



'''simple docstring'''




import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


UpperCamelCase__       :	Any    = 16
UpperCamelCase__       :	Dict    = 32


def 				lowerCAmelCase_   (	_lowerCamelCase:   int					):
		return int(x / 2**20					)






class 						_UpperCamelCase  :


			'''simple docstring'''

			def __enter__(							self     : Optional[Any]						):





					"""simple docstring"""






					gc.collect()
					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
					__SCREAMING_SNAKE_CASE       :		int           =		torch.cuda.memory_allocated()
					return self



			def __exit__(							self     : Any							,     *lowerCAmelCase__     : Optional[int]						):





					"""simple docstring"""






					gc.collect()
					torch.cuda.empty_cache()
					__SCREAMING_SNAKE_CASE       :		Dict           =		torch.cuda.memory_allocated()
					__SCREAMING_SNAKE_CASE       :		Any           =		torch.cuda.max_memory_allocated()
					__SCREAMING_SNAKE_CASE       :		str           =		bamb(self.end - self.begin						)
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		bamb(self.peak - self.begin						)
					# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


def 				lowerCAmelCase_   (	_lowerCamelCase:   Accelerator       ,      _lowerCamelCase:   int = 16       ,      _lowerCamelCase:   str = "bert-base-cased"       ,      _lowerCamelCase:   int = 3_20       ,      _lowerCamelCase:   int = 1_60       ,      ):
		__SCREAMING_SNAKE_CASE       :		Dict           =		AutoTokenizer.from_pretrained(_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Tuple           =		load_dataset(
		    """glue"""       ,      """mrpc"""       ,      split={"""train""": F"train[:{n_train}]", """validation""": F"validation[:{n_val}]"}					)

		def tokenize_function(_lowerCamelCase:   List[Any]					):
				# max_length=None => use the model max length (it's actually the default)
				__SCREAMING_SNAKE_CASE       :		int           =		tokenizer(examples["""sentence1"""]       ,      examples["""sentence2"""]       ,      truncation=_lowerCamelCase       ,      max_length=_lowerCamelCase					)
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		__SCREAMING_SNAKE_CASE       :		Tuple           =		datasets.map(
		    _lowerCamelCase       ,      batched=_lowerCamelCase       ,      remove_columns=["""idx""", """sentence1""", """sentence2"""]       ,      load_from_cache_file=_lowerCamelCase					)

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

		def collate_fn(_lowerCamelCase:   List[str]					):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				if accelerator.distributed_type == DistributedType.TPU:
						return tokenizer.pad(_lowerCamelCase       ,      padding="""max_length"""       ,      max_length=1_28       ,      return_tensors="""pt"""					)
				return tokenizer.pad(_lowerCamelCase       ,      padding="""longest"""       ,      return_tensors="""pt"""					)

		# Instantiate dataloaders.
		__SCREAMING_SNAKE_CASE       :		int           =		DataLoader(
		    tokenized_datasets["""train"""]       ,      shuffle=_lowerCamelCase       ,      collate_fn=_lowerCamelCase       ,      batch_size=_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		str           =		DataLoader(
		    tokenized_datasets["""validation"""]       ,      shuffle=_lowerCamelCase       ,      collate_fn=_lowerCamelCase       ,      batch_size=_lowerCamelCase					)

		return train_dataloader, eval_dataloader


def 				lowerCAmelCase_   (	_lowerCamelCase:   List[Any]       ,      _lowerCamelCase:   int					):
		# Initialize accelerator
		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		Accelerator()

		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		config["""lr"""]
		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		int(config["""num_epochs"""]					)
		__SCREAMING_SNAKE_CASE       :		int           =		int(config["""seed"""]					)
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		int(config["""batch_size"""]					)
		__SCREAMING_SNAKE_CASE       :		List[str]           =		args.model_name_or_path

		set_seed(_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		get_dataloaders(_lowerCamelCase       ,      _lowerCamelCase       ,      _lowerCamelCase       ,      args.n_train       ,      args.n_val					)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		__SCREAMING_SNAKE_CASE       :		Dict           =		AutoModelForSequenceClassification.from_pretrained(_lowerCamelCase       ,      return_dict=_lowerCamelCase					)

		# Instantiate optimizer
		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		(
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		__SCREAMING_SNAKE_CASE       :		Tuple           =		optimizer_cls(params=model.parameters()       ,      lr=_lowerCamelCase					)

		if accelerator.state.deepspeed_plugin is not None:
				__SCREAMING_SNAKE_CASE       :		str           =		accelerator.state.deepspeed_plugin.deepspeed_config[
				    """gradient_accumulation_steps"""
				]
		else:
				__SCREAMING_SNAKE_CASE       :		Tuple           =		1
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		(len(_lowerCamelCase					) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		get_linear_schedule_with_warmup(
				    optimizer=_lowerCamelCase       ,      num_warmup_steps=0       ,      num_training_steps=_lowerCamelCase       ,      )
		else:
				__SCREAMING_SNAKE_CASE       :		str           =		DummyScheduler(_lowerCamelCase       ,      total_num_steps=_lowerCamelCase       ,      warmup_num_steps=0					)

		# Prepare everything
		# 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.
		__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		accelerator.prepare(
		    _lowerCamelCase       ,      _lowerCamelCase       ,      _lowerCamelCase       ,      _lowerCamelCase       ,      _lowerCamelCase					)

		# We need to keep track of how many total steps we have iterated over
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		0
		# We also need to keep track of the stating epoch so files are named properly
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		0

		# Now we train the model
		__SCREAMING_SNAKE_CASE       :		int           =		{}
		for epoch in range(_lowerCamelCase       ,      _lowerCamelCase					):
				with TorchTracemalloc() as tracemalloc:
						model.train()
						for step, batch in enumerate(_lowerCamelCase					):
								__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		model(**_lowerCamelCase					)
								__SCREAMING_SNAKE_CASE       :		Tuple           =		outputs.loss
								__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		loss / gradient_accumulation_steps
								accelerator.backward(_lowerCamelCase					)
								if step % gradient_accumulation_steps == 0:
										optimizer.step()
										lr_scheduler.step()
										optimizer.zero_grad()

								overall_step += 1

        # 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					)					)					)
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		tracemalloc.peaked + bamb(tracemalloc.begin					)
				if args.peak_memory_upper_bound is not None:
						assert (
						    train_total_peak_memory[F"epoch-{epoch}"] <= args.peak_memory_upper_bound
						), "Peak memory usage exceeded the upper bound"

		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
				with open(os.path.join(args.output_dir       ,      """peak_memory_utilization.json"""					)       ,      """w"""					) as f:
						json.dump(_lowerCamelCase       ,      _lowerCamelCase					)


def 				lowerCAmelCase_   (	):
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""					)
		parser.add_argument(
		    """--model_name_or_path"""       ,      type=_lowerCamelCase       ,      default="""bert-base-cased"""       ,      help="""Path to pretrained model or model identifier from huggingface.co/models."""       ,      required=_lowerCamelCase       ,      )
		parser.add_argument(
		    """--output_dir"""       ,      type=_lowerCamelCase       ,      default="""."""       ,      help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""       ,      )
		parser.add_argument(
		    """--peak_memory_upper_bound"""       ,      type=_lowerCamelCase       ,      default=_lowerCamelCase       ,      help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."""       ,      )
		parser.add_argument(
		    """--n_train"""       ,      type=_lowerCamelCase       ,      default=3_20       ,      help="""Number of training examples to use."""       ,      )
		parser.add_argument(
		    """--n_val"""       ,      type=_lowerCamelCase       ,      default=1_60       ,      help="""Number of validation examples to use."""       ,      )
		parser.add_argument(
		    """--num_epochs"""       ,      type=_lowerCamelCase       ,      default=1       ,      help="""Number of train epochs."""       ,      )
		__SCREAMING_SNAKE_CASE       :		int           =		parser.parse_args()
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
		training_function(_lowerCamelCase       ,      _lowerCamelCase					)


if __name__ == "__main__":
						main()




'''simple docstring'''




import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


UpperCamelCase__       :	List[str]    = logging.get_logger(__name__)


UpperCamelCase__       :	Tuple    = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all LED models at https://huggingface.co/models?filter=LED
UpperCamelCase__       :	Tuple    = {
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

UpperCamelCase__       :	List[Any]    = {
    '''allenai/led-base-16384''': 1_63_84,
}


@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 				lowerCAmelCase_   (	):
		__SCREAMING_SNAKE_CASE       :		Tuple           =		(
		    list(range(ord("""!"""					)       ,      ord("""~"""					) + 1					)					) + list(range(ord("""¡"""					)       ,      ord("""¬"""					) + 1					)					) + list(range(ord("""®"""					)       ,      ord("""ÿ"""					) + 1					)					)
		)
		__SCREAMING_SNAKE_CASE       :		Any           =		bs[:]
		__SCREAMING_SNAKE_CASE       :		Tuple           =		0
		for b in range(2**8					):
				if b not in bs:
						bs.append(_lowerCamelCase					)
						cs.append(2**8 + n					)
						n += 1
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		[chr(_lowerCamelCase					) for n in cs]
		return dict(zip(_lowerCamelCase       ,      _lowerCamelCase					)					)


def 				lowerCAmelCase_   (	_lowerCamelCase:   Optional[int]					):
		__SCREAMING_SNAKE_CASE       :		Dict           =		set()
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)					)
				__SCREAMING_SNAKE_CASE       :		str           =		char
		return pairs






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		Union[str, Any]			    =		VOCAB_FILES_NAMES
			_A      :		Any			    =		PRETRAINED_VOCAB_FILES_MAP
			_A      :		Any			    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_A      :		Optional[Any]			    =		['''input_ids''', '''attention_mask''']

			def __init__(							self     : Tuple							,     lowerCAmelCase__     : List[str]							,     lowerCAmelCase__     : Optional[Any]							,     lowerCAmelCase__     : Union[str, Any]="replace"							,     lowerCAmelCase__     : Dict="<s>"							,     lowerCAmelCase__     : List[str]="</s>"							,     lowerCAmelCase__     : Tuple="</s>"							,     lowerCAmelCase__     : Tuple="<s>"							,     lowerCAmelCase__     : Union[str, Any]="<unk>"							,     lowerCAmelCase__     : Union[str, Any]="<pad>"							,     lowerCAmelCase__     : int="<mask>"							,     lowerCAmelCase__     : str=False							,     **lowerCAmelCase__     : int							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		AddedToken(lowerCAmelCase__							,     lstrip=lowerCAmelCase__							,     rstrip=lowerCAmelCase__						) if isinstance(lowerCAmelCase__							,     lowerCAmelCase__						) else bos_token
					__SCREAMING_SNAKE_CASE       :		List[str]           =		AddedToken(lowerCAmelCase__							,     lstrip=lowerCAmelCase__							,     rstrip=lowerCAmelCase__						) if isinstance(lowerCAmelCase__							,     lowerCAmelCase__						) else eos_token
					__SCREAMING_SNAKE_CASE       :		List[str]           =		AddedToken(lowerCAmelCase__							,     lstrip=lowerCAmelCase__							,     rstrip=lowerCAmelCase__						) if isinstance(lowerCAmelCase__							,     lowerCAmelCase__						) else sep_token
					__SCREAMING_SNAKE_CASE       :		Dict           =		AddedToken(lowerCAmelCase__							,     lstrip=lowerCAmelCase__							,     rstrip=lowerCAmelCase__						) if isinstance(lowerCAmelCase__							,     lowerCAmelCase__						) else cls_token
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		AddedToken(lowerCAmelCase__							,     lstrip=lowerCAmelCase__							,     rstrip=lowerCAmelCase__						) if isinstance(lowerCAmelCase__							,     lowerCAmelCase__						) else unk_token
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		AddedToken(lowerCAmelCase__							,     lstrip=lowerCAmelCase__							,     rstrip=lowerCAmelCase__						) if isinstance(lowerCAmelCase__							,     lowerCAmelCase__						) else pad_token

					# Mask token behave like a normal word, i.e. include the space before it
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		AddedToken(lowerCAmelCase__							,     lstrip=lowerCAmelCase__							,     rstrip=lowerCAmelCase__						) if isinstance(lowerCAmelCase__							,     lowerCAmelCase__						) else mask_token

					super().__init__(
					    errors=lowerCAmelCase__							,     bos_token=lowerCAmelCase__							,     eos_token=lowerCAmelCase__							,     unk_token=lowerCAmelCase__							,     sep_token=lowerCAmelCase__							,     cls_token=lowerCAmelCase__							,     pad_token=lowerCAmelCase__							,     mask_token=lowerCAmelCase__							,     add_prefix_space=lowerCAmelCase__							,     **lowerCAmelCase__							,     )

					with open(lowerCAmelCase__							,     encoding="""utf-8"""						) as vocab_handle:
							__SCREAMING_SNAKE_CASE       :		str           =		json.load(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		List[str]           =		{v: k for k, v in self.encoder.items()}
					__SCREAMING_SNAKE_CASE       :		Dict           =		errors  # how to handle errors in decoding
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		bytes_to_unicode()
					__SCREAMING_SNAKE_CASE       :		Tuple           =		{v: k for k, v in self.byte_encoder.items()}
					with open(lowerCAmelCase__							,     encoding="""utf-8"""						) as merges_handle:
							__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		merges_handle.read().split("""\n"""						)[1:-1]
					__SCREAMING_SNAKE_CASE       :		int           =		[tuple(merge.split()						) for merge in bpe_merges]
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		dict(zip(lowerCAmelCase__							,     range(len(lowerCAmelCase__						)						)						)						)
					__SCREAMING_SNAKE_CASE       :		int           =		{}
					__SCREAMING_SNAKE_CASE       :		Any           =		add_prefix_space

					# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
					__SCREAMING_SNAKE_CASE       :		str           =		re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""						)

			@property
			# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
			def UpperCamelCase__					(							self     : Union[str, Any]						):





					"""simple docstring"""






					return len(self.encoder						)

			def UpperCamelCase__					(							self     : Any						):





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : Dict							,     lowerCAmelCase__     : Any						):





					"""simple docstring"""






					if token in self.cache:
							return self.cache[token]
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		tuple(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Dict           =		get_pairs(lowerCAmelCase__						)

					if not pairs:
							return token

					while True:
							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		min(lowerCAmelCase__							,     key=lambda lowerCAmelCase__						: self.bpe_ranks.get(lowerCAmelCase__							,     float("""inf"""						)						)						)
							if bigram not in self.bpe_ranks:
									break
							__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Dict           =		bigram
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		[]
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		0
							while i < len(lowerCAmelCase__						):
									try:
											__SCREAMING_SNAKE_CASE       :		Dict           =		word.index(lowerCAmelCase__							,     lowerCAmelCase__						)
									except ValueError:
											new_word.extend(word[i:]						)
											break
									else:
											new_word.extend(word[i:j]						)
											__SCREAMING_SNAKE_CASE       :		Dict           =		j

									if word[i] == first and i < len(lowerCAmelCase__						) - 1 and word[i + 1] == second:
											new_word.append(first + second						)
											i += 2
									else:
											new_word.append(word[i]						)
											i += 1
							__SCREAMING_SNAKE_CASE       :		Tuple           =		tuple(lowerCAmelCase__						)
							__SCREAMING_SNAKE_CASE       :		Any           =		new_word
							if len(lowerCAmelCase__						) == 1:
									break
							else:
									__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		get_pairs(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		""" """.join(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		word
					return word

			def UpperCamelCase__					(							self     : Optional[Any]							,     lowerCAmelCase__     : Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		[]
					for token in re.findall(self.pat							,     lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE       :		Any           =		"""""".join(
							    self.byte_encoder[b] for b in token.encode("""utf-8"""						)						)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
							bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase__						).split(""" """						)						)
					return bpe_tokens

			def UpperCamelCase__					(							self     : List[str]							,     lowerCAmelCase__     : List[str]						):





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : int							,     lowerCAmelCase__     : Optional[int]						):





					"""simple docstring"""






					return self.decoder.get(lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Optional[Any]							,     lowerCAmelCase__     : List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		"""""".join(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		bytearray([self.byte_decoder[c] for c in text]						).decode("""utf-8"""							,     errors=self.errors						)
					return text

			def UpperCamelCase__					(							self     : List[str]							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : Optional[str] = None						):





					"""simple docstring"""






					if not os.path.isdir(lowerCAmelCase__						):
							logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
							return
					__SCREAMING_SNAKE_CASE       :		int           =		os.path.join(
					    lowerCAmelCase__							,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		os.path.join(
					    lowerCAmelCase__							,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]						)

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

					__SCREAMING_SNAKE_CASE       :		Tuple           =		0
					with open(lowerCAmelCase__							,     """w"""							,     encoding="""utf-8"""						) as writer:
							writer.write("""#version: 0.2\n"""						)
							for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							,     key=lambda lowerCAmelCase__						: kv[1]						):
									if index != token_index:
											logger.warning(
											    F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
											    """ Please check that the tokenizer is not corrupted!"""						)
											__SCREAMING_SNAKE_CASE       :		List[Any]           =		token_index
									writer.write(""" """.join(lowerCAmelCase__						) + """\n"""						)
									index += 1

					return vocab_file, merge_file

			def UpperCamelCase__					(							self     : str							,     lowerCAmelCase__     : List[int]							,     lowerCAmelCase__     : Optional[List[int]] = None						):





					"""simple docstring"""






					if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		[self.cls_token_id]
					__SCREAMING_SNAKE_CASE       :		str           =		[self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep

			def UpperCamelCase__					(							self     : Any							,     lowerCAmelCase__     : List[int]							,     lowerCAmelCase__     : Optional[List[int]] = None							,     lowerCAmelCase__     : bool = False						):





					"""simple docstring"""






					if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=lowerCAmelCase__							,     token_ids_a=lowerCAmelCase__							,     already_has_special_tokens=lowerCAmelCase__						)

					if token_ids_a is None:
							return [1] + ([0] * len(lowerCAmelCase__						)) + [1]
					return [1] + ([0] * len(lowerCAmelCase__						)) + [1, 1] + ([0] * len(lowerCAmelCase__						)) + [1]

			def UpperCamelCase__					(							self     : Any							,     lowerCAmelCase__     : List[int]							,     lowerCAmelCase__     : Optional[List[int]] = None						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		int           =		[self.sep_token_id]
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		[self.cls_token_id]

					if token_ids_a is None:
							return len(cls + token_ids_a + sep						) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]

			def UpperCamelCase__					(							self     : Optional[Any]							,     lowerCAmelCase__     : int							,     lowerCAmelCase__     : List[Any]=False							,     **lowerCAmelCase__     : Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		kwargs.pop("""add_prefix_space"""							,     self.add_prefix_space						)
					if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase__						) > 0 and not text[0].isspace()):
							__SCREAMING_SNAKE_CASE       :		int           =		""" """ + text
					return (text, kwargs)



			def UpperCamelCase__					(							self     : Optional[Any]							,     lowerCAmelCase__     : Union[Dict[str, EncodedInput], BatchEncoding]							,     lowerCAmelCase__     : Optional[int] = None							,     lowerCAmelCase__     : PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,     lowerCAmelCase__     : Optional[int] = None							,     lowerCAmelCase__     : Optional[bool] = None							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		super()._pad(
					    encoded_inputs=lowerCAmelCase__							,     max_length=lowerCAmelCase__							,     padding_strategy=lowerCAmelCase__							,     pad_to_multiple_of=lowerCAmelCase__							,     return_attention_mask=lowerCAmelCase__							,     )

					# Load from model defaults
					if return_attention_mask is None:
							__SCREAMING_SNAKE_CASE       :		Tuple           =		"""attention_mask""" in self.model_input_names

					if return_attention_mask and "global_attention_mask" in encoded_inputs:
							__SCREAMING_SNAKE_CASE       :		str           =		encoded_inputs[self.model_input_names[0]]
							# `global_attention_mask` need to have the same length as other (sequential) inputs.
							__SCREAMING_SNAKE_CASE       :		str           =		len(encoded_inputs["""global_attention_mask"""]						) != len(lowerCAmelCase__						)

							if needs_to_be_padded:
									__SCREAMING_SNAKE_CASE       :		Dict           =		len(lowerCAmelCase__						) - len(encoded_inputs["""global_attention_mask"""]						)

									if self.padding_side == "right":
											# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
											__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		(
											    encoded_inputs["""global_attention_mask"""] + [-1] * difference
											)
									elif self.padding_side == "left":
											__SCREAMING_SNAKE_CASE       :		Dict           =		[-1] * difference + encoded_inputs[
											    """global_attention_mask"""
											]
									else:
											raise ValueError("""Invalid padding strategy:""" + str(self.padding_side						)						)

					return encoded_inputs