

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_    :						Any    		=      logging.getLogger(__name__)




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




@dataclass
class      UpperCamelCase :
    __UpperCamelCase         =field(
        metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}   )
    __UpperCamelCase         =field(
        default="summarization"	,			metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"}	,			)
    __UpperCamelCase         =field(
        default=10_24	,			metadata={
            "help": (
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        }	,			)
    __UpperCamelCase         =field(
        default=1_28	,			metadata={
            "help": (
                "The maximum total sequence length for target text after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        }	,			)
    __UpperCamelCase         =field(
        default=1_42	,			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``."
            )
        }	,			)
    __UpperCamelCase         =field(
        default=1_42	,			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."
            )
        }	,			)
    __UpperCamelCase         =field(default=-1	,			metadata={"help": "# training examples. -1 means use all."}   )
    __UpperCamelCase         =field(default=-1	,			metadata={"help": "# validation examples. -1 means use all."}   )
    __UpperCamelCase         =field(default=-1	,			metadata={"help": "# test examples. -1 means use all."}   )
    __UpperCamelCase         =field(default=__a	,			metadata={"help": "Source language id for translation."}   )
    __UpperCamelCase         =field(default=__a	,			metadata={"help": "Target language id for translation."}   )
    __UpperCamelCase         =field(default=__a	,			metadata={"help": "# num_beams to use for evaluation."}   )
    __UpperCamelCase         =field(
        default=__a	,			metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}	,			)



def        __lowerCAmelCase    (       _UpperCamelCase		:  str		,							_UpperCamelCase		:  Union[str, Any]		,							_UpperCamelCase		:  Optional[Any]	)		->							Optional[Any]:
 '''simple docstring'''


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



def        __lowerCAmelCase    (       )		->							str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			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.
  SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
 else:
  SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			parser.parse_args_into_dataclasses()

 check_output_dir(_UpperCamelCase	)

 # 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'		,							_UpperCamelCase	)

 # 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.

 SCREAMING_SNAKE_CASE								=			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		,							)

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

 SCREAMING_SNAKE_CASE								=			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		,							)
 SCREAMING_SNAKE_CASE								=			AutoModelForSeqaSeqLM.from_pretrained(
     model_args.model_name_or_path		,							from_tf='.ckpt' in model_args.model_name_or_path		,							config=_UpperCamelCase		,							cache_dir=model_args.cache_dir		,							)

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

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

 # set decoder_start_token_id for MBart
 if model.config.decoder_start_token_id is None and isinstance(_UpperCamelCase		,							(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(_UpperCamelCase		,							_UpperCamelCase	):
   SCREAMING_SNAKE_CASE								=			tokenizer.lang_code_to_id[data_args.tgt_lang]
  else:
   SCREAMING_SNAKE_CASE								=			tokenizer.convert_tokens_to_ids(data_args.tgt_lang	)

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

 SCREAMING_SNAKE_CASE								=			SeqaSeqDataset

 # Get datasets
 SCREAMING_SNAKE_CASE								=			(
     dataset_class(
         _UpperCamelCase		,							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
 )
 SCREAMING_SNAKE_CASE								=			(
     dataset_class(
         _UpperCamelCase		,							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
 )
 SCREAMING_SNAKE_CASE								=			(
     dataset_class(
         _UpperCamelCase		,							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
 SCREAMING_SNAKE_CASE								=			(
     build_compute_metrics_fn(data_args.task		,							_UpperCamelCase	) if training_args.predict_with_generate else None
 )
 SCREAMING_SNAKE_CASE								=			SeqaSeqTrainer(
     model=_UpperCamelCase		,							args=_UpperCamelCase		,							data_args=_UpperCamelCase		,							train_dataset=_UpperCamelCase		,							eval_dataset=_UpperCamelCase		,							data_collator=SeqaSeqDataCollator(
         _UpperCamelCase		,							_UpperCamelCase		,							model.config.decoder_start_token_id		,							training_args.tpu_num_cores	)		,							compute_metrics=_UpperCamelCase		,							tokenizer=_UpperCamelCase		,							)

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

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

  trainer.save_model()  # this also saves the tokenizer

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

   # 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 ***'	)

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

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

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

  SCREAMING_SNAKE_CASE								=			trainer.predict(test_dataset=_UpperCamelCase		,							metric_key_prefix='test'	)
  SCREAMING_SNAKE_CASE								=			test_output.metrics
  SCREAMING_SNAKE_CASE								=			data_args.n_test

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

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

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

 return all_metrics



def        __lowerCAmelCase    (       _UpperCamelCase		:  List[str]	)		->							List[str]:
 '''simple docstring'''


 main()


if __name__ == "__main__":
      main()




import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


a_    :						Optional[int]    		=      logging.get_logger(__name__)


a_    :						Union[str, Any]    		=      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

a_    :						Any    		=      {
    "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",
    },
}

a_    :						List[Any]    		=      {
    "allenai/led-base-16384": 1_6384,
}




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         =VOCAB_FILES_NAMES
    __UpperCamelCase         =PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase         =LEDTokenizer
    __UpperCamelCase         =["input_ids", "attention_mask"]

    def __init__(			self :      Tuple							,    snake_case__ :      List[Any]=None							,    snake_case__ :      List[str]=None							,    snake_case__ :      List[str]=None							,    snake_case__ :      Dict="replace"							,    snake_case__ :      Tuple="<s>"							,    snake_case__ :      Optional[Any]="</s>"							,    snake_case__ :      int="</s>"							,    snake_case__ :      Dict="<s>"							,    snake_case__ :      Union[str, Any]="<unk>"							,    snake_case__ :      Optional[int]="<pad>"							,    snake_case__ :      List[str]="<mask>"							,    snake_case__ :      List[Any]=False							,    snake_case__ :      int=True							,    **snake_case__ :      Dict							,    ):

     """simple docstring"""






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

     SCREAMING_SNAKE_CASE								=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
     if pre_tok_state.get('add_prefix_space'							,    snake_case__    ) != add_prefix_space:
      SCREAMING_SNAKE_CASE								=			getattr(snake_case__							,    pre_tok_state.pop('type'    )    )
      SCREAMING_SNAKE_CASE								=			add_prefix_space
      SCREAMING_SNAKE_CASE								=			pre_tok_class(**snake_case__    )

     SCREAMING_SNAKE_CASE								=			add_prefix_space

     # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
     SCREAMING_SNAKE_CASE								=			'post_processor'
     SCREAMING_SNAKE_CASE								=			getattr(self.backend_tokenizer							,    snake_case__							,    snake_case__    )
     if tokenizer_component_instance:
      SCREAMING_SNAKE_CASE								=			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:
       SCREAMING_SNAKE_CASE								=			tuple(state['sep']    )
      if "cls" in state:
       SCREAMING_SNAKE_CASE								=			tuple(state['cls']    )

      SCREAMING_SNAKE_CASE								=			False

      if state.get('add_prefix_space'							,    snake_case__    ) != add_prefix_space:
       SCREAMING_SNAKE_CASE								=			add_prefix_space
       SCREAMING_SNAKE_CASE								=			True

      if state.get('trim_offsets'							,    snake_case__    ) != trim_offsets:
       SCREAMING_SNAKE_CASE								=			trim_offsets
       SCREAMING_SNAKE_CASE								=			True

      if changes_to_apply:
       SCREAMING_SNAKE_CASE								=			getattr(snake_case__							,    state.pop('type'    )    )
       SCREAMING_SNAKE_CASE								=			component_class(**snake_case__    )
       setattr(self.backend_tokenizer							,    snake_case__							,    snake_case__    )

    @property
    # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     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 UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			AddedToken(snake_case__							,    lstrip=snake_case__							,    rstrip=snake_case__    ) if isinstance(snake_case__							,    snake_case__    ) else value
     SCREAMING_SNAKE_CASE								=			value

    def UpperCamelCase			(			self :      Dict							,    *snake_case__ :      Optional[Any]							,    **snake_case__ :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			kwargs.get('is_split_into_words'							,    snake_case__    )

     if is_split_into_words and not self.add_prefix_space:
      raise ValueError(
          F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
          'to use it with pretokenized inputs.'    )

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

    def UpperCamelCase			(			self :      List[str]							,    *snake_case__ :      List[Any]							,    **snake_case__ :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			kwargs.get('is_split_into_words'							,    snake_case__    )

     if is_split_into_words and not self.add_prefix_space:
      raise ValueError(
          F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
          'to use it with pretokenized inputs.'    )

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

    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      str							,    snake_case__ :      Optional[str] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self._tokenizer.model.save(snake_case__							,    name=snake_case__    )
     return tuple(snake_case__    )

    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      int							,    snake_case__ :      Tuple=None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
      return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]

    def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      List[int]							,    snake_case__ :      Optional[List[int]] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[self.sep_token_id]
     SCREAMING_SNAKE_CASE								=			[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]							,    snake_case__ :      Union[Dict[str, EncodedInput], BatchEncoding]							,    snake_case__ :      Optional[int] = None							,    snake_case__ :      PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,    snake_case__ :      Optional[int] = None							,    snake_case__ :      Optional[bool] = None							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			super()._pad(
         encoded_inputs=snake_case__							,    max_length=snake_case__							,    padding_strategy=snake_case__							,    pad_to_multiple_of=snake_case__							,    return_attention_mask=snake_case__							,    )

     # Load from model defaults
     if return_attention_mask is None:
      SCREAMING_SNAKE_CASE								=			'attention_mask' in self.model_input_names

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

      if needs_to_be_padded:
       SCREAMING_SNAKE_CASE								=			len(snake_case__    ) - 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								=			(
            encoded_inputs['global_attention_mask'] + [-1] * difference
        )
       elif self.padding_side == "left":
        SCREAMING_SNAKE_CASE								=			[-1] * difference + encoded_inputs[
            'global_attention_mask'
        ]
       else:
        raise ValueError('Invalid padding strategy:' + str(self.padding_side    )    )

     return encoded_inputs

