





'''simple docstring'''


import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM




def  __a			(  UpperCAmelCase     )   ->Optional[int]:



	"""simple docstring"""







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

	return orig_key




def  __a			(  UpperCAmelCase							,      UpperCAmelCase     )   ->Union[str, Any]:



	"""simple docstring"""







	for key in orig_state_dict.copy().keys():
		A					=   orig_state_dict.pop(_lowerCamelCase     )

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

	A					=   orig_state_dict["cls.predictions.decoder.bias"]
	A					=   (
	    torch.arange(config.max_position_embeddings     ).expand((1, -1)     ) + 2
	)

	return orig_state_dict




def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase     )   ->Optional[int]:



	"""simple docstring"""







	A					=   torch.load(_lowerCamelCase							,      map_location="""cpu"""     )["model_state_dict"]
	A					=   NystromformerConfig.from_json_file(_lowerCamelCase     )
	A					=   NystromformerForMaskedLM(_lowerCamelCase     )

	A					=   convert_checkpoint_helper(_lowerCamelCase							,      _lowerCamelCase     )

	model.load_state_dict(_lowerCamelCase     )
	model.eval()
	model.save_pretrained(_lowerCamelCase     )

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


if __name__ == "__main__":
				_lowerCamelCase					:						List[Any]					     =							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.'
				)
				_lowerCamelCase					:						Optional[Any]					     =							parser.parse_args()
				convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase    				=		{'''configuration_vit_hybrid''': ['''VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTHybridConfig''']}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase    				=		[
          '''VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ViTHybridForImageClassification''',
          '''ViTHybridModel''',
          '''ViTHybridPreTrainedModel''',
      ]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase    				=		['''ViTHybridImageProcessor''']


if TYPE_CHECKING:
      from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_vit_hybrid import (
                VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
                ViTHybridForImageClassification,
                ViTHybridModel,
                ViTHybridPreTrainedModel,
            )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
      import sys

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



