import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer




class  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    str  			=   '''Wav2Vec2FeatureExtractor'''
   lowerCAmelCase      :    Any  			=   '''AutoTokenizer'''



   def __init__(	self					:  List[Any]		,  _lowercase					:  Dict		,  _lowercase					:  List[str]							):



    """simple docstring"""



    super().__init__(_lowercase		,  _lowercase							)
    _UpperCamelCase:     Dict							= self.feature_extractor
    _UpperCamelCase:     str							= False



   @classmethod
   def   lowerCAmelCase						(	cls					:  List[Any]		,  _lowercase					:  Any		,  **_lowercase					:  Any							):



    """simple docstring"""



    try:
     return super().from_pretrained(_lowercase		,  **_lowercase							)
    except OSError:
     warnings.warn(
         f"""Loading a tokenizer inside {cls.__name__} from a config that does not"""
         ''' include a `tokenizer_class` attribute is deprecated and will be '''
         '''removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'''
         ''' attribute to either your `config.json` or `tokenizer_config.json` '''
         '''file to suppress this warning: '''		,  _lowercase		,  )

     _UpperCamelCase:     Dict							= WavaVecaFeatureExtractor.from_pretrained(_lowercase		,  **_lowercase							)
     _UpperCamelCase:     List[str]							= WavaVecaCTCTokenizer.from_pretrained(_lowercase		,  **_lowercase							)

     return cls(feature_extractor=_lowercase		,  tokenizer=_lowercase							)



   def __call__(	self					:  Optional[int]		,  *_lowercase					:  Union[str, Any]		,  **_lowercase					:  Union[str, Any]							):



    """simple docstring"""



    if self._in_target_context_manager:
     return self.current_processor(*_lowercase		,  **_lowercase							)

    if "raw_speech" in kwargs:
     warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''							)
     _UpperCamelCase:     List[Any]							= kwargs.pop('''raw_speech'''							)
    else:
     _UpperCamelCase:     Dict							= kwargs.pop('''audio'''		,  _lowercase							)
    _UpperCamelCase:     Union[str, Any]							= kwargs.pop('''sampling_rate'''		,  _lowercase							)
    _UpperCamelCase:     Dict							= kwargs.pop('''text'''		,  _lowercase							)
    if len(_lowercase							) > 0:
     _UpperCamelCase:     Dict							= args[0]
     _UpperCamelCase:     List[str]							= args[1:]

    if audio is None and text is None:
     raise ValueError('''You need to specify either an `audio` or `text` input to process.'''							)

    if audio is not None:
     _UpperCamelCase:     Dict							= self.feature_extractor(_lowercase		,  *_lowercase		,  sampling_rate=_lowercase		,  **_lowercase							)
    if text is not None:
     _UpperCamelCase:     Optional[int]							= self.tokenizer(_lowercase		,  **_lowercase							)

    if text is None:
     return inputs
    elif audio is None:
     return encodings
    else:
     _UpperCamelCase:     Any							= encodings['''input_ids''']
     return inputs



   def   lowerCAmelCase						(	self					:  List[Any]		,  *_lowercase					:  Tuple		,  **_lowercase					:  Tuple							):



    """simple docstring"""



    if self._in_target_context_manager:
     return self.current_processor.pad(*_lowercase		,  **_lowercase							)

    _UpperCamelCase:     str							= kwargs.pop('''input_features'''		,  _lowercase							)
    _UpperCamelCase:     str							= kwargs.pop('''labels'''		,  _lowercase							)
    if len(_lowercase							) > 0:
     _UpperCamelCase:     Tuple							= args[0]
     _UpperCamelCase:     List[Any]							= args[1:]

    if input_features is not None:
     _UpperCamelCase:     List[str]							= self.feature_extractor.pad(_lowercase		,  *_lowercase		,  **_lowercase							)
    if labels is not None:
     _UpperCamelCase:     str							= self.tokenizer.pad(_lowercase		,  **_lowercase							)

    if labels is None:
     return input_features
    elif input_features is None:
     return labels
    else:
     _UpperCamelCase:     Any							= labels['''input_ids''']
     return input_features



   def   lowerCAmelCase						(	self					:  int		,  *_lowercase					:  List[Any]		,  **_lowercase					:  Optional[int]							):



    """simple docstring"""



    return self.tokenizer.batch_decode(*_lowercase		,  **_lowercase							)



   def   lowerCAmelCase						(	self					:  Union[str, Any]		,  *_lowercase					:  Tuple		,  **_lowercase					:  List[str]							):



    """simple docstring"""



    return self.tokenizer.decode(*_lowercase		,  **_lowercase							)



   @contextmanager
   def   lowerCAmelCase						(	self					:  Any							):



    """simple docstring"""



    warnings.warn(
        '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
        '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
        '''your audio inputs, or in a separate call.'''							)
    _UpperCamelCase:     int							= True
    _UpperCamelCase:     Dict							= self.tokenizer
    yield
    _UpperCamelCase:     Dict							= self.feature_extractor
    _UpperCamelCase:     Optional[int]							= False
import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline




def       lowerCAmelCase_     (		lowercase:							Optional[int]  ,					lowercase:							Any  ,					lowercase:							str  ,					lowercase:							List[str]  ,					lowercase:							Optional[int]		)    ->				int:







 '''simple docstring'''
 # load base model
 _UpperCamelCase:     Optional[int]							= StableDiffusionPipeline.from_pretrained(lowercase  ,					torch_dtype=torch.floataa		)

 # load LoRA weight from .safetensors
 _UpperCamelCase:     List[str]							= load_file(lowercase		)

 _UpperCamelCase:     Optional[int]							= []

 # directly update weight in diffusers model
 for key in state_dict:
  # it is suggested to print out the key, it usually will be something like below
  # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

  # as we have set the alpha beforehand, so just skip
  if ".alpha" in key or key in visited:
   continue

  if "text" in key:
   _UpperCamelCase:     int							= key.split('''.'''		)[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''		)[-1].split('''_'''		)
   _UpperCamelCase:     str							= pipeline.text_encoder
  else:
   _UpperCamelCase:     Optional[Any]							= key.split('''.'''		)[0].split(LORA_PREFIX_UNET + '''_'''		)[-1].split('''_'''		)
   _UpperCamelCase:     Any							= pipeline.unet

  # find the target layer
  _UpperCamelCase:     Union[str, Any]							= layer_infos.pop(0		)
  while len(lowercase		) > -1:
   try:
    _UpperCamelCase:     Union[str, Any]							= curr_layer.__getattr__(lowercase		)
    if len(lowercase		) > 0:
     _UpperCamelCase:     Tuple							= layer_infos.pop(0		)
    elif len(lowercase		) == 0:
     break
   except Exception:
    if len(lowercase		) > 0:
     temp_name += "_" + layer_infos.pop(0		)
    else:
     _UpperCamelCase:     List[Any]							= layer_infos.pop(0		)

  _UpperCamelCase:     Any							= []
  if "lora_down" in key:
   pair_keys.append(key.replace('''lora_down'''  ,					'''lora_up'''		)		)
   pair_keys.append(lowercase		)
  else:
   pair_keys.append(lowercase		)
   pair_keys.append(key.replace('''lora_up'''  ,					'''lora_down'''		)		)

  # update weight
  if len(state_dict[pair_keys[0]].shape		) == 4:
   _UpperCamelCase:     str							= state_dict[pair_keys[0]].squeeze(3		).squeeze(2		).to(torch.floataa		)
   _UpperCamelCase:     Optional[int]							= state_dict[pair_keys[1]].squeeze(3		).squeeze(2		).to(torch.floataa		)
   curr_layer.weight.data += alpha * torch.mm(lowercase  ,					lowercase		).unsqueeze(2		).unsqueeze(3		)
  else:
   _UpperCamelCase:     str							= state_dict[pair_keys[0]].to(torch.floataa		)
   _UpperCamelCase:     Dict							= state_dict[pair_keys[1]].to(torch.floataa		)
   curr_layer.weight.data += alpha * torch.mm(lowercase  ,					lowercase		)

  # update visited list
  for item in pair_keys:
   visited.append(lowercase		)

 return pipeline


if __name__ == "__main__":
  UpperCAmelCase_			    =							argparse.ArgumentParser()

  parser.add_argument(
      '''--base_model_path''', default=None, type=str, required=True, help='''Path to the base model in diffusers format.'''
  )
  parser.add_argument(
      '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
  )
  parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
  parser.add_argument(
      '''--lora_prefix_unet''', default='''lora_unet''', type=str, help='''The prefix of UNet weight in safetensors'''
  )
  parser.add_argument(
      '''--lora_prefix_text_encoder''',
      default='''lora_te''',
      type=str,
      help='''The prefix of text encoder weight in safetensors''',
  )
  parser.add_argument('''--alpha''', default=0.7_5, type=float, help='''The merging ratio in W = W0 + alpha * deltaW''')
  parser.add_argument(
      '''--to_safetensors''', action='''store_true''', help='''Whether to store pipeline in safetensors format or not.'''
  )
  parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')

  UpperCAmelCase_			    =							parser.parse_args()

  UpperCAmelCase_			    =							args.base_model_path
  UpperCAmelCase_			    =							args.checkpoint_path
  UpperCAmelCase_			    =							args.dump_path
  UpperCAmelCase_			    =							args.lora_prefix_unet
  UpperCAmelCase_			    =							args.lora_prefix_text_encoder
  UpperCAmelCase_			    =							args.alpha

  UpperCAmelCase_			    =							convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

  UpperCAmelCase_			    =							pipe.to(args.device)
  pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)