


import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		int=1						):




      """simple docstring"""





      if n_shave_prefix_segments >= 0:
            return ".".join(path.split('''.'''						)[n_shave_prefix_segments:]						)
      else:
            return ".".join(path.split('''.'''						)[:n_shave_prefix_segments]						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		str=0						):




      """simple docstring"""





      a						:Any 							=       []
      for old_item in old_list:
            a						:List[Any] 							=       old_item.replace('''in_layers.0''' ,  '''norm1'''						)
            a						:Union[str, Any] 							=       new_item.replace('''in_layers.2''' ,  '''conv1'''						)

            a						:Optional[Any] 							=       new_item.replace('''out_layers.0''' ,  '''norm2'''						)
            a						:int 							=       new_item.replace('''out_layers.3''' ,  '''conv2'''						)

            a						:Optional[Any] 							=       new_item.replace('''emb_layers.1''' ,  '''time_emb_proj'''						)
            a						:Union[str, Any] 							=       new_item.replace('''skip_connection''' ,  '''conv_shortcut'''						)

            a						:int 							=       shave_segments(UpperCAmelCase_ ,  n_shave_prefix_segments=UpperCAmelCase_						)

            mapping.append({'''old''': old_item, '''new''': new_item}						)

      return mapping
def 				__lowerCamelCase						( UpperCAmelCase_						:		Tuple ,  UpperCAmelCase_						:		Optional[int]=0						):




      """simple docstring"""





      a						:List[str] 							=       []
      for old_item in old_list:
            a						:List[str] 							=       old_item

            a						:Tuple 							=       new_item.replace('''norm.weight''' ,  '''group_norm.weight'''						)
            a						:Optional[int] 							=       new_item.replace('''norm.bias''' ,  '''group_norm.bias'''						)

            a						:str 							=       new_item.replace('''proj_out.weight''' ,  '''proj_attn.weight'''						)
            a						:Tuple 							=       new_item.replace('''proj_out.bias''' ,  '''proj_attn.bias'''						)

            a						:str 							=       shave_segments(UpperCAmelCase_ ,  n_shave_prefix_segments=UpperCAmelCase_						)

            mapping.append({'''old''': old_item, '''new''': new_item}						)

      return mapping
def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		Tuple ,  UpperCAmelCase_						:		Dict=None ,  UpperCAmelCase_						:		List[Any]=None ,  UpperCAmelCase_						:		Optional[Any]=None						):




      """simple docstring"""





      assert isinstance(UpperCAmelCase_ ,  UpperCAmelCase_						), "Paths should be a list of dicts containing 'old' and 'new' keys."

      # Splits the attention layers into three variables.
      if attention_paths_to_split is not None:
            for path, path_map in attention_paths_to_split.items():
                  a						:int 							=       old_checkpoint[path]
                  a						:List[str] 							=       old_tensor.shape[0] // 3

                  a						:int 							=       (-1, channels) if len(old_tensor.shape						) == 3 else (-1)

                  a						:int 							=       old_tensor.shape[0] // config['''num_head_channels'''] // 3

                  a						:int 							=       old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]						)
                  a							,				a							,				a						:Dict 							=       old_tensor.split(channels // num_heads ,  dim=1						)

                  a						:str 							=       query.reshape(UpperCAmelCase_						)
                  a						:List[str] 							=       key.reshape(UpperCAmelCase_						)
                  a						:str 							=       value.reshape(UpperCAmelCase_						)

      for path in paths:
            a						:Optional[Any] 							=       path['''new''']

            # These have already been assigned
            if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                  continue

            # Global renaming happens here
            a						:Optional[Any] 							=       new_path.replace('''middle_block.0''' ,  '''mid_block.resnets.0'''						)
            a						:Optional[int] 							=       new_path.replace('''middle_block.1''' ,  '''mid_block.attentions.0'''						)
            a						:Any 							=       new_path.replace('''middle_block.2''' ,  '''mid_block.resnets.1'''						)

            if additional_replacements is not None:
                  for replacement in additional_replacements:
                        a						:Optional[Any] 							=       new_path.replace(replacement['''old'''] ,  replacement['''new''']						)

        # proj_attn.weight has to be converted from conv 1D to linear
            if "proj_attn.weight" in new_path:
                  a						:List[str] 							=       old_checkpoint[path['''old''']][:, :, 0]
            else:
                  a						:Any 							=       old_checkpoint[path['''old''']]
def 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any] ,  UpperCAmelCase_						:		Dict						):




      """simple docstring"""





      a						:Dict 							=       {}

      a						:Optional[int] 							=       checkpoint['''time_embed.0.weight''']
      a						:Union[str, Any] 							=       checkpoint['''time_embed.0.bias''']
      a						:List[Any] 							=       checkpoint['''time_embed.2.weight''']
      a						:List[str] 							=       checkpoint['''time_embed.2.bias''']

      a						:Dict 							=       checkpoint['''input_blocks.0.0.weight''']
      a						:List[str] 							=       checkpoint['''input_blocks.0.0.bias''']

      a						:List[Any] 							=       checkpoint['''out.0.weight''']
      a						:Any 							=       checkpoint['''out.0.bias''']
      a						:Any 							=       checkpoint['''out.2.weight''']
      a						:Optional[int] 							=       checkpoint['''out.2.bias''']

      # Retrieves the keys for the input blocks only
      a						:Tuple 							=       len({'''.'''.join(layer.split('''.'''						)[:2]						) for layer in checkpoint if '''input_blocks''' in layer}						)
      a						:List[str] 							=       {
          layer_id: [key for key in checkpoint if F'''input_blocks.{layer_id}''' in key]
          for layer_id in range(UpperCAmelCase_						)
      }

      # Retrieves the keys for the middle blocks only
      a						:List[str] 							=       len({'''.'''.join(layer.split('''.'''						)[:2]						) for layer in checkpoint if '''middle_block''' in layer}						)
      a						:List[str] 							=       {
          layer_id: [key for key in checkpoint if F'''middle_block.{layer_id}''' in key]
          for layer_id in range(UpperCAmelCase_						)
      }

      # Retrieves the keys for the output blocks only
      a						:Dict 							=       len({'''.'''.join(layer.split('''.'''						)[:2]						) for layer in checkpoint if '''output_blocks''' in layer}						)
      a						:Any 							=       {
          layer_id: [key for key in checkpoint if F'''output_blocks.{layer_id}''' in key]
          for layer_id in range(UpperCAmelCase_						)
      }

      for i in range(1 ,  UpperCAmelCase_						):
            a						:Dict 							=       (i - 1) // (config['''num_res_blocks'''] + 1)
            a						:List[str] 							=       (i - 1) % (config['''num_res_blocks'''] + 1)

            a						:Optional[int] 							=       [key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key]
            a						:Any 							=       [key for key in input_blocks[i] if F'''input_blocks.{i}.1''' in key]

            if F'''input_blocks.{i}.0.op.weight''' in checkpoint:
                  a						:Optional[int] 							=       checkpoint[
                      F'''input_blocks.{i}.0.op.weight'''
                  ]
                  a						:int 							=       checkpoint[
                      F'''input_blocks.{i}.0.op.bias'''
                  ]
                  continue

            a						:Tuple 							=       renew_resnet_paths(UpperCAmelCase_						)
            a						:str 							=       {'''old''': F'''input_blocks.{i}.0''', '''new''': F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
            a						:Union[str, Any] 							=       {'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
            assign_to_checkpoint(
                UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  additional_replacements=[meta_path, resnet_op] ,  config=UpperCAmelCase_						)

            if len(UpperCAmelCase_						):
                  a						:Optional[Any] 							=       renew_attention_paths(UpperCAmelCase_						)
                  a						:str 							=       {
                      '''old''': F'''input_blocks.{i}.1''',
                      '''new''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
                  }
                  a						:Dict 							=       {
                      F'''input_blocks.{i}.1.qkv.bias''': {
                          '''key''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
                          '''query''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
                          '''value''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
                      },
                      F'''input_blocks.{i}.1.qkv.weight''': {
                          '''key''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
                          '''query''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
                          '''value''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
                      },
                  }
                  assign_to_checkpoint(
                      UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  additional_replacements=[meta_path] ,  attention_paths_to_split=UpperCAmelCase_ ,  config=UpperCAmelCase_ ,  )

      a						:Optional[int] 							=       middle_blocks[0]
      a						:Union[str, Any] 							=       middle_blocks[1]
      a						:int 							=       middle_blocks[2]

      a						:List[str] 							=       renew_resnet_paths(UpperCAmelCase_						)
      assign_to_checkpoint(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  config=UpperCAmelCase_						)

      a						:Union[str, Any] 							=       renew_resnet_paths(UpperCAmelCase_						)
      assign_to_checkpoint(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  config=UpperCAmelCase_						)

      a						:int 							=       renew_attention_paths(UpperCAmelCase_						)
      a						:Optional[int] 							=       {
          '''middle_block.1.qkv.bias''': {
              '''key''': '''mid_block.attentions.0.key.bias''',
              '''query''': '''mid_block.attentions.0.query.bias''',
              '''value''': '''mid_block.attentions.0.value.bias''',
          },
          '''middle_block.1.qkv.weight''': {
              '''key''': '''mid_block.attentions.0.key.weight''',
              '''query''': '''mid_block.attentions.0.query.weight''',
              '''value''': '''mid_block.attentions.0.value.weight''',
          },
      }
      assign_to_checkpoint(
          UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  attention_paths_to_split=UpperCAmelCase_ ,  config=UpperCAmelCase_						)

      for i in range(UpperCAmelCase_						):
            a						:Tuple 							=       i // (config['''num_res_blocks'''] + 1)
            a						:str 							=       i % (config['''num_res_blocks'''] + 1)
            a						:int 							=       [shave_segments(UpperCAmelCase_ ,  2						) for name in output_blocks[i]]
            a						:Union[str, Any] 							=       {}

            for layer in output_block_layers:
                  a							,				a						:Dict 							=       layer.split('''.'''						)[0], shave_segments(UpperCAmelCase_ ,  1						)
                  if layer_id in output_block_list:
                        output_block_list[layer_id].append(UpperCAmelCase_						)
                  else:
                        a						:List[str] 							=       [layer_name]

            if len(UpperCAmelCase_						) > 1:
                  a						:Any 							=       [key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
                  a						:Union[str, Any] 							=       [key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]

                  a						:Tuple 							=       renew_resnet_paths(UpperCAmelCase_						)
                  a						:Optional[Any] 							=       renew_resnet_paths(UpperCAmelCase_						)

                  a						:Dict 							=       {'''old''': F'''output_blocks.{i}.0''', '''new''': F'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
                  assign_to_checkpoint(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  additional_replacements=[meta_path] ,  config=UpperCAmelCase_						)

                  if ["conv.weight", "conv.bias"] in output_block_list.values():
                        a						:List[str] 							=       list(output_block_list.values()						).index(['''conv.weight''', '''conv.bias''']						)
                        a						:Optional[int] 							=       checkpoint[
                            F'''output_blocks.{i}.{index}.conv.weight'''
                        ]
                        a						:int 							=       checkpoint[
                            F'''output_blocks.{i}.{index}.conv.bias'''
                        ]

                        # Clear attentions as they have been attributed above.
                        if len(UpperCAmelCase_						) == 2:
                              a						:Optional[Any] 							=       []

                  if len(UpperCAmelCase_						):
                        a						:Tuple 							=       renew_attention_paths(UpperCAmelCase_						)
                        a						:Optional[Any] 							=       {
                            '''old''': F'''output_blocks.{i}.1''',
                            '''new''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
                        }
                        a						:int 							=       {
                            F'''output_blocks.{i}.1.qkv.bias''': {
                                '''key''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
                                '''query''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
                                '''value''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
                            },
                            F'''output_blocks.{i}.1.qkv.weight''': {
                                '''key''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
                                '''query''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
                                '''value''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
                            },
                        }
                        assign_to_checkpoint(
                            UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  additional_replacements=[meta_path] ,  attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions						) else None ,  config=UpperCAmelCase_ ,  )
            else:
                  a						:Optional[Any] 							=       renew_resnet_paths(UpperCAmelCase_ ,  n_shave_prefix_segments=1						)
                  for path in resnet_0_paths:
                        a						:Any 							=       '''.'''.join(['''output_blocks''', str(UpperCAmelCase_						), path['''old''']]						)
                        a						:str 							=       '''.'''.join(['''up_blocks''', str(UpperCAmelCase_						), '''resnets''', str(UpperCAmelCase_						), path['''new''']]						)

                        a						:List[Any] 							=       checkpoint[old_path]

      return new_checkpoint


if __name__ == "__main__":
     snake_case				:    str       				=     argparse.ArgumentParser()

     parser.add_argument(
         '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
     )

     parser.add_argument(
         '''--config_file''',
         default=None,
         type=str,
         required=True,
         help='''The config json file corresponding to the architecture.''',
     )

     parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

     snake_case				:    str       				=     parser.parse_args()

     snake_case				:    List[str]       				=     torch.load(args.checkpoint_path)

     with open(args.config_file) as f:
          snake_case				:    Any       				=     json.loads(f.read())

     snake_case				:    Any       				=     convert_ldm_checkpoint(checkpoint, config)

     if "ldm" in config:
          del config["ldm"]

     snake_case				:    Tuple       				=     UNetaDModel(**config)
     model.load_state_dict(converted_checkpoint)

     try:
          snake_case				:    Optional[int]       				=     DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
          snake_case				:    int       				=     VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

          snake_case				:    Optional[int]       				=     LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
          pipe.save_pretrained(args.dump_path)
     except:  # noqa: E722
          model.save_pretrained(args.dump_path)









from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       'ClapFeatureExtractor'
 SCREAMING_SNAKE_CASE__      =       ('RobertaTokenizer', 'RobertaTokenizerFast')




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase    ):
       super().__init__(_lowerCamelCase					,  _lowerCamelCase    )




 def __call__(		self					,  _lowerCamelCase=None					,  _lowerCamelCase=None					,  _lowerCamelCase=None					,  **_lowerCamelCase    ):
       a						:Dict 							=       kwargs.pop('''sampling_rate'''					,  _lowerCamelCase    )

       if text is None and audios is None:
             raise ValueError('''You have to specify either text or audios. Both cannot be none.'''    )

       if text is not None:
             a						:Optional[int] 							=       self.tokenizer(_lowerCamelCase					,  return_tensors=_lowerCamelCase					,  **_lowerCamelCase    )

       if audios is not None:
             a						:Tuple 							=       self.feature_extractor(
                 _lowerCamelCase					,  sampling_rate=_lowerCamelCase					,  return_tensors=_lowerCamelCase					,  **_lowerCamelCase    )

       if text is not None and audios is not None:
             a						:Union[str, Any] 							=       audio_features.input_features
             return encoding
       elif text is not None:
             return encoding
       else:
             return BatchEncoding(data=dict(**_lowerCamelCase    )					,  tensor_type=_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  *_lowerCamelCase					,  **_lowerCamelCase    ):
       return self.tokenizer.batch_decode(*_lowerCamelCase					,  **_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  *_lowerCamelCase					,  **_lowerCamelCase    ):
       return self.tokenizer.decode(*_lowerCamelCase					,  **_lowerCamelCase    )




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[Any] 							=       self.tokenizer.model_input_names
       a						:str 							=       self.feature_extractor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names    )    )





