



"""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							:     Tuple   =					data_utils.TransfoXLTokenizer
__snake_case							:     Any   =					data_utils.TransfoXLCorpus
__snake_case							:     Union[str, Any]   =					data_utils
__snake_case							:     int   =					data_utils


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case      ,__snake_case			)		->     Any:




			if transfo_xl_dataset_file:
						# Convert a pre-processed corpus (see original TensorFlow repo)
						with open(_lowerCAmelCase      ,"rb"			) as fp:
									__lowerCAmelCase   :						List[Any]				= pickle.load(_lowerCAmelCase      ,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   :						Any				= corpus.vocab.__dict__
						torch.save(_lowerCAmelCase      ,_lowerCAmelCase			)

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

						__lowerCAmelCase   :						List[str]				= load_tf_weights_in_transfo_xl(_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase			)
						# Save pytorch-model
						__lowerCAmelCase   :						List[str]				= os.path.join(_lowerCAmelCase      ,_lowerCAmelCase			)
						__lowerCAmelCase   :						Union[str, Any]				= 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__":
				__snake_case							:     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.',
				)
				__snake_case							:     int   =					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"""






def     _lowercase       (						__snake_case      ,__snake_case			)		->     list:
			__lowerCAmelCase   :						List[str]				= len(__snake_case			)
			__lowerCAmelCase   :						Dict				= []
			for i in range(len(__snake_case			) - pat_len + 1			):
						__lowerCAmelCase   :						List[Any]				= True
						for j in range(__snake_case			):
									if s[i + j] != pattern[j]:
												__lowerCAmelCase   :						Union[str, Any]				= False
												break
						if match_found:
									position.append(__snake_case			)
			return position


if __name__ == "__main__":
	assert naive_pattern_search('ABCDEFG', 'DE') == [3]
	print(naive_pattern_search('ABAAABCDBBABCDDEBCABC', 'ABC'))