





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




@dataclass
class lowerCamelCase__	:
   __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=1024	,					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=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."""
           )
       }	,					)
   __UpperCAmelCase										=       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``."""
           )
       }	,					)
   __UpperCAmelCase										=       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."""
           )
       }	,					)
   __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=UpperCamelCase_	,					metadata={"""help""": """Source language id for translation."""}					)
   __UpperCAmelCase										=       field(default=UpperCamelCase_	,					metadata={"""help""": """Target language id for translation."""}					)
   __UpperCAmelCase										=       field(default=UpperCamelCase_	,					metadata={"""help""": """# num_beams to use for evaluation."""}					)
   __UpperCAmelCase										=       field(
       default=UpperCamelCase_	,					metadata={"""help""": """If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."""}	,					)




def       __snake_case    (   __A			,__A			,__A     )		->					Optional[int]:
   logger.info(F'''***** {split} metrics *****'''     )
   for key in sorted(metrics.keys()     ):
      logger.info(F'''  {key} = {metrics[key]}'''     )
   save_json(SCREAMING_SNAKE_CASE__			,os.path.join(SCREAMING_SNAKE_CASE__			,F'''{split}_results.json'''     )     )




def       __snake_case    (   )		->					Dict:
   lowercase     :      Tuple	=							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     :      Any	=							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
   else:
      lowercase     :      List[Any]	=							parser.parse_args_into_dataclasses()

   check_output_dir(SCREAMING_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"""			,SCREAMING_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     :      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			,)

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

   lowercase     :      Union[str, 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			,)
   lowercase     :      str	=							AutoModelForSeqaSeqLM.from_pretrained(
       model_args.model_name_or_path			,from_tf=""".ckpt""" in model_args.model_name_or_path			,config=SCREAMING_SNAKE_CASE__			,cache_dir=model_args.cache_dir			,)

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

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

   # set decoder_start_token_id for MBart
   if model.config.decoder_start_token_id is None and isinstance(SCREAMING_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(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__     ):
         lowercase     :      Tuple	=							tokenizer.lang_code_to_id[data_args.tgt_lang]
      else:
         lowercase     :      Dict	=							tokenizer.convert_tokens_to_ids(data_args.tgt_lang     )

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

   lowercase     :      List[str]	=							SeqaSeqDataset

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

   lowercase     :      List[Any]	=							{}
   # Training
   if training_args.do_train:
      logger.info("""*** Train ***"""     )

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

      trainer.save_model()  # this also saves the tokenizer

      if trainer.is_world_process_zero():
         handle_metrics("""train"""			,SCREAMING_SNAKE_CASE__			,training_args.output_dir     )
         all_metrics.update(SCREAMING_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     :      List[Any]	=							trainer.evaluate(metric_key_prefix="""val"""     )
      lowercase     :      int	=							data_args.n_val
      lowercase     :      Union[str, Any]	=							round(metrics["""val_loss"""]			,4     )

      if trainer.is_world_process_zero():
         handle_metrics("""val"""			,SCREAMING_SNAKE_CASE__			,training_args.output_dir     )
         all_metrics.update(SCREAMING_SNAKE_CASE__     )

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

      lowercase     :      Tuple	=							trainer.predict(test_dataset=SCREAMING_SNAKE_CASE__			,metric_key_prefix="""test"""     )
      lowercase     :      Optional[Any]	=							test_output.metrics
      lowercase     :      int	=							data_args.n_test

      if trainer.is_world_process_zero():
         lowercase     :      List[Any]	=							round(metrics["""test_loss"""]			,4     )
         handle_metrics("""test"""			,SCREAMING_SNAKE_CASE__			,training_args.output_dir     )
         all_metrics.update(SCREAMING_SNAKE_CASE__     )

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

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

   return all_metrics




def       __snake_case    (   __A     )		->					Tuple:
   main()


if __name__ == "__main__":
   main()






from timeit import timeit

lowerCamelCase		   =			{
    'MALAYALAM': True,
    'String': False,
    'rotor': True,
    'level': True,
    'A': True,
    'BB': True,
    'ABC': False,
    'amanaplanacanalpanama': True,  # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())




def       a_ (       SCREAMING_SNAKE_CASE__   : str	):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =0
       _lowerCamelCase			:						Union[str, Any]            =len(SCREAMING_SNAKE_CASE__	) - 1
       while start_i < end_i:
              if s[start_i] == s[end_i]:
                     start_i += 1
                     end_i -= 1
              else:
                     return False
       return True




def       a_ (       SCREAMING_SNAKE_CASE__   : str	):
       '''simple docstring'''





       _lowerCamelCase			:						List[str]            =len(SCREAMING_SNAKE_CASE__	) // 2
       _lowerCamelCase			:						Optional[Any]            =len(SCREAMING_SNAKE_CASE__	)

       # We need to traverse till half of the length of string
       # as we can get access of the i'th last element from
       # i'th index.
       # eg: [0,1,2,3,4,5] => 4th index can be accessed
       # with the help of 1st index (i==n-i-1)
       # where n is length of string
       return all(s[i] == s[n - i - 1] for i in range(SCREAMING_SNAKE_CASE__	)	)




def       a_ (       SCREAMING_SNAKE_CASE__   : str	):
       '''simple docstring'''





       if len(SCREAMING_SNAKE_CASE__	) <= 2:
              return True
       if s[0] == s[len(SCREAMING_SNAKE_CASE__	) - 1]:
              return is_palindrome_recursive(s[1:-1]	)
       else:
              return False




def       a_ (       SCREAMING_SNAKE_CASE__   : str	):
       '''simple docstring'''





       return s == s[::-1]




def       a_ (       SCREAMING_SNAKE_CASE__   : str	):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =F'''all({name}(key) is value for key, value in test_data.items())'''
       _lowerCamelCase			:						List[Any]            =F'''from __main__ import test_data, {name}'''
       _lowerCamelCase			:						Any            =500_000
       _lowerCamelCase			:						Dict            =timeit(stmt=SCREAMING_SNAKE_CASE__						,     setup=SCREAMING_SNAKE_CASE__						,     number=SCREAMING_SNAKE_CASE__	)
       print(F'''{name:<35} finished {number:,} runs in {result:.5f} seconds'''	)


if __name__ == "__main__":
 for key, value in test_data.items():
  assert is_palindrome(key) is is_palindrome_recursive(key)
  assert is_palindrome(key) is is_palindrome_slice(key)
  print(F"""{key:21} {value}""")
 print('a man a plan a canal panama')

 # finished 500,000 runs in 0.46793 seconds
 benchmark_function('is_palindrome_slice')
 # finished 500,000 runs in 0.85234 seconds
 benchmark_function('is_palindrome')
 # finished 500,000 runs in 1.32028 seconds
 benchmark_function('is_palindrome_recursive')
 # finished 500,000 runs in 2.08679 seconds
 benchmark_function('is_palindrome_traversal')


