


import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def 		_A		(		_lowercase )					-> str:


     """simple docstring"""


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

     return orig_key






def 		_A		(		_lowercase					,  _lowercase )					-> List[Any]:


     """simple docstring"""


     for key in orig_state_dict.copy().keys():
          __UpperCamelCase =			orig_state_dict.pop(_lowercase )

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

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

     return orig_state_dict






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase )					-> Optional[Any]:


     """simple docstring"""


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

     __UpperCamelCase =			convert_checkpoint_helper(config.max_position_embeddings					,  _lowercase )

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

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


if __name__ == "__main__":
    __snake_case   							=						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.'''
    )
    __snake_case   							=						parser.parse_args()
    convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)







from __future__ import annotations

from fractions import Fraction


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   bool:
			return (
			    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
			)


def A    (							__UpperCamelCase       )    ->   list[str]:
			A__	  =     []
			A__	  =     11
			A__	  =     int('1' + '0' * digit_len       )
			for num in range(__UpperCamelCase			,						__UpperCamelCase       ):
						while den <= 99:
									if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
												if is_digit_cancelling(__UpperCamelCase			,						__UpperCamelCase       ):
															solutions.append(f'''{num}/{den}'''       )
									den += 1
						num += 1
						A__	  =     10
			return solutions


def A    (							__UpperCamelCase = 2       )    ->   int:
			A__	  =     1.0
			for fraction in fraction_list(__UpperCamelCase       ):
						A__	  =     Fraction(__UpperCamelCase       )
						result *= frac.denominator / frac.numerator
			return int(__UpperCamelCase       )


if __name__ == "__main__":
							print(solution())




