






'''simple docstring'''





from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class       snake_case__	( UpperCamelCase):
					a_								= 42
					a_								= 42


if is_transformers_available() and is_torch_available():
						from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline





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
lowercase_									=   data_utils.TransfoXLTokenizer
lowercase_									=   data_utils.TransfoXLCorpus
lowercase_									=   data_utils
lowercase_									=   data_utils
def      a      (			A__	:   int      ,   A__	:   Dict      ,   A__	:   Union[str, Any]      ,   A__	:   Union[str, Any]     )    ->      List[str]:





      """simple docstring"""



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

            _lowercase	       =corpus.__dict__
            corpus_dict_no_vocab.pop('vocab'      ,   A__     )
            _lowercase	       =pytorch_dump_folder_path + '/' + CORPUS_NAME
            print(F'''Save dataset to {pytorch_dataset_dump_path}'''     )
            torch.save(A__      ,   A__     )

      if tf_checkpoint_path:
            # Convert a pre-trained TensorFlow model
            _lowercase	       =os.path.abspath(A__     )
            _lowercase	       =os.path.abspath(A__     )

            print(F'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'''     )
            # Initialise PyTorch model
            if transfo_xl_config_file == "":
                  _lowercase	       =TransfoXLConfig()
            else:
                  _lowercase	       =TransfoXLConfig.from_json_file(A__     )
            print(F'''Building PyTorch model from configuration: {config}'''     )
            _lowercase	       =TransfoXLLMHeadModel(A__     )

            _lowercase	       =load_tf_weights_in_transfo_xl(A__      ,   A__      ,   A__     )
            # Save pytorch-model
            _lowercase	       =os.path.join(A__      ,   A__     )
            _lowercase	       =os.path.join(A__      ,   A__     )
            print(F'''Save PyTorch model to {os.path.abspath(A__     )}'''     )
            torch.save(model.state_dict()      ,   A__     )
            print(F'''Save configuration file to {os.path.abspath(A__     )}'''     )
            with open(A__      ,   'w'      ,   encoding='utf-8'     ) as f:
                  f.write(config.to_json_string()     )


if __name__ == "__main__":
  lowercase_									=   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.',
  )
  lowercase_									=   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,
  )


