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						=		data_utils.TransfoXLTokenizer
_UpperCAmelCase						=		data_utils.TransfoXLCorpus
_UpperCAmelCase						=		data_utils
_UpperCAmelCase						=		data_utils




def 				UpperCamelCase						(       __lowercase						: Union[str, Any]						,__lowercase						: Tuple						,__lowercase						: Dict						,__lowercase						: int	):
		'''simple docstring'''




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

				A_		:				List[str]     =      corpus.__dict__
				corpus_dict_no_vocab.pop('vocab'						,lowercase__	)
				A_		:				Optional[int]     =      pytorch_dump_folder_path + '/' + CORPUS_NAME
				print(f'''Save dataset to {pytorch_dataset_dump_path}'''	)
				torch.save(lowercase__						,lowercase__	)

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

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

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


if __name__ == "__main__":
	_UpperCAmelCase						=		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						=		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,
	)










def        _lowerCamelCase(	lowercase__	)						->	int:

		'''simple docstring'''







		if collection == []:
				return []

		# get some information about the collection
		__lowercase=			len(lowercase__	)
		__lowercase=			max(lowercase__	)
		__lowercase=			min(lowercase__	)

		# create the counting array
		__lowercase=			coll_max + 1 - coll_min
		__lowercase=			[0] * counting_arr_length

		# count how much a number appears in the collection
		for number in collection:
				counting_arr[number - coll_min] += 1

		# sum each position with it's predecessors. now, counting_arr[i] tells
		# us how many elements <= i has in the collection
		for i in range(1   , lowercase__	):
				__lowercase=			counting_arr[i] + counting_arr[i - 1]

		# create the output collection
		__lowercase=			[0] * coll_len

		# place the elements in the output, respecting the original order (stable
		# sort) from end to begin, updating counting_arr
		for i in reversed(range(0   , lowercase__	)	):
				__lowercase=			collection[i]
				counting_arr[collection[i] - coll_min] -= 1

		return ordered







def        _lowerCamelCase(	lowercase__	)						->	List[str]:

		'''simple docstring'''







		return "".join([chr(lowercase__	) for i in counting_sort([ord(lowercase__	) for c in string]	)]	)


if __name__ == "__main__":
	# Test string sort
	assert counting_sort_string('''thisisthestring''') == "eghhiiinrsssttt"

	lowerCAmelCase           =		input('''Enter numbers separated by a comma:\n''').strip()
	lowerCAmelCase           =		[int(item) for item in user_input.split(''',''')]
	print(counting_sort(unsorted))






