



'''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'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase											=      {
    '''configuration_align''': [
        '''ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''AlignConfig''',
        '''AlignTextConfig''',
        '''AlignVisionConfig''',
    ],
    '''processing_align''': ['''AlignProcessor'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase											=      [
      '''ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''AlignModel''',
      '''AlignPreTrainedModel''',
      '''AlignTextModel''',
      '''AlignVisionModel''',
  ]

if TYPE_CHECKING:
  from .configuration_align import (
      ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
      AlignConfig,
      AlignTextConfig,
      AlignVisionConfig,
  )
  from .processing_align import AlignProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_align import (
        ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
        AlignModel,
        AlignPreTrainedModel,
        AlignTextModel,
        AlignVisionModel,
    )

else:
  import sys

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


