"""simple docstring"""






import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
       import numpy as np
       import torch

       from . import (
           AlbertForPreTraining,
           BartForConditionalGeneration,
           BertForPreTraining,
           BertForQuestionAnswering,
           BertForSequenceClassification,
           CamembertForMaskedLM,
           CTRLLMHeadModel,
           DistilBertForMaskedLM,
           DistilBertForQuestionAnswering,
           DPRContextEncoder,
           DPRQuestionEncoder,
           DPRReader,
           ElectraForPreTraining,
           FlaubertWithLMHeadModel,
           GPTaLMHeadModel,
           LayoutLMForMaskedLM,
           LxmertForPreTraining,
           LxmertVisualFeatureEncoder,
           OpenAIGPTLMHeadModel,
           RobertaForMaskedLM,
           RobertaForSequenceClassification,
           TaForConditionalGeneration,
           TransfoXLLMHeadModel,
           XLMRobertaForMaskedLM,
           XLMWithLMHeadModel,
           XLNetLMHeadModel,
       )


logging.set_verbosity_info()

__lowerCAmelCase					: Optional[int]        ={
    """bart""": (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """bert""": (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-base-cased-finetuned-mrpc""": (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """dpr""": (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """gpt2""": (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlnet""": (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm""": (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm-roberta""": (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """transfo-xl""": (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """openai-gpt""": (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """roberta""": (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """layoutlm""": (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """roberta-large-mnli""": (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """camembert""": (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """flaubert""": (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert""": (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert-base-distilled-squad""": (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert""": (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert-visual-feature-encoder""": (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """ctrl""": (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """albert""": (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """t5""": (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """electra""": (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """wav2vec2""": (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}


def     UpperCAmelCase__ (       lowerCAmelCase__  :Any      ,	lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :List[Any]=False      ,	lowerCAmelCase__  :Any=True	)				->					Union[str, Any]:
      '''simple docstring'''


      if model_type not in MODEL_CLASSES:
            raise ValueError(f'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()	)}.'	)

      lowercase							,							lowercase							,							lowercase							,							lowercase														=			MODEL_CLASSES[model_type]

      # Initialise TF model
      if config_file in aws_config_map:
            lowercase														=			cached_file(lowerCAmelCase__      ,	lowerCAmelCase__      ,	force_download=not use_cached_models	)
      lowercase														=			config_class.from_json_file(lowerCAmelCase__	)
      lowercase														=			True
      lowercase														=			True
      print(f'Building TensorFlow model from configuration: {config}'	)
      lowercase														=			model_class(lowerCAmelCase__	)

      # Load weights from tf checkpoint
      if pytorch_checkpoint_path in aws_config_map.keys():
            lowercase														=			cached_file(
                lowerCAmelCase__      ,	lowerCAmelCase__      ,	force_download=not use_cached_models	)
      # Load PyTorch checkpoint in tf2 model:
      lowercase														=			load_pytorch_checkpoint_in_tfa_model(lowerCAmelCase__      ,	lowerCAmelCase__	)

      if compare_with_pt_model:
            lowercase														=			tf_model(tf_model.dummy_inputs      ,	training=lowerCAmelCase__	)  # build the network

            lowercase														=			torch.load(lowerCAmelCase__      ,	map_location="""cpu"""	)
            lowercase														=			pt_model_class.from_pretrained(
                pretrained_model_name_or_path=lowerCAmelCase__      ,	config=lowerCAmelCase__      ,	state_dict=lowerCAmelCase__	)

            with torch.no_grad():
                  lowercase														=			pt_model(**pt_model.dummy_inputs	)

            lowercase														=			pto[0].numpy()
            lowercase														=			tfo[0].numpy()
            lowercase														=			np.amax(np.abs(np_pt - np_tf	)	)
            print(f'Max absolute difference between models outputs {diff}'	)
            assert diff <= 2e-2, f'Error, model absolute difference is >2e-2: {diff}'

      # Save pytorch-model
      print(f'Save TensorFlow model to {tf_dump_path}'	)
      tf_model.save_weights(lowerCAmelCase__      ,	save_format="""h5"""	)


def     UpperCAmelCase__ (       lowerCAmelCase__  :Optional[int]      ,	lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :Union[str, Any]=None      ,	lowerCAmelCase__  :Optional[Any]=None      ,	lowerCAmelCase__  :Union[str, Any]=False      ,	lowerCAmelCase__  :Union[str, Any]=False      ,	lowerCAmelCase__  :Tuple=False      ,	lowerCAmelCase__  :Union[str, Any]=False      ,	)				->					Optional[Any]:
      '''simple docstring'''


      if args_model_type is None:
            lowercase														=			list(MODEL_CLASSES.keys()	)
      else:
            lowercase														=			[args_model_type]

      for j, model_type in enumerate(lowerCAmelCase__      ,	start=1	):
            print("""=""" * 1_0_0	)
            print(f' Converting model type {j}/{len(lowerCAmelCase__	)}: {model_type}'	)
            print("""=""" * 1_0_0	)
            if model_type not in MODEL_CLASSES:
                  raise ValueError(f'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()	)}.'	)

            lowercase							,							lowercase							,							lowercase							,							lowercase							,							lowercase														=			MODEL_CLASSES[model_type]

            if model_shortcut_names_or_path is None:
                  lowercase														=			list(aws_model_maps.keys()	)
            if config_shortcut_names_or_path is None:
                  lowercase														=			model_shortcut_names_or_path

            for i, (model_shortcut_name, config_shortcut_name) in enumerate(
                zip(lowerCAmelCase__      ,	lowerCAmelCase__	)      ,	start=1	):
                  print("""-""" * 1_0_0	)
                  if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
                        if not only_convert_finetuned_models:
                              print(f'    Skipping finetuned checkpoint {model_shortcut_name}'	)
                              continue
                        lowercase														=			model_shortcut_name
                  elif only_convert_finetuned_models:
                        print(f'    Skipping not finetuned checkpoint {model_shortcut_name}'	)
                        continue
                  print(
                      f'    Converting checkpoint {i}/{len(lowerCAmelCase__	)}: {model_shortcut_name} - model_type {model_type}'	)
                  print("""-""" * 1_0_0	)

                  if config_shortcut_name in aws_config_map:
                        lowercase														=			cached_file(lowerCAmelCase__      ,	lowerCAmelCase__      ,	force_download=not use_cached_models	)
                  else:
                        lowercase														=			config_shortcut_name

                  if model_shortcut_name in aws_model_maps:
                        lowercase														=			cached_file(lowerCAmelCase__      ,	lowerCAmelCase__      ,	force_download=not use_cached_models	)
                  else:
                        lowercase														=			model_shortcut_name

                  if os.path.isfile(lowerCAmelCase__	):
                        lowercase														=			"""converted_model"""

                  convert_pt_checkpoint_to_tf(
                      model_type=lowerCAmelCase__      ,	pytorch_checkpoint_path=lowerCAmelCase__      ,	config_file=lowerCAmelCase__      ,	tf_dump_path=os.path.join(lowerCAmelCase__      ,	model_shortcut_name + """-tf_model.h5"""	)      ,	compare_with_pt_model=lowerCAmelCase__      ,	)
                  if remove_cached_files:
                        os.remove(lowerCAmelCase__	)
                        os.remove(lowerCAmelCase__	)


if __name__ == "__main__":
       __lowerCAmelCase					: Union[str, Any]        =argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--tf_dump_path""", default=None, type=str, required=True, help="""Path to the output Tensorflow dump file."""
       )
       parser.add_argument(
           """--model_type""",
           default=None,
           type=str,
           help=(
               F"""Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and """
               """convert all the models from AWS."""
           ),
       )
       parser.add_argument(
           """--pytorch_checkpoint_path""",
           default=None,
           type=str,
           help=(
               """Path to the PyTorch checkpoint path or shortcut name to download from AWS. """
               """If not given, will download and convert all the checkpoints from AWS."""
           ),
       )
       parser.add_argument(
           """--config_file""",
           default=None,
           type=str,
           help=(
               """The config json file corresponding to the pre-trained model. \n"""
               """This specifies the model architecture. If not given and """
               """--pytorch_checkpoint_path is not given or is a shortcut name """
               """use the configuration associated to the shortcut name on the AWS"""
           ),
       )
       parser.add_argument(
           """--compare_with_pt_model""", action="""store_true""", help="""Compare Tensorflow and PyTorch model predictions."""
       )
       parser.add_argument(
           """--use_cached_models""",
           action="""store_true""",
           help="""Use cached models if possible instead of updating to latest checkpoint versions.""",
       )
       parser.add_argument(
           """--remove_cached_files""",
           action="""store_true""",
           help="""Remove pytorch models after conversion (save memory when converting in batches).""",
       )
       parser.add_argument("""--only_convert_finetuned_models""", action="""store_true""", help="""Only convert finetuned models.""")
       __lowerCAmelCase					: int        =parser.parse_args()

       # if args.pytorch_checkpoint_path is not None:
       #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
       #                                 args.pytorch_checkpoint_path,
       #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
       #                                 args.tf_dump_path,
       #                                 compare_with_pt_model=args.compare_with_pt_model,
       #                                 use_cached_models=args.use_cached_models)
       # else:
       convert_all_pt_checkpoints_to_tf(
           args.model_type.lower() if args.model_type is not None else None,
           args.tf_dump_path,
           model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
           if args.pytorch_checkpoint_path is not None
           else None,
           config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
           compare_with_pt_model=args.compare_with_pt_model,
           use_cached_models=args.use_cached_models,
           remove_cached_files=args.remove_cached_files,
           only_convert_finetuned_models=args.only_convert_finetuned_models,
       )




"""simple docstring"""






import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


__lowerCAmelCase					: List[Any]        ="""\
    Text data.
    Second line of data."""

__lowerCAmelCase					: Any        ="""file"""


@pytest.fixture(scope="""session"""	)
def     UpperCAmelCase__ (       lowerCAmelCase__  :str	)				->					Union[str, Any]:
      '''simple docstring'''


      lowercase														=			tmp_path_factory.mktemp("""data"""	) / (FILE_PATH + """.zstd""")
      lowercase														=			bytes(lowerCAmelCase__      ,	"""utf-8"""	)
      with zstd.open(lowerCAmelCase__      ,	"""wb"""	) as f:
            f.write(lowerCAmelCase__	)
      return path


@pytest.fixture
def     UpperCAmelCase__ (       lowerCAmelCase__  :Tuple	)				->					Dict:
      '''simple docstring'''


      with open(os.path.join(tmpfs.local_root_dir      ,	lowerCAmelCase__	)      ,	"""w"""	) as f:
            f.write(lowerCAmelCase__	)
      return FILE_PATH


@pytest.mark.parametrize("""compression_format"""      ,	["""gzip""", """xz""", """zstd"""]	)
def     UpperCAmelCase__ (       lowerCAmelCase__  :Any      ,	lowerCAmelCase__  :Any      ,	lowerCAmelCase__  :List[Any]      ,	lowerCAmelCase__  :Optional[Any]      ,	lowerCAmelCase__  :Any      ,	lowerCAmelCase__  :int	)				->					Union[str, Any]:
      '''simple docstring'''


      lowercase														=			{"""gzip""": gz_file, """xz""": xz_file, """zstd""": zstd_path}
      lowercase														=			input_paths[compression_format]
      lowercase														=			tmp_path / """cache"""
      lowercase														=			DownloadConfig(cache_dir=lowerCAmelCase__      ,	extract_compressed_file=lowerCAmelCase__	)
      lowercase														=			cached_path(lowerCAmelCase__      ,	download_config=lowerCAmelCase__	)
      with open(lowerCAmelCase__	) as f:
            lowercase														=			f.read()
      with open(lowerCAmelCase__	) as f:
            lowercase														=			f.read()
      assert extracted_file_content == expected_file_content


@pytest.mark.parametrize("""default_extracted"""      ,	[True, False]	)
@pytest.mark.parametrize("""default_cache_dir"""      ,	[True, False]	)
def     UpperCAmelCase__ (       lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :Tuple      ,	lowerCAmelCase__  :Tuple      ,	lowerCAmelCase__  :Union[str, Any]      ,	lowerCAmelCase__  :List[str]	)				->					Any:
      '''simple docstring'''


      lowercase														=			"""custom_cache"""
      lowercase														=			"""custom_extracted_dir"""
      lowercase														=			tmp_path / """custom_extracted_path"""
      if default_extracted:
            lowercase														=			("""downloads""" if default_cache_dir else custom_cache_dir, """extracted""")
      else:
            monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_DIR"""      ,	lowerCAmelCase__	)
            monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH"""      ,	str(lowerCAmelCase__	)	)
            lowercase														=			custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

      lowercase														=			xz_file
      lowercase														=			(
          DownloadConfig(extract_compressed_file=lowerCAmelCase__	)
          if default_cache_dir
          else DownloadConfig(cache_dir=tmp_path / custom_cache_dir      ,	extract_compressed_file=lowerCAmelCase__	)
      )
      lowercase														=			cached_path(lowerCAmelCase__      ,	download_config=lowerCAmelCase__	)
      assert Path(lowerCAmelCase__	).parent.parts[-2:] == expected


def     UpperCAmelCase__ (       lowerCAmelCase__  :List[str]	)				->					Tuple:
      '''simple docstring'''


      lowercase														=			str(Path(lowerCAmelCase__	).resolve()	)
      assert cached_path(lowerCAmelCase__	) == text_file
      # relative path
      lowercase														=			str(Path(lowerCAmelCase__	).resolve().relative_to(Path(os.getcwd()	)	)	)
      assert cached_path(lowerCAmelCase__	) == text_file


def     UpperCAmelCase__ (       lowerCAmelCase__  :Any	)				->					List[Any]:
      '''simple docstring'''


      lowercase														=			str(tmp_path.resolve() / """__missing_file__.txt"""	)
      with pytest.raises(lowerCAmelCase__	):
            cached_path(lowerCAmelCase__	)
      # relative path
      lowercase														=			"""./__missing_file__.txt"""
      with pytest.raises(lowerCAmelCase__	):
            cached_path(lowerCAmelCase__	)


def     UpperCAmelCase__ (       lowerCAmelCase__  :Any	)				->					Union[str, Any]:
      '''simple docstring'''


      lowercase														=			get_from_cache(f'tmp://{tmpfs_file}'	)
      with open(lowerCAmelCase__	) as f:
            lowercase														=			f.read()
      assert output_file_content == FILE_CONTENT


@patch("""datasets.config.HF_DATASETS_OFFLINE"""      ,	lowerCAmelCase__	)
def     UpperCAmelCase__ (       )				->					str:
      '''simple docstring'''


      with pytest.raises(lowerCAmelCase__	):
            cached_path("""https://huggingface.co"""	)


@patch("""datasets.config.HF_DATASETS_OFFLINE"""      ,	lowerCAmelCase__	)
def     UpperCAmelCase__ (       lowerCAmelCase__  :Tuple	)				->					Any:
      '''simple docstring'''


      lowercase														=			tmp_path_factory.mktemp("""data"""	) / """file.html"""
      with pytest.raises(lowerCAmelCase__	):
            http_get("""https://huggingface.co"""      ,	temp_file=lowerCAmelCase__	)
      with pytest.raises(lowerCAmelCase__	):
            http_head("""https://huggingface.co"""	)


@patch("""datasets.config.HF_DATASETS_OFFLINE"""      ,	lowerCAmelCase__	)
def     UpperCAmelCase__ (       lowerCAmelCase__  :str	)				->					Any:
      '''simple docstring'''


      lowercase														=			tmp_path_factory.mktemp("""data"""	) / """file.html"""
      with pytest.raises(lowerCAmelCase__	):
            ftp_get("""ftp://huggingface.co"""      ,	temp_file=lowerCAmelCase__	)
      with pytest.raises(lowerCAmelCase__	):
            ftp_head("""ftp://huggingface.co"""	)






@patch("""datasets.config.HF_DATASETS_OFFLINE"""      ,	lowerCAmelCase__	)
def     UpperCAmelCase__ (       lowerCAmelCase__  :Any	)				->					Dict:
      '''simple docstring'''


      lowercase														=			tmp_path_factory.mktemp("""data"""	) / """file.html"""
      with pytest.raises(lowerCAmelCase__	):
            fsspec_get("""s3://huggingface.co"""      ,	temp_file=lowerCAmelCase__	)
      with pytest.raises(lowerCAmelCase__	):
            fsspec_head("""s3://huggingface.co"""	)



