






"""simple docstring"""


import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM




def       snake_case  (A_				:int				):







  '''simple docstring'''

  if "model" in orig_key:
    a					:      Any    =       orig_key.replace('model.'     ,      ''				)
  if "norm1" in orig_key:
    a					:      List[Any]    =       orig_key.replace('norm1'     ,      'attention.output.LayerNorm'				)
  if "norm2" in orig_key:
    a					:      Tuple    =       orig_key.replace('norm2'     ,      'output.LayerNorm'				)
  if "norm" in orig_key:
    a					:      str    =       orig_key.replace('norm'     ,      'LayerNorm'				)
  if "transformer" in orig_key:
    a					:      Optional[Any]    =       orig_key.split('.'				)[0].split('_'				)[-1]
    a					:      Dict    =       orig_key.replace(f'''transformer_{layer_num}'''     ,      f'''encoder.layer.{layer_num}'''				)
  if "mha.attn" in orig_key:
    a					:      Dict    =       orig_key.replace('mha.attn'     ,      'attention.self'				)
  if "mha" in orig_key:
    a					:      Tuple    =       orig_key.replace('mha'     ,      'attention'				)
  if "W_q" in orig_key:
    a					:      List[Any]    =       orig_key.replace('W_q'     ,      'self.query'				)
  if "W_k" in orig_key:
    a					:      List[str]    =       orig_key.replace('W_k'     ,      'self.key'				)
  if "W_v" in orig_key:
    a					:      Dict    =       orig_key.replace('W_v'     ,      'self.value'				)
  if "ff1" in orig_key:
    a					:      List[str]    =       orig_key.replace('ff1'     ,      'intermediate.dense'				)
  if "ff2" in orig_key:
    a					:      Any    =       orig_key.replace('ff2'     ,      'output.dense'				)
  if "ff" in orig_key:
    a					:      Tuple    =       orig_key.replace('ff'     ,      'output.dense'				)
  if "mlm_class" in orig_key:
    a					:      Optional[int]    =       orig_key.replace('mlm.mlm_class'     ,      'cls.predictions.decoder'				)
  if "mlm" in orig_key:
    a					:      Tuple    =       orig_key.replace('mlm'     ,      'cls.predictions.transform'				)
  if "cls" not in orig_key:
    a					:      Optional[Any]    =       'yoso.' + orig_key

  return orig_key




def       snake_case  (A_				:List[str]     ,      A_				:Tuple				):







  '''simple docstring'''

  for key in orig_state_dict.copy().keys():
    a					:      Any    =       orig_state_dict.pop(A_				)

    if ("pooler" in key) or ("sen_class" in key):
      continue
    else:
      a					:      Optional[Any]    =       val

  a					:      Optional[Any]    =       orig_state_dict['cls.predictions.decoder.bias']
  a					:      Optional[Any]    =       torch.arange(A_				).expand((1, -1)				) + 2

  return orig_state_dict




def       snake_case  (A_				:Tuple     ,      A_				:str     ,      A_				:List[str]				):







  '''simple docstring'''

  a					:      Any    =       torch.load(A_     ,      map_location='cpu'				)['model_state_dict']
  a					:      Optional[Any]    =       YosoConfig.from_json_file(A_				)
  a					:      List[str]    =       YosoForMaskedLM(A_				)

  a					:      Union[str, Any]    =       convert_checkpoint_helper(config.max_position_embeddings     ,      A_				)

  print(model.load_state_dict(A_				)				)
  model.eval()
  model.save_pretrained(A_				)

  print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''				)


if __name__ == "__main__":
   _UpperCamelCase    :   Tuple   			=						argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
   )
   parser.add_argument(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help='The json file for YOSO model config.',
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _UpperCamelCase    :   List[Any]   			=						parser.parse_args()
   convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)












"""simple docstring"""


import argparse
from collections import defaultdict

import yaml


_UpperCamelCase    :   int   			=						'docs/source/en/_toctree.yml'




def       snake_case  (A_				:Optional[Any]				):







  '''simple docstring'''

  a					:      List[Any]    =       defaultdict(A_				)
  for doc in model_doc:
    counts[doc["local"]] += 1
  a					:      Optional[Any]    =       [key for key, value in counts.items() if value > 1]

  a					:      List[str]    =       []
  for duplicate_key in duplicates:
    a					:      int    =       list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key}				)
    if len(A_				) > 1:
      raise ValueError(
          f'''{duplicate_key} is present several times in the documentation table of content at '''
          '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
          'others.'				)
    # Only add this once
    new_doc.append({'local': duplicate_key, 'title': titles[0]}				)

  # Add none duplicate-keys
  new_doc.extend([doc for doc in model_doc if counts[doc['local']] == 1]				)

  # Sort
  return sorted(A_     ,      key=lambda A_				: s["title"].lower()				)




def       snake_case  (A_				:List[str]=False				):







  '''simple docstring'''

  with open(A_     ,      encoding='utf-8'				) as f:
    a					:      Dict    =       yaml.safe_load(f.read()				)

  # Get to the API doc
  a					:      Optional[Any]    =       0
  while content[api_idx]["title"] != "API":
    api_idx += 1
  a					:      List[str]    =       content[api_idx]['sections']

  # Then to the model doc
  a					:      Optional[int]    =       0
  while api_doc[model_idx]["title"] != "Models":
    model_idx += 1

  a					:      Optional[Any]    =       api_doc[model_idx]['sections']

  a					:      Dict    =       [(idx, section) for idx, section in enumerate(A_				) if 'sections' in section]
  a					:      List[str]    =       False
  for idx, modality_doc in modalities_docs:
    a					:      str    =       modality_doc['sections']
    a					:      str    =       clean_model_doc_toc(A_				)

    if old_modality_doc != new_modality_doc:
      a					:      str    =       True
      if overwrite:
        a					:      Any    =       new_modality_doc

  if diff:
    if overwrite:
      a					:      Any    =       model_doc
      a					:      str    =       api_doc
      with open(A_     ,      'w'     ,      encoding='utf-8'				) as f:
        f.write(yaml.dump(A_     ,      allow_unicode=A_				)				)
    else:
      raise ValueError(
          'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'				)


if __name__ == "__main__":
   _UpperCamelCase    :   Optional[int]   			=						argparse.ArgumentParser()
   parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
   _UpperCamelCase    :   Any   			=						parser.parse_args()

   check_model_doc(args.fix_and_overwrite)




