



"""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
lowerCAmelCase__        =				data_utils.TransfoXLTokenizer
lowerCAmelCase__        =				data_utils.TransfoXLCorpus
lowerCAmelCase__        =				data_utils
lowerCAmelCase__        =				data_utils






def        snake_case_  (      A_					:		Optional[Any],				A_					:		List[Any],				A_					:		Any,				A_					:		Optional[Any]						):


       '''simple docstring'''





       if transfo_xl_dataset_file:
              # Convert a pre-processed corpus (see original TensorFlow repo)
              with open(A_,				'''rb'''						) as fp:
                     _lowerCamelCase						:				Union[str, Any]    			= pickle.load(A_,				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						:				Union[str, Any]    			= corpus.vocab.__dict__
              torch.save(A_,				A_						)

              _lowerCamelCase						:				List[str]    			= corpus.__dict__
              corpus_dict_no_vocab.pop('''vocab''',				A_						)
              _lowerCamelCase						:				Union[str, Any]    			= 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
              _lowerCamelCase						:				List[str]    			= os.path.abspath(A_						)
              _lowerCamelCase						:				Optional[Any]    			= 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 == "":
                     _lowerCamelCase						:				Dict    			= TransfoXLConfig()
              else:
                     _lowerCamelCase						:				Dict    			= TransfoXLConfig.from_json_file(A_						)
              print(F'''Building PyTorch model from configuration: {config}'''						)
              _lowerCamelCase						:				str    			= TransfoXLLMHeadModel(A_						)

              _lowerCamelCase						:				Dict    			= load_tf_weights_in_transfo_xl(A_,				A_,				A_						)
              # Save pytorch-model
              _lowerCamelCase						:				Dict    			= os.path.join(A_,				A_						)
              _lowerCamelCase						:				Union[str, Any]    			= 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__":
   lowerCAmelCase__        =				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.''',
   )
   lowerCAmelCase__        =				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'''

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import (
          ImageTextPipelineOutput,
          UniDiffuserPipeline,
      )
else:
      from .modeling_text_decoder import UniDiffuserTextDecoder
      from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
      from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline



