



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
lowerCAmelCase__       =  data_utils.TransfoXLTokenizer
lowerCAmelCase__       =  data_utils.TransfoXLCorpus
lowerCAmelCase__       =  data_utils
lowerCAmelCase__       =  data_utils


def 					__lowerCamelCase					(   lowerCamelCase__     , lowerCamelCase__     , lowerCamelCase__     , lowerCamelCase__						):

				"""simple docstring"""


				if transfo_xl_dataset_file:
								# Convert a pre-processed corpus (see original TensorFlow repo)
								with open(_snake_case     , "rb"						) as fp:
												lowercase__     :    str													=     pickle.load(_snake_case     , encoding="latin1"						)
								# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
								lowercase__     :    List[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(_snake_case     , _snake_case						)

								lowercase__     :    Optional[int]													=     corpus.__dict__
								corpus_dict_no_vocab.pop("vocab"     , _snake_case						)
								lowercase__     :    Optional[Any]													=     pytorch_dump_folder_path + "/" + CORPUS_NAME
								print(F"""Save dataset to {pytorch_dataset_dump_path}"""						)
								torch.save(_snake_case     , _snake_case						)

				if tf_checkpoint_path:
								# Convert a pre-trained TensorFlow model
								lowercase__     :    List[str]													=     os.path.abspath(_snake_case						)
								lowercase__     :    int													=     os.path.abspath(_snake_case						)

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

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


if __name__ == "__main__":
				lowerCAmelCase__       =  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.''',
				)
				lowerCAmelCase__       =  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 multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
_lowerCAmelCase					:			Optional[Any]  				=			Lock()






def        UpperCamelCase_(							_snake_case   :  List[str]       ,     _snake_case   :  Optional[int]       ,     _snake_case   :  Dict       ,     _snake_case   :  Any       ,     _snake_case   :  List[str]       ,     _snake_case   :  Tuple       ,     _snake_case   :  List[str]	):




			"""simple docstring"""






			global process_lock

			# we perform n swaps since after n swaps we know we are sorted
			# we *could* stop early if we are sorted already, but it takes as long to
			# find out we are sorted as it does to sort the list with this algorithm
			for i in range(0       ,     10	):
						if (i + position) % 2 == 0 and r_send is not None:
									# send your value to your right neighbor
									process_lock.acquire()
									r_send[1].send(_snake_case	)
									process_lock.release()

									# receive your right neighbor's value
									process_lock.acquire()
									__a        =rr_cv[0].recv()
									process_lock.release()

									# take the lower value since you are on the left
									__a        =min(_snake_case       ,     _snake_case	)
						elif (i + position) % 2 != 0 and l_send is not None:
									# send your value to your left neighbor
									process_lock.acquire()
									l_send[1].send(_snake_case	)
									process_lock.release()

									# receive your left neighbor's value
									process_lock.acquire()
									__a        =lr_cv[0].recv()
									process_lock.release()

									# take the higher value since you are on the right
									__a        =max(_snake_case       ,     _snake_case	)
    # after all swaps are performed, send the values back to main
			result_pipe[1].send(_snake_case	)






def        UpperCamelCase_(							_snake_case   :  List[str]	):




			"""simple docstring"""






			__a        =[]
			__a        =[]
			# initialize the list of pipes where the values will be retrieved
			for _ in arr:
						result_pipe.append(Pipe()	)
			# creates the processes
			# the first and last process only have one neighbor so they are made outside
			# of the loop
			__a        =Pipe()
			__a        =Pipe()
			process_array_.append(
			    Process(
			        target=_snake_case       ,     args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])       ,     )	)
			__a        =temp_rs
			__a        =temp_rr

			for i in range(1       ,     len(_snake_case	) - 1	):
						__a        =Pipe()
						__a        =Pipe()
						process_array_.append(
						    Process(
						        target=_snake_case       ,     args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])       ,     )	)
						__a        =temp_rs
						__a        =temp_rr

			process_array_.append(
			    Process(
			        target=_snake_case       ,     args=(
			            len(_snake_case	) - 1,
			            arr[len(_snake_case	) - 1],
			            temp_ls,
			            None,
			            temp_lr,
			            None,
			            result_pipe[len(_snake_case	) - 1],
			        )       ,     )	)

			# start the processes
			for p in process_array_:
						p.start()

			# wait for the processes to end and write their values to the list
			for p in range(0       ,     len(_snake_case	)	):
						__a        =result_pipe[p][0].recv()
						process_array_[p].join()
			return arr






def        UpperCamelCase_(							):




			"""simple docstring"""






			__a        =list(range(10       ,     0       ,     -1	)	)
			print('Initial List'	)
			print(*_snake_case	)
			__a        =odd_even_transposition(_snake_case	)
			print('Sorted List\n'	)
			print(*_snake_case	)


if __name__ == "__main__":
						main()






