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,
)


UpperCAmelCase_			    =							logging.getLogger(__name__)




@dataclass
class  __magic_name__  :





   """simple docstring"""




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




@dataclass
class  __magic_name__  :





   """simple docstring"""




   lowerCAmelCase      :    str  			=   field(
       metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}    )
   lowerCAmelCase      :    Optional[str]  			=   field(
       default='''summarization'''      ,	metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''}      ,	)
   lowerCAmelCase      :    Optional[int]  			=   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.'''
           )
       }      ,	)
   lowerCAmelCase      :    Optional[int]  			=   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.'''
           )
       }      ,	)
   lowerCAmelCase      :    Optional[int]  			=   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``.'''
           )
       }      ,	)
   lowerCAmelCase      :    Optional[int]  			=   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.'''
           )
       }      ,	)
   lowerCAmelCase      :    Optional[int]  			=   field(default=-1      ,	metadata={'''help''': '''# training examples. -1 means use all.'''}    )
   lowerCAmelCase      :    Optional[int]  			=   field(default=-1      ,	metadata={'''help''': '''# validation examples. -1 means use all.'''}    )
   lowerCAmelCase      :    Optional[int]  			=   field(default=-1      ,	metadata={'''help''': '''# test examples. -1 means use all.'''}    )
   lowerCAmelCase      :    Optional[str]  			=   field(default=__a      ,	metadata={'''help''': '''Source language id for translation.'''}    )
   lowerCAmelCase      :    Optional[str]  			=   field(default=__a      ,	metadata={'''help''': '''Target language id for translation.'''}    )
   lowerCAmelCase      :    Optional[int]  			=   field(default=__a      ,	metadata={'''help''': '''# num_beams to use for evaluation.'''}    )
   lowerCAmelCase      :    bool  			=   field(
       default=__a      ,	metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''}      ,	)




def       lowerCAmelCase_     (		lowercase:							Dict  ,					lowercase:							Tuple  ,					lowercase:							str		)    ->				int:







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




def       lowerCAmelCase_     (		)    ->				List[Any]:







 '''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.

 _UpperCamelCase:     Union[str, Any]							= 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.
  _UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase:     List[str]							= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
 else:
  _UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase:     Tuple							= parser.parse_args_into_dataclasses()

 check_output_dir(lowercase		)

 # 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'''  ,					lowercase		)

 # 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.

 _UpperCamelCase:     Dict							= 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  ,					)

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

 _UpperCamelCase:     Dict							= 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  ,					)
 _UpperCamelCase:     List[Any]							= AutoModelForSeqaSeqLM.from_pretrained(
     model_args.model_name_or_path  ,					from_tf='''.ckpt''' in model_args.model_name_or_path  ,					config=lowercase  ,					cache_dir=model_args.cache_dir  ,					)

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

 # set num_beams for evaluation
 if data_args.eval_beams is None:
  _UpperCamelCase:     List[Any]							= model.config.num_beams

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

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

 _UpperCamelCase:     int							= SeqaSeqDataset

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

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

  _UpperCamelCase:     Union[str, Any]							= trainer.train(
      model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path		) else None		)
  _UpperCamelCase:     int							= train_result.metrics
  _UpperCamelCase:     int							= data_args.n_train

  trainer.save_model()  # this also saves the tokenizer

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

   # 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 ***'''		)

  _UpperCamelCase:     Tuple							= trainer.evaluate(metric_key_prefix='''val'''		)
  _UpperCamelCase:     Union[str, Any]							= data_args.n_val
  _UpperCamelCase:     List[str]							= round(metrics['''val_loss''']  ,					4		)

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

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

  _UpperCamelCase:     int							= trainer.predict(test_dataset=lowercase  ,					metric_key_prefix='''test'''		)
  _UpperCamelCase:     Tuple							= test_output.metrics
  _UpperCamelCase:     str							= data_args.n_test

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

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

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

 return all_metrics




def       lowerCAmelCase_     (		lowercase:							Dict		)    ->				List[str]:







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


if __name__ == "__main__":
  main()
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool




class  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    Optional[Any]  			=   '''philschmid/bart-large-cnn-samsum'''
   lowerCAmelCase      :    Any  			=   (
       '''This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '''
       '''and returns a summary of the text.'''
   )
   lowerCAmelCase      :    Any  			=   '''summarizer'''
   lowerCAmelCase      :    Tuple  			=   AutoTokenizer
   lowerCAmelCase      :    Optional[Any]  			=   AutoModelForSeqaSeqLM

   lowerCAmelCase      :    Union[str, Any]  			=   ['''text''']
   lowerCAmelCase      :    Dict  			=   ['''text''']



   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  Union[str, Any]							):



    """simple docstring"""



    return self.pre_processor(_lowercase		,  return_tensors='''pt'''		,  truncation=_lowercase							)



   def   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  Optional[Any]							):



    """simple docstring"""



    return self.model.generate(**_lowercase							)[0]



   def   lowerCAmelCase						(	self					:  int		,  _lowercase					:  List[str]							):



    """simple docstring"""



    return self.pre_processor.decode(_lowercase		,  skip_special_tokens=_lowercase		,  clean_up_tokenization_spaces=_lowercase							)