


"""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
snake_case__		:     List[str]    				=				data_utils.TransfoXLTokenizer
snake_case__		:     Optional[int]    				=				data_utils.TransfoXLCorpus
snake_case__		:     Optional[int]    				=				data_utils
snake_case__		:     int    				=				data_utils


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

            lowerCAmelCase					:	int       =   corpus.__dict__
            corpus_dict_no_vocab.pop('''vocab''' ,   _SCREAMING_SNAKE_CASE			)
            lowerCAmelCase					:	Dict       =   pytorch_dump_folder_path + '''/''' + CORPUS_NAME
            print(f'''Save dataset to {pytorch_dataset_dump_path}'''			)
            torch.save(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE			)

      if tf_checkpoint_path:
            # Convert a pre-trained TensorFlow model
            lowerCAmelCase					:	int       =   os.path.abspath(_SCREAMING_SNAKE_CASE			)
            lowerCAmelCase					:	Dict       =   os.path.abspath(_SCREAMING_SNAKE_CASE			)

            print(f'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'''			)
            # Initialise PyTorch model
            if transfo_xl_config_file == "":
                  lowerCAmelCase					:	Optional[Any]       =   TransfoXLConfig()
            else:
                  lowerCAmelCase					:	List[Any]       =   TransfoXLConfig.from_json_file(_SCREAMING_SNAKE_CASE			)
            print(f'''Building PyTorch model from configuration: {config}'''			)
            lowerCAmelCase					:	Optional[int]       =   TransfoXLLMHeadModel(_SCREAMING_SNAKE_CASE			)

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


if __name__ == "__main__":
 snake_case__		:     List[str]    				=				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.''',
 )
 snake_case__		:     Union[str, Any]    				=				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 __future__ import annotations

from typing import Any



class 		snake_case_(       a__			):
     pass



class 		snake_case_:






     def __init__(	self  :					Any					,				UpperCamelCase_  :					Any				):
           lowerCAmelCase					:	Any       =   data
           lowerCAmelCase					:	Node | None       =   None






     def __iter__(	self  :					int				):
           lowerCAmelCase					:	Any       =   self
           lowerCAmelCase					:	Union[str, Any]       =   []
           while node:
                 if node in visited:
                       raise ContainsLoopError
                 visited.append(UpperCamelCase_				)
                 yield node.data
                 lowerCAmelCase					:	Optional[int]       =   node.next_node






     @property
     def      lowerCamelCase__     (	self  :					str				):
           try:
                 list(self				)
                 return False
           except ContainsLoopError:
                 return True


if __name__ == "__main__":
 snake_case__		:     Dict    				=				Node(1)
 snake_case__		:     Any    				=				Node(2)
 snake_case__		:     int    				=				Node(3)
 snake_case__		:     Any    				=				Node(4)
 print(root_node.has_loop)  # False
 snake_case__		:     Tuple    				=				root_node.next_node
 print(root_node.has_loop)  # True

 snake_case__		:     List[Any]    				=				Node(5)
 snake_case__		:     int    				=				Node(6)
 snake_case__		:     List[Any]    				=				Node(5)
 snake_case__		:     Dict    				=				Node(6)
 print(root_node.has_loop)  # False

 snake_case__		:     Any    				=				Node(1)
 print(root_node.has_loop)  # False


