






'''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,
)


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







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







def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	List[str],					SCREAMING_SNAKE_CASE__:	Tuple,					SCREAMING_SNAKE_CASE__:	Union[str, Any]    )  ->			Union[str, Any]:

   """simple docstring"""
   logger.info(f"""***** {split} metrics *****"""    )
   for key in sorted(metrics.keys()    ):
      logger.info(f"""  {key} = {metrics[key]}"""    )
   save_json(UpperCAmelCase__,					os.path.join(UpperCAmelCase__,					f"""{split}_results.json"""    )    )



def    __UpperCAmelCase	(				)  ->			int:

   """simple docstring"""
   __a				     =    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.
      __a     ,			__a     ,			__a				     =    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
   else:
      __a     ,			__a     ,			__a				     =    parser.parse_args_into_dataclasses()

   check_output_dir(UpperCAmelCase__    )

   # 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',					UpperCAmelCase__    )

   # 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.

   __a				     =    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,					)

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

   __a				     =    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,					)
   __a				     =    AutoModelForSeqaSeqLM.from_pretrained(
       model_args.model_name_or_path,					from_tf='.ckpt' in model_args.model_name_or_path,					config=UpperCAmelCase__,					cache_dir=model_args.cache_dir,					)

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

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

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

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

   __a				     =    SeqaSeqDataset

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

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

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

      trainer.save_model()  # this also saves the tokenizer

      if trainer.is_world_process_zero():
         handle_metrics('train',					UpperCAmelCase__,					training_args.output_dir    )
         all_metrics.update(UpperCAmelCase__    )

         # 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 ***'    )

      __a				     =    trainer.evaluate(metric_key_prefix='val'    )
      __a				     =    data_args.n_val
      __a				     =    round(metrics['val_loss'],					4    )

      if trainer.is_world_process_zero():
         handle_metrics('val',					UpperCAmelCase__,					training_args.output_dir    )
         all_metrics.update(UpperCAmelCase__    )

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

      __a				     =    trainer.predict(test_dataset=UpperCAmelCase__,					metric_key_prefix='test'    )
      __a				     =    test_output.metrics
      __a				     =    data_args.n_test

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

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

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

   return all_metrics



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Union[str, Any]    )  ->			List[str]:

   """simple docstring"""
   main()


if __name__ == "__main__":
 main()







'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase      :       List[str]      			=      logging.get_logger(__name__)

__UpperCamelCase      :       Tuple      			=      {
    """sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="vit_msn"




      def __init__(  self ,   lowerCamelCase=768 ,   lowerCamelCase=12 ,   lowerCamelCase=12 ,   lowerCamelCase=3072 ,   lowerCamelCase="gelu" ,   lowerCamelCase=0.0 ,   lowerCamelCase=0.0 ,   lowerCamelCase=0.02 ,   lowerCamelCase=1e-06 ,   lowerCamelCase=224 ,   lowerCamelCase=16 ,   lowerCamelCase=3 ,   lowerCamelCase=True ,   **lowerCamelCase ,   )							->Tuple:



         '''simple docstring'''
         super().__init__(**lowerCamelCase      )

         __a				     =    hidden_size
         __a				     =    num_hidden_layers
         __a				     =    num_attention_heads
         __a				     =    intermediate_size
         __a				     =    hidden_act
         __a				     =    hidden_dropout_prob
         __a				     =    attention_probs_dropout_prob
         __a				     =    initializer_range
         __a				     =    layer_norm_eps
         __a				     =    image_size
         __a				     =    patch_size
         __a				     =    num_channels
         __a				     =    qkv_bias