import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						Optional[Any]:




       '''simple docstring'''






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

       return orig_key

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						Tuple:




       '''simple docstring'''






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

              if ("pooler" in key) or ("sen_class" in key):
                     continue
              else:
                     _A=	val

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

       return orig_state_dict

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						Optional[Any]:




       '''simple docstring'''






       _A=	torch.load(lowerCAmelCase_       ,					map_location='cpu'   )['model_state_dict']
       _A=	YosoConfig.from_json_file(lowerCAmelCase_   )
       _A=	YosoForMaskedLM(lowerCAmelCase_   )

       _A=	convert_checkpoint_helper(config.max_position_embeddings       ,					lowerCAmelCase_   )

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

       print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"   )


if __name__ == "__main__":
      UpperCAmelCase_								=							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_								=							parser.parse_args()
      convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
from typing import TYPE_CHECKING

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


UpperCAmelCase_								=							{
    '''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_								=							[
          '''ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ErnieForCausalLM''',
          '''ErnieForMaskedLM''',
          '''ErnieForMultipleChoice''',
          '''ErnieForNextSentencePrediction''',
          '''ErnieForPreTraining''',
          '''ErnieForQuestionAnswering''',
          '''ErnieForSequenceClassification''',
          '''ErnieForTokenClassification''',
          '''ErnieModel''',
          '''ErniePreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_ernie import (
                ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
                ErnieForCausalLM,
                ErnieForMaskedLM,
                ErnieForMultipleChoice,
                ErnieForNextSentencePrediction,
                ErnieForPreTraining,
                ErnieForQuestionAnswering,
                ErnieForSequenceClassification,
                ErnieForTokenClassification,
                ErnieModel,
                ErniePreTrainedModel,
            )

else:
      import sys

      UpperCAmelCase_								=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)