"""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
_UpperCamelCase  :		Optional[int]           =       data_utils.TransfoXLTokenizer
_UpperCamelCase  :		Dict           =       data_utils.TransfoXLCorpus
_UpperCamelCase  :		Tuple           =       data_utils
_UpperCamelCase  :		str           =       data_utils





def 							a_	( _lowerCAmelCase    :					List[str]  ,					_lowerCAmelCase    :					str  ,					_lowerCAmelCase    :					Union[str, Any]  ,					_lowerCAmelCase    :					Dict	):





       '''simple docstring'''

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

              lowercase__     :						Union[str, Any]         =      corpus.__dict__
              corpus_dict_no_vocab.pop('vocab'  ,					UpperCAmelCase_	)
              lowercase__     :						str         =      pytorch_dump_folder_path + '/' + CORPUS_NAME
              print(f"""Save dataset to {pytorch_dataset_dump_path}"""	)
              torch.save(UpperCAmelCase_  ,					UpperCAmelCase_	)

       if tf_checkpoint_path:
              # Convert a pre-trained TensorFlow model
              lowercase__     :						Any         =      os.path.abspath(UpperCAmelCase_	)
              lowercase__     :						Dict         =      os.path.abspath(UpperCAmelCase_	)

              print(f"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""	)
              # Initialise PyTorch model
              if transfo_xl_config_file == "":
                     lowercase__     :						Union[str, Any]         =      TransfoXLConfig()
              else:
                     lowercase__     :						str         =      TransfoXLConfig.from_json_file(UpperCAmelCase_	)
              print(f"""Building PyTorch model from configuration: {config}"""	)
              lowercase__     :						Any         =      TransfoXLLMHeadModel(UpperCAmelCase_	)

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


if __name__ == "__main__":
       _UpperCamelCase  :		Union[str, Any]           =       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.",
       )
       _UpperCamelCase  :		List[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

import math




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->int:

	if depth < 0:
		raise ValueError('Depth cannot be less than 0'							)

	if not scores:
		raise ValueError('Scores cannot be empty'							)

	if depth == height:
		return scores[node_index]

	return (
	    max(
	        minimax(depth + 1					,				node_index * 2					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)					,				minimax(depth + 1					,				node_index * 2 + 1					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)					,				)
	    if is_max
	    else min(
	        minimax(depth + 1					,				node_index * 2					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)					,				minimax(depth + 1					,				node_index * 2 + 1					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)					,				)
	)




def 						__lowerCamelCase			(	)   ->None:
	snake_case__	       =   [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
	snake_case__	       =   math.log(len(UpperCAmelCase_							)					,				2							)
	print(f'''Optimal value : {minimax(0					,				0					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)}'''							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()



