






import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM





def 						A_					(						lowercase_			)				->str:







 """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 "ff1" in orig_key:
  SCREAMING_SNAKE_CASE				    =		orig_key.replace('ff1'	,  'intermediate.dense'			)
 if "ff2" in orig_key:
  SCREAMING_SNAKE_CASE				    =		orig_key.replace('ff2'	,  '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 "cls" not in orig_key:
  SCREAMING_SNAKE_CASE				    =		'nystromformer.' + orig_key

 return orig_key





def 						A_					(						lowercase_	,  lowercase_			)				->Tuple:







 """simple docstring"""

 for key in orig_state_dict.copy().keys():
  SCREAMING_SNAKE_CASE				    =		orig_state_dict.pop(lowercase_			)

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

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

 return orig_state_dict





def 						A_					(						lowercase_	,  lowercase_	,  lowercase_			)				->str:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		torch.load(lowercase_	,  map_location='cpu'			)['model_state_dict']
 SCREAMING_SNAKE_CASE				    =		NystromformerConfig.from_json_file(lowercase_			)
 SCREAMING_SNAKE_CASE				    =		NystromformerForMaskedLM(lowercase_			)

 SCREAMING_SNAKE_CASE				    =		convert_checkpoint_helper(lowercase_	,  lowercase_			)

 model.load_state_dict(lowercase_			)
 model.eval()
 model.save_pretrained(lowercase_			)

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


if __name__ == "__main__":
      __UpperCAmelCase      =				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."
      )
      __UpperCAmelCase      =				parser.parse_args()
      convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)












from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
		if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.27.0''')):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import (
		    AudioLDMPipeline,
		)
else:
		from .pipeline_audioldm import AudioLDMPipeline

