
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__     :		Dict        = logging.getLogger(__name__)



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



@dataclass
class    _snake_case :
 __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_024	,    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=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.'
         )
     }	,    )
 __lowerCAmelCase	:  Optional[int]	   =      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``.'
         )
     }	,    )
 __lowerCAmelCase	:  Optional[int]	   =      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.'
         )
     }	,    )
 __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=UpperCAmelCase_	,    metadata={'help': 'Source language id for translation.'}				)
 __lowerCAmelCase	:  Optional[str]	   =      field(default=UpperCAmelCase_	,    metadata={'help': 'Target language id for translation.'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(default=UpperCAmelCase_	,    metadata={'help': '# num_beams to use for evaluation.'}				)
 __lowerCAmelCase	:  bool	   =      field(
     default=UpperCAmelCase_	,    metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}	,    )

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						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 		UpperCamelCase						(   )		->						List[Any]:


  '''simple docstring'''






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

  lowercase__	: str     		=   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__	: Tuple     		=   ("""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_ ) )

  lowercase__	: Tuple     		=   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=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:
    lowercase__	: Tuple     		=   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_ ):
      lowercase__	: Union[str, Any]     		=   tokenizer.lang_code_to_id[data_args.tgt_lang]
    else:
      lowercase__	: Tuple     		=   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() )

  lowercase__	: Tuple     		=   SeqaSeqDataset

  # Get datasets
  lowercase__	: Dict     		=   (
      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
  )
  lowercase__	: Union[str, Any]     		=   (
      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
  )
  lowercase__	: Union[str, Any]     		=   (
      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
  lowercase__	: List[Any]     		=   (
      build_compute_metrics_fn(data_args.task			,			lowercase_ ) if training_args.predict_with_generate else None
  )
  lowercase__	: Optional[int]     		=   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_			,			)

  lowercase__	: Any     		=   {}
  # Training
  if training_args.do_train:
    logger.info("""*** Train ***""" )

    lowercase__	: Optional[Any]     		=   trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
    lowercase__	: Optional[Any]     		=   train_result.metrics
    lowercase__	: str     		=   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 ***""" )

    lowercase__	: Any     		=   trainer.evaluate(metric_key_prefix="""val""" )
    lowercase__	: int     		=   data_args.n_val
    lowercase__	: Any     		=   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 ***""" )

    lowercase__	: Optional[Any]     		=   trainer.predict(test_dataset=lowercase_			,			metric_key_prefix="""test""" )
    lowercase__	: List[Any]     		=   test_output.metrics
    lowercase__	: Any     		=   data_args.n_test

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

      if training_args.predict_with_generate:
        lowercase__	: Dict     		=   tokenizer.batch_decode(
            test_output.predictions			,			skip_special_tokens=lowercase_			,			clean_up_tokenization_spaces=lowercase_ )
        lowercase__	: Optional[int]     		=   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 		UpperCamelCase						(   lowercase_ )		->						Optional[int]:


  '''simple docstring'''






  main()


if __name__ == "__main__":
     main()








from typing import List

import datasets
from datasets.tasks import AudioClassification

from ..folder_based_builder import folder_based_builder


lowerCamelCase__     :		Any        = datasets.utils.logging.get_logger(__name__)



class    _snake_case (						folder_based_builder.FolderBasedBuilderConfig				):
 __lowerCAmelCase	:  bool	   =      None
 __lowerCAmelCase	:  bool	   =      None



class    _snake_case (						folder_based_builder.FolderBasedBuilder				):
 __lowerCAmelCase	:  Optional[Any]	   =      datasets.Audio()
 __lowerCAmelCase	:  Union[str, Any]	   =      'audio'
 __lowerCAmelCase	:  str	   =      AudioFolderConfig
 __lowerCAmelCase	:  List[str]  # definition at the bottom of the script
 __lowerCAmelCase	:  Optional[int]	   =      AudioClassification(audio_column='audio'	,    label_column='label'				)

lowerCamelCase__     :		int        = [
    """.aiff""",
    """.au""",
    """.avr""",
    """.caf""",
    """.flac""",
    """.htk""",
    """.svx""",
    """.mat4""",
    """.mat5""",
    """.mpc2k""",
    """.ogg""",
    """.paf""",
    """.pvf""",
    """.raw""",
    """.rf64""",
    """.sd2""",
    """.sds""",
    """.ircam""",
    """.voc""",
    """.w64""",
    """.wav""",
    """.nist""",
    """.wavex""",
    """.wve""",
    """.xi""",
    """.mp3""",
    """.opus""",
]
lowerCamelCase__     :		int        = AUDIO_EXTENSIONS






