






"""simple docstring"""







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
lowerCAmelCase_						:     List[Any]							  =   logging.get_logger(__name__)

lowerCAmelCase_						:     str							  =   {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
lowerCAmelCase_						:     List[str]							  =   [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]






def    _lowerCAmelCase							(   lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase						     =      {}
       with open(A__						,   """r"""		) as file:
              for line_number, line in enumerate(A__		):
                     UpperCAmelCase						     =      line.strip()
                     if line:
                            UpperCAmelCase						     =      line.split()
                            UpperCAmelCase						     =      line_number
                            UpperCAmelCase						     =      words[0]
                            UpperCAmelCase						     =      value
       return result






def    _lowerCAmelCase							(   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase		):







       '''simple docstring'''





       for attribute in key.split("""."""		):
              UpperCAmelCase						     =      getattr(A__						,   A__		)

       UpperCAmelCase						     =      None
       for param_key in PARAM_MAPPING.keys():
              if full_name.endswith(A__		):
                     UpperCAmelCase						     =      PARAM_MAPPING[full_name.split("""."""		)[-1]]
                     UpperCAmelCase						     =      """param"""

       if weight_type is not None and weight_type != "param":
              UpperCAmelCase						     =      getattr(A__						,   A__		).shape
       elif weight_type is not None and weight_type == "param":
              UpperCAmelCase						     =      hf_pointer
              for attribute in hf_param_name.split("""."""		):
                     UpperCAmelCase						     =      getattr(A__						,   A__		)
              UpperCAmelCase						     =      shape_pointer.shape

              # let's reduce dimension
              UpperCAmelCase						     =      value[0]
       else:
              UpperCAmelCase						     =      hf_pointer.shape

       if hf_shape != value.shape:
              raise ValueError(
                  F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
                  F''' {value.shape} for {full_name}'''		)

       if weight_type == "weight":
              UpperCAmelCase						     =      value
       elif weight_type == "weight_g":
              UpperCAmelCase						     =      value
       elif weight_type == "weight_v":
              UpperCAmelCase						     =      value
       elif weight_type == "bias":
              UpperCAmelCase						     =      value
       elif weight_type == "param":
              for attribute in hf_param_name.split("""."""		):
                     UpperCAmelCase						     =      getattr(A__						,   A__		)
              UpperCAmelCase						     =      value
       else:
              UpperCAmelCase						     =      value

       logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''		)






def    _lowerCAmelCase							(   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase						     =      None
       for param_key in PARAM_MAPPING.keys():
              if full_name.endswith(A__		):
                     UpperCAmelCase						     =      PARAM_MAPPING[full_name.split("""."""		)[-1]]
                     UpperCAmelCase						     =      """param"""

       if weight_type is not None and weight_type != "param":
              UpperCAmelCase						     =      """.""".join([key, weight_type]		)
       elif weight_type is not None and weight_type == "param":
              UpperCAmelCase						     =      """.""".join([key, hf_param_name]		)
       else:
              UpperCAmelCase						     =      key

       UpperCAmelCase						     =      value if """lm_head""" in full_key else value[0]


lowerCAmelCase_						:     Optional[int]							  =   {
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}






def    _lowerCAmelCase							(   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase=None						,   lowerCAmelCase=None		):







       '''simple docstring'''
       UpperCAmelCase						     =      False
       for key, mapped_key in MAPPING.items():
              UpperCAmelCase						     =      """wav2vec2.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
              if key in name or key.split("""w2v_model."""		)[-1] == name.split("""."""		)[0]:
                     UpperCAmelCase						     =      True
                     if "*" in mapped_key:
                            UpperCAmelCase						     =      name.split(A__		)[0].split("""."""		)[-2]
                            UpperCAmelCase						     =      mapped_key.replace("""*"""						,   A__		)
                     if "weight_g" in name:
                            UpperCAmelCase						     =      """weight_g"""
                     elif "weight_v" in name:
                            UpperCAmelCase						     =      """weight_v"""
                     elif "bias" in name:
                            UpperCAmelCase						     =      """bias"""
                     elif "weight" in name:
                            # TODO: don't match quantizer.weight_proj
                            UpperCAmelCase						     =      """weight"""
                     else:
                            UpperCAmelCase						     =      None
                     if hf_dict is not None:
                            rename_dict(A__						,   A__						,   A__						,   A__						,   A__		)
                     else:
                            set_recursively(A__						,   A__						,   A__						,   A__						,   A__		)
                     return is_used
       return is_used






def    _lowerCAmelCase							(   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase						     =      []
       UpperCAmelCase						     =      fairseq_model.state_dict()

       UpperCAmelCase						     =      hf_model.wavaveca.feature_extractor

       for name, value in fairseq_dict.items():
              UpperCAmelCase						     =      False
              if "conv_layers" in name:
                     load_conv_layer(
                         A__						,   A__						,   A__						,   A__						,   hf_model.config.feat_extract_norm == """group"""						,   )
                     UpperCAmelCase						     =      True
              else:
                     UpperCAmelCase						     =      load_wavaveca_layer(A__						,   A__						,   A__		)
              if not is_used:
                     unused_weights.append(A__		)

       logger.warning(F'''Unused weights: {unused_weights}'''		)






def    _lowerCAmelCase							(   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase						     =      full_name.split("""conv_layers."""		)[-1]
       UpperCAmelCase						     =      name.split("""."""		)
       UpperCAmelCase						     =      int(items[0]		)
       UpperCAmelCase						     =      int(items[1]		)

       if type_id == 0:
              if "bias" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                            raise ValueError(
                                F'''{full_name} has size {value.shape}, but'''
                                F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''		)
                     UpperCAmelCase						     =      value
                     logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''		)
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                            raise ValueError(
                                F'''{full_name} has size {value.shape}, but'''
                                F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''		)
                     UpperCAmelCase						     =      value
                     logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''		)
       elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
              if "bias" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                            raise ValueError(
                                F'''{full_name} has size {value.shape}, but'''
                                F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''		)
                     UpperCAmelCase						     =      value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                            raise ValueError(
                                F'''{full_name} has size {value.shape}, but'''
                                F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''		)
                     UpperCAmelCase						     =      value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
       else:
              unused_weights.append(A__		)






@torch.no_grad()
def    _lowerCAmelCase							(   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase=None						,   lowerCAmelCase=None						,   lowerCAmelCase=True						,   lowerCAmelCase=False		):







       '''simple docstring'''





       if config_path is not None:
              UpperCAmelCase						     =      WavaVecaConfig.from_pretrained(A__		)
       else:
              UpperCAmelCase						     =      WavaVecaConfig()

       if is_seq_class:
              UpperCAmelCase						     =      read_txt_into_dict(A__		)
              UpperCAmelCase						     =      idalabel
              UpperCAmelCase						     =      WavaVecaForSequenceClassification(A__		)
              UpperCAmelCase						     =      WavaVecaFeatureExtractor(
                  feature_size=1						,   sampling_rate=16000						,   padding_value=0						,   do_normalize=A__						,   return_attention_mask=A__						,   )
              feature_extractor.save_pretrained(A__		)

       elif is_finetuned:
              if dict_path:
                     UpperCAmelCase						     =      Dictionary.load(A__		)

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     UpperCAmelCase						     =      target_dict.pad_index
                     UpperCAmelCase						     =      target_dict.bos_index
                     UpperCAmelCase						     =      target_dict.eos_index
                     UpperCAmelCase						     =      len(target_dict.symbols		)
                     UpperCAmelCase						     =      os.path.join(A__						,   """vocab.json"""		)
                     if not os.path.isdir(A__		):
                            logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(A__		)		)
                            return
                     os.makedirs(A__						,   exist_ok=A__		)
                     UpperCAmelCase						     =      target_dict.indices

                     # fairseq has the <pad> and <s> switched
                     UpperCAmelCase						     =      0
                     UpperCAmelCase						     =      1
                     with open(A__						,   """w"""						,   encoding="""utf-8"""		) as vocab_handle:
                            json.dump(A__						,   A__		)
                     UpperCAmelCase						     =      WavaVecaCTCTokenizer(
                         A__						,   unk_token=target_dict.unk_word						,   pad_token=target_dict.pad_word						,   bos_token=target_dict.bos_word						,   eos_token=target_dict.eos_word						,   word_delimiter_token="""|"""						,   do_lower_case=A__						,   )
                     UpperCAmelCase						     =      True if config.feat_extract_norm == """layer""" else False
                     UpperCAmelCase						     =      WavaVecaFeatureExtractor(
                         feature_size=1						,   sampling_rate=16000						,   padding_value=0						,   do_normalize=A__						,   return_attention_mask=A__						,   )
                     UpperCAmelCase						     =      WavaVecaProcessor(feature_extractor=A__						,   tokenizer=A__		)
                     processor.save_pretrained(A__		)

              UpperCAmelCase						     =      WavaVecaForCTC(A__		)
       else:
              UpperCAmelCase						     =      WavaVecaForPreTraining(A__		)

       if is_finetuned or is_seq_class:
              UpperCAmelCase						     =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]						,   arg_overrides={"""data""": """/""".join(dict_path.split("""/"""		)[:-1]		)}		)
       else:
              UpperCAmelCase						     =      argparse.Namespace(task="""audio_pretraining"""		)
              UpperCAmelCase						     =      fairseq.tasks.setup_task(A__		)

              UpperCAmelCase						     =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						,   task=A__		)

       UpperCAmelCase						     =      model[0].eval()

       recursively_load_weights(A__						,   A__						,   not is_finetuned		)

       hf_wavavec.save_pretrained(A__		)


if __name__ == "__main__":
 lowerCAmelCase_						:     str							  =   argparse.ArgumentParser()
 parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
 parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
 parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
 parser.add_argument(
     '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
 )
 parser.add_argument(
     '''--is_seq_class''',
     action='''store_true''',
     help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
 )
 lowerCAmelCase_						:     List[str]							  =   parser.parse_args()

 lowerCAmelCase_						:     Optional[int]							  =   not args.not_finetuned and not args.is_seq_class
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.dict_path,
     is_finetuned,
     args.is_seq_class,
 )




'''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,
)


__A   :    str											= logging.getLogger(__name__)






@dataclass
class   __snake_case			:







    """simple docstring"""



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







@dataclass
class   __snake_case			:







    """simple docstring"""



    lowercase  					=   field(
        metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'})
    lowercase  					=   field(
        default='summarization'      ,metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}      ,)
    lowercase  					=   field(
        default=10_24      ,metadata={
            'help': (
                'The maximum total input sequence length after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }      ,)
    lowercase  					=   field(
        default=1_28      ,metadata={
            'help': (
                'The maximum total sequence length for target text after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }      ,)
    lowercase  					=   field(
        default=1_42      ,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``.'
            )
        }      ,)
    lowercase  					=   field(
        default=1_42      ,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.'
            )
        }      ,)
    lowercase  					=   field(default=-1      ,metadata={'help': '# training examples. -1 means use all.'})
    lowercase  					=   field(default=-1      ,metadata={'help': '# validation examples. -1 means use all.'})
    lowercase  					=   field(default=-1      ,metadata={'help': '# test examples. -1 means use all.'})
    lowercase  					=   field(default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Source language id for translation.'})
    lowercase  					=   field(default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Target language id for translation.'})
    lowercase  					=   field(default=_SCREAMING_SNAKE_CASE      ,metadata={'help': '# num_beams to use for evaluation.'})
    lowercase  					=   field(
        default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}      ,)







def 							UpperCamelCase_     (       A__   :  Tuple				,       A__   :  List[str]				,       A__   :  str   ):
  '''simple docstring'''





  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_     (       ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  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.
    lowerCAmelCase_,				lowerCAmelCase_,				lowerCAmelCase_     : Optional[int]     =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
  else:
    lowerCAmelCase_,				lowerCAmelCase_,				lowerCAmelCase_     : Union[str, Any]     =  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.

  lowerCAmelCase_     : List[Any]     =  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				,       )

  lowerCAmelCase_     : List[Any]     =  ("""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__   )   )

  lowerCAmelCase_     : 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				,       )
  lowerCAmelCase_     : Union[str, Any]     =  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:
    lowerCAmelCase_     : List[Any]     =  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__   ):
      lowerCAmelCase_     : List[str]     =  tokenizer.lang_code_to_id[data_args.tgt_lang]
    else:
      lowerCAmelCase_     : int     =  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()   )

  lowerCAmelCase_     : Any     =  SeqaSeqDataset

  # Get datasets
  lowerCAmelCase_     : List[str]     =  (
      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
  )
  lowerCAmelCase_     : List[Any]     =  (
      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
  )
  lowerCAmelCase_     : List[Any]     =  (
      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
  lowerCAmelCase_     : Dict     =  (
      build_compute_metrics_fn(data_args.task				,       A__   ) if training_args.predict_with_generate else None
  )
  lowerCAmelCase_     : str     =  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__				,       )

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

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

    lowerCAmelCase_     : Any     =  trainer.evaluate(metric_key_prefix="""val"""   )
    lowerCAmelCase_     : Optional[int]     =  data_args.n_val
    lowerCAmelCase_     : Any     =  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 ***"""   )

    lowerCAmelCase_     : Union[str, Any]     =  trainer.predict(test_dataset=A__				,       metric_key_prefix="""test"""   )
    lowerCAmelCase_     : Optional[int]     =  test_output.metrics
    lowerCAmelCase_     : List[Any]     =  data_args.n_test

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

      if training_args.predict_with_generate:
        lowerCAmelCase_     : int     =  tokenizer.batch_decode(
            test_output.predictions				,       skip_special_tokens=A__				,       clean_up_tokenization_spaces=A__   )
        lowerCAmelCase_     : List[Any]     =  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__   :  Optional[int]   ):
  '''simple docstring'''





  main()


if __name__ == "__main__":
  main()



