"""simple docstring"""
import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_ )      ->Optional[int]:







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

    return orig_key



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_ )      ->List[Any]:







    for key in orig_state_dict.copy().keys():
        _lowerCamelCase	: str       =       orig_state_dict.pop(_A )

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            _lowerCamelCase	: Union[str, Any]       =       val

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

    return orig_state_dict



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_ )      ->Optional[Any]:
    _lowerCamelCase	: List[str]       =       torch.load(_A						,	map_location='''cpu''' )['model_state_dict']
    _lowerCamelCase	: List[Any]       =       YosoConfig.from_json_file(_A )
    _lowerCamelCase	: str       =       YosoForMaskedLM(_A )

    _lowerCamelCase	: Optional[int]       =       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__":
  SCREAMING_SNAKE_CASE__   :      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.'
  )
  SCREAMING_SNAKE_CASE__   :      List[str]							=parser.parse_args()
  convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase     :  Any											=    {
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Tuple											=    [
           """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """CanineForMultipleChoice""",
           """CanineForQuestionAnswering""",
           """CanineForSequenceClassification""",
           """CanineForTokenClassification""",
           """CanineLayer""",
           """CanineModel""",
           """CaninePreTrainedModel""",
           """load_tf_weights_in_canine""",
       ]


if TYPE_CHECKING:
       from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
       from .tokenization_canine import CanineTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_canine import (
                  CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CanineForMultipleChoice,
                  CanineForQuestionAnswering,
                  CanineForSequenceClassification,
                  CanineForTokenClassification,
                  CanineLayer,
                  CanineModel,
                  CaninePreTrainedModel,
                  load_tf_weights_in_canine,
              )


else:
       import sys

       lowerCAmelCase     :  Optional[Any]											=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

