamelCase : List[Any] = analy... | 315 |
import math
def a ( SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) and (
number >= 0
), "'number' must been an int and positive"
if 1... | 315 | 1 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageP... | 315 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
__UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
class UpperCAmelCase_ ( _a):
'''simple docstring'''
def __init__( self , ... | 315 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
clas... | 315 |
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
if is_torch_avail... | 315 | 1 |
# Lint as: python3
import itertools
import os
import re
__UpperCAmelCase : Dict = re.compile(r"([A-Z]+)([A-Z][a-z])")
__UpperCAmelCase : int = re.compile(r"([a-z\d])([A-Z])")
__UpperCAmelCase : Tuple = re.compile(r"(?<!_)_(?!_)")
__UpperCAmelCase : Optional[int] = re.compi... | 315 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def a ( SCREAMING_SNAKE_CASE_ : dict ):
... | 315 | 1 |
def a ( SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
UpperCamelCase : list[list[int]] = [[0 for _ in range(SCREAMING_SNAKE_CASE_ )] for _ in range(m + 1 )]
for i in range(m + 1 ):
UpperCamelCase ... | 315 |
__UpperCAmelCase : str = "\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/transformers.git\n"
__UpperCAmelCase : Dict ... | 315 | 1 |
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
__UpperCAmelCase : Any = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
"text-classif... | 315 |
import collections
import os
import re
from pathlib import Path
__UpperCAmelCase : List[str] = "src/transformers"
# Matches is_xxx_available()
__UpperCAmelCase : int = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
__UpperCAmelCase : Optional[int... | 315 | 1 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
__UpperCAmelCase : Any = ... | 315 |
def a ( SCREAMING_SNAKE_CASE_ : str = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
UpperCamelCase : Any = set()
# Replace all the whitespace in our sentence
UpperCamelCase : Unio... | 315 | 1 |
import os
import sys
import unittest
__UpperCAmelCase : Any = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_ma... | 315 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
__UpperCAmelCase : Union[str, Any] = logging.getL... | 315 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase : Tuple = logging.get_logger(__name__)
__UpperCAmelCase : Union[str, Any] = {
"kssteven/i... | 315 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase : Tuple = logging.get_logger(__name__)
__UpperCAmelCase : Union[str, Any] = {
"kssteven/i... | 315 | 1 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common imp... | 315 |
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 : int = logging.get_logger(__name__)
class ... | 315 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase : Tuple = logging.get_logger(__name__)
__UpperCAmelCase : Optional[Any] = {
"Salesforce/blip-vqa-base": "https://huggingface.co/Salesfor... | 315 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_r... | 315 | 1 |
def a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
UpperCamelCase : list[list[str]] = [[] for _ in range(SCREAMING_SNAKE_CASE_ )]
UpperCamelCase : Union[str, Any] ... | 315 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class UpperCAmelCase_ ( _a):
'''simple docstring'''
__UpperCamelCase : int = ["image_processor", "tokenizer"]
__UpperCamelCase : List[st... | 315 | 1 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str , **SCREAMING_SNAKE_CASE_ : Dict ):
"""simple docstring"""
Upper... | 315 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless require... | 315 | 1 |
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