
import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowercase__							:							Dict													=     1_6
lowercase__							:							int													=     3_2




def 		A_	(							snake_case							:				Union[str, Any]	) ->      Any:



						'''simple docstring'''

						return int(x / 2**20	)
class     SCREAMING_SNAKE_CASE__      :

							"""simple docstring"""






							def __enter__(					self     )->				Optional[int]:





													'''simple docstring'''






													gc.collect()
													torch.cuda.empty_cache()
													torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
													__UpperCamelCase       					=     torch.cuda.memory_allocated()
													__UpperCamelCase       					=     psutil.Process()

													__UpperCamelCase       					=     self.cpu_mem_used()
													__UpperCamelCase       					=     True
													__UpperCamelCase       					=     threading.Thread(target=self.peak_monitor_func     )
													__UpperCamelCase       					=     True
													peak_monitor_thread.start()
													return self



							def       A__						(					self     )->				Optional[int]:





													'''simple docstring'''






													return self.process.memory_info().rss



							def       A__						(					self     )->				Tuple:





													'''simple docstring'''






													__UpperCamelCase       					=     -1

													while True:
																			__UpperCamelCase       					=     max(self.cpu_mem_used()       ,		self.cpu_peak     )

																			# can't sleep or will not catch the peak right (this comment is here on purpose)
																			# time.sleep(0.001) # 1msec

																			if not self.peak_monitoring:
																									break



							def __exit__(					self       ,		*SCREAMING_SNAKE_CASE_     )->				Any:





													'''simple docstring'''






													__UpperCamelCase       					=     False

													gc.collect()
													torch.cuda.empty_cache()
													__UpperCamelCase       					=     torch.cuda.memory_allocated()
													__UpperCamelCase       					=     torch.cuda.max_memory_allocated()
													__UpperCamelCase       					=     bamb(self.end - self.begin     )
													__UpperCamelCase       					=     bamb(self.peak - self.begin     )

													__UpperCamelCase       					=     self.cpu_mem_used()
													__UpperCamelCase       					=     bamb(self.cpu_end - self.cpu_begin     )
													__UpperCamelCase       					=     bamb(self.cpu_peak - self.cpu_begin     )
													# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
				from accelerate.test_utils.training import mocked_dataloaders

				lowercase__							:							Union[str, Any]													=     mocked_dataloaders  # noqa: F811




def 		A_	(							snake_case							:				Dict	, snake_case							:				List[str]	) ->      Any:



						'''simple docstring'''

						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''	, snake_case	) == "1":
												__UpperCamelCase       					=     2

						# New Code #
						# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
						__UpperCamelCase       					=     FullyShardedDataParallelPlugin(
						    state_dict_config=FullStateDictConfig(offload_to_cpu=snake_case	, ranka_only=snake_case	)	, optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=snake_case	, ranka_only=snake_case	)	, )

						# Initialize accelerator
						if args.with_tracking:
												__UpperCamelCase       					=     Accelerator(
												    cpu=args.cpu	, mixed_precision=args.mixed_precision	, log_with='''wandb'''	, project_dir=args.logging_dir	, fsdp_plugin=snake_case	, )
						else:
												__UpperCamelCase       					=     Accelerator(fsdp_plugin=snake_case	)
						accelerator.print(accelerator.distributed_type	)

						if hasattr(args.checkpointing_steps	, '''isdigit'''	):
												if args.checkpointing_steps == "epoch":
																		__UpperCamelCase       					=     args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		__UpperCamelCase       					=     int(args.checkpointing_steps	)
												else:
																		raise ValueError(
																		    f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."	)
						else:
												__UpperCamelCase       					=     None
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						__UpperCamelCase       					=     config['''lr''']
						__UpperCamelCase       					=     int(config['''num_epochs''']	)
						__UpperCamelCase       					=     int(config['''seed''']	)
						__UpperCamelCase       					=     int(config['''batch_size''']	)

						# We need to initialize the trackers we use, and also store our configuration
						if args.with_tracking:
												__UpperCamelCase       					=     vars(snake_case	)
												accelerator.init_trackers('''fsdp_glue_no_trainer'''	, snake_case	)

						__UpperCamelCase       					=     AutoTokenizer.from_pretrained(args.model_name_or_path	)
						__UpperCamelCase       					=     load_dataset('''glue'''	, '''mrpc'''	)
						__UpperCamelCase       					=     evaluate.load('''glue'''	, '''mrpc'''	)

						def tokenize_function(snake_case							:				Optional[int]	):
												# max_length=None => use the model max length (it's actually the default)
												__UpperCamelCase       					=     tokenizer(examples['''sentence1''']	, examples['''sentence2''']	, truncation=snake_case	, max_length=snake_case	)
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												__UpperCamelCase       					=     datasets.map(
												    snake_case	, batched=snake_case	, remove_columns=['''idx''', '''sentence1''', '''sentence2''']	, )

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						__UpperCamelCase       					=     tokenized_datasets.rename_column('''label'''	, '''labels'''	)

						# If the batch size is too big we use gradient accumulation
						__UpperCamelCase       					=     1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												__UpperCamelCase       					=     batch_size // MAX_GPU_BATCH_SIZE
												__UpperCamelCase       					=     MAX_GPU_BATCH_SIZE

						def collate_fn(snake_case							:				Union[str, Any]	):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												__UpperCamelCase       					=     128 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		__UpperCamelCase       					=     16
												elif accelerator.mixed_precision != "no":
																		__UpperCamelCase       					=     8
												else:
																		__UpperCamelCase       					=     None

												return tokenizer.pad(
												    snake_case	, padding='''longest'''	, max_length=snake_case	, pad_to_multiple_of=snake_case	, return_tensors='''pt'''	, )

						# Instantiate dataloaders.
						__UpperCamelCase       					=     DataLoader(
						    tokenized_datasets['''train''']	, shuffle=snake_case	, collate_fn=snake_case	, batch_size=snake_case	)
						__UpperCamelCase       					=     DataLoader(
						    tokenized_datasets['''validation''']	, shuffle=snake_case	, collate_fn=snake_case	, batch_size=snake_case	)

						set_seed(snake_case	)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__UpperCamelCase       					=     AutoModelForSequenceClassification.from_pretrained(
						    args.model_name_or_path	, return_dict=snake_case	, low_cpu_mem_usage=snake_case	)

						# New Code #
						# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
						__UpperCamelCase       					=     accelerator.prepare(snake_case	)
						accelerator.print(snake_case	)

						# Instantiate optimizer
						# New Code #
						# For FSDP feature, at present it doesn't support multiple parameter groups,
						# so we need to create a single parameter group for the whole model
						__UpperCamelCase       					=     torch.optim.AdamW(params=model.parameters()	, lr=snake_case	, weight_decay=2e-4	)

						# Instantiate scheduler
						__UpperCamelCase       					=     get_linear_schedule_with_warmup(
						    optimizer=snake_case	, num_warmup_steps=10	, num_training_steps=(len(snake_case	) * num_epochs) // gradient_accumulation_steps	, )

						# New Code #
						# For FSDP feature, prepare everything except the model as we have already prepared the model
						# before creating the optimizer
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase       					=     accelerator.prepare(
						    snake_case	, snake_case	, snake_case	, snake_case	)

						__UpperCamelCase       					=     0

						# Potentially load in the weights and states from a previous save
						if args.resume_from_checkpoint:
												if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																		accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}"	)
																		accelerator.load_state(args.resume_from_checkpoint	)
																		__UpperCamelCase       					=     os.path.basename(args.resume_from_checkpoint	)
												else:
																		# Get the most recent checkpoint
																		__UpperCamelCase       					=     [f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
																		dirs.sort(key=os.path.getctime	)
																		__UpperCamelCase       					=     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												__UpperCamelCase       					=     os.path.splitext(snake_case	)[0]

												if "epoch" in training_difference:
																		num_epochs -= int(training_difference.replace('''epoch_'''	, ''''''	)	)
																		__UpperCamelCase       					=     None
												else:
																		__UpperCamelCase       					=     int(training_difference.replace('''step_'''	, ''''''	)	)
																		num_epochs -= resume_step // len(snake_case	)
																		# If resuming by step, we also need to know exactly how far into the DataLoader we went
																		__UpperCamelCase       					=     (num_epochs * len(snake_case	)) - resume_step

    # Now we train the model
						for epoch in range(snake_case	):
												# New Code #
												# context manager to track the peak memory usage during the training epoch
												with TorchTracemalloc() as tracemalloc:
																		model.train()
																		if args.with_tracking:
																								__UpperCamelCase       					=     0
																		for step, batch in enumerate(snake_case	):
																								# We need to skip steps until we reach the resumed step
																								if args.resume_from_checkpoint and epoch == 0:
																														if resume_step is not None and step < resume_step:
																																				pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
																								batch.to(accelerator.device	)
																								__UpperCamelCase       					=     model(**snake_case	)
																								__UpperCamelCase       					=     outputs.loss
																								__UpperCamelCase       					=     loss / gradient_accumulation_steps
																								# We keep track of the loss at each epoch
																								if args.with_tracking:
																														total_loss += loss.detach().float()
																								accelerator.backward(snake_case	)
																								if step % gradient_accumulation_steps == 0:
																														optimizer.step()
																														lr_scheduler.step()
																														optimizer.zero_grad()
																														# accelerator.print(lr_scheduler.get_lr())

																								overall_step += 1

																								if isinstance(snake_case	, snake_case	):
																														__UpperCamelCase       					=     f"step_{overall_step}"
																														if overall_step % checkpointing_steps == 0:
																																				if args.output_dir is not None:
																																										__UpperCamelCase       					=     os.path.join(args.output_dir	, snake_case	)
																																				accelerator.save_state(snake_case	)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
												accelerator.print('''Memory before entering the train : {}'''.format(bamb(tracemalloc.begin	)	)	)
												accelerator.print('''Memory consumed at the end of the train (end-begin): {}'''.format(tracemalloc.used	)	)
												accelerator.print('''Peak Memory consumed during the train (max-begin): {}'''.format(tracemalloc.peaked	)	)
												accelerator.print(
												    '''Total Peak Memory consumed during the train (max): {}'''.format(
												        tracemalloc.peaked + bamb(tracemalloc.begin	)	)	)
												# Logging the peak memory usage of the GPU to the tracker
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        '''train_total_peak_memory''': tracemalloc.peaked + bamb(tracemalloc.begin	),
																		    }	, step=snake_case	, )

												# New Code #
												# context manager to track the peak memory usage during the evaluation
												with TorchTracemalloc() as tracemalloc:
																		model.eval()
																		for step, batch in enumerate(snake_case	):
																								# We could avoid this line since we set the accelerator with `device_placement=True`.
																								batch.to(accelerator.device	)
																								with torch.no_grad():
																														__UpperCamelCase       					=     model(**snake_case	)
																								__UpperCamelCase       					=     outputs.logits.argmax(dim=-1	)
																								__UpperCamelCase					,							__UpperCamelCase       					=     accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
																								metric.add_batch(
																								    predictions=snake_case	, references=snake_case	, )

																		__UpperCamelCase       					=     metric.compute()
																		# Use accelerator.print to print only on the main process.
																		accelerator.print(f"epoch {epoch}:"	, snake_case	)
																		if args.with_tracking:
																								accelerator.log(
																								    {
																								        '''accuracy''': eval_metric['''accuracy'''],
																								        '''f1''': eval_metric['''f1'''],
																								        '''train_loss''': total_loss.item() / len(snake_case	),
																								    }	, step=snake_case	, )

																		if checkpointing_steps == "epoch":
																								__UpperCamelCase       					=     f"epoch_{epoch}"
																								if args.output_dir is not None:
																														__UpperCamelCase       					=     os.path.join(args.output_dir	, snake_case	)
																								accelerator.save_state(snake_case	)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
												accelerator.print('''Memory before entering the eval : {}'''.format(bamb(tracemalloc.begin	)	)	)
												accelerator.print('''Memory consumed at the end of the eval (end-begin): {}'''.format(tracemalloc.used	)	)
												accelerator.print('''Peak Memory consumed during the eval (max-begin): {}'''.format(tracemalloc.peaked	)	)
												accelerator.print(
												    '''Total Peak Memory consumed during the eval (max): {}'''.format(tracemalloc.peaked + bamb(tracemalloc.begin	)	)	)
												# Logging the peak memory usage of the GPU to the tracker
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        '''eval_total_peak_memory''': tracemalloc.peaked + bamb(tracemalloc.begin	),
																		    }	, step=snake_case	, )

						if args.with_tracking:
												accelerator.end_training()




def 		A_	(							) ->      str:



						'''simple docstring'''

						__UpperCamelCase       					=     argparse.ArgumentParser(description='''Simple example of training script.'''	)
						parser.add_argument(
						    '''--mixed_precision'''	, type=snake_case	, default=snake_case	, choices=['''no''', '''fp16''', '''bf16''', '''fp8''']	, help='''Whether to use mixed precision. Choose'''
						    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
						    '''and an Nvidia Ampere GPU.'''	, )
						parser.add_argument('''--cpu'''	, action='''store_true'''	, help='''If passed, will train on the CPU.'''	)
						parser.add_argument(
						    '''--checkpointing_steps'''	, type=snake_case	, default=snake_case	, help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''	, )
						parser.add_argument(
						    '''--resume_from_checkpoint'''	, type=snake_case	, default=snake_case	, help='''If the training should continue from a checkpoint folder.'''	, )
						parser.add_argument(
						    '''--with_tracking'''	, action='''store_true'''	, help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''	, )
						parser.add_argument(
						    '''--output_dir'''	, type=snake_case	, default='''.'''	, help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''	, )
						parser.add_argument(
						    '''--logging_dir'''	, type=snake_case	, default='''logs'''	, help='''Location on where to store experiment tracking logs`'''	, )
						parser.add_argument(
						    '''--model_name_or_path'''	, type=snake_case	, help='''Path to pretrained model or model identifier from huggingface.co/models.'''	, required=snake_case	, )
						__UpperCamelCase       					=     parser.parse_args()
						__UpperCamelCase       					=     {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(snake_case	, snake_case	)


if __name__ == "__main__":
				main()






import json
import os
import re
import unicodedata
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase__							:							Optional[Any]													=     logging.get_logger(__name__)

lowercase__							:							str													=     {
    "src_vocab_file": "vocab-src.json",
    "tgt_vocab_file": "vocab-tgt.json",
    "merges_file": "merges.txt",
}

lowercase__							:							int													=     {
    "src_vocab_file": {
        "stas/tiny-wmt19-en-de": "https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-src.json"
    },
    "tgt_vocab_file": {
        "stas/tiny-wmt19-en-de": "https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-tgt.json"
    },
    "merges_file": {"stas/tiny-wmt19-en-de": "https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/merges.txt"},
}

lowercase__							:							Any													=     {"stas/tiny-wmt19-en-de": 1_0_2_4}
lowercase__							:							List[str]													=     {
    "stas/tiny-wmt19-en-de": {
        "langs": ["en", "de"],
        "model_max_length": 1_0_2_4,
        "special_tokens_map_file": None,
        "full_tokenizer_file": None,
    }
}




def 		A_	(							snake_case							:				Union[str, Any]	) ->      Any:



						'''simple docstring'''

						__UpperCamelCase       					=     set()
						__UpperCamelCase       					=     word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)	)
												__UpperCamelCase       					=     char
						return pairs




def 		A_	(							snake_case							:				Union[str, Any]	) ->      int:



						'''simple docstring'''

						__UpperCamelCase       					=     text.replace('''，'''	, ''','''	)
						__UpperCamelCase       					=     re.sub(r'''。\s*'''	, '''. '''	, snake_case	)
						__UpperCamelCase       					=     text.replace('''、'''	, ''','''	)
						__UpperCamelCase       					=     text.replace('''”'''	, '''"'''	)
						__UpperCamelCase       					=     text.replace('''“'''	, '''"'''	)
						__UpperCamelCase       					=     text.replace('''∶'''	, ''':'''	)
						__UpperCamelCase       					=     text.replace('''：'''	, ''':'''	)
						__UpperCamelCase       					=     text.replace('''？'''	, '''?'''	)
						__UpperCamelCase       					=     text.replace('''《'''	, '''"'''	)
						__UpperCamelCase       					=     text.replace('''》'''	, '''"'''	)
						__UpperCamelCase       					=     text.replace('''）'''	, ''')'''	)
						__UpperCamelCase       					=     text.replace('''！'''	, '''!'''	)
						__UpperCamelCase       					=     text.replace('''（'''	, '''('''	)
						__UpperCamelCase       					=     text.replace('''；'''	, ''';'''	)
						__UpperCamelCase       					=     text.replace('''１'''	, '''1'''	)
						__UpperCamelCase       					=     text.replace('''」'''	, '''"'''	)
						__UpperCamelCase       					=     text.replace('''「'''	, '''"'''	)
						__UpperCamelCase       					=     text.replace('''０'''	, '''0'''	)
						__UpperCamelCase       					=     text.replace('''３'''	, '''3'''	)
						__UpperCamelCase       					=     text.replace('''２'''	, '''2'''	)
						__UpperCamelCase       					=     text.replace('''５'''	, '''5'''	)
						__UpperCamelCase       					=     text.replace('''６'''	, '''6'''	)
						__UpperCamelCase       					=     text.replace('''９'''	, '''9'''	)
						__UpperCamelCase       					=     text.replace('''７'''	, '''7'''	)
						__UpperCamelCase       					=     text.replace('''８'''	, '''8'''	)
						__UpperCamelCase       					=     text.replace('''４'''	, '''4'''	)
						__UpperCamelCase       					=     re.sub(r'''．\s*'''	, '''. '''	, snake_case	)
						__UpperCamelCase       					=     text.replace('''～'''	, '''~'''	)
						__UpperCamelCase       					=     text.replace('''’'''	, '''\''''	)
						__UpperCamelCase       					=     text.replace('''…'''	, '''...'''	)
						__UpperCamelCase       					=     text.replace('''━'''	, '''-'''	)
						__UpperCamelCase       					=     text.replace('''〈'''	, '''<'''	)
						__UpperCamelCase       					=     text.replace('''〉'''	, '''>'''	)
						__UpperCamelCase       					=     text.replace('''【'''	, '''['''	)
						__UpperCamelCase       					=     text.replace('''】'''	, ''']'''	)
						__UpperCamelCase       					=     text.replace('''％'''	, '''%'''	)
						return text




def 		A_	(							snake_case							:				List[str]	) ->      Optional[Any]:



						'''simple docstring'''

						__UpperCamelCase       					=     []
						for char in text:
												__UpperCamelCase       					=     unicodedata.category(snake_case	)
												if cat.startswith('''C'''	):
																		continue
												output.append(snake_case	)
						return "".join(snake_case	)






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							_snake_case       						=					VOCAB_FILES_NAMES
							_snake_case       						=					PRETRAINED_VOCAB_FILES_MAP
							_snake_case       						=					PRETRAINED_INIT_CONFIGURATION
							_snake_case       						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_snake_case       						=					['input_ids', 'attention_mask']



							def __init__(					self       ,		SCREAMING_SNAKE_CASE_=None       ,		SCREAMING_SNAKE_CASE_=None       ,		SCREAMING_SNAKE_CASE_=None       ,		SCREAMING_SNAKE_CASE_=None       ,		SCREAMING_SNAKE_CASE_=False       ,		SCREAMING_SNAKE_CASE_="<unk>"       ,		SCREAMING_SNAKE_CASE_="<s>"       ,		SCREAMING_SNAKE_CASE_="</s>"       ,		SCREAMING_SNAKE_CASE_="<pad>"       ,		**SCREAMING_SNAKE_CASE_       ,		)->				str:





													'''simple docstring'''






													super().__init__(
													    langs=SCREAMING_SNAKE_CASE_       ,		src_vocab_file=SCREAMING_SNAKE_CASE_       ,		tgt_vocab_file=SCREAMING_SNAKE_CASE_       ,		merges_file=SCREAMING_SNAKE_CASE_       ,		do_lower_case=SCREAMING_SNAKE_CASE_       ,		unk_token=SCREAMING_SNAKE_CASE_       ,		bos_token=SCREAMING_SNAKE_CASE_       ,		sep_token=SCREAMING_SNAKE_CASE_       ,		pad_token=SCREAMING_SNAKE_CASE_       ,		**SCREAMING_SNAKE_CASE_       ,		)

													try:
																			import sacremoses
													except ImportError:
																			raise ImportError(
																			    '''You need to install sacremoses to use XLMTokenizer. '''
																			    '''See https://pypi.org/project/sacremoses/ for installation.'''     )

													__UpperCamelCase       					=     sacremoses

													__UpperCamelCase       					=     src_vocab_file
													__UpperCamelCase       					=     tgt_vocab_file
													__UpperCamelCase       					=     merges_file
													__UpperCamelCase       					=     do_lower_case

													# cache of sm.MosesPunctNormalizer instance
													__UpperCamelCase       					=     {}
													# cache of sm.MosesTokenizer instance
													__UpperCamelCase       					=     {}
													__UpperCamelCase       					=     {}

													if langs and len(SCREAMING_SNAKE_CASE_     ) == 2:
																			__UpperCamelCase					,							__UpperCamelCase       					=     langs
													else:
																			raise ValueError(
																			    F"arg `langs` needs to be a list of 2 langs, e.g. ['en', 'ru'], but got {langs}. "
																			    '''Usually that means that tokenizer can\'t find a mapping for the given model path '''
																			    '''in PRETRAINED_VOCAB_FILES_MAP, and other maps of this tokenizer.'''     )

													with open(SCREAMING_SNAKE_CASE_       ,		encoding='''utf-8'''     ) as src_vocab_handle:
																			__UpperCamelCase       					=     json.load(SCREAMING_SNAKE_CASE_     )
													with open(SCREAMING_SNAKE_CASE_       ,		encoding='''utf-8'''     ) as tgt_vocab_handle:
																			__UpperCamelCase       					=     json.load(SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     {v: k for k, v in tgt_vocab.items()}
													with open(SCREAMING_SNAKE_CASE_       ,		encoding='''utf-8'''     ) as merges_handle:
																			__UpperCamelCase       					=     merges_handle.read().split('''\n'''     )[:-1]
													__UpperCamelCase       					=     [tuple(merge.split()[:2]     ) for merge in merges]
													__UpperCamelCase       					=     dict(zip(SCREAMING_SNAKE_CASE_       ,		range(len(SCREAMING_SNAKE_CASE_     )     )     )     )
													__UpperCamelCase       					=     {}



							def       A__						(					self     )->				Dict[str, int]:





													'''simple docstring'''






													return self.get_src_vocab()



							@property
							def       A__						(					self     )->				int:





													'''simple docstring'''






													return self.src_vocab_size



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				int:





													'''simple docstring'''






													if lang not in self.cache_moses_punct_normalizer:
																			__UpperCamelCase       					=     self.sm.MosesPunctNormalizer(lang=SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     punct_normalizer
													return self.cache_moses_punct_normalizer[lang].normalize(SCREAMING_SNAKE_CASE_     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				Tuple:





													'''simple docstring'''






													if lang not in self.cache_moses_tokenizer:
																			__UpperCamelCase       					=     self.sm.MosesTokenizer(lang=SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     moses_tokenizer
													return self.cache_moses_tokenizer[lang].tokenize(
													    SCREAMING_SNAKE_CASE_       ,		aggressive_dash_splits=SCREAMING_SNAKE_CASE_       ,		return_str=SCREAMING_SNAKE_CASE_       ,		escape=SCREAMING_SNAKE_CASE_     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				Tuple:





													'''simple docstring'''






													if lang not in self.cache_moses_detokenizer:
																			__UpperCamelCase       					=     self.sm.MosesDetokenizer(lang=SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     moses_detokenizer
													return self.cache_moses_detokenizer[lang].detokenize(SCREAMING_SNAKE_CASE_     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				List[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     replace_unicode_punct(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     self.moses_punct_norm(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     remove_non_printing_char(SCREAMING_SNAKE_CASE_     )
													return text



							@property
							def       A__						(					self     )->				List[str]:





													'''simple docstring'''






													return len(self.encoder     )



							@property
							def       A__						(					self     )->				Any:





													'''simple docstring'''






													return len(self.decoder     )



							def       A__						(					self     )->				int:





													'''simple docstring'''






													return dict(self.encoder       ,		**self.added_tokens_encoder     )



							def       A__						(					self     )->				Any:





													'''simple docstring'''






													return dict(self.decoder       ,		**self.added_tokens_decoder     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     tuple(token[:-1]     ) + (token[-1] + '''</w>''',)
													if token in self.cache:
																			return self.cache[token]
													__UpperCamelCase       					=     get_pairs(SCREAMING_SNAKE_CASE_     )

													if not pairs:
																			return token + "</w>"

													while True:
																			__UpperCamelCase       					=     min(SCREAMING_SNAKE_CASE_       ,		key=lambda SCREAMING_SNAKE_CASE_     : self.bpe_ranks.get(SCREAMING_SNAKE_CASE_       ,		float('''inf'''     )     )     )
																			if bigram not in self.bpe_ranks:
																									break
																			__UpperCamelCase					,							__UpperCamelCase       					=     bigram
																			__UpperCamelCase       					=     []
																			__UpperCamelCase       					=     0
																			while i < len(SCREAMING_SNAKE_CASE_     ):
																									try:
																															__UpperCamelCase       					=     word.index(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
																									except ValueError:
																															new_word.extend(word[i:]     )
																															break
																									else:
																															new_word.extend(word[i:j]     )
																															__UpperCamelCase       					=     j

																									if word[i] == first and i < len(SCREAMING_SNAKE_CASE_     ) - 1 and word[i + 1] == second:
																															new_word.append(first + second     )
																															i += 2
																									else:
																															new_word.append(word[i]     )
																															i += 1
																			__UpperCamelCase       					=     tuple(SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     new_word
																			if len(SCREAMING_SNAKE_CASE_     ) == 1:
																									break
																			else:
																									__UpperCamelCase       					=     get_pairs(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     ''' '''.join(SCREAMING_SNAKE_CASE_     )
													if word == "\n  </w>":
																			__UpperCamelCase       					=     '''\n</w>'''
													__UpperCamelCase       					=     word
													return word



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_="en"       ,		SCREAMING_SNAKE_CASE_=False     )->				List[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     self.src_lang

													if self.do_lower_case:
																			__UpperCamelCase       					=     text.lower()

													if bypass_tokenizer:
																			__UpperCamelCase       					=     text.split()
													else:
																			__UpperCamelCase       					=     self.moses_pipeline(SCREAMING_SNAKE_CASE_       ,		lang=SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     self.moses_tokenize(SCREAMING_SNAKE_CASE_       ,		lang=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     []
													for token in text:
																			if token:
																									split_tokens.extend(list(self.bpe(SCREAMING_SNAKE_CASE_     ).split(''' '''     )     )     )

													return split_tokens



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Any:





													'''simple docstring'''






													return self.encoder.get(SCREAMING_SNAKE_CASE_       ,		self.encoder.get(self.unk_token     )     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Tuple:





													'''simple docstring'''






													return self.decoder.get(SCREAMING_SNAKE_CASE_       ,		self.unk_token     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				List[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     [t.replace(''' '''       ,		''''''     ).replace('''</w>'''       ,		''' '''     ) for t in tokens]
													__UpperCamelCase       					=     ''''''.join(SCREAMING_SNAKE_CASE_     ).split()
													# detokenize
													__UpperCamelCase       					=     self.moses_detokenize(SCREAMING_SNAKE_CASE_       ,		self.tgt_lang     )
													return text



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = None     )->				List[int]:





													'''simple docstring'''






													__UpperCamelCase       					=     [self.sep_token_id]

													# no bos used in fairseq
													if token_ids_a is None:
																			return token_ids_a + sep
													return token_ids_a + sep + token_ids_a + sep



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_ = False     )->				List[int]:





													'''simple docstring'''






													if already_has_special_tokens:
																			return super().get_special_tokens_mask(
																			    token_ids_a=SCREAMING_SNAKE_CASE_       ,		token_ids_a=SCREAMING_SNAKE_CASE_       ,		already_has_special_tokens=SCREAMING_SNAKE_CASE_     )
													# no bos used in fairseq
													if token_ids_a is not None:
																			return ([0] * len(SCREAMING_SNAKE_CASE_     )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_     )) + [1]
													return ([0] * len(SCREAMING_SNAKE_CASE_     )) + [1]



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = None     )->				List[int]:





													'''simple docstring'''






													__UpperCamelCase       					=     [self.sep_token_id]

													# no bos used in fairseq
													if token_ids_a is None:
																			return len(token_ids_a + sep     ) * [0]
													return len(token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1]



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = None     )->				Tuple[str]:





													'''simple docstring'''






													if not os.path.isdir(SCREAMING_SNAKE_CASE_     ):
																			logger.error(F"Vocabulary path ({save_directory}) should be a directory"     )
																			return

													__UpperCamelCase       					=     os.path.join(
													    SCREAMING_SNAKE_CASE_       ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''src_vocab_file''']     )
													__UpperCamelCase       					=     os.path.join(
													    SCREAMING_SNAKE_CASE_       ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''tgt_vocab_file''']     )
													__UpperCamelCase       					=     os.path.join(
													    SCREAMING_SNAKE_CASE_       ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']     )

													with open(SCREAMING_SNAKE_CASE_       ,		'''w'''       ,		encoding='''utf-8'''     ) as f:
																			f.write(json.dumps(self.encoder       ,		indent=2       ,		sort_keys=SCREAMING_SNAKE_CASE_       ,		ensure_ascii=SCREAMING_SNAKE_CASE_     ) + '''\n'''     )

													with open(SCREAMING_SNAKE_CASE_       ,		'''w'''       ,		encoding='''utf-8'''     ) as f:
																			__UpperCamelCase       					=     {v: k for k, v in self.decoder.items()}
																			f.write(json.dumps(SCREAMING_SNAKE_CASE_       ,		indent=2       ,		sort_keys=SCREAMING_SNAKE_CASE_       ,		ensure_ascii=SCREAMING_SNAKE_CASE_     ) + '''\n'''     )

													__UpperCamelCase       					=     0
													with open(SCREAMING_SNAKE_CASE_       ,		'''w'''       ,		encoding='''utf-8'''     ) as writer:
																			for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,		key=lambda SCREAMING_SNAKE_CASE_     : kv[1]     ):
																									if index != token_index:
																															logger.warning(
																															    F"Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."
																															    ''' Please check that the tokenizer is not corrupted!'''     )
																															__UpperCamelCase       					=     token_index
																									writer.write(''' '''.join(SCREAMING_SNAKE_CASE_     ) + '''\n'''     )
																									index += 1

													return src_vocab_file, tgt_vocab_file, merges_file



							def __getstate__(					self     )->				str:





													'''simple docstring'''






													__UpperCamelCase       					=     self.__dict__.copy()
													__UpperCamelCase       					=     None
													return state



							def __setstate__(					self       ,		SCREAMING_SNAKE_CASE_     )->				List[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     d

													try:
																			import sacremoses
													except ImportError:
																			raise ImportError(
																			    '''You need to install sacremoses to use XLMTokenizer. '''
																			    '''See https://pypi.org/project/sacremoses/ for installation.'''     )

													__UpperCamelCase       					=     sacremoses




