


'''simple docstring'''

from typing import Any






def __UpperCamelCase   (			__lowerCamelCase		: list      ,  __lowerCamelCase		: list      ,  __lowerCamelCase		: dict      ,  __lowerCamelCase		: dict      ,  __lowerCamelCase		: dict      ,  )      ->     list:







     '''simple docstring'''






     _validation(
         __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  )
     # Creates data structures and fill initial step
     _a						=		{}
     _a						=		{}
     for state in states_space:
          _a						=		observations_space[0]
          _a						=		(
              initial_probabilities[state] * emission_probabilities[state][observation]
          )
          _a						=		None

     # Fills the data structure with the probabilities of
     # different transitions and pointers to previous states
     for o in range(1      ,  len(__lowerCamelCase				)				):
          _a						=		observations_space[o]
          _a						=		observations_space[o - 1]
          for state in states_space:
               # Calculates the argmax for probability function
               _a						=		""
               _a						=		-1
               for k_state in states_space:
                    _a						=		(
                        probabilities[(k_state, prior_observation)]
                        * transition_probabilities[k_state][state]
                        * emission_probabilities[state][observation]
                    )
                    if probability > max_probability:
                         _a						=		probability
                         _a						=		k_state

            # Update probabilities and pointers dicts
               _a						=		(
                   probabilities[(arg_max, prior_observation)]
                   * transition_probabilities[arg_max][state]
                   * emission_probabilities[state][observation]
               )

               _a						=		arg_max

    # The final observation
     _a						=		observations_space[len(__lowerCamelCase				) - 1]

     # argmax for given final observation
     _a						=		""
     _a						=		-1
     for k_state in states_space:
          _a						=		probabilities[(k_state, final_observation)]
          if probability > max_probability:
               _a						=		probability
               _a						=		k_state
     _a						=		arg_max

     # Process pointers backwards
     _a						=		last_state
     _a						=		[]
     for o in range(len(__lowerCamelCase				) - 1      ,  -1      ,  -1				):
          result.append(__lowerCamelCase				)
          _a						=		pointers[previous, observations_space[o]]
     result.reverse()

     return result






def __UpperCamelCase   (			__lowerCamelCase		: Any      ,  __lowerCamelCase		: Any      ,  __lowerCamelCase		: Any      ,  __lowerCamelCase		: Any      ,  __lowerCamelCase		: Any      ,  )      ->     None:







     '''simple docstring'''






     _validate_not_empty(
         __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  )
     _validate_lists(__lowerCamelCase      ,  __lowerCamelCase				)
     _validate_dicts(
         __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase				)






def __UpperCamelCase   (			__lowerCamelCase		: Any      ,  __lowerCamelCase		: Any      ,  __lowerCamelCase		: Any      ,  __lowerCamelCase		: Any      ,  __lowerCamelCase		: Any      ,  )      ->     None:







     '''simple docstring'''






     if not all(
         [
             observations_space,
             states_space,
             initial_probabilities,
             transition_probabilities,
             emission_probabilities,
         ]				):
          raise ValueError("There's an empty parameter"				)






def __UpperCamelCase   (			__lowerCamelCase		: Any      ,  __lowerCamelCase		: Any				)      ->     None:







     '''simple docstring'''






     _validate_list(__lowerCamelCase      ,  "observations_space"				)
     _validate_list(__lowerCamelCase      ,  "states_space"				)






def __UpperCamelCase   (			__lowerCamelCase		: Any      ,  __lowerCamelCase		: str				)      ->     None:







     '''simple docstring'''






     if not isinstance(_object      ,  __lowerCamelCase				):
          _a						=		F"{var_name} must be a list"
          raise ValueError(__lowerCamelCase				)
     else:
          for x in _object:
               if not isinstance(__lowerCamelCase      ,  __lowerCamelCase				):
                    _a						=		F"{var_name} must be a list of strings"
                    raise ValueError(__lowerCamelCase				)






def __UpperCamelCase   (			__lowerCamelCase		: Any      ,  __lowerCamelCase		: Any      ,  __lowerCamelCase		: Any      ,  )      ->     None:







     '''simple docstring'''






     _validate_dict(__lowerCamelCase      ,  "initial_probabilities"      ,  __lowerCamelCase				)
     _validate_nested_dict(__lowerCamelCase      ,  "transition_probabilities"				)
     _validate_nested_dict(__lowerCamelCase      ,  "emission_probabilities"				)






def __UpperCamelCase   (			__lowerCamelCase		: Any      ,  __lowerCamelCase		: str				)      ->     None:







     '''simple docstring'''






     _validate_dict(_object      ,  __lowerCamelCase      ,  __lowerCamelCase				)
     for x in _object.values():
          _validate_dict(__lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase				)






def __UpperCamelCase   (			__lowerCamelCase		: Any      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: type      ,  __lowerCamelCase		: bool = False				)      ->     None:







     '''simple docstring'''






     if not isinstance(_object      ,  __lowerCamelCase				):
          _a						=		F"{var_name} must be a dict"
          raise ValueError(__lowerCamelCase				)
     if not all(isinstance(__lowerCamelCase      ,  __lowerCamelCase				) for x in _object				):
          _a						=		F"{var_name} all keys must be strings"
          raise ValueError(__lowerCamelCase				)
     if not all(isinstance(__lowerCamelCase      ,  __lowerCamelCase				) for x in _object.values()				):
          _a						=		"nested dictionary " if nested else ""
          _a						=		F"{var_name} {nested_text}all values must be {value_type.__name__}"
          raise ValueError(__lowerCamelCase				)


if __name__ == "__main__":
   from doctest import testmod

   testmod()





'''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
lowercase__				     =		data_utils.TransfoXLTokenizer
lowercase__				     =		data_utils.TransfoXLCorpus
lowercase__				     =		data_utils
lowercase__				     =		data_utils






def __UpperCamelCase   (			__lowerCamelCase		: str      ,  __lowerCamelCase		: Tuple      ,  __lowerCamelCase		: Optional[int]      ,  __lowerCamelCase		: Tuple				)      ->     Dict:







     '''simple docstring'''






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

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

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

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

          _a						=		load_tf_weights_in_transfo_xl(__lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase				)
          # Save pytorch-model
          _a						=		os.path.join(__lowerCamelCase      ,  __lowerCamelCase				)
          _a						=		os.path.join(__lowerCamelCase      ,  __lowerCamelCase				)
          print(F"Save PyTorch model to {os.path.abspath(__lowerCamelCase				)}"				)
          torch.save(model.state_dict()      ,  __lowerCamelCase				)
          print(F"Save configuration file to {os.path.abspath(__lowerCamelCase				)}"				)
          with open(__lowerCamelCase      ,  "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,
   )

