


'''simple docstring'''







import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     "M-CLIP"







     def __init__(			self   ,						snake_case__=1024   ,						snake_case__=768   ,						**snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							transformerDimSize
            _lowerCAmelCase     :				Tuple       =							imageDimSize
            super().__init__(**snake_case__ )
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     MCLIPConfig







     def __init__(			self   ,						snake_case__   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            super().__init__(snake_case__   ,						*snake_case__   ,						**snake_case__ )
            _lowerCAmelCase     :				int       =							XLMRobertaModel(snake_case__ )
            _lowerCAmelCase     :				List[Any]       =							torch.nn.Linear(
                in_features=config.transformerDimensions   ,						out_features=config.numDims )







     def a						(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							self.transformer(input_ids=snake_case__   ,						attention_mask=snake_case__ )[0]
            _lowerCAmelCase     :				Any       =							(embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
            return self.LinearTransformation(snake_case__ ), embs





'''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,
)


lowerCAmelCase     :  Optional[Any]											=    logging.getLogger(__name__)
@dataclass
class 						UpperCamelCase__       :



     """simple docstring"""






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



     """simple docstring"""






     __magic_name__           =     field(
         metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}						)
     __magic_name__           =     field(
         default="summarization"     ,       metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"}     ,       )
     __magic_name__           =     field(
         default=1_0_2_4     ,       metadata={
             "help": (
                 "The maximum total input sequence length after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )
         }     ,       )
     __magic_name__           =     field(
         default=1_2_8     ,       metadata={
             "help": (
                 "The maximum total sequence length for target text after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )
         }     ,       )
     __magic_name__           =     field(
         default=1_4_2     ,       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``."
             )
         }     ,       )
     __magic_name__           =     field(
         default=1_4_2     ,       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."
             )
         }     ,       )
     __magic_name__           =     field(default=-1     ,       metadata={"help": "# training examples. -1 means use all."}						)
     __magic_name__           =     field(default=-1     ,       metadata={"help": "# validation examples. -1 means use all."}						)
     __magic_name__           =     field(default=-1     ,       metadata={"help": "# test examples. -1 means use all."}						)
     __magic_name__           =     field(default=SCREAMING_SNAKE_CASE_     ,       metadata={"help": "Source language id for translation."}						)
     __magic_name__           =     field(default=SCREAMING_SNAKE_CASE_     ,       metadata={"help": "Target language id for translation."}						)
     __magic_name__           =     field(default=SCREAMING_SNAKE_CASE_     ,       metadata={"help": "# num_beams to use for evaluation."}						)
     __magic_name__           =     field(
         default=SCREAMING_SNAKE_CASE_     ,       metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}     ,       )






def       lowercase					(_A ,    _A ,    _A       ):







       """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       lowercase					():







       """simple docstring"""







       _lowerCAmelCase     :				int       =							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     :				Tuple       =							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
       else:
              _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				Optional[int]       =							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     :				Optional[int]       =							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     :				Tuple       =							('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     :				str       =							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     :				Union[str, 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     :				Union[str, Any]       =							tokenizer.lang_code_to_id[data_args.tgt_lang]
              else:
                     _lowerCAmelCase     :				Union[str, Any]       =							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[Any]       =							(
           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     :				Optional[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     :				Union[str, 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     :				Optional[Any]       =							(
           build_compute_metrics_fn(data_args.task ,    _A       ) if training_args.predict_with_generate else None
       )
       _lowerCAmelCase     :				int       =							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     :				str       =							{}
       # Training
       if training_args.do_train:
              logger.info('*** Train ***'       )

              _lowerCAmelCase     :				Dict       =							trainer.train(
                  model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
              _lowerCAmelCase     :				Any       =							train_result.metrics
              _lowerCAmelCase     :				str       =							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     :				List[str]       =							trainer.evaluate(metric_key_prefix='val'       )
              _lowerCAmelCase     :				int       =							data_args.n_val
              _lowerCAmelCase     :				List[str]       =							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     :				Any       =							trainer.predict(test_dataset=_A ,    metric_key_prefix='test'       )
              _lowerCAmelCase     :				Optional[int]       =							test_output.metrics
              _lowerCAmelCase     :				Optional[int]       =							data_args.n_test

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

                     if training_args.predict_with_generate:
                            _lowerCAmelCase     :				Union[str, Any]       =							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       lowercase					(_A       ):







       """simple docstring"""







       main()


if __name__ == "__main__":
       main()

