




"""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							      =     logging.getLogger(__name__)





@dataclass
class 							lowercase_			:



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





@dataclass
class 							lowercase_			:



 '''simple docstring'''
 UpperCAmelCase       :	str     =      field(
     metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}				)
 UpperCAmelCase       :	Optional[str]     =      field(
     default='''summarization'''     ,      metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''}     ,      )
 UpperCAmelCase       :	Optional[int]     =      field(
     default=1024     ,      metadata={
         '''help''': (
             '''The maximum total input sequence length after tokenization. Sequences longer '''
             '''than this will be truncated, sequences shorter will be padded.'''
         )
     }     ,      )
 UpperCAmelCase       :	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.'''
         )
     }     ,      )
 UpperCAmelCase       :	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``.'''
         )
     }     ,      )
 UpperCAmelCase       :	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.'''
         )
     }     ,      )
 UpperCAmelCase       :	Optional[int]     =      field(default=-1     ,      metadata={'''help''': '''# training examples. -1 means use all.'''}				)
 UpperCAmelCase       :	Optional[int]     =      field(default=-1     ,      metadata={'''help''': '''# validation examples. -1 means use all.'''}				)
 UpperCAmelCase       :	Optional[int]     =      field(default=-1     ,      metadata={'''help''': '''# test examples. -1 means use all.'''}				)
 UpperCAmelCase       :	Optional[str]     =      field(default=__lowerCAmelCase     ,      metadata={'''help''': '''Source language id for translation.'''}				)
 UpperCAmelCase       :	Optional[str]     =      field(default=__lowerCAmelCase     ,      metadata={'''help''': '''Target language id for translation.'''}				)
 UpperCAmelCase       :	Optional[int]     =      field(default=__lowerCAmelCase     ,      metadata={'''help''': '''# num_beams to use for evaluation.'''}				)
 UpperCAmelCase       :	bool     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''}     ,      )






def 		_snake_case	(   _snake_case				:   Optional[Any]							,    _snake_case				:   int							,    _snake_case				:   Optional[Any]				)       ->   str:






      '''simple docstring'''


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






def 		_snake_case	(   )       ->   Tuple:






      '''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(_snake_case				)

      # 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'							,    _snake_case				)

      # 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(_snake_case							,    _snake_case							,    _snake_case				):
                  assert hasattr(_snake_case							,    _snake_case				), F'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
                  setattr(_snake_case							,    _snake_case							,    getattr(_snake_case							,    _snake_case				)				)

      _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=_snake_case							,    cache_dir=model_args.cache_dir							,    )

      # use task specific params
      use_task_specific_params(_snake_case							,    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(_snake_case							,    (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(_snake_case							,    _snake_case				):
                  _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(_snake_case				)
      if model_args.freeze_encoder:
            freeze_params(model.get_encoder()				)
            assert_all_frozen(model.get_encoder()				)

      _A     =  SeqaSeqDataset

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

      _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'							,    _snake_case							,    training_args.output_dir				)
                  all_metrics.update(_snake_case				)

                  # 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'							,    _snake_case							,    training_args.output_dir				)
                  all_metrics.update(_snake_case				)

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

            _A     =  trainer.predict(test_dataset=_snake_case							,    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'							,    _snake_case							,    training_args.output_dir				)
                  all_metrics.update(_snake_case				)

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

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

      return all_metrics






def 		_snake_case	(   _snake_case				:   List[Any]				)       ->   int:






      '''simple docstring'''


      main()


if __name__ == "__main__":
       main()







"""simple docstring"""

import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
a							      =     logging.get_logger(__name__)

a							      =     [
    ('''bert.bert''', '''visual_bert'''),
    ('''bert.cls''', '''cls'''),
    ('''bert.classifier''', '''cls'''),
    ('''token_type_embeddings_visual''', '''visual_token_type_embeddings'''),
    ('''position_embeddings_visual''', '''visual_position_embeddings'''),
    ('''projection''', '''visual_projection'''),
]

a							      =     [
    '''nlvr2_coco_pre_trained.th''',
    '''nlvr2_fine_tuned.th''',
    '''nlvr2_pre_trained.th''',
    '''vcr_coco_pre_train.th''',
    '''vcr_fine_tune.th''',
    '''vcr_pre_train.th''',
    '''vqa_coco_pre_trained.th''',
    '''vqa_fine_tuned.th''',
    '''vqa_pre_trained.th''',
]






def 		_snake_case	(   _snake_case				:   Optional[Any]				)       ->   str:






      '''simple docstring'''


      _A     =  torch.load(_snake_case							,    map_location='cpu'				)
      return sd






def 		_snake_case	(   _snake_case				:   Union[str, Any]							,    _snake_case				:   str							,    _snake_case				:   Tuple=rename_keys_prefix				)       ->   List[str]:






      '''simple docstring'''


      _A     =  OrderedDict()
      _A     =  torch.arange(config.max_position_embeddings				).expand((1, -1)				)
      # detector_d = OrderedDict()
      for key in d:
            if "detector" in key:
                  # detector_d[key.replace('detector.','')] = d[key]
                  continue
            _A     =  key
            for name_pair in rename_keys_prefix:
                  _A     =  new_key.replace(name_pair[0]							,    name_pair[1]				)
            _A     =  d[key]
            if key == "bert.cls.predictions.decoder.weight":
                  # Old bert code didn't have `decoder.bias`, but was added separately
                  _A     =  new_d['cls.predictions.bias']
      return new_d






@torch.no_grad()
def 		_snake_case	(   _snake_case				:   List[str]							,    _snake_case				:   Dict				)       ->   Dict:






      '''simple docstring'''


      assert (
          checkpoint_path.split('/'				)[-1] in ACCEPTABLE_CHECKPOINTS
      ), F'''The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'''

      # Get Config
      if "pre" in checkpoint_path:
            _A     =  'pretraining'
            if "vcr" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 5_12}
            elif "vqa_advanced" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 20_48}
            elif "vqa" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 20_48}
            elif "nlvr" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 10_24}
            else:
                  raise NotImplementedError(F'''No implementation found for `{checkpoint_path}`.'''				)
      else:
            if "vcr" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 5_12}
                  _A     =  'multichoice'
            elif "vqa_advanced" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 20_48}
                  _A     =  'vqa_advanced'
            elif "vqa" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 20_48, 'num_labels': 31_29}
                  _A     =  'vqa'
            elif "nlvr" in checkpoint_path:
                  _A     =  {
                      'visual_embedding_dim': 10_24,
                      'num_labels': 2,
                  }
                  _A     =  'nlvr'

      _A     =  VisualBertConfig(**_snake_case				)

      # Load State Dict
      _A     =  load_state_dict(_snake_case				)

      _A     =  get_new_dict(_snake_case							,    _snake_case				)

      if model_type == "pretraining":
            _A     =  VisualBertForPreTraining(_snake_case				)
      elif model_type == "vqa":
            _A     =  VisualBertForQuestionAnswering(_snake_case				)
      elif model_type == "nlvr":
            _A     =  VisualBertForVisualReasoning(_snake_case				)
      elif model_type == "multichoice":
            _A     =  VisualBertForMultipleChoice(_snake_case				)

      model.load_state_dict(_snake_case				)
      # Save Checkpoints
      Path(_snake_case				).mkdir(exist_ok=_snake_case				)
      model.save_pretrained(_snake_case				)


if __name__ == "__main__":
       a							      =     argparse.ArgumentParser()
       # Required parameters
       parser.add_argument('''orig_checkpoint_path''', type=str, help='''A path to .th on local filesystem.''')
       parser.add_argument('''pytorch_dump_folder_path''', type=str, help='''Path to the output PyTorch model.''')
       a							      =     parser.parse_args()
       convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)

