
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__a         =   logging.getLogger(__name__)
@dataclass
class 					lowercase__:



	"""simple docstring"""




	a     :str       	=      field(
	    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}				)
	a     :Optional[str]       	=      field(
	    default=UpperCAmelCase					,		metadata={'help': 'Pretrained config name or path if not the same as model_name'}				)
	a     :Optional[str]       	=      field(
	    default=UpperCAmelCase					,		metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}				)
	a     :Optional[str]       	=      field(
	    default=UpperCAmelCase					,		metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}					,		)
	a     :bool       	=      field(default=UpperCAmelCase					,		metadata={'help': 'Whether tp freeze the encoder.'}				)
	a     :bool       	=      field(default=UpperCAmelCase					,		metadata={'help': 'Whether  to freeze the embeddings.'}				)

@dataclass
class 					lowercase__:



	"""simple docstring"""




	a     :str       	=      field(
	    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}				)
	a     :Optional[str]       	=      field(
	    default='summarization'					,		metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}					,		)
	a     :Optional[int]       	=      field(
	    default=1_024					,		metadata={
	        'help': (
	            'The maximum total input sequence length after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }					,		)
	a     :Optional[int]       	=      field(
	    default=128					,		metadata={
	        'help': (
	            'The maximum total sequence length for target text after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }					,		)
	a     :Optional[int]       	=      field(
	    default=142					,		metadata={
	        'help': (
	            'The maximum total sequence length for validation target text after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded. '
	            'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
	            'during ``evaluate`` and ``predict``.'
	        )
	    }					,		)
	a     :Optional[int]       	=      field(
	    default=142					,		metadata={
	        'help': (
	            'The maximum total sequence length for test target text after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }					,		)
	a     :Optional[int]       	=      field(default=-1					,		metadata={'help': '# training examples. -1 means use all.'}				)
	a     :Optional[int]       	=      field(default=-1					,		metadata={'help': '# validation examples. -1 means use all.'}				)
	a     :Optional[int]       	=      field(default=-1					,		metadata={'help': '# test examples. -1 means use all.'}				)
	a     :Optional[str]       	=      field(default=UpperCAmelCase					,		metadata={'help': 'Source language id for translation.'}				)
	a     :Optional[str]       	=      field(default=UpperCAmelCase					,		metadata={'help': 'Target language id for translation.'}				)
	a     :Optional[int]       	=      field(default=UpperCAmelCase					,		metadata={'help': '# num_beams to use for evaluation.'}				)
	a     :bool       	=      field(
	    default=UpperCAmelCase					,		metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}					,		)




def 							a		(				snake_case__:   Dict		,  snake_case__:   Optional[int]		,  snake_case__:   List[str]   ):


				'''simple docstring'''
				logger.info(F'''***** {split} metrics *****'''   )
				for key in sorted(metrics.keys()   ):
								logger.info(F'''  {key} = {metrics[key]}'''   )
				save_json(snake_case__		,  os.path.join(snake_case__		,  F'''{split}_results.json'''   )   )




def 							a		(				):


				'''simple docstring'''
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				lowercase_	    =   HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)   )

				if len(sys.argv   ) == 2 and sys.argv[1].endswith('''.json'''   ):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								lowercase_    ,      lowercase_    ,      lowercase_	    =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
				else:
								lowercase_    ,      lowercase_    ,      lowercase_	    =   parser.parse_args_into_dataclasses()

				check_output_dir(snake_case__   )

				# Setup logging
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''		,  datefmt='''%m/%d/%Y %H:%M:%S'''		,  level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN		,  )
				logger.warning(
				    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''		,  training_args.local_rank		,  training_args.device		,  training_args.n_gpu		,  bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED   )		,  training_args.fpaa		,  )
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank   ):
								transformers.utils.logging.set_verbosity_info()
				logger.info('''Training/evaluation parameters %s'''		,  snake_case__   )

				# Set seed
				set_seed(training_args.seed   )

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				lowercase_	    =   AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path		,  cache_dir=model_args.cache_dir		,  )

				lowercase_	    =   ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
				for p in extra_model_params:
								if getattr(snake_case__		,  snake_case__		,  snake_case__   ):
												assert hasattr(snake_case__		,  snake_case__   ), F'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
												setattr(snake_case__		,  snake_case__		,  getattr(snake_case__		,  snake_case__   )   )

				lowercase_	    =   AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path		,  cache_dir=model_args.cache_dir		,  )
				lowercase_	    =   AutoModelForSeqaSeqLM.from_pretrained(
				    model_args.model_name_or_path		,  from_tf='''.ckpt''' in model_args.model_name_or_path		,  config=snake_case__		,  cache_dir=model_args.cache_dir		,  )

				# use task specific params
				use_task_specific_params(snake_case__		,  data_args.task   )

				# set num_beams for evaluation
				if data_args.eval_beams is None:
								lowercase_	    =   model.config.num_beams

				# set decoder_start_token_id for MBart
				if model.config.decoder_start_token_id is None and isinstance(snake_case__		,  (MBartTokenizer, MBartTokenizerFast)   ):
								assert (
								    data_args.tgt_lang is not None and data_args.src_lang is not None
								), "mBart requires --tgt_lang and --src_lang"
								if isinstance(snake_case__		,  snake_case__   ):
												lowercase_	    =   tokenizer.lang_code_to_id[data_args.tgt_lang]
								else:
												lowercase_	    =   tokenizer.convert_tokens_to_ids(data_args.tgt_lang   )

				if model_args.freeze_embeds:
								freeze_embeds(snake_case__   )
				if model_args.freeze_encoder:
								freeze_params(model.get_encoder()   )
								assert_all_frozen(model.get_encoder()   )

				lowercase_	    =   SeqaSeqDataset

				# Get datasets
				lowercase_	    =   (
				    dataset_class(
				        snake_case__		,  type_path='''train'''		,  data_dir=data_args.data_dir		,  n_obs=data_args.n_train		,  max_target_length=data_args.max_target_length		,  max_source_length=data_args.max_source_length		,  prefix=model.config.prefix or ''''''		,  )
				    if training_args.do_train
				    else None
				)
				lowercase_	    =   (
				    dataset_class(
				        snake_case__		,  type_path='''val'''		,  data_dir=data_args.data_dir		,  n_obs=data_args.n_val		,  max_target_length=data_args.val_max_target_length		,  max_source_length=data_args.max_source_length		,  prefix=model.config.prefix or ''''''		,  )
				    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
				    else None
				)
				lowercase_	    =   (
				    dataset_class(
				        snake_case__		,  type_path='''test'''		,  data_dir=data_args.data_dir		,  n_obs=data_args.n_test		,  max_target_length=data_args.test_max_target_length		,  max_source_length=data_args.max_source_length		,  prefix=model.config.prefix or ''''''		,  )
				    if training_args.do_predict
				    else None
				)

				# Initialize our Trainer
				lowercase_	    =   (
				    build_compute_metrics_fn(data_args.task		,  snake_case__   ) if training_args.predict_with_generate else None
				)
				lowercase_	    =   SeqaSeqTrainer(
				    model=snake_case__		,  args=snake_case__		,  data_args=snake_case__		,  train_dataset=snake_case__		,  eval_dataset=snake_case__		,  data_collator=SeqaSeqDataCollator(
				        snake_case__		,  snake_case__		,  model.config.decoder_start_token_id		,  training_args.tpu_num_cores   )		,  compute_metrics=snake_case__		,  tokenizer=snake_case__		,  )

				lowercase_	    =   {}
				# Training
				if training_args.do_train:
								logger.info('''*** Train ***'''   )

								lowercase_	    =   trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
								lowercase_	    =   train_result.metrics
								lowercase_	    =   data_args.n_train

								trainer.save_model()  # this also saves the tokenizer

								if trainer.is_world_process_zero():
												handle_metrics('''train'''		,  snake_case__		,  training_args.output_dir   )
												all_metrics.update(snake_case__   )

												# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
												trainer.state.save_to_json(os.path.join(training_args.output_dir		,  '''trainer_state.json'''   )   )

												# For convenience, we also re-save the tokenizer to the same directory,
												# so that you can share your model easily on huggingface.co/models =)
												tokenizer.save_pretrained(training_args.output_dir   )

    # Evaluation
				if training_args.do_eval:
								logger.info('''*** Evaluate ***'''   )

								lowercase_	    =   trainer.evaluate(metric_key_prefix='''val'''   )
								lowercase_	    =   data_args.n_val
								lowercase_	    =   round(metrics['''val_loss''']		,  4   )

								if trainer.is_world_process_zero():
												handle_metrics('''val'''		,  snake_case__		,  training_args.output_dir   )
												all_metrics.update(snake_case__   )

				if training_args.do_predict:
								logger.info('''*** Predict ***'''   )

								lowercase_	    =   trainer.predict(test_dataset=snake_case__		,  metric_key_prefix='''test'''   )
								lowercase_	    =   test_output.metrics
								lowercase_	    =   data_args.n_test

								if trainer.is_world_process_zero():
												lowercase_	    =   round(metrics['''test_loss''']		,  4   )
												handle_metrics('''test'''		,  snake_case__		,  training_args.output_dir   )
												all_metrics.update(snake_case__   )

												if training_args.predict_with_generate:
																lowercase_	    =   tokenizer.batch_decode(
																    test_output.predictions		,  skip_special_tokens=snake_case__		,  clean_up_tokenization_spaces=snake_case__   )
																lowercase_	    =   lmap(str.strip		,  snake_case__   )
																write_txt_file(snake_case__		,  os.path.join(training_args.output_dir		,  '''test_generations.txt'''   )   )

				if trainer.is_world_process_zero():
								save_json(snake_case__		,  os.path.join(training_args.output_dir		,  '''all_results.json'''   )   )

				return all_metrics




def 							a		(				snake_case__:   List[str]   ):


				'''simple docstring'''
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
						main()








"""simple docstring"""



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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__snake_case       =       logging.get_logger(__name__)


__snake_case       =       {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

__snake_case       =       {
    """vocab_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
    },
}

__snake_case       =       {
    """allenai/longformer-base-4096""": 4096,
    """allenai/longformer-large-4096""": 4096,
    """allenai/longformer-large-4096-finetuned-triviaqa""": 4096,
    """allenai/longformer-base-4096-extra.pos.embd.only""": 4096,
    """allenai/longformer-large-4096-extra.pos.embd.only""": 4096,
}







@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def     __lowerCAmelCase		(		)      ->		str:


    """simple docstring"""





    snake_case       : int    =       (
        list(range(ord("!"					)		,				ord("~"					) + 1					)					) + list(range(ord("¡"					)		,				ord("¬"					) + 1					)					) + list(range(ord("®"					)		,				ord("ÿ"					) + 1					)					)
    )
    snake_case       : Any    =       bs[:]
    snake_case       : str    =       0
    for b in range(2**8					):
        if b not in bs:
            bs.append(lowercase					)
            cs.append(2**8 + n					)
            n += 1
    snake_case       : str    =       [chr(lowercase					) for n in cs]
    return dict(zip(lowercase		,				lowercase					)					)







def     __lowerCAmelCase		(		lowercase    :    int					)      ->		Tuple:


    """simple docstring"""





    snake_case       : Optional[Any]    =       set()
    snake_case       : Dict    =       word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)					)
        snake_case       : Optional[int]    =       char
    return pairs







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			Dict				    =    VOCAB_FILES_NAMES
   __UpperCAmelCase					:			List[Any]				    =    PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase					:			str				    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase					:			Dict				    =    ['''input_ids''', '''attention_mask''']







   def __init__(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__="replace"		,      UpperCamelCase__="<s>"		,      UpperCamelCase__="</s>"		,      UpperCamelCase__="</s>"		,      UpperCamelCase__="<s>"		,      UpperCamelCase__="<unk>"		,      UpperCamelCase__="<pad>"		,      UpperCamelCase__="<mask>"		,      UpperCamelCase__=False		,      **UpperCamelCase__		,      )      ->						Union[str, Any]:

       '''simple docstring'''




       snake_case       : Any    =       AddedToken(UpperCamelCase__		,      lstrip=UpperCamelCase__		,      rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__		,      UpperCamelCase__		) else bos_token
       snake_case       : int    =       AddedToken(UpperCamelCase__		,      lstrip=UpperCamelCase__		,      rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__		,      UpperCamelCase__		) else eos_token
       snake_case       : Dict    =       AddedToken(UpperCamelCase__		,      lstrip=UpperCamelCase__		,      rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__		,      UpperCamelCase__		) else sep_token
       snake_case       : Optional[int]    =       AddedToken(UpperCamelCase__		,      lstrip=UpperCamelCase__		,      rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__		,      UpperCamelCase__		) else cls_token
       snake_case       : Dict    =       AddedToken(UpperCamelCase__		,      lstrip=UpperCamelCase__		,      rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__		,      UpperCamelCase__		) else unk_token
       snake_case       : Tuple    =       AddedToken(UpperCamelCase__		,      lstrip=UpperCamelCase__		,      rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__		,      UpperCamelCase__		) else pad_token

       # Mask token behave like a normal word, i.e. include the space before it
       snake_case       : Any    =       AddedToken(UpperCamelCase__		,      lstrip=UpperCamelCase__		,      rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__		,      UpperCamelCase__		) else mask_token

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

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

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







   @property
   def 					lowerCamelCase						(      self		)      ->						Optional[Any]:

       '''simple docstring'''




       return len(self.encoder		)







   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Tuple:

       '''simple docstring'''




       if token in self.cache:
           return self.cache[token]
       snake_case       : Optional[Any]    =       tuple(UpperCamelCase__		)
       snake_case       : str    =       get_pairs(UpperCamelCase__		)

       if not pairs:
           return token

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

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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						List[Any]:

       '''simple docstring'''




       snake_case       : Optional[int]    =       []
       for token in re.findall(self.pat		,      UpperCamelCase__		):
           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(UpperCamelCase__		).split(" "		)		)
       return bpe_tokens







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						List[Any]:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						List[str]:

       '''simple docstring'''




       return self.decoder.get(UpperCamelCase__		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						str:

       '''simple docstring'''




       snake_case       : int    =       "".join(UpperCamelCase__		)
       snake_case       : Dict    =       bytearray([self.byte_decoder[c] for c in text]		).decode("utf-8"		,      errors=self.errors		)
       return text







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__ = None		)      ->						Tuple[str]:

       '''simple docstring'''




       if not os.path.isdir(UpperCamelCase__		):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory'		)
           return
       snake_case       : Dict    =       os.path.join(
           UpperCamelCase__		,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
       snake_case       : List[Any]    =       os.path.join(
           UpperCamelCase__		,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]		)

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

       snake_case       : List[Any]    =       0
       with open(UpperCamelCase__		,      "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 UpperCamelCase__		: 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!"		)
                   snake_case       : Optional[Any]    =       token_index
               writer.write(" ".join(UpperCamelCase__		) + "\n"		)
               index += 1

       return vocab_file, merge_file







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__ = None		)      ->						List[int]:

       '''simple docstring'''




       if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       snake_case       : Union[str, Any]    =       [self.cls_token_id]
       snake_case       : Optional[int]    =       [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__ = None		,      UpperCamelCase__ = False		)      ->						List[int]:

       '''simple docstring'''




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

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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__ = None		)      ->						List[int]:

       '''simple docstring'''




       snake_case       : Any    =       [self.sep_token_id]
       snake_case       : List[str]    =       [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 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__=False		,      **UpperCamelCase__		)      ->						Any:

       '''simple docstring'''




       snake_case       : Optional[Any]    =       kwargs.pop("add_prefix_space"		,      self.add_prefix_space		)
       if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase__		) > 0 and not text[0].isspace()):
           snake_case       : str    =       " " + text
       return (text, kwargs)





