'''simple docstring'''







from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml




class _SCREAMING_SNAKE_CASE(							yaml.SafeLoader					):




						def      __lowerCamelCase       (    self :       List[str]							,							UpperCamelCase_ :       List[Any]      )     ->				Dict:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = [self.constructed_objects[key_node] for key_node, _ in node.value]
													SCREAMING_SNAKE_CASE__     :str						    = [tuple(_UpperCAmelCase      ) if isinstance(_UpperCAmelCase							,							_UpperCAmelCase      ) else key for key in keys]
													SCREAMING_SNAKE_CASE__     :Any						    = Counter(_UpperCAmelCase      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = [key for key in counter if counter[key] > 1]
													if duplicate_keys:
																				raise TypeError(f'''Got duplicate yaml keys: {duplicate_keys}'''      )




						def      __lowerCamelCase       (    self :       Tuple							,							UpperCamelCase_ :       int							,							UpperCamelCase_ :       Union[str, Any]=False      )     ->				Tuple:
													SCREAMING_SNAKE_CASE__     :List[Any]						    = super().construct_mapping(_UpperCAmelCase							,							deep=_UpperCAmelCase      )
													self._check_no_duplicates_on_constructed_node(_UpperCAmelCase      )
													return mapping


def    lowerCamelCase       (			UpperCAmelCase__				:  str		)			->	Tuple[Optional[str], str]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :str						    = list(readme_content.splitlines()		)
							if full_content and full_content[0] == "---" and "---" in full_content[1:]:
														SCREAMING_SNAKE_CASE__     :Optional[Any]						    = full_content[1:].index('---'		) + 1
														SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = '\n'.join(full_content[1:sep_idx]		)
														return yamlblock, "\n".join(full_content[sep_idx + 1 :]		)

							return None, "\n".join(_snake_case		)




class _SCREAMING_SNAKE_CASE(							__lowerCAmelCase					):
						A_			:							Union[str, Any]     		= {'''train_eval_index'''}  # train-eval-index in the YAML metadata




						@classmethod
						def      __lowerCamelCase       (    cls :       int							,							UpperCamelCase_ :       Path      )     ->				Union[str, Any]:
													with open(_UpperCAmelCase							,							encoding='utf-8'      ) as readme_file:
																				SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Any						    = _split_yaml_from_readme(readme_file.read()      )
													if yaml_string is not None:
																				return cls.from_yaml_string(_UpperCAmelCase      )
													else:
																				return cls()




						def      __lowerCamelCase       (    self :       str							,							UpperCamelCase_ :       Path      )     ->				Optional[int]:
													if path.exists():
																				with open(_UpperCAmelCase							,							encoding='utf-8'      ) as readme_file:
																											SCREAMING_SNAKE_CASE__     :Dict						    = readme_file.read()
													else:
																				SCREAMING_SNAKE_CASE__     :Any						    = None
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self._to_readme(_UpperCAmelCase      )
													with open(_UpperCAmelCase							,							'w'							,							encoding='utf-8'      ) as readme_file:
																				readme_file.write(_UpperCAmelCase      )




						def      __lowerCamelCase       (    self :       int							,							UpperCamelCase_ :       Optional[str] = None      )     ->				List[str]:
													if readme_content is not None:
																				SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :str						    = _split_yaml_from_readme(_UpperCAmelCase      )
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = '---\n' + self.to_yaml_string() + '---\n' + content
													else:
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = '---\n' + self.to_yaml_string() + '---\n'
													return full_content




						@classmethod
						def      __lowerCamelCase       (    cls :       List[str]							,							UpperCamelCase_ :       str      )     ->				List[str]:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = yaml.load(_UpperCAmelCase							,							Loader=_NoDuplicateSafeLoader      ) or {}

													# Convert the YAML keys to DatasetMetadata fields
													SCREAMING_SNAKE_CASE__     :Tuple						    = {
													    (key.replace('-'							,							'_'      ) if key.replace('-'							,							'_'      ) in cls._FIELDS_WITH_DASHES else key): value
													    for key, value in metadata_dict.items()
													}
													return cls(**_UpperCAmelCase      )




						def      __lowerCamelCase       (    self :       Dict      )     ->				Optional[Any]:
													return yaml.safe_dump(
													    {
													        (key.replace('_'							,							'-'      ) if key in self._FIELDS_WITH_DASHES else key): value
													        for key, value in self.items()
													    }							,							sort_keys=_UpperCAmelCase							,							allow_unicode=_UpperCAmelCase							,							encoding='utf-8'							,							).decode('utf-8'      )
UpperCamelCase_      	=	{
    '''image-classification''': [],
    '''translation''': [],
    '''image-segmentation''': [],
    '''fill-mask''': [],
    '''automatic-speech-recognition''': [],
    '''token-classification''': [],
    '''sentence-similarity''': [],
    '''audio-classification''': [],
    '''question-answering''': [],
    '''summarization''': [],
    '''zero-shot-classification''': [],
    '''table-to-text''': [],
    '''feature-extraction''': [],
    '''other''': [],
    '''multiple-choice''': [],
    '''text-classification''': [],
    '''text-to-image''': [],
    '''text2text-generation''': [],
    '''zero-shot-image-classification''': [],
    '''tabular-classification''': [],
    '''tabular-regression''': [],
    '''image-to-image''': [],
    '''tabular-to-text''': [],
    '''unconditional-image-generation''': [],
    '''text-retrieval''': [],
    '''text-to-speech''': [],
    '''object-detection''': [],
    '''audio-to-audio''': [],
    '''text-generation''': [],
    '''conversational''': [],
    '''table-question-answering''': [],
    '''visual-question-answering''': [],
    '''image-to-text''': [],
    '''reinforcement-learning''': [],
    '''voice-activity-detection''': [],
    '''time-series-forecasting''': [],
    '''document-question-answering''': [],
}


if __name__ == "__main__":
		from argparse import ArgumentParser

		UpperCamelCase_      	=	ArgumentParser(usage='''Validate the yaml metadata block of a README.md file.''')
		ap.add_argument('''readme_filepath''')
		UpperCamelCase_      	=	ap.parse_args()

		UpperCamelCase_      	=	Path(args.readme_filepath)
		UpperCamelCase_      	=	DatasetMetadata.from_readme(readme_filepath)
		print(dataset_metadata)
		dataset_metadata.to_readme(readme_filepath)












"""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)

