



'''simple docstring'''







import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM







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

		return orig_key







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		for key in orig_state_dict.copy().keys():
				SCREAMING_SNAKE_CASE_						:Dict										=      orig_state_dict.pop(SCREAMING_SNAKE_CASE     )

				if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
						continue
				else:
						SCREAMING_SNAKE_CASE_						:str										=      val

		SCREAMING_SNAKE_CASE_						:Tuple										=      orig_state_dict['cls.predictions.decoder.bias']
		SCREAMING_SNAKE_CASE_						:Dict										=      (
		    torch.arange(config.max_position_embeddings     ).expand((1, -1)     ) + 2
		)

		return orig_state_dict







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:List[str]										=      torch.load(SCREAMING_SNAKE_CASE ,		map_location='cpu'     )['model_state_dict']
		SCREAMING_SNAKE_CASE_						:Any										=      NystromformerConfig.from_json_file(SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      NystromformerForMaskedLM(SCREAMING_SNAKE_CASE     )

		SCREAMING_SNAKE_CASE_						:str										=      convert_checkpoint_helper(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )

		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__":
							SCREAMING_SNAKE_CASE__     : int				=   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to Nystromformer pytorch checkpoint."""
							)
							parser.add_argument(
							    """--config_file""",
							    default=None,
							    type=str,
							    required=True,
							    help="""The json file for Nystromformer 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_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





'''simple docstring'''







import warnings

from ...utils import logging
from .image_processing_maskformer import MaskFormerImageProcessor


SCREAMING_SNAKE_CASE__     : Dict				=   logging.get_logger(__name__)




class 				__lowerCAmelCase(							lowerCAmelCase__      ):






			def __init__(   self  :							List[Any]      ,       *SCREAMING_SNAKE_CASE  :							int      ,       **SCREAMING_SNAKE_CASE  :							Optional[int]	):




					"""simple docstring"""







					warnings.warn(
					    'The class MaskFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
					    ' Please use MaskFormerImageProcessor instead.'      ,       SCREAMING_SNAKE_CASE      ,       )
					super().__init__(*SCREAMING_SNAKE_CASE      ,       **SCREAMING_SNAKE_CASE	)
