import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM


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

       return orig_key


def SCREAMING_SNAKE_CASE			(       lowercase_:		Any						,  lowercase_:		Tuple			):
       for key in orig_state_dict.copy().keys():
              _UpperCamelCase       :    int					  =					orig_state_dict.pop(lowercase_			)

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

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

       return orig_state_dict


def SCREAMING_SNAKE_CASE			(       lowercase_:		Any						,  lowercase_:		Any						,  lowercase_:		int			):
       _UpperCamelCase       :    str					  =					torch.load(lowercase_						,  map_location='cpu'			)['model_state_dict']
       _UpperCamelCase       :    Tuple					  =					NystromformerConfig.from_json_file(lowercase_			)
       _UpperCamelCase       :    List[str]					  =					NystromformerForMaskedLM(lowercase_			)

       _UpperCamelCase       :    str					  =					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 typing import TYPE_CHECKING

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


UpperCAmelCase     						=			{
    '''configuration_speech_to_text_2''': ['''SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2Text2Config'''],
    '''processing_speech_to_text_2''': ['''Speech2Text2Processor'''],
    '''tokenization_speech_to_text_2''': ['''Speech2Text2Tokenizer'''],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			[
     '''SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Speech2Text2ForCausalLM''',
     '''Speech2Text2PreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
 from .processing_speech_to_text_a import SpeechaTextaProcessor
 from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text_a import (
      SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextaForCausalLM,
      SpeechaTextaPreTrainedModel,
  )

else:
 import sys

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



