
'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase	=    logging.get_logger(__name__)

__lowercase	=    {
    '''google/pegasus-x-base''': '''https://huggingface.co/google/pegasus-x-base/resolve/main/config.json''',
    '''google/pegasus-x-large''': '''https://huggingface.co/google/pegasus-x-large/resolve/main/config.json''',
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}

class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	_UpperCamelCase							:  Dict								=		'''pegasus_x'''
	_UpperCamelCase							:  Optional[int]								=		['''past_key_values''']
	_UpperCamelCase							:  str								=		{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




	def __init__(						self				: Any					,						UpperCamelCase_				: Tuple=96103					,						UpperCamelCase_				: List[str]=16384					,						UpperCamelCase_				: Any=16					,						UpperCamelCase_				: Dict=4096					,						UpperCamelCase_				: str=16					,						UpperCamelCase_				: Union[str, Any]=16					,						UpperCamelCase_				: str=4096					,						UpperCamelCase_				: Any=16					,						UpperCamelCase_				: str=0.0					,						UpperCamelCase_				: Dict=0.0					,						UpperCamelCase_				: Union[str, Any]=True					,						UpperCamelCase_				: Union[str, Any]=True					,						UpperCamelCase_				: List[str]="gelu"					,						UpperCamelCase_				: Dict=1024					,						UpperCamelCase_				: str=0.1					,						UpperCamelCase_				: str=0.0					,						UpperCamelCase_				: Optional[Any]=0.0					,						UpperCamelCase_				: str=0.0_2					,						UpperCamelCase_				: Optional[Any]=0					,						UpperCamelCase_				: Tuple=True					,						UpperCamelCase_				: Tuple=0					,						UpperCamelCase_				: Optional[int]=1					,						UpperCamelCase_				: List[Any]=1					,						UpperCamelCase_				: List[Any]=32					,						UpperCamelCase_				: Optional[int]=512					,						UpperCamelCase_				: Any=True					,						**UpperCamelCase_				: Dict					,						):
			lowerCAmelCase_   :       List[Any]    							=vocab_size
			lowerCAmelCase_   :       Any    							=max_position_embeddings
			lowerCAmelCase_   :       int    							=d_model
			lowerCAmelCase_   :       List[Any]    							=encoder_ffn_dim
			lowerCAmelCase_   :       Union[str, Any]    							=encoder_layers
			lowerCAmelCase_   :       Tuple    							=encoder_attention_heads
			lowerCAmelCase_   :       List[Any]    							=decoder_ffn_dim
			lowerCAmelCase_   :       List[Any]    							=decoder_layers
			lowerCAmelCase_   :       List[str]    							=decoder_attention_heads
			lowerCAmelCase_   :       str    							=dropout
			lowerCAmelCase_   :       Any    							=attention_dropout
			lowerCAmelCase_   :       Any    							=activation_dropout
			lowerCAmelCase_   :       Optional[int]    							=activation_function
			lowerCAmelCase_   :       List[Any]    							=init_std
			lowerCAmelCase_   :       List[str]    							=encoder_layerdrop
			lowerCAmelCase_   :       Optional[int]    							=decoder_layerdrop
			lowerCAmelCase_   :       int    							=use_cache
			lowerCAmelCase_   :       Union[str, Any]    							=encoder_layers
			lowerCAmelCase_   :       Tuple    							=scale_embedding  # scale factor will be sqrt(d_model) if True

			lowerCAmelCase_   :       Optional[int]    							=num_global_tokens
			lowerCAmelCase_   :       List[str]    							=block_size
			lowerCAmelCase_   :       Optional[int]    							=stagger_local_blocks

			super().__init__(
			    pad_token_id=UpperCamelCase_					,						eos_token_id=UpperCamelCase_					,						is_encoder_decoder=UpperCamelCase_					,						decoder_start_token_id=UpperCamelCase_					,						forced_eos_token_id=UpperCamelCase_					,						**UpperCamelCase_					,						)




	@property
	def 	__A      (						self				: List[str]      ):
			return self.encoder_attention_heads





	@property
	def 	__A      (						self				: int      ):
			return self.d_model








'''simple docstring'''






import argparse

import torch

from transformers import MraConfig, MraForMaskedLM







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ):
		if "model" in orig_key:
				lowerCAmelCase_   :       Union[str, Any]    							=orig_key.replace('''model.'''  ,					''''''  )
		if "norm1" in orig_key:
				lowerCAmelCase_   :       List[Any]    							=orig_key.replace('''norm1'''  ,					'''attention.output.LayerNorm'''  )
		if "norm2" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''norm2'''  ,					'''output.LayerNorm'''  )
		if "norm" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''norm'''  ,					'''LayerNorm'''  )
		if "transformer" in orig_key:
				lowerCAmelCase_   :       str    							=orig_key.split('''.'''  )[0].split('''_'''  )[-1]
				lowerCAmelCase_   :       Optional[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_   :       Any    							=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_   :       Any    							=orig_key.replace('''W_v'''  ,					'''self.value'''  )
		if "ff.0" in orig_key:
				lowerCAmelCase_   :       Dict    							=orig_key.replace('''ff.0'''  ,					'''intermediate.dense'''  )
		if "ff.2" in orig_key:
				lowerCAmelCase_   :       str    							=orig_key.replace('''ff.2'''  ,					'''output.dense'''  )
		if "ff" in orig_key:
				lowerCAmelCase_   :       int    							=orig_key.replace('''ff'''  ,					'''output.dense'''  )
		if "mlm_class" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''mlm.mlm_class'''  ,					'''cls.predictions.decoder'''  )
		if "mlm" in orig_key:
				lowerCAmelCase_   :       int    							=orig_key.replace('''mlm'''  ,					'''cls.predictions.transform'''  )
		if "backbone.backbone.encoders" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''backbone.backbone.encoders'''  ,					'''encoder.layer'''  )
		if "cls" not in orig_key:
				lowerCAmelCase_   :       List[Any]    							='''mra.''' + orig_key

		return orig_key







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		for key in orig_state_dict.copy().keys():
				lowerCAmelCase_   :       Tuple    							=orig_state_dict.pop(_SCREAMING_SNAKE_CASE  )

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

		lowerCAmelCase_   :       Union[str, Any]    							=orig_state_dict['''cls.predictions.decoder.bias''']
		lowerCAmelCase_   :       int    							=torch.arange(_SCREAMING_SNAKE_CASE  ).expand((1, -1)  ) + 2

		return orig_state_dict







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       Tuple    							=torch.load(_SCREAMING_SNAKE_CASE  ,					map_location='''cpu'''  )['''model_state_dict''']
		lowerCAmelCase_   :       Optional[int]    							=MraConfig.from_json_file(_SCREAMING_SNAKE_CASE  )
		lowerCAmelCase_   :       Optional[Any]    							=MraForMaskedLM(_SCREAMING_SNAKE_CASE  )

		lowerCAmelCase_   :       str    							=convert_checkpoint_helper(config.max_position_embeddings  ,					_SCREAMING_SNAKE_CASE  )

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

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


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






