



"""simple docstring"""


import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

snake_case_    					=	2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
snake_case_    					=	{
    # fairseq:
    """wmt19-ru-en""": {"""length_penalty""": 1.1},
    """wmt19-en-ru""": {"""length_penalty""": 1.15},
    """wmt19-en-de""": {"""length_penalty""": 1.0},
    """wmt19-de-en""": {"""length_penalty""": 1.1},
    # allenai:
    """wmt16-en-de-dist-12-1""": {"""length_penalty""": 0.6},
    """wmt16-en-de-dist-6-1""": {"""length_penalty""": 0.6},
    """wmt16-en-de-12-1""": {"""length_penalty""": 0.8},
    """wmt19-de-en-6-6-base""": {"""length_penalty""": 0.6},
    """wmt19-de-en-6-6-big""": {"""length_penalty""": 0.6},
}

# this remaps the different models to their organization names
snake_case_    					=	{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
				snake_case_    					=	"""facebook"""
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
				snake_case_    					=	"""allenai"""

def 			_lowerCAmelCase				(       lowercase_						):
							# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
							# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
							UpperCAmelCase					=			dict((re.sub(R'@@$'		,     ''		,     lowercase_						), v) if k.endswith('@@'						) else (re.sub(R'$'		,     '</w>'		,     lowercase_						), v) for k, v in d.items()						)
							UpperCAmelCase					=			'<s> <pad> </s> <unk>'.split()
							# restore the special tokens
							for k in keep_keys:
														del da[F"""{k}</w>"""]
														UpperCAmelCase					=			d[k]  # restore
							return da

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_						):
							# prep
							assert os.path.exists(lowercase_						)
							os.makedirs(lowercase_		,     exist_ok=lowercase_						)
							print(F"""Writing results to {pytorch_dump_folder_path}"""						)

							# handle various types of models

							UpperCAmelCase					=			basename(lowercase_						)
							UpperCAmelCase					=			dirname(lowercase_						)

							UpperCAmelCase					=			fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
							UpperCAmelCase					=			cls.hub_models()
							UpperCAmelCase					=			{'bpe': 'fastbpe', 'tokenizer': 'moses'}
							UpperCAmelCase					=			'.'
							# note: since the model dump is old, fairseq has upgraded its model some
							# time later, and it does a whole lot of rewrites and splits on the saved
							# weights, therefore we can't use torch.load() directly on the model file.
							# see: upgrade_state_dict(state_dict) in fairseq_model.py
							print(F"""using checkpoint {checkpoint_file}"""						)
							UpperCAmelCase					=			hub_utils.from_pretrained(
							    lowercase_		,     lowercase_		,     lowercase_		,     archive_map=lowercase_		,     **lowercase_						)

							UpperCAmelCase					=			vars(chkpt['args']['model']						)

							UpperCAmelCase					=			args['source_lang']
							UpperCAmelCase					=			args['target_lang']

							UpperCAmelCase					=			dirname(lowercase_						)
							UpperCAmelCase					=			basename(lowercase_						)

							# dicts
							UpperCAmelCase					=			os.path.join(lowercase_		,     F"""dict.{src_lang}.txt"""						)
							UpperCAmelCase					=			os.path.join(lowercase_		,     F"""dict.{tgt_lang}.txt"""						)

							UpperCAmelCase					=			Dictionary.load(lowercase_						)
							UpperCAmelCase					=			rewrite_dict_keys(src_dict.indices						)
							UpperCAmelCase					=			len(lowercase_						)
							UpperCAmelCase					=			os.path.join(lowercase_		,     'vocab-src.json'						)
							print(F"""Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records"""						)
							with open(lowercase_		,     'w'		,     encoding='utf-8'						) as f:
														f.write(json.dumps(lowercase_		,     ensure_ascii=lowercase_		,     indent=lowercase_						)						)

							# detect whether this is a do_lower_case situation, which can be derived by checking whether we
							# have at least one uppercase letter in the source vocab
							UpperCAmelCase					=			True
							for k in src_vocab.keys():
														if not k.islower():
																					UpperCAmelCase					=			False
																					break

							UpperCAmelCase					=			Dictionary.load(lowercase_						)
							UpperCAmelCase					=			rewrite_dict_keys(tgt_dict.indices						)
							UpperCAmelCase					=			len(lowercase_						)
							UpperCAmelCase					=			os.path.join(lowercase_		,     'vocab-tgt.json'						)
							print(F"""Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records"""						)
							with open(lowercase_		,     'w'		,     encoding='utf-8'						) as f:
														f.write(json.dumps(lowercase_		,     ensure_ascii=lowercase_		,     indent=lowercase_						)						)

							# merges_file (bpecodes)
							UpperCAmelCase					=			os.path.join(lowercase_		,     VOCAB_FILES_NAMES['merges_file']						)
							for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
														UpperCAmelCase					=			os.path.join(lowercase_		,     lowercase_						)
														if os.path.exists(lowercase_						):
																					break
							with open(lowercase_		,     encoding='utf-8'						) as fin:
														UpperCAmelCase					=			fin.read()
							UpperCAmelCase					=			re.sub(R' \d+$'		,     ''		,     lowercase_		,     0		,     re.M						)  # remove frequency number
							print(F"""Generating {merges_file}"""						)
							with open(lowercase_		,     'w'		,     encoding='utf-8'						) as fout:
														fout.write(lowercase_						)

							# model config
							UpperCAmelCase					=			os.path.join(lowercase_		,     'config.json'						)

							# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
							# may have to modify the tokenizer if a different type is used by a future model
							assert args["bpe"] == "fastbpe", F"""need to extend tokenizer to support bpe={args['bpe']}"""
							assert args["tokenizer"] == "moses", F"""need to extend tokenizer to support bpe={args['tokenizer']}"""

							UpperCAmelCase					=			{
							    'architectures': ['FSMTForConditionalGeneration'],
							    'model_type': 'fsmt',
							    'activation_dropout': args['activation_dropout'],
							    'activation_function': 'relu',
							    'attention_dropout': args['attention_dropout'],
							    'd_model': args['decoder_embed_dim'],
							    'dropout': args['dropout'],
							    'init_std': 0.0_2,
							    'max_position_embeddings': args['max_source_positions'],
							    'num_hidden_layers': args['encoder_layers'],
							    'src_vocab_size': src_vocab_size,
							    'tgt_vocab_size': tgt_vocab_size,
							    'langs': [src_lang, tgt_lang],
							    'encoder_attention_heads': args['encoder_attention_heads'],
							    'encoder_ffn_dim': args['encoder_ffn_embed_dim'],
							    'encoder_layerdrop': args['encoder_layerdrop'],
							    'encoder_layers': args['encoder_layers'],
							    'decoder_attention_heads': args['decoder_attention_heads'],
							    'decoder_ffn_dim': args['decoder_ffn_embed_dim'],
							    'decoder_layerdrop': args['decoder_layerdrop'],
							    'decoder_layers': args['decoder_layers'],
							    'bos_token_id': 0,
							    'pad_token_id': 1,
							    'eos_token_id': 2,
							    'is_encoder_decoder': True,
							    'scale_embedding': not args['no_scale_embedding'],
							    'tie_word_embeddings': args['share_all_embeddings'],
							}

							# good hparam defaults to start with
							UpperCAmelCase					=			5
							UpperCAmelCase					=			False
							if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
														UpperCAmelCase					=			best_score_hparams[model_dir]['length_penalty']
							else:
														UpperCAmelCase					=			1.0

							print(F"""Generating {fsmt_model_config_file}"""						)
							with open(lowercase_		,     'w'		,     encoding='utf-8'						) as f:
														f.write(json.dumps(lowercase_		,     ensure_ascii=lowercase_		,     indent=lowercase_						)						)

							# tokenizer config
							UpperCAmelCase					=			os.path.join(lowercase_		,     lowercase_						)

							UpperCAmelCase					=			{
							    'langs': [src_lang, tgt_lang],
							    'model_max_length': 1024,
							    'do_lower_case': do_lower_case,
							}

							print(F"""Generating {fsmt_tokenizer_config_file}"""						)
							with open(lowercase_		,     'w'		,     encoding='utf-8'						) as f:
														f.write(json.dumps(lowercase_		,     ensure_ascii=lowercase_		,     indent=lowercase_						)						)

							# model
							UpperCAmelCase					=			chkpt['models'][0]
							UpperCAmelCase					=			model.state_dict()

							# rename keys to start with 'model.'
							UpperCAmelCase					=			OrderedDict(('model.' + k, v) for k, v in model_state_dict.items()						)

							# remove unneeded keys
							UpperCAmelCase					=			[
							    'model.model',
							    'model.encoder.version',
							    'model.decoder.version',
							    'model.encoder_embed_tokens.weight',
							    'model.decoder_embed_tokens.weight',
							    'model.encoder.embed_positions._float_tensor',
							    'model.decoder.embed_positions._float_tensor',
							]
							for k in ignore_keys:
														model_state_dict.pop(lowercase_		,     lowercase_						)

							UpperCAmelCase					=			FSMTConfig.from_pretrained(lowercase_						)
							UpperCAmelCase					=			FSMTForConditionalGeneration(lowercase_						)

							# check that it loads ok
							model_new.load_state_dict(lowercase_		,     strict=lowercase_						)

							# save
							UpperCAmelCase					=			os.path.join(lowercase_		,     lowercase_						)
							print(F"""Generating {pytorch_weights_dump_path}"""						)
							torch.save(lowercase_		,     lowercase_						)

							print('Conversion is done!'						)
							print('\nLast step is to upload the files to s3'						)
							print(F"""cd {data_root}"""						)
							print(F"""transformers-cli upload {model_dir}"""						)


if __name__ == "__main__":
				snake_case_    					=	argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--fsmt_checkpoint_path""",
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
				        """ bpecodes, etc."""
				    ),
				)
				parser.add_argument(
				    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
				)
				snake_case_    					=	parser.parse_args()
				convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)










"""simple docstring"""


from collections import deque







class 				A_      :



						"""simple docstring"""






						def __init__( self			:Any       ,  lowercase_			:str       ,  lowercase_			:int       ,  lowercase_			:int )    ->      None:
												UpperCAmelCase					=			process_name  # process name
												UpperCAmelCase					=			arrival_time  # arrival time of the process
												# completion time of finished process or last interrupted time
												UpperCAmelCase					=			arrival_time
												UpperCAmelCase					=			burst_time  # remaining burst time
												UpperCAmelCase					=			0  # total time of the process wait in ready queue
												UpperCAmelCase					=			0  # time from arrival time to completion time







class 				A_      :



						"""simple docstring"""






						def __init__( self			:Any       ,  lowercase_			:int       ,  lowercase_			:list[int]       ,  lowercase_			:deque[Process]       ,  lowercase_			:int       ,  )    ->      None:
												# total number of mlfq's queues
												UpperCAmelCase					=			number_of_queues
												# time slice of queues that round robin algorithm applied
												UpperCAmelCase					=			time_slices
												# unfinished process is in this ready_queue
												UpperCAmelCase					=			queue
												# current time
												UpperCAmelCase					=			current_time
												# finished process is in this sequence queue
												UpperCAmelCase					=			deque()






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      list[str]:
												UpperCAmelCase					=			[]
												for i in range(len(self.finish_queue ) ):
																		sequence.append(self.finish_queue[i].process_name )
												return sequence






						def      UpperCAmelCase__					( self			:List[str]       ,  lowercase_			:list[Process] )    ->      list[int]:
												UpperCAmelCase					=			[]
												for i in range(len(lowercase_ ) ):
																		waiting_times.append(queue[i].waiting_time )
												return waiting_times






						def      UpperCAmelCase__					( self			:List[str]       ,  lowercase_			:list[Process] )    ->      list[int]:
												UpperCAmelCase					=			[]
												for i in range(len(lowercase_ ) ):
																		turnaround_times.append(queue[i].turnaround_time )
												return turnaround_times






						def      UpperCAmelCase__					( self			:Dict       ,  lowercase_			:list[Process] )    ->      list[int]:
												UpperCAmelCase					=			[]
												for i in range(len(lowercase_ ) ):
																		completion_times.append(queue[i].stop_time )
												return completion_times






						def      UpperCAmelCase__					( self			:str       ,  lowercase_			:deque[Process] )    ->      list[int]:
												return [q.burst_time for q in queue]






						def      UpperCAmelCase__					( self			:int       ,  lowercase_			:Process )    ->      int:
												process.waiting_time += self.current_time - process.stop_time
												return process.waiting_time






						def      UpperCAmelCase__					( self			:Optional[int]       ,  lowercase_			:deque[Process] )    ->      deque[Process]:
												UpperCAmelCase					=			deque()  # sequence deque of finished process
												while len(lowercase_ ) != 0:
																		UpperCAmelCase					=			ready_queue.popleft()  # current process

																		# if process's arrival time is later than current time, update current time
																		if self.current_time < cp.arrival_time:
																								self.current_time += cp.arrival_time

																		# update waiting time of current process
																		self.update_waiting_time(lowercase_ )
																		# update current time
																		self.current_time += cp.burst_time
																		# finish the process and set the process's burst-time 0
																		UpperCAmelCase					=			0
																		# set the process's turnaround time because it is finished
																		UpperCAmelCase					=			self.current_time - cp.arrival_time
																		# set the completion time
																		UpperCAmelCase					=			self.current_time
																		# add the process to queue that has finished queue
																		finished.append(lowercase_ )

												self.finish_queue.extend(lowercase_ )  # add finished process to finish queue
												# FCFS will finish all remaining processes
												return finished






						def      UpperCAmelCase__					( self			:Tuple       ,  lowercase_			:deque[Process]       ,  lowercase_			:int )    ->      tuple[deque[Process], deque[Process]]:
												UpperCAmelCase					=			deque()  # sequence deque of terminated process
												# just for 1 cycle and unfinished processes will go back to queue
												for _ in range(len(lowercase_ ) ):
																		UpperCAmelCase					=			ready_queue.popleft()  # current process

																		# if process's arrival time is later than current time, update current time
																		if self.current_time < cp.arrival_time:
																								self.current_time += cp.arrival_time

																		# update waiting time of unfinished processes
																		self.update_waiting_time(lowercase_ )
																		# if the burst time of process is bigger than time-slice
																		if cp.burst_time > time_slice:
																								# use CPU for only time-slice
																								self.current_time += time_slice
																								# update remaining burst time
																								cp.burst_time -= time_slice
																								# update end point time
																								UpperCAmelCase					=			self.current_time
																								# locate the process behind the queue because it is not finished
																								ready_queue.append(lowercase_ )
																		else:
																								# use CPU for remaining burst time
																								self.current_time += cp.burst_time
																								# set burst time 0 because the process is finished
																								UpperCAmelCase					=			0
																								# set the finish time
																								UpperCAmelCase					=			self.current_time
																								# update the process' turnaround time because it is finished
																								UpperCAmelCase					=			self.current_time - cp.arrival_time
																								# add the process to queue that has finished queue
																								finished.append(lowercase_ )

												self.finish_queue.extend(lowercase_ )  # add finished process to finish queue
												# return finished processes queue and remaining processes queue
												return finished, ready_queue






						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      deque[Process]:

												#  all queues except last one have round_robin algorithm
												for i in range(self.number_of_queues - 1 ):
																		UpperCAmelCase				,   UpperCAmelCase					=			self.round_robin(
																		    self.ready_queue       ,  self.time_slices[i] )
												#  the last queue has first_come_first_served algorithm
												self.first_come_first_served(self.ready_queue )

												return self.finish_queue


if __name__ == "__main__":
				import doctest

				snake_case_    					=	Process("""P1""", 0, 53)
				snake_case_    					=	Process("""P2""", 0, 17)
				snake_case_    					=	Process("""P3""", 0, 68)
				snake_case_    					=	Process("""P4""", 0, 24)
				snake_case_    					=	3
				snake_case_    					=	[17, 25]
				snake_case_    					=	deque([Pa, Pa, Pa, Pa])

				if len(time_slices) != number_of_queues - 1:
								raise SystemExit(0)

				doctest.testmod(extraglobs={"""queue""": deque([Pa, Pa, Pa, Pa])})

				snake_case_    					=	Process("""P1""", 0, 53)
				snake_case_    					=	Process("""P2""", 0, 17)
				snake_case_    					=	Process("""P3""", 0, 68)
				snake_case_    					=	Process("""P4""", 0, 24)
				snake_case_    					=	3
				snake_case_    					=	[17, 25]
				snake_case_    					=	deque([Pa, Pa, Pa, Pa])
				snake_case_    					=	MLFQ(number_of_queues, time_slices, queue, 0)
				snake_case_    					=	mlfq.multi_level_feedback_queue()

				# print total waiting times of processes(P1, P2, P3, P4)
				print(
				    f'''waiting time:\
        \t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}'''
				)
				# print completion times of processes(P1, P2, P3, P4)
				print(
				    f'''completion time:\
        \t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}'''
				)
				# print total turnaround times of processes(P1, P2, P3, P4)
				print(
				    f'''turnaround time:\
        \t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}'''
				)
				# print sequence of finished processes
				print(
				    f'''sequence of finished processes:\
        {mlfq.calculate_sequence_of_finish_queue()}'''
				)





