




"""simple docstring"""


import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
_A													=      data_utils.TransfoXLTokenizer
_A													=      data_utils.TransfoXLCorpus
_A													=      data_utils
_A													=      data_utils



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Any:
      if transfo_xl_dataset_file:
            # Convert a pre-processed corpus (see original TensorFlow repo)
            with open(__UpperCAmelCase    ,		"""rb"""				) as fp:
                  lowerCAmelCase__      :			str   =	pickle.load(__UpperCAmelCase    ,		encoding="""latin1"""				)
            # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
            lowerCAmelCase__      :			Union[str, Any]   =	pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""pretrained_vocab_file"""]
            print(f"""Save vocabulary to {pytorch_vocab_dump_path}"""				)
            lowerCAmelCase__      :			Any   =	corpus.vocab.__dict__
            torch.save(__UpperCAmelCase    ,		__UpperCAmelCase				)

            lowerCAmelCase__      :			Optional[Any]   =	corpus.__dict__
            corpus_dict_no_vocab.pop("""vocab"""    ,		__UpperCAmelCase				)
            lowerCAmelCase__      :			Dict   =	pytorch_dump_folder_path + """/""" + CORPUS_NAME
            print(f"""Save dataset to {pytorch_dataset_dump_path}"""				)
            torch.save(__UpperCAmelCase    ,		__UpperCAmelCase				)

      if tf_checkpoint_path:
            # Convert a pre-trained TensorFlow model
            lowerCAmelCase__      :			int   =	os.path.abspath(__UpperCAmelCase				)
            lowerCAmelCase__      :			Optional[int]   =	os.path.abspath(__UpperCAmelCase				)

            print(f"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""				)
            # Initialise PyTorch model
            if transfo_xl_config_file == "":
                  lowerCAmelCase__      :			str   =	TransfoXLConfig()
            else:
                  lowerCAmelCase__      :			Any   =	TransfoXLConfig.from_json_file(__UpperCAmelCase				)
            print(f"""Building PyTorch model from configuration: {config}"""				)
            lowerCAmelCase__      :			int   =	TransfoXLLMHeadModel(__UpperCAmelCase				)

            lowerCAmelCase__      :			int   =	load_tf_weights_in_transfo_xl(__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)
            # Save pytorch-model
            lowerCAmelCase__      :			Optional[Any]   =	os.path.join(__UpperCAmelCase    ,		__UpperCAmelCase				)
            lowerCAmelCase__      :			List[Any]   =	os.path.join(__UpperCAmelCase    ,		__UpperCAmelCase				)
            print(f"""Save PyTorch model to {os.path.abspath(__UpperCAmelCase				)}"""				)
            torch.save(model.state_dict()    ,		__UpperCAmelCase				)
            print(f"""Save configuration file to {os.path.abspath(__UpperCAmelCase				)}"""				)
            with open(__UpperCAmelCase    ,		"""w"""    ,		encoding="""utf-8"""				) as f:
                  f.write(config.to_json_string()				)


if __name__ == "__main__":
  _A													=      argparse.ArgumentParser()
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default=None,
      type=str,
      required=True,
      help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
  )
  parser.add_argument(
      """--tf_checkpoint_path""",
      default="""""",
      type=str,
      help="""An optional path to a TensorFlow checkpoint path to be converted.""",
  )
  parser.add_argument(
      """--transfo_xl_config_file""",
      default="""""",
      type=str,
      help=(
          """An optional config json file corresponding to the pre-trained BERT model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--transfo_xl_dataset_file""",
      default="""""",
      type=str,
      help="""An optional dataset file to be converted in a vocabulary.""",
  )
  _A													=      parser.parse_args()
  convert_transfo_xl_checkpoint_to_pytorch(
      args.tf_checkpoint_path,
      args.transfo_xl_config_file,
      args.pytorch_dump_folder_path,
      args.transfo_xl_dataset_file,
  )











"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


_A													=      logging.get_logger(__name__)


class _lowerCamelCase    (					a_						):






   def __init__(		self       :		Union[str, Any]   ,     *UpperCamelCase       :		int   ,     **UpperCamelCase       :		List[Any]   )			->						None:

         """simple docstring"""
         warnings.warn(
             """The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
             """ use CLIPImageProcessor instead."""   ,     UpperCamelCase   ,     )
         super().__init__(*UpperCamelCase   ,     **UpperCamelCase   )





