






"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation

lowerCamelCase_							      = logging.get_logger(__name__)


lowerCamelCase_							      = {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCamelCase_							      = {
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

lowerCamelCase_							      = {'''facebook/blenderbot-3B''': 128}







class      UpperCamelCase_			(__A	):
	__magic_name__       =							VOCAB_FILES_NAMES
	__magic_name__       =							PRETRAINED_VOCAB_FILES_MAP
	__magic_name__       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__       =							['''input_ids''', '''attention_mask''']
	__magic_name__       =							BlenderbotTokenizer





	def __init__( self   :					List[str]      ,      lowerCAmelCase_   :					List[Any]=None      ,      lowerCAmelCase_   :					Tuple=None      ,      lowerCAmelCase_   :					str=None      ,      lowerCAmelCase_   :					List[Any]="replace"      ,      lowerCAmelCase_   :					List[Any]="<s>"      ,      lowerCAmelCase_   :					Tuple="</s>"      ,      lowerCAmelCase_   :					str="</s>"      ,      lowerCAmelCase_   :					List[str]="<s>"      ,      lowerCAmelCase_   :					int="<unk>"      ,      lowerCAmelCase_   :					Any="<pad>"      ,      lowerCAmelCase_   :					str="<mask>"      ,      lowerCAmelCase_   :					Optional[int]=False      ,      lowerCAmelCase_   :					Dict=True      ,      **lowerCAmelCase_   :					Any      ,      )		->     Dict:
				super().__init__(
				    lowerCAmelCase_      ,      lowerCAmelCase_      ,      tokenizer_file=lowerCAmelCase_      ,      errors=lowerCAmelCase_      ,      bos_token=lowerCAmelCase_      ,      eos_token=lowerCAmelCase_      ,      sep_token=lowerCAmelCase_      ,      cls_token=lowerCAmelCase_      ,      unk_token=lowerCAmelCase_      ,      pad_token=lowerCAmelCase_      ,      mask_token=lowerCAmelCase_      ,      add_prefix_space=lowerCAmelCase_      ,      trim_offsets=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )

				UpperCAmelCase_	:					Optional[int]     	=   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
				if pre_tok_state.get("add_prefix_space"      ,      lowerCAmelCase_		) != add_prefix_space:
							UpperCAmelCase_	:					str     	=   getattr(lowerCAmelCase_      ,      pre_tok_state.pop("type"		)		)
							UpperCAmelCase_	:					Optional[Any]     	=   add_prefix_space
							UpperCAmelCase_	:					Any     	=   pre_tok_class(**lowerCAmelCase_		)

				UpperCAmelCase_	:					Optional[Any]     	=   add_prefix_space

				UpperCAmelCase_	:					Union[str, Any]     	=   "post_processor"
				UpperCAmelCase_	:					Dict     	=   getattr(self.backend_tokenizer      ,      lowerCAmelCase_      ,      lowerCAmelCase_		)
				if tokenizer_component_instance:
							UpperCAmelCase_	:					Any     	=   json.loads(tokenizer_component_instance.__getstate__()		)

							# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
							if "sep" in state:
										UpperCAmelCase_	:					Optional[Any]     	=   tuple(state["sep"]		)
							if "cls" in state:
										UpperCAmelCase_	:					Union[str, Any]     	=   tuple(state["cls"]		)

							UpperCAmelCase_	:					str     	=   False

							if state.get("add_prefix_space"      ,      lowerCAmelCase_		) != add_prefix_space:
										UpperCAmelCase_	:					List[str]     	=   add_prefix_space
										UpperCAmelCase_	:					Union[str, Any]     	=   True

							if state.get("trim_offsets"      ,      lowerCAmelCase_		) != trim_offsets:
										UpperCAmelCase_	:					Any     	=   trim_offsets
										UpperCAmelCase_	:					Any     	=   True

							if changes_to_apply:
										UpperCAmelCase_	:					int     	=   getattr(lowerCAmelCase_      ,      state.pop("type"		)		)
										UpperCAmelCase_	:					Dict     	=   component_class(**lowerCAmelCase_		)
										setattr(self.backend_tokenizer      ,      lowerCAmelCase_      ,      lowerCAmelCase_		)





	@property
	# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     str:
				if self._mask_token is None:
							if self.verbose:
										logger.error("Using mask_token, but it is not set yet."		)
							return None
				return str(self._mask_token		)





	@mask_token.setter
	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					Tuple		)		->     Tuple:
				UpperCAmelCase_	:					Optional[Any]     	=   AddedToken(lowerCAmelCase_      ,      lstrip=lowerCAmelCase_      ,      rstrip=lowerCAmelCase_		) if isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		) else value
				UpperCAmelCase_	:					Any     	=   value





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      *lowerCAmelCase_   :					Optional[Any]      ,      **lowerCAmelCase_   :					List[str]		)		->     BatchEncoding:
				UpperCAmelCase_	:					List[Any]     	=   kwargs.get("is_split_into_words"      ,      lowerCAmelCase_		)
				assert self.add_prefix_space or not is_split_into_words, (
				    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
				    "to use it with pretokenized inputs."
				)

				return super()._batch_encode_plus(*lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]      ,      *lowerCAmelCase_   :					List[str]      ,      **lowerCAmelCase_   :					Tuple		)		->     BatchEncoding:
				UpperCAmelCase_	:					Optional[int]     	=   kwargs.get("is_split_into_words"      ,      lowerCAmelCase_		)

				assert self.add_prefix_space or not is_split_into_words, (
				    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
				    "to use it with pretokenized inputs."
				)

				return super()._encode_plus(*lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					Optional[str] = None		)		->     Tuple[str]:
				UpperCAmelCase_	:					Union[str, Any]     	=   self._tokenizer.model.save(lowerCAmelCase_      ,      name=lowerCAmelCase_		)
				return tuple(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					Optional[List[int]] = None		)		->     List[int]:
				UpperCAmelCase_	:					Dict     	=   [self.sep_token_id]
				UpperCAmelCase_	:					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 	_SCREAMING_SNAKE_CASE ( self   :					int      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					Optional[List[int]] = None		)		->     Optional[Any]:
				return token_ids_a + [self.eos_token_id]





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					"Conversation"		)		->     List[int]:
				UpperCAmelCase_	:					Tuple     	=   []
				for is_user, text in conversation.iter_texts():
							if is_user:
										# We need to space prefix as it's being done within blenderbot
										inputs.append(" " + text		)
							else:
										# Generated responses should contain them already.
										inputs.append(lowerCAmelCase_		)

				UpperCAmelCase_	:					Dict     	=   "  ".join(lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   self.encode(lowerCAmelCase_		)
				if len(lowerCAmelCase_		) > self.model_max_length:
							UpperCAmelCase_	:					Optional[int]     	=   input_ids[-self.model_max_length :]
							logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""		)
				return input_ids







'''simple docstring'''







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,
)


lowerCAmelCase     :  Optional[Any]											=    logging.getLogger(__name__)
@dataclass
class 						UpperCamelCase__       :



     """simple docstring"""






     __magic_name__           =     field(
         metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}						)
     __magic_name__           =     field(
         default=SCREAMING_SNAKE_CASE_     ,       metadata={"help": "Pretrained config name or path if not the same as model_name"}						)
     __magic_name__           =     field(
         default=SCREAMING_SNAKE_CASE_     ,       metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}						)
     __magic_name__           =     field(
         default=SCREAMING_SNAKE_CASE_     ,       metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}     ,       )
     __magic_name__           =     field(default=SCREAMING_SNAKE_CASE_     ,       metadata={"help": "Whether tp freeze the encoder."}						)
     __magic_name__           =     field(default=SCREAMING_SNAKE_CASE_     ,       metadata={"help": "Whether  to freeze the embeddings."}						)
@dataclass
class 						UpperCamelCase__       :



     """simple docstring"""






     __magic_name__           =     field(
         metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}						)
     __magic_name__           =     field(
         default="summarization"     ,       metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"}     ,       )
     __magic_name__           =     field(
         default=1_0_2_4     ,       metadata={
             "help": (
                 "The maximum total input sequence length after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )
         }     ,       )
     __magic_name__           =     field(
         default=1_2_8     ,       metadata={
             "help": (
                 "The maximum total sequence length for target text after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )
         }     ,       )
     __magic_name__           =     field(
         default=1_4_2     ,       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``."
             )
         }     ,       )
     __magic_name__           =     field(
         default=1_4_2     ,       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."
             )
         }     ,       )
     __magic_name__           =     field(default=-1     ,       metadata={"help": "# training examples. -1 means use all."}						)
     __magic_name__           =     field(default=-1     ,       metadata={"help": "# validation examples. -1 means use all."}						)
     __magic_name__           =     field(default=-1     ,       metadata={"help": "# test examples. -1 means use all."}						)
     __magic_name__           =     field(default=SCREAMING_SNAKE_CASE_     ,       metadata={"help": "Source language id for translation."}						)
     __magic_name__           =     field(default=SCREAMING_SNAKE_CASE_     ,       metadata={"help": "Target language id for translation."}						)
     __magic_name__           =     field(default=SCREAMING_SNAKE_CASE_     ,       metadata={"help": "# num_beams to use for evaluation."}						)
     __magic_name__           =     field(
         default=SCREAMING_SNAKE_CASE_     ,       metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}     ,       )






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







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






def       lowercase					():







       """simple docstring"""







       _lowerCAmelCase     :				int       =							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.
              _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				Tuple       =							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
       else:
              _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				Optional[int]       =							parser.parse_args_into_dataclasses()

       check_output_dir(_A       )

       # 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' ,    _A       )

       # 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.

       _lowerCAmelCase     :				Optional[int]       =							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 ,    )

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

       _lowerCAmelCase     :				Any       =							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 ,    )
       _lowerCAmelCase     :				str       =							AutoModelForSeqaSeqLM.from_pretrained(
           model_args.model_name_or_path ,    from_tf='.ckpt' in model_args.model_name_or_path ,    config=_A ,    cache_dir=model_args.cache_dir ,    )

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

       # set num_beams for evaluation
       if data_args.eval_beams is None:
              _lowerCAmelCase     :				Union[str, Any]       =							model.config.num_beams

       # set decoder_start_token_id for MBart
       if model.config.decoder_start_token_id is None and isinstance(_A ,    (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(_A ,    _A       ):
                     _lowerCAmelCase     :				Union[str, Any]       =							tokenizer.lang_code_to_id[data_args.tgt_lang]
              else:
                     _lowerCAmelCase     :				Union[str, Any]       =							tokenizer.convert_tokens_to_ids(data_args.tgt_lang       )

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

       _lowerCAmelCase     :				Any       =							SeqaSeqDataset

       # Get datasets
       _lowerCAmelCase     :				List[Any]       =							(
           dataset_class(
               _A ,    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
       )
       _lowerCAmelCase     :				Optional[Any]       =							(
           dataset_class(
               _A ,    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
       )
       _lowerCAmelCase     :				Union[str, Any]       =							(
           dataset_class(
               _A ,    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
       _lowerCAmelCase     :				Optional[Any]       =							(
           build_compute_metrics_fn(data_args.task ,    _A       ) if training_args.predict_with_generate else None
       )
       _lowerCAmelCase     :				int       =							SeqaSeqTrainer(
           model=_A ,    args=_A ,    data_args=_A ,    train_dataset=_A ,    eval_dataset=_A ,    data_collator=SeqaSeqDataCollator(
               _A ,    _A ,    model.config.decoder_start_token_id ,    training_args.tpu_num_cores       ) ,    compute_metrics=_A ,    tokenizer=_A ,    )

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

              _lowerCAmelCase     :				Dict       =							trainer.train(
                  model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
              _lowerCAmelCase     :				Any       =							train_result.metrics
              _lowerCAmelCase     :				str       =							data_args.n_train

              trainer.save_model()  # this also saves the tokenizer

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

                     # 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 ***'       )

              _lowerCAmelCase     :				List[str]       =							trainer.evaluate(metric_key_prefix='val'       )
              _lowerCAmelCase     :				int       =							data_args.n_val
              _lowerCAmelCase     :				List[str]       =							round(metrics['val_loss'] ,    4       )

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

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

              _lowerCAmelCase     :				Any       =							trainer.predict(test_dataset=_A ,    metric_key_prefix='test'       )
              _lowerCAmelCase     :				Optional[int]       =							test_output.metrics
              _lowerCAmelCase     :				Optional[int]       =							data_args.n_test

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

                     if training_args.predict_with_generate:
                            _lowerCAmelCase     :				Union[str, Any]       =							tokenizer.batch_decode(
                                test_output.predictions ,    skip_special_tokens=_A ,    clean_up_tokenization_spaces=_A       )
                            _lowerCAmelCase     :				List[Any]       =							lmap(str.strip ,    _A       )
                            write_txt_file(_A ,    os.path.join(training_args.output_dir ,    'test_generations.txt'       )       )

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

       return all_metrics






def       lowercase					(_A       ):







       """simple docstring"""







       main()


if __name__ == "__main__":
       main()

