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
__magic_name__								=data_utils.TransfoXLTokenizer
__magic_name__								=data_utils.TransfoXLCorpus
__magic_name__								=data_utils
__magic_name__								=data_utils







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

          UpperCamelCase__										=      corpus.__dict__
          corpus_dict_no_vocab.pop('''vocab''' ,							A							)
          UpperCamelCase__										=      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
          UpperCamelCase__										=      os.path.abspath(A							)
          UpperCamelCase__										=      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 == "":
               UpperCamelCase__										=      TransfoXLConfig()
          else:
               UpperCamelCase__										=      TransfoXLConfig.from_json_file(A							)
          print(f"Building PyTorch model from configuration: {config}"							)
          UpperCamelCase__										=      TransfoXLLMHeadModel(A							)

          UpperCamelCase__										=      load_tf_weights_in_transfo_xl(A ,							A ,							A							)
          # Save pytorch-model
          UpperCamelCase__										=      os.path.join(A ,							A							)
          UpperCamelCase__										=      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__":
     __magic_name__								=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.''',
     )
     __magic_name__								=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,
     )




from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin



class     _A   (    __UpperCamelCase	, __UpperCamelCase							):






  @register_to_config
  def __init__(self   ,				SCREAMING_SNAKE_CASE_ = 768   ,				)		->      Tuple:





       '''simple docstring'''





       super().__init__()

       UpperCamelCase__										=      nn.Parameter(torch.zeros(1   ,				SCREAMING_SNAKE_CASE_	)	)
       UpperCamelCase__										=      nn.Parameter(torch.ones(1   ,				SCREAMING_SNAKE_CASE_	)	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      nn.Parameter(self.mean.to(SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	)	)
       UpperCamelCase__										=      nn.Parameter(self.std.to(SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	)	)
       return self






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      (embeds - self.mean) * 1.0 / self.std
       return embeds






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      (embeds * self.std) + self.mean
       return embeds



