





from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


_snake_case           =			logging.get_logger(__name__)





class  lowerCAmelCase							(       __SCREAMING_SNAKE_CASE ):







  def __init__(      self  :List[str]   ,							_lowercase  :int   ,							_lowercase  :int   ,							_lowercase  :float   ,							**_lowercase  :Any ):






     '''simple docstring'''





     lowercase__			       =  feature_size
     lowercase__			       =  sampling_rate
     lowercase__			       =  padding_value

     lowercase__			       =  kwargs.pop("padding_side"   ,							"right" )
     lowercase__			       =  kwargs.pop("return_attention_mask"   ,							UpperCamelCase__ )

     super().__init__(**UpperCamelCase__ )







  def   UpperCAmelCase					(      self  :Any   ,							_lowercase  :Union[
          BatchFeature,
          List[BatchFeature],
          Dict[str, BatchFeature],
          Dict[str, List[BatchFeature]],
          List[Dict[str, BatchFeature]],
      ]   ,							_lowercase  :Union[bool, str, PaddingStrategy] = True   ,							_lowercase  :Optional[int] = None   ,							_lowercase  :bool = False   ,							_lowercase  :Optional[int] = None   ,							_lowercase  :Optional[bool] = None   ,							_lowercase  :Optional[Union[str, TensorType]] = None   ,							):






     '''simple docstring'''





     if isinstance(UpperCamelCase__   ,							(list, tuple) ) and isinstance(processed_features[0]   ,							(dict, BatchFeature) ):
        lowercase__			       =  {
            key: [example[key] for example in processed_features] for key in processed_features[0].keys()
        }

     # The model's main input name, usually `input_values`, has be passed for padding
     if self.model_input_names[0] not in processed_features:
        raise ValueError(
            "You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
            f''' to this method that includes {self.model_input_names[0]}, but you provided'''
            f''' {list(processed_features.keys() )}''' )

     lowercase__			       =  processed_features[self.model_input_names[0]]
     lowercase__			       =  (
         return_attention_mask if return_attention_mask is not None else self.return_attention_mask
     )

     if len(UpperCamelCase__ ) == 0:
        if return_attention_mask:
           lowercase__			       =  []
        return processed_features

     # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
     # and rebuild them afterwards if no return_tensors is specified
     # Note that we lose the specific device the tensor may be on for PyTorch

     lowercase__			       =  required_input[0]
     if isinstance(UpperCamelCase__   ,							(list, tuple) ):
        # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
        lowercase__			       =  0
        while len(required_input[index] ) == 0:
           index += 1
        if index < len(UpperCamelCase__ ):
           lowercase__			       =  required_input[index][0]

     if return_tensors is None:
        if is_tf_tensor(UpperCamelCase__ ):
           lowercase__			       =  '''tf'''
        elif is_torch_tensor(UpperCamelCase__ ):
           lowercase__			       =  '''pt'''
        elif isinstance(UpperCamelCase__   ,							(int, float, list, tuple, np.ndarray) ):
           lowercase__			       =  '''np'''
        else:
           raise ValueError(
               f'''type of {first_element} unknown: {type(UpperCamelCase__ )}. '''
               "Should be one of a python, numpy, pytorch or tensorflow object." )

     for key, value in processed_features.items():
        if isinstance(value[0]   ,							(int, float) ):
           lowercase__			       =  to_numpy(UpperCamelCase__ )
        else:
           lowercase__			       =  [to_numpy(UpperCamelCase__ ) for v in value]

        # Convert padding_strategy in PaddingStrategy
     lowercase__			       =  self._get_padding_strategies(padding=UpperCamelCase__   ,							max_length=UpperCamelCase__ )

     lowercase__			       =  processed_features[self.model_input_names[0]]

     lowercase__			       =  len(UpperCamelCase__ )
     if not all(len(UpperCamelCase__ ) == batch_size for v in processed_features.values() ):
        raise ValueError("Some items in the output dictionary have a different batch size than others." )

     lowercase__			       =  []
     for i in range(UpperCamelCase__ ):
        lowercase__			       =  {k: v[i] for k, v in processed_features.items()}
        # truncation
        lowercase__			       =  self._truncate(
            UpperCamelCase__   ,							max_length=UpperCamelCase__   ,							pad_to_multiple_of=UpperCamelCase__   ,							truncation=UpperCamelCase__   ,							)
        truncated_inputs.append(UpperCamelCase__ )

     if padding_strategy == PaddingStrategy.LONGEST:
        # make sure that `max_length` cannot be longer than the longest truncated length
        lowercase__			       =  max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
        lowercase__			       =  PaddingStrategy.MAX_LENGTH

     lowercase__			       =  {}
     for i in range(UpperCamelCase__ ):
        # padding
        lowercase__			       =  self._pad(
            truncated_inputs[i]   ,							max_length=UpperCamelCase__   ,							padding_strategy=UpperCamelCase__   ,							pad_to_multiple_of=UpperCamelCase__   ,							return_attention_mask=UpperCamelCase__   ,							)

        for key, value in outputs.items():
           if key not in batch_outputs:
              lowercase__			       =  []
           if value.dtype is np.dtype(np.floataa ):
              lowercase__			       =  value.astype(np.floataa )
           batch_outputs[key].append(UpperCamelCase__ )

     return BatchFeature(UpperCamelCase__   ,							tensor_type=UpperCamelCase__ )







  def   UpperCAmelCase					(      self  :str   ,							_lowercase  :Union[Dict[str, np.ndarray], BatchFeature]   ,							_lowercase  :Optional[int] = None   ,							_lowercase  :PaddingStrategy = PaddingStrategy.DO_NOT_PAD   ,							_lowercase  :Optional[int] = None   ,							_lowercase  :Optional[bool] = None   ,							):






     '''simple docstring'''





     lowercase__			       =  processed_features[self.model_input_names[0]]

     if padding_strategy == PaddingStrategy.LONGEST:
        lowercase__			       =  len(UpperCamelCase__ )

     if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
        lowercase__			       =  ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

     lowercase__			       =  padding_strategy != PaddingStrategy.DO_NOT_PAD and len(UpperCamelCase__ ) < max_length

     if return_attention_mask and "attention_mask" not in processed_features:
        lowercase__			       =  np.ones(len(UpperCamelCase__ )   ,							dtype=np.intaa )

     if needs_to_be_padded:
        lowercase__			       =  max_length - len(UpperCamelCase__ )
        if self.padding_side == "right":
           if return_attention_mask:
              lowercase__			       =  np.pad(
                  processed_features["attention_mask"]   ,							(0, difference) )
           lowercase__			       =  ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
           lowercase__			       =  np.pad(
               UpperCamelCase__   ,							UpperCamelCase__   ,							"constant"   ,							constant_values=self.padding_value )
        elif self.padding_side == "left":
           if return_attention_mask:
              lowercase__			       =  np.pad(
                  processed_features["attention_mask"]   ,							(difference, 0) )
           lowercase__			       =  ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
           lowercase__			       =  np.pad(
               UpperCamelCase__   ,							UpperCamelCase__   ,							"constant"   ,							constant_values=self.padding_value )
        else:
           raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )

     return processed_features







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :Union[Dict[str, np.ndarray], BatchFeature]   ,							_lowercase  :Optional[int] = None   ,							_lowercase  :Optional[int] = None   ,							_lowercase  :Optional[bool] = None   ,							):






     '''simple docstring'''





     if not truncation:
        return processed_features
     elif truncation and max_length is None:
        raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined." )

     lowercase__			       =  processed_features[self.model_input_names[0]]

     # find `max_length` that fits `pad_to_multiple_of`
     if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
        lowercase__			       =  ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

     lowercase__			       =  len(UpperCamelCase__ ) > max_length

     if needs_to_be_truncated:
        lowercase__			       =  processed_features[self.model_input_names[0]][:max_length]
        if "attention_mask" in processed_features:
           lowercase__			       =  processed_features['''attention_mask'''][:max_length]

     return processed_features







  def   UpperCAmelCase					(      self  :Any   ,							_lowercase  :Tuple=False   ,							_lowercase  :Optional[Any]=None ):






     '''simple docstring'''





     if padding is not False:
        if padding is True:
           lowercase__			       =  PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
        elif not isinstance(UpperCamelCase__   ,							UpperCamelCase__ ):
           lowercase__			       =  PaddingStrategy(UpperCamelCase__ )
        elif isinstance(UpperCamelCase__   ,							UpperCamelCase__ ):
           lowercase__			       =  padding
     else:
        lowercase__			       =  PaddingStrategy.DO_NOT_PAD

     # Set max length if needed
     if max_length is None:
        if padding_strategy == PaddingStrategy.MAX_LENGTH:
           raise ValueError(
               f'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined''' )

        # Test if we have a padding value
     if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
        raise ValueError(
            "Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
            " as `padding_value`. For example: `feature_extractor.padding_value = 0.0`." )

     return padding_strategy













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,
)


_snake_case           =			logging.getLogger(__name__)





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





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


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


def _A       (   ):
   # 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.

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

   check_output_dir(__magic_name__	)

   # 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"  , __magic_name__	)

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

   lowercase__			       =  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__			       =  AutoModelForSeqaSeqLM.from_pretrained(
       model_args.model_name_or_path  , from_tf=".ckpt" in model_args.model_name_or_path  , config=__magic_name__  , cache_dir=model_args.cache_dir  , )

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

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

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

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

   lowercase__			       =  SeqaSeqDataset

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

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

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

      trainer.save_model()  # this also saves the tokenizer

      if trainer.is_world_process_zero():
         handle_metrics("train"  , __magic_name__  , training_args.output_dir	)
         all_metrics.update(__magic_name__	)

         # 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__			       =  trainer.evaluate(metric_key_prefix="val"	)
      lowercase__			       =  data_args.n_val
      lowercase__			       =  round(metrics["val_loss"]  , 4	)

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

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

      lowercase__			       =  trainer.predict(test_dataset=__magic_name__  , metric_key_prefix="test"	)
      lowercase__			       =  test_output.metrics
      lowercase__			       =  data_args.n_test

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

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

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

   return all_metrics


def _A       (   __magic_name__	):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
  main()






