code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from collections import namedtuple
import requests
from lxml import html # type: ignore
_lowerCamelCase = namedtuple("""covid_data""", """cases deaths recovered""")
def a__ ( _SCREAMING_SNAKE_CASE : str = "https://www.worldometers.info/coronavirus... | 71 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/mai... | 18 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformer... | 379 | def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = [1]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = 0, 0, 0
SCREAMING_SNAKE_C... | 379 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-e... | 94 | '''simple docstring'''
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_... | 309 | 0 |
"""simple docstring"""
def _snake_case ( snake_case__ : int ):
if not isinstance(snake_case__ , snake_case__ ):
A = F'Input value of [number={number}] must be an integer'
raise TypeError(snake_case__ )
if number < 0:
return False
A = number * number
whil... | 708 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_lowercase = {
'''configuration_perceiver''': ['''PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_... | 22 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a = {
"""configuration_blenderbot""": [
... | 19 |
"""simple docstring"""
from typing import 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 BatchF... | 65 | 0 |
"""simple docstring"""
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_ST... | 31 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
... | 31 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from trans... | 82 |
"""simple docstring"""
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging... | 616 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, lo... | 7 |
'''simple docstring'''
from __future__ import annotations
def _UpperCAmelCase ( a : int = 4 ) -> list[list[int]]:
"""simple docstring"""
lowercase_ : Tuple = abs(a ) or 4
return [[1 + x + y * row_size for x in range(a )] for y in range(a ... | 7 | 1 |
"""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_prope... | 52 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
__lowerCAmelCase = logging.get_logger("transformers.models.speecht5")
def __UpperCamelCase ( lowerc... | 536 | 0 |
import operator as op
def UpperCAmelCase ( lowercase__ : str ):
'''simple docstring'''
a__ = []
a__ = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer division operation
a__ = {
"""^""": op.pow,
... | 412 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto impo... | 412 | 1 |
'''simple docstring'''
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_... | 466 |
'''simple docstring'''
import math
def _UpperCAmelCase ( __A : int ):
a_ : str = []
a_ : Tuple = 2
a_ : Optional[Any] = int(math.sqrt(__A ) ) # Size of every segment
a_ : Optional[Any] = [True] * (end ... | 466 | 1 |
'''simple docstring'''
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enabl... | 61 |
'''simple docstring'''
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enabl... | 61 | 1 |
'''simple docstring'''
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
a = logging.getLogger()
def a... | 350 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
@staticmethod
@abstractmethod
def A_ ( __a : ArgumentParser ) -> List[str]:
'''simple docstring'''
raise Not... | 286 | 0 |
"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
__SCREAMING_SNAKE_CASE : Any = '\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understandin... | 2 |
"""simple docstring"""
from math import factorial
def _a ( _SCREAMING_SNAKE_CASE = 100 ) -> int:
return sum(int(_SCREAMING_SNAKE_CASE ) for x in str(factorial(_SCREAMING_SNAKE_CASE ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').s... | 2 | 1 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, Par... | 401 | import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import A... | 401 | 1 |
def a ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
UpperCamelCase : List[Any] = 0
# if input_string is "aba" than new_input_string become "a|b|a"
UpperCamelCase : Optional[Any] = ''''''
UpperCamelCase ... | 643 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
f... | 643 | 1 |
"""simple docstring"""
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 SCREAMING_SNAKE_CASE__ ( ... | 169 |
"""simple docstring"""
from math import sqrt
def lowercase (snake_case__ : int ) -> int:
'''simple docstring'''
lowerCAmelCase = 0
for i in range(1 , int(sqrt(snake_case__ ) + 1 ) ):
if n % i == 0 and i != sqrt(snake_case__ ):
... | 169 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ :Optional[Any] = logging.get_logger(__name__)
A_ :List[str] = {
'''xlm-mlm-en-2048''': '''htt... | 154 |
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __A ( a , unittest.TestCase ):
... | 154 | 1 |
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 _snake_case ( __snake_case ):
_UpperCamelCase = test_file.split(os.path.sep )
if components[0:2] != ["te... | 10 | import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, rand... | 10 | 1 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def lowerCamelCase ( )-> Tuple:
"""simple docstring"""
with offline(OfflineSimulati... | 703 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase = {
'''configuration_trajectory_transformer''': [
'''TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TrajectoryTransformerConfig''',
... | 321 | 0 |
'''simple docstring'''
def lowercase_ ( __A : int ) -> int:
"""simple docstring"""
lowercase : Dict =1
for i in range(1 , num + 1 ):
fact *= i
return fact
def lowercase_ ( __A : int ) -> int:
"""simple... | 94 |
"""simple docstring"""
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
lowerCAmelCa... | 174 | 0 |
import os
def lowerCamelCase__ (__lowerCamelCase = "matrix.txt" ):
with open(os.path.join(os.path.dirname(__UpperCamelCase ), __UpperCamelCase ) ) as in_file:
_SCREAMING_SNAKE_CASE : List[Any] = in_file.read()
_SCREAMING_SNAKE_CASE : Dict ... | 713 |
import cmath
import math
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
_SCREAMING_SNAKE_CASE : Any = math.radians(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Tuple = math.radians(__lo... | 381 | 0 |
"""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/LICENSE-2.0
... | 46 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE_:List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:List[Any] = {
"""google/bit-50""": ... | 662 | 0 |
'''simple docstring'''
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCamelCase_ ( lowerCAmelCase: Optional[int] )-> List[Any]:
def wrapper(*lowerCAmelCase: Tuple , **lowerCAme... | 717 |
def lowerCamelCase_ ( lowerCAmelCase: int )-> int:
if not isinstance(lowerCAmelCase , lowerCAmelCase ):
_snake_case : Union[str, Any] = F"""Input value of [number={number}] must be an integer"""
raise TypeError(lowerCAmelCase )
if number < 1:
_snake_... | 669 | 0 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ..... | 418 |
'''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 import Backbone... | 418 | 1 |
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import A... | 702 | import numpy as np
import qiskit
def UpperCamelCase__ ( A__ = 8 , A__ = None ) -> str:
snake_case__ : Optional[int] = np.random.default_rng(seed=A__ )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
sn... | 699 | 0 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_... | 676 |
'''simple docstring'''
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_ : int = logging.get_logger(__name__)
a_ : str = {"""vocab... | 676 | 1 |
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
... | 715 |
import unittest
from transformers import XLMConfig, 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 import ModelTesterMixin, ids... | 456 | 0 |
'''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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import loggin... | 372 |
'''simple docstring'''
from manim import *
class _UpperCamelCase ( SCREAMING_SNAKE_CASE):
'''simple docstring'''
def a__ ( self ) -> List[str]:
lowercase : List[Any] = Rectangle(height=0.5 , width=0.5 )
lowercase : str = ... | 372 | 1 |
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_ava... | 411 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_tor... | 411 | 1 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, l... | 590 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
def __init__( self , *_SCRE... | 590 | 1 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
return "".join(chr(ord(_UpperCamelCase ) - 32 ) if "a" <= char <= "z" else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 282 |
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
A : Dict = logging.get_logger(__name__) # pylint: disable=invalid-name
class _UpperCame... | 282 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : List[str] = logging.get_logger(__name__)
UpperCamelCase__ : Optional[int] = {
"weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/r... | 591 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : int = logging.get_logger(__name__)
UpperCamelCase__ : Optional[Any] = {
"microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
... | 591 | 1 |
import logging
from transformers.configuration_utils import PretrainedConfig
UpperCAmelCase_ = logging.getLogger(__name__)
class UpperCamelCase_ ( _lowerCamelCase ):
lowerCAmelCase_ = '''masked_bert'''
def __init__( self , lowerCAmelCase_=3_0522 , l... | 720 |
def lowerCamelCase__ ( UpperCamelCase__ : list[int] , UpperCamelCase__ : int ) -> bool:
'''simple docstring'''
_snake_case = len(UpperCamelCase__ )
_snake_case = [[False] * (required_sum + 1) for _ in range(arr_len + 1 ... | 541 | 0 |
'''simple docstring'''
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
A__ : Union[str, Any] = [
"""... | 13 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
snake_case__ : int = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ASTCon... | 408 | 0 |
'''simple docstring'''
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 To... | 703 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCamelCase__ : Union[str, Any] = {'tokenization_byt5': ['ByT5Tokenizer']}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
UpperCamelCase__ : List[str] = _LazyModule... | 496 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ (metaclass=_UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase : str = ['torch', 'torchsde']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREA... | 13 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.datacla... | 282 | 0 |
"""simple docstring"""
from datetime import datetime as dt
import os
from github import Github
__UpperCAmelCase =[
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""feature request""",
"""new model""",
"""wip""",
]
def __a ( ) ->... | 261 |
"""simple docstring"""
# Lint as: python3
import itertools
import os
import re
__UpperCAmelCase =re.compile(r"""([A-Z]+)([A-Z][a-z])""")
__UpperCAmelCase =re.compile(r"""([a-z\d])([A-Z])""")
__UpperCAmelCase =re.compile(r"""(?<!_)_(?!_)""")
__UpperCAmelCase =re.compile(r"""... | 261 | 1 |
from __future__ import annotations
SCREAMING_SNAKE_CASE : Dict = tuple[int, int, int]
SCREAMING_SNAKE_CASE : str = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
SCREAMING_SNAKE_CASE : Tuple = "ABCDEFGHIJKLMNOPQR... | 141 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( __snake_case , __snake_case , __snake_case ) -> str:
# Initialise PyTorch mo... | 317 | 0 |
"""simple docstring"""
UpperCAmelCase__ = tuple[float, float, float]
UpperCAmelCase__ = tuple[float, float, float]
def __UpperCAmelCase ( lowercase ,lowercase ):
"""simple docstring"""
_UpperCAmelCase = end_pointa[0] - end_pointa[0]
_UpperCAmelCase = ... | 700 | """simple docstring"""
from __future__ import annotations
from random import choice
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
return choice(lowercase )
def __UpperCAmelCase ( lowercase ,lowercase ):
"""simple docstring"""
_UpperCAmelCase ... | 275 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils impo... | 422 | from heapq import heappop, heappush
import numpy as np
def __lowercase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ) -> tuple[float | int, list[tuple[int, int]]]:
'''simple docstring'''
__lowercase , __lowercase = grid.shape
__low... | 321 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.ut... | 701 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
lowercase : Dict = logging.getLogger(__name__)
class __lowercase :
"""simple docs... | 423 | 0 |
'''simple docstring'''
import socket
def _UpperCAmelCase ( ):
a_ : Dict = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
a_ : Optional[Any] = socket.gethostname()
a_ : Union[str, Any] = 1_23_12
sock.connect((h... | 466 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils im... | 466 | 1 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplica... | 717 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import requ... | 564 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
"""BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgeto... | 318 |
# 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... | 318 | 1 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ..... | 78 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 78 | 1 |
"""simple docstring"""
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 Mode... | 7 |
"""simple docstring"""
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 7 | 1 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> None:
'''simple docstring'''
__UpperCAmelCase , __UpperCAmelCase : Tuple = analyze_text... | 675 |
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
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 ImageProcessingSa... | 675 | 1 |
def A_ ( a ):
"""simple docstring"""
if not numbers:
return 0
if not isinstance(a , (list, tuple) ) or not all(
isinstance(a , a ) for number in numbers ):
raise ValueError('numbers must be an iterable of integers' )
SCREAMING_SNAKE_CASE_... | 511 | 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
_lowercase: str = '''sshleifer/bart-tiny-random'''
_lowercase: ... | 192 | 0 |
'''simple docstring'''
import os
from pathlib import Path
def lowercase_ ( ) -> str:
"""simple docstring"""
from torch.utils.cpp_extension import load
lowercase : Dict =Path(__A ).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
lowercas... | 8 |
'''simple docstring'''
def lowercase_ ( __A : int , __A : int ) -> str:
"""simple docstring"""
if number < 0 or shift_amount < 0:
raise ValueError('''both inputs must be positive integers''' )
lowercase : List[Any] =str(bin(__A ) )
... | 8 | 1 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
... | 216 | class a__ :
def __init__( self : Any,_A : str = "",_A : bool = False ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : dict[str, RadixNode] = {}
# A node will be a leaf if the tree contains its word
SCREAMING_SNAKE_CASE_ : ... | 216 | 1 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
A_ = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def __UpperCAmelCase ( Uppe... | 708 | 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
A_ = logging.get_logger(__name__)
A_ = {"vocab_file": "vocab.json", "me... | 479 | 0 |
"""simple docstring"""
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
SCREAMING_SNAKE_CASE_ = {
'n_samples': 64,
'horizon': 32,
'num_inference_steps': 20,
'n_guide_steps': 2, # can set to 0 for faster sampling, does not use v... | 34 |
"""simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock imp... | 34 | 1 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( _UpperCAmelCase ):
"""simple docstring"""
A = ['image_processor', 'tokenizer']
A = 'AutoImageProcessor'
A = 'AutoTokenizer'
... | 719 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
... | 220 | 0 |
'''simple docstring'''
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,
DistilB... | 42 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers... | 42 | 1 |
'''simple docstring'''
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
f... | 714 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if... | 350 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from tran... | 197 | import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="session" )
def __A ( ):
"""simple doc... | 197 | 1 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _a ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(SCREAM... | 552 |
from math import sqrt
def _a ( SCREAMING_SNAKE_CASE_ : int ):
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
... | 552 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def UpperCAmelCase ( a_ ) -> int:
"""simple doc... | 55 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase_ : Optional[Any] = {
'''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBer... | 185 | 0 |
"""simple docstring"""
from pathlib import Path
import numpy as np
from PIL import Image
def __snake_case ( SCREAMING_SNAKE_CASE: List[Any] ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = rgb[:, :, 0], rgb[:,... | 710 |
"""simple docstring"""
def __snake_case ( SCREAMING_SNAKE_CASE: int ):
"""simple docstring"""
_lowerCAmelCase = generate_pascal_triangle(SCREAMING_SNAKE_CASE )
for row_idx in range(SCREAMING_SNAKE_CASE ):
# Print left spaces
... | 491 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : int = logging.get_logger(__name__)
snake_case_ : List[Any] = {
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/conf... | 595 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from tran... | 595 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE_ : int = logging.get_... | 720 |
"""simple docstring"""
from collections import defaultdict
def _snake_case ( UpperCAmelCase_ : int ):
A__ = 1
A__ = True
for v in tree[start]:
if v not in visited:
ret += dfs(UpperCAmelCase_ )
if ret % 2 == 0:
... | 500 | 0 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def a ( snake_case__: Any ... | 97 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : dict ,_UpperCAmelCase : str ,_UpperCAmelCase : set ,_UpperCAmelCase : set ... | 694 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is... | 701 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
S... | 95 | 0 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
_lowerCAmelCase : Dict =logging.get_logger(__name__)
class __UpperCamelCase ( _a ):
'''simple docstring'''
def __init__( self , *lowerCamelCase__ , **lowerCam... | 113 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_a... | 113 | 1 |
"""simple docstring"""
def snake_case (A_ :str ):
'''simple docstring'''
if n_term == "":
return []
a : list = []
for temp in range(int(A_ ) ):
series.append(f'''1/{temp + 1}''' if series else '1' )
return series
if __name__ == "__main__":
_UpperC... | 719 |
"""simple docstring"""
_UpperCamelCase : Tuple = '\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/transform... | 118 | 0 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.m... | 569 |
"""simple docstring"""
import warnings
from functools import wraps
from typing import Callable
def _UpperCamelCase ( _A ) -> Callable:
"""simple docstring"""
@wraps(_A )
def _inner_fn(*_A , **_A ):
warnings.warn(
(F"""'{fn.__name__}' is experiment... | 555 | 0 |
"""simple docstring"""
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transform... | 719 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...s... | 194 | 0 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( a , a , a , a ) -> list:
__A : str = []
__A , __A : str = input_list[low:mid], input_list[mid : high + 1]
while left and right:
result.append((... | 239 |
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
MaxNe... | 239 | 1 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerI... | 715 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def _A (UpperCamelCase : str ) ->None:
'''simple docstring'''
lowerCamelCase__ ,lowerCamelCase__ : List[str] = analyze_text(UpperCamelCase )
l... | 96 | 0 |
'''simple docstring'''
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMTForConditionalGenerat... | 476 |
'''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 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class _UpperCamelCase ( __A ):
'''simple docstring'''
... | 706 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ = {
'configuration_bridgetower': [
'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BridgeTowerConfig',
'BridgeTowerTextConfig',
... | 193 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class snake_case_ ( a_ ):
__lowerCA... | 237 |
"""simple docstring"""
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class snake_case_ ( a_ ):
def __init__( self , a_="" , a_="train" ):
assert os.path.isdir(a_ )
a_ : List[Any] = []
... | 237 | 1 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import ... | 311 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a ni... | 311 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a_ :List[str] = {
'configuration_swiftformer': [
'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SwiftFormerConfig',
'SwiftFormerOnnxConfig',
... | 35 |
"""simple docstring"""
lowercase = 9.80_665
def UpperCAmelCase ( A : float , A : float , A : float = g ):
'''simple docstring'''
if fluid_density <= 0:
raise ValueError('Impossible fluid density' )
if volume ... | 573 | 0 |
def SCREAMING_SNAKE_CASE_ ( __A : int = 1_00 ) -> int:
"""simple docstring"""
a_ : Dict = set()
a_ : Union[str, Any] = 0
a_ : str = n + 1 # maximum limit
for a in range(2 , __A ):
... | 443 |
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_propert... | 443 | 1 |
def A_ ( lowercase_ , lowercase_ , lowercase_ ) -> int:
def update_area_of_max_square(lowercase_ , lowercase_ ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
_snake_case : Tuple = update_a... | 326 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class A :
_SCREAMING_SNAKE_CASE = field(
default="""codeparrot/codeparrot""" ,metadata={"""help""": """Model name or path of model to be trained."""} )
_SCREAMING_SNAKE_CASE = ... | 326 | 1 |
'''simple docstring'''
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple... | 692 |
'''simple docstring'''
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
__UpperCAmelCase = [
# tf -> hf
("/", "."),
("layer_", "layers."),
("k... | 692 | 1 |
"""simple docstring"""
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcess... | 571 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __snake_case ( ) -> Any:
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
... | 100 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a= logging.get_logger(__name__)
a= {
'''microsoft/unispeech-sat-base-100h-libri-ft''': (
'''https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve... | 715 | '''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _UpperCamelCase ( _a : NDArray[floataa] , _a : NDArray[floataa] , _a : list[int] , _a : int , ):
"""simple docstring"""
__Up... | 287 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE : str = {'''configuration_ibert''': ['''IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''IBertConfig''', '''IBertOnnxConfig''']}
try:
if not is_torch_a... | 141 | from typing import TYPE_CHECKING
from ...utils import _LazyModule
__lowerCamelCase : Union[str, Any] = {'''processing_wav2vec2_with_lm''': ['''Wav2Vec2ProcessorWithLM''']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
__lowerCamelCase ... | 216 | 0 |
"""simple docstring"""
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import Grad... | 721 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__snake_case = logging.get_logger(__name__)
class __lowerCamelCase ( a__ ):
'''simple docstring'''
... | 285 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : List[str] = {
"configuration_whisper": ["WHISPER_PRETRAI... | 656 | """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):
'''simple docstring'''
def ... | 656 | 1 |
from typing import Any
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self , snake_case ):
'''simple docstring'''
UpperCamelCase__ = data
UpperCamelCase__ = None
class lowerCamelCase__ :
... | 715 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase = {
'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig'... | 185 | 0 |
'''simple docstring'''
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggi... | 523 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'tanreinama/GPTSAN-2.8B-spout_is_uniform': (
'https://huggingface.co/tanreinama/GPTSAN-2.8B-sp... | 523 | 1 |
"""simple docstring"""
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def _A ( __lowercase , __lowercase , __lowercase ):
"""simple docstring"""
lowerCamelCase__ = AutoConfig.from_pretr... | 258 |
"""simple docstring"""
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def _A ( __lowercase , __lowercase , __lowercase ... | 258 | 1 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def _UpperCamelCase ( UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : List[Any] = [
... | 77 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
A = pd.read_csv("""sample_data.csv""", header=None)
A = ... | 77 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : Union[str, Any] = {'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FocalNetConfig... | 47 |
'''simple docstring'''
from collections.abc import Generator
def _a ( ):
"""simple docstring"""
_snake_case , _snake_case : Union[str, Any] = 0, 1
while True:
_snake_case , _snake_case : List[str] = b, a + b
... | 47 | 1 |
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfA... | 34 | '''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def UpperCamelCase__ ( _lowercase : Dict ) -> str:
for param in module.parameters():
__UpperCAmelCase: int = False
def UpperCamelCase__ ( ) ... | 523 | 0 |
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from d... | 714 |
import datasets
from .evaluate import evaluate
_lowerCAmelCase = """\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP},
year={2016}
}
"""
... | 481 | 0 |
'''simple docstring'''
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
_SCREAMING_SNAKE_CASE = {
# 1536-bit
5: {
"p... | 18 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
"configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
"feature_extraction_mctct": ["MCTCTFeatureE... | 18 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
snake_case_ = logging.get_logger(__name__)
s... | 355 |
'''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,
)
from torch.utils.dat... | 355 | 1 |
def _A ( lowerCAmelCase_ : list[int] , lowerCAmelCase_ : str ):
"""simple docstring"""
lowerCAmelCase__ = int(lowerCAmelCase_ )
# Initialize Result
lowerCAmelCase__ = []
# Traverse through all denomination
for den... | 61 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transfo... | 61 | 1 |
def __lowercase ( _A , _A ) -> int:
assert x is not None
assert y is not None
SCREAMING_SNAKE_CASE : int = len(_A )
SCREAMING_SNAKE_CASE : Union[str, Any] = len(_A )
# declaring the array for storing the dp values
... | 717 |
from math import ceil
def __lowercase ( _A = 1001 ) -> int:
SCREAMING_SNAKE_CASE : Union[str, Any] = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
SCREAMING_SNAKE_CASE : Dict = 2 * i + 1
SCREAMING_... | 446 | 0 |
'''simple docstring'''
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
a : int = logging.get_logger(__name__)
a : Any = r'''
Arg... | 69 |
'''simple docstring'''
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
UpperCamelCase =TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow("", "|",... | 208 | 0 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __UpperCamelCase ( _A : Dict ) -> int:
"""simple docstring"""
... | 709 |
'''simple docstring'''
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase : Union[str, Any] = get_tests_dir('fixture... | 646 | 0 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils i... | 690 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase__ :
def __init__( self : Optional[Any] , _lowercase : int=2 , _lowercase : Option... | 690 | 1 |
"""simple docstring"""
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel... | 42 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from... | 42 | 1 |
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_channel... | 519 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def _UpperCAmelCase ( ):
"""simple docstring"""
__lowerCamelCase : Any = {
"""repo_name""": ["""test_rep... | 519 | 1 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
requir... | 702 |
# 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 switchi... | 388 | 0 |
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