

import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Optional[int]    ):





				"""simple docstring"""


				if "model" in orig_key:
								SCREAMING_SNAKE_CASE_													=  orig_key.replace('model.'	,				''    )
				if "norm1" in orig_key:
								SCREAMING_SNAKE_CASE_													=  orig_key.replace('norm1'	,				'attention.output.LayerNorm'    )
				if "norm2" in orig_key:
								SCREAMING_SNAKE_CASE_													=  orig_key.replace('norm2'	,				'output.LayerNorm'    )
				if "norm" in orig_key:
								SCREAMING_SNAKE_CASE_													=  orig_key.replace('norm'	,				'LayerNorm'    )
				if "transformer" in orig_key:
								SCREAMING_SNAKE_CASE_													=  orig_key.split('.'    )[0].split('_'    )[-1]
								SCREAMING_SNAKE_CASE_													=  orig_key.replace(f"""transformer_{layer_num}"""	,				f"""encoder.layer.{layer_num}"""    )
				if "mha.attn" in orig_key:
								SCREAMING_SNAKE_CASE_													=  orig_key.replace('mha.attn'	,				'attention.self'    )
				if "mha" in orig_key:
								SCREAMING_SNAKE_CASE_													=  orig_key.replace('mha'	,				'attention'    )
				if "W_q" in orig_key:
								SCREAMING_SNAKE_CASE_													=  orig_key.replace('W_q'	,				'self.query'    )
				if "W_k" in orig_key:
								SCREAMING_SNAKE_CASE_													=  orig_key.replace('W_k'	,				'self.key'    )
				if "W_v" in orig_key:
								SCREAMING_SNAKE_CASE_													=  orig_key.replace('W_v'	,				'self.value'    )
				if "ff.0" in orig_key:
								SCREAMING_SNAKE_CASE_													=  orig_key.replace('ff.0'	,				'intermediate.dense'    )
				if "ff.2" in orig_key:
								SCREAMING_SNAKE_CASE_													=  orig_key.replace('ff.2'	,				'output.dense'    )
				if "ff" in orig_key:
								SCREAMING_SNAKE_CASE_													=  orig_key.replace('ff'	,				'output.dense'    )
				if "mlm_class" in orig_key:
								SCREAMING_SNAKE_CASE_													=  orig_key.replace('mlm.mlm_class'	,				'cls.predictions.decoder'    )
				if "mlm" in orig_key:
								SCREAMING_SNAKE_CASE_													=  orig_key.replace('mlm'	,				'cls.predictions.transform'    )
				if "backbone.backbone.encoders" in orig_key:
								SCREAMING_SNAKE_CASE_													=  orig_key.replace('backbone.backbone.encoders'	,				'encoder.layer'    )
				if "cls" not in orig_key:
								SCREAMING_SNAKE_CASE_													=  'mra.' + orig_key

				return orig_key


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Dict	,				_SCREAMING_SNAKE_CASE   :						Optional[Any]    ):





				"""simple docstring"""


				for key in orig_state_dict.copy().keys():
								SCREAMING_SNAKE_CASE_													=  orig_state_dict.pop(_SCREAMING_SNAKE_CASE    )

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

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

				return orig_state_dict


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						List[Any]	,				_SCREAMING_SNAKE_CASE   :						List[Any]	,				_SCREAMING_SNAKE_CASE   :						List[str]    ):





				"""simple docstring"""


				SCREAMING_SNAKE_CASE_													=  torch.load(_SCREAMING_SNAKE_CASE	,				map_location='cpu'    )['model_state_dict']
				SCREAMING_SNAKE_CASE_													=  MraConfig.from_json_file(_SCREAMING_SNAKE_CASE    )
				SCREAMING_SNAKE_CASE_													=  MraForMaskedLM(_SCREAMING_SNAKE_CASE    )

				SCREAMING_SNAKE_CASE_													=  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__":
							UpperCamelCase__					: Tuple								=      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."
							)
							UpperCamelCase__					: Optional[int]								=      parser.parse_args()
							convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





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


UpperCamelCase__					: Optional[int]								=      logging.get_logger(__name__)

UpperCamelCase__					: int								=      {
    "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__    ):
  __lowerCAmelCase       :		List[Any]  	=					'pegasus_x'
  __lowerCAmelCase       :		Optional[int]  	=					['past_key_values']
  __lowerCAmelCase       :		List[str]  	=					{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

  def __init__(   self  ,						_A=96103  ,						_A=16384  ,						_A=16  ,						_A=4096  ,						_A=16  ,						_A=16  ,						_A=4096  ,						_A=16  ,						_A=0.0  ,						_A=0.0  ,						_A=True  ,						_A=True  ,						_A="gelu"  ,						_A=1024  ,						_A=0.1  ,						_A=0.0  ,						_A=0.0  ,						_A=0.0_2  ,						_A=0  ,						_A=True  ,						_A=0  ,						_A=1  ,						_A=1  ,						_A=32  ,						_A=512  ,						_A=True  ,						**_A  ,						):
      SCREAMING_SNAKE_CASE_													=  vocab_size
      SCREAMING_SNAKE_CASE_													=  max_position_embeddings
      SCREAMING_SNAKE_CASE_													=  d_model
      SCREAMING_SNAKE_CASE_													=  encoder_ffn_dim
      SCREAMING_SNAKE_CASE_													=  encoder_layers
      SCREAMING_SNAKE_CASE_													=  encoder_attention_heads
      SCREAMING_SNAKE_CASE_													=  decoder_ffn_dim
      SCREAMING_SNAKE_CASE_													=  decoder_layers
      SCREAMING_SNAKE_CASE_													=  decoder_attention_heads
      SCREAMING_SNAKE_CASE_													=  dropout
      SCREAMING_SNAKE_CASE_													=  attention_dropout
      SCREAMING_SNAKE_CASE_													=  activation_dropout
      SCREAMING_SNAKE_CASE_													=  activation_function
      SCREAMING_SNAKE_CASE_													=  init_std
      SCREAMING_SNAKE_CASE_													=  encoder_layerdrop
      SCREAMING_SNAKE_CASE_													=  decoder_layerdrop
      SCREAMING_SNAKE_CASE_													=  use_cache
      SCREAMING_SNAKE_CASE_													=  encoder_layers
      SCREAMING_SNAKE_CASE_													=  scale_embedding  # scale factor will be sqrt(d_model) if True

      SCREAMING_SNAKE_CASE_													=  num_global_tokens
      SCREAMING_SNAKE_CASE_													=  block_size
      SCREAMING_SNAKE_CASE_													=  stagger_local_blocks

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

  @property
  def   lowerCAmelCase__       (   self):
      return self.encoder_attention_heads


  @property
  def   lowerCAmelCase__       (   self):
      return self.d_model


