





'''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,
)


_lowercase       						=			logging.getLogger(__name__)
@dataclass
class 			_lowercase						:
     _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 			_lowercase						:
     _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=1_024				,		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=__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    __UpperCamelCase	(	a    : str					,   a    : Union[str, Any]					,   a    : Optional[Any]     ) ->Optional[Any]:
 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    __UpperCamelCase	(	) ->str:
 # 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.

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

 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					,   )

 snake_case				=			('''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     )     )

 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					,   )
 snake_case				=			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:
  snake_case				=			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     ):
   snake_case				=			tokenizer.lang_code_to_id[data_args.tgt_lang]
  else:
   snake_case				=			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()     )

 snake_case				=			SeqaSeqDataset

 # Get datasets
 snake_case				=			(
     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
 )
 snake_case				=			(
     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
 )
 snake_case				=			(
     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
 snake_case				=			(
     build_compute_metrics_fn(data_args.task					,   a     ) if training_args.predict_with_generate else None
 )
 snake_case				=			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					,   )

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

  snake_case				=			trainer.train(
      model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path     ) else None     )
  snake_case				=			train_result.metrics
  snake_case				=			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 ***'''     )

  snake_case				=			trainer.evaluate(metric_key_prefix='''val'''     )
  snake_case				=			data_args.n_val
  snake_case				=			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 ***'''     )

  snake_case				=			trainer.predict(test_dataset=a					,   metric_key_prefix='''test'''     )
  snake_case				=			test_output.metrics
  snake_case				=			data_args.n_test

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

   if training_args.predict_with_generate:
    snake_case				=			tokenizer.batch_decode(
        test_output.predictions					,   skip_special_tokens=a					,   clean_up_tokenization_spaces=a     )
    snake_case				=			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    __UpperCamelCase	(	a    : List[str]     ) ->List[str]:
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
   main()








'''simple docstring'''







import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text

def    __UpperCamelCase	(	a    : List[str]     ) ->str:
 snake_case				=			[]
 for line in lines:
  snake_case				=			re.sub(R'''#.*'''					,   ''''''					,   a     )  # remove comments
  if line:
   filtered_lines.append(a     )
 snake_case				=			'''\n'''.join(a     )

 # Make a hash from all this code
 snake_case				=			full_str.encode('''utf-8'''     )
 return shaaaa(a     ).hexdigest()


# get importable module names and hash for caching
_lowercase       						=			{
    'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
_lowercase       						=			{
    '.csv': ('csv', {}),
    '.tsv': ('csv', {'sep': '\t'}),
    '.json': ('json', {}),
    '.jsonl': ('json', {}),
    '.parquet': ('parquet', {}),
    '.arrow': ('arrow', {}),
    '.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_lowercase       						=			{'imagefolder', 'audiofolder'}

# Used to filter data files based on extensions given a module name
_lowercase       						=			{}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
   _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')






_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')

