



'''simple docstring'''






import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_=1		)->		Union[str, Any]:





       '''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     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_=0		)->		Optional[Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						int			     = []
       for old_item in old_list:
              _UpperCAmelCase  :						str			     = old_item.replace("""in_layers.0"""       ,   """norm1"""		)
              _UpperCAmelCase  :						int			     = new_item.replace("""in_layers.2"""       ,   """conv1"""		)

              _UpperCAmelCase  :						int			     = new_item.replace("""out_layers.0"""       ,   """norm2"""		)
              _UpperCAmelCase  :						Any			     = new_item.replace("""out_layers.3"""       ,   """conv2"""		)

              _UpperCAmelCase  :						Union[str, Any]			     = new_item.replace("""emb_layers.1"""       ,   """time_emb_proj"""		)
              _UpperCAmelCase  :						Dict			     = new_item.replace("""skip_connection"""       ,   """conv_shortcut"""		)

              _UpperCAmelCase  :						Dict			     = shave_segments(lowerCAmelCase_       ,   n_shave_prefix_segments=lowerCAmelCase_		)

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

       return mapping




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_=0		)->		Dict:





       '''simple docstring'''


       _UpperCAmelCase  :						Optional[int]			     = []
       for old_item in old_list:
              _UpperCAmelCase  :						Any			     = old_item

              _UpperCAmelCase  :						Tuple			     = new_item.replace("""norm.weight"""       ,   """group_norm.weight"""		)
              _UpperCAmelCase  :						Any			     = new_item.replace("""norm.bias"""       ,   """group_norm.bias"""		)

              _UpperCAmelCase  :						Dict			     = new_item.replace("""proj_out.weight"""       ,   """proj_attn.weight"""		)
              _UpperCAmelCase  :						List[Any]			     = new_item.replace("""proj_out.bias"""       ,   """proj_attn.bias"""		)

              _UpperCAmelCase  :						List[Any]			     = shave_segments(lowerCAmelCase_       ,   n_shave_prefix_segments=lowerCAmelCase_		)

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

       return mapping




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_=None       ,   lowerCAmelCase_=None       ,   lowerCAmelCase_=None		)->		List[Any]:





       '''simple docstring'''


       assert isinstance(lowerCAmelCase_       ,   lowerCAmelCase_		), "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():
                     _UpperCAmelCase  :						Union[str, Any]			     = old_checkpoint[path]
                     _UpperCAmelCase  :						Optional[int]			     = old_tensor.shape[0] // 3

                     _UpperCAmelCase  :						Optional[Any]			     = (-1, channels) if len(old_tensor.shape		) == 3 else (-1)

                     _UpperCAmelCase  :						List[Any]			     = old_tensor.shape[0] // config["""num_head_channels"""] // 3

                     _UpperCAmelCase  :						Tuple			     = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]		)
                     _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase  :						List[Any]			     = old_tensor.split(channels // num_heads       ,   dim=1		)

                     _UpperCAmelCase  :						Any			     = query.reshape(lowerCAmelCase_		)
                     _UpperCAmelCase  :						Any			     = key.reshape(lowerCAmelCase_		)
                     _UpperCAmelCase  :						Union[str, Any]			     = value.reshape(lowerCAmelCase_		)

       for path in paths:
              _UpperCAmelCase  :						Tuple			     = 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
              _UpperCAmelCase  :						Any			     = new_path.replace("""middle_block.0"""       ,   """mid_block.resnets.0"""		)
              _UpperCAmelCase  :						int			     = new_path.replace("""middle_block.1"""       ,   """mid_block.attentions.0"""		)
              _UpperCAmelCase  :						int			     = new_path.replace("""middle_block.2"""       ,   """mid_block.resnets.1"""		)

              if additional_replacements is not None:
                     for replacement in additional_replacements:
                            _UpperCAmelCase  :						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:
                     _UpperCAmelCase  :						str			     = old_checkpoint[path["""old"""]][:, :, 0]
              else:
                     _UpperCAmelCase  :						Optional[int]			     = old_checkpoint[path["""old"""]]




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		int:





       '''simple docstring'''


       _UpperCAmelCase  :						Union[str, Any]			     = {}

       _UpperCAmelCase  :						Tuple			     = checkpoint["""time_embed.0.weight"""]
       _UpperCAmelCase  :						List[str]			     = checkpoint["""time_embed.0.bias"""]
       _UpperCAmelCase  :						Optional[int]			     = checkpoint["""time_embed.2.weight"""]
       _UpperCAmelCase  :						Union[str, Any]			     = checkpoint["""time_embed.2.bias"""]

       _UpperCAmelCase  :						Any			     = checkpoint["""input_blocks.0.0.weight"""]
       _UpperCAmelCase  :						int			     = checkpoint["""input_blocks.0.0.bias"""]

       _UpperCAmelCase  :						str			     = checkpoint["""out.0.weight"""]
       _UpperCAmelCase  :						Optional[int]			     = checkpoint["""out.0.bias"""]
       _UpperCAmelCase  :						str			     = checkpoint["""out.2.weight"""]
       _UpperCAmelCase  :						Dict			     = checkpoint["""out.2.bias"""]

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

       # Retrieves the keys for the middle blocks only
       _UpperCAmelCase  :						Optional[Any]			     = len({""".""".join(layer.split("""."""		)[:2]		) for layer in checkpoint if """middle_block""" in layer}		)
       _UpperCAmelCase  :						int			     = {
           layer_id: [key for key in checkpoint if F'''middle_block.{layer_id}''' in key]
           for layer_id in range(lowerCAmelCase_		)
       }

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

       for i in range(1       ,   lowerCAmelCase_		):
              _UpperCAmelCase  :						Any			     = (i - 1) // (config["""num_res_blocks"""] + 1)
              _UpperCAmelCase  :						Any			     = (i - 1) % (config["""num_res_blocks"""] + 1)

              _UpperCAmelCase  :						Any			     = [key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key]
              _UpperCAmelCase  :						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:
                     _UpperCAmelCase  :						Dict			     = checkpoint[
                         F'''input_blocks.{i}.0.op.weight'''
                     ]
                     _UpperCAmelCase  :						Tuple			     = checkpoint[
                         F'''input_blocks.{i}.0.op.bias'''
                     ]
                     continue

              _UpperCAmelCase  :						Optional[Any]			     = renew_resnet_paths(lowerCAmelCase_		)
              _UpperCAmelCase  :						Optional[Any]			     = {"""old""": F'''input_blocks.{i}.0''', """new""": F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
              _UpperCAmelCase  :						Optional[int]			     = {"""old""": """resnets.2.op""", """new""": """downsamplers.0.op"""}
              assign_to_checkpoint(
                  lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   additional_replacements=[meta_path, resnet_op]       ,   config=lowerCAmelCase_		)

              if len(lowerCAmelCase_		):
                     _UpperCAmelCase  :						Optional[Any]			     = renew_attention_paths(lowerCAmelCase_		)
                     _UpperCAmelCase  :						Optional[Any]			     = {
                         """old""": F'''input_blocks.{i}.1''',
                         """new""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
                     }
                     _UpperCAmelCase  :						int			     = {
                         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(
                         lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   additional_replacements=[meta_path]       ,   attention_paths_to_split=lowerCAmelCase_       ,   config=lowerCAmelCase_       ,   )

       _UpperCAmelCase  :						int			     = middle_blocks[0]
       _UpperCAmelCase  :						Dict			     = middle_blocks[1]
       _UpperCAmelCase  :						Optional[int]			     = middle_blocks[2]

       _UpperCAmelCase  :						Tuple			     = renew_resnet_paths(lowerCAmelCase_		)
       assign_to_checkpoint(lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   config=lowerCAmelCase_		)

       _UpperCAmelCase  :						int			     = renew_resnet_paths(lowerCAmelCase_		)
       assign_to_checkpoint(lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   config=lowerCAmelCase_		)

       _UpperCAmelCase  :						str			     = renew_attention_paths(lowerCAmelCase_		)
       _UpperCAmelCase  :						List[Any]			     = {
           """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(
           lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   attention_paths_to_split=lowerCAmelCase_       ,   config=lowerCAmelCase_		)

       for i in range(lowerCAmelCase_		):
              _UpperCAmelCase  :						List[str]			     = i // (config["""num_res_blocks"""] + 1)
              _UpperCAmelCase  :						str			     = i % (config["""num_res_blocks"""] + 1)
              _UpperCAmelCase  :						List[Any]			     = [shave_segments(lowerCAmelCase_       ,   2		) for name in output_blocks[i]]
              _UpperCAmelCase  :						Optional[int]			     = {}

              for layer in output_block_layers:
                     _UpperCAmelCase     ,_UpperCAmelCase  :						Tuple			     = layer.split("""."""		)[0], shave_segments(lowerCAmelCase_       ,   1		)
                     if layer_id in output_block_list:
                            output_block_list[layer_id].append(lowerCAmelCase_		)
                     else:
                            _UpperCAmelCase  :						Optional[int]			     = [layer_name]

              if len(lowerCAmelCase_		) > 1:
                     _UpperCAmelCase  :						Tuple			     = [key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
                     _UpperCAmelCase  :						Any			     = [key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]

                     _UpperCAmelCase  :						Dict			     = renew_resnet_paths(lowerCAmelCase_		)
                     _UpperCAmelCase  :						Union[str, Any]			     = renew_resnet_paths(lowerCAmelCase_		)

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

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

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

                     if len(lowerCAmelCase_		):
                            _UpperCAmelCase  :						Union[str, Any]			     = renew_attention_paths(lowerCAmelCase_		)
                            _UpperCAmelCase  :						Tuple			     = {
                                """old""": F'''output_blocks.{i}.1''',
                                """new""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
                            }
                            _UpperCAmelCase  :						Dict			     = {
                                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(
                                lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   additional_replacements=[meta_path]       ,   attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions		) else None       ,   config=lowerCAmelCase_       ,   )
              else:
                     _UpperCAmelCase  :						List[Any]			     = renew_resnet_paths(lowerCAmelCase_       ,   n_shave_prefix_segments=1		)
                     for path in resnet_0_paths:
                            _UpperCAmelCase  :						Optional[Any]			     = """.""".join(["""output_blocks""", str(lowerCAmelCase_		), path["""old"""]]		)
                            _UpperCAmelCase  :						str			     = """.""".join(["""up_blocks""", str(lowerCAmelCase_		), """resnets""", str(lowerCAmelCase_		), path["""new"""]]		)

                            _UpperCAmelCase  :						Optional[Any]			     = checkpoint[old_path]

       return new_checkpoint


if __name__ == "__main__":
   A_							: Tuple						 =	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.""")

   A_							: List[str]						 =	parser.parse_args()

   A_							: Any						 =	torch.load(args.checkpoint_path)

   with open(args.config_file) as f:
      A_							: Union[str, Any]						 =	json.loads(f.read())

   A_							: List[Any]						 =	convert_ldm_checkpoint(checkpoint, config)

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

   A_							: List[str]						 =	UNetaDModel(**config)
   model.load_state_dict(converted_checkpoint)

   try:
      A_							: Union[str, Any]						 =	DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
      A_							: str						 =	VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

      A_							: Any						 =	LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
      pipe.save_pretrained(args.dump_path)
   except:  # noqa: E722
      model.save_pretrained(args.dump_path)








'''simple docstring'''






import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list



@require_torchaudio
@require_sentencepiece
class 							lowercase       (  unittest.TestCase				):


 """simple docstring"""


 def _snake_case (							self							)		->   Optional[Any]:
        _UpperCAmelCase  :						str			     = """laion/clap-htsat-unfused"""
        _UpperCAmelCase  :						int			     = tempfile.mkdtemp()







 def _snake_case (							self  ,**a_							)		->   str:
        return RobertaTokenizer.from_pretrained(self.checkpoint  ,**a_							)







 def _snake_case (							self  ,**a_							)		->   Tuple:
        return ClapFeatureExtractor.from_pretrained(self.checkpoint  ,**a_							)







 def _snake_case (							self							)		->   int:
        shutil.rmtree(self.tmpdirname							)







 def _snake_case (							self							)		->   Optional[int]:
        _UpperCAmelCase  :						str			     = self.get_tokenizer()
        _UpperCAmelCase  :						Any			     = self.get_feature_extractor()

        _UpperCAmelCase  :						int			     = ClapProcessor(tokenizer=a_  ,feature_extractor=a_							)

        processor.save_pretrained(self.tmpdirname							)
        _UpperCAmelCase  :						List[Any]			     = ClapProcessor.from_pretrained(self.tmpdirname							)

        self.assertEqual(processor.tokenizer.get_vocab()  ,tokenizer.get_vocab()							)
        self.assertIsInstance(processor.tokenizer  ,a_							)

        self.assertEqual(processor.feature_extractor.to_json_string()  ,feature_extractor.to_json_string()							)
        self.assertIsInstance(processor.feature_extractor  ,a_							)







 def _snake_case (							self							)		->   List[Any]:
        _UpperCAmelCase  :						int			     = ClapProcessor(tokenizer=self.get_tokenizer()  ,feature_extractor=self.get_feature_extractor()							)
        processor.save_pretrained(self.tmpdirname							)

        _UpperCAmelCase  :						List[str]			     = self.get_tokenizer(bos_token="""(BOS)"""  ,eos_token="""(EOS)"""							)
        _UpperCAmelCase  :						List[Any]			     = self.get_feature_extractor(do_normalize=a_  ,padding_value=1.0							)

        _UpperCAmelCase  :						Optional[Any]			     = ClapProcessor.from_pretrained(
            self.tmpdirname  ,bos_token="""(BOS)"""  ,eos_token="""(EOS)"""  ,do_normalize=a_  ,padding_value=1.0							)

        self.assertEqual(processor.tokenizer.get_vocab()  ,tokenizer_add_kwargs.get_vocab()							)
        self.assertIsInstance(processor.tokenizer  ,a_							)

        self.assertEqual(processor.feature_extractor.to_json_string()  ,feature_extractor_add_kwargs.to_json_string()							)
        self.assertIsInstance(processor.feature_extractor  ,a_							)







 def _snake_case (							self							)		->   str:
        _UpperCAmelCase  :						Tuple			     = self.get_feature_extractor()
        _UpperCAmelCase  :						Dict			     = self.get_tokenizer()

        _UpperCAmelCase  :						str			     = ClapProcessor(tokenizer=a_  ,feature_extractor=a_							)

        _UpperCAmelCase  :						Tuple			     = floats_list((3, 1_000)							)

        _UpperCAmelCase  :						int			     = feature_extractor(a_  ,return_tensors="""np"""							)
        _UpperCAmelCase  :						Union[str, Any]			     = processor(audios=a_  ,return_tensors="""np"""							)

        for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()  ,input_processor[key].sum()  ,delta=1E-2							)







 def _snake_case (							self							)		->   Tuple:
        _UpperCAmelCase  :						List[Any]			     = self.get_feature_extractor()
        _UpperCAmelCase  :						Any			     = self.get_tokenizer()

        _UpperCAmelCase  :						Optional[int]			     = ClapProcessor(tokenizer=a_  ,feature_extractor=a_							)

        _UpperCAmelCase  :						Union[str, Any]			     = """This is a test string"""

        _UpperCAmelCase  :						Optional[Any]			     = processor(text=a_							)

        _UpperCAmelCase  :						Any			     = tokenizer(a_							)

        for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]  ,encoded_processor[key]							)







 def _snake_case (							self							)		->   List[Any]:
        _UpperCAmelCase  :						str			     = self.get_feature_extractor()
        _UpperCAmelCase  :						List[str]			     = self.get_tokenizer()

        _UpperCAmelCase  :						Any			     = ClapProcessor(tokenizer=a_  ,feature_extractor=a_							)

        _UpperCAmelCase  :						List[str]			     = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _UpperCAmelCase  :						Dict			     = processor.batch_decode(a_							)
        _UpperCAmelCase  :						Any			     = tokenizer.batch_decode(a_							)

        self.assertListEqual(a_  ,a_							)







 def _snake_case (							self							)		->   Dict:
        _UpperCAmelCase  :						List[str]			     = self.get_feature_extractor()
        _UpperCAmelCase  :						int			     = self.get_tokenizer()

        _UpperCAmelCase  :						Dict			     = ClapProcessor(tokenizer=a_  ,feature_extractor=a_							)

        self.assertListEqual(
            processor.model_input_names[2:]  ,feature_extractor.model_input_names  ,msg="""`processor` and `feature_extractor` model input names do not match"""  ,)



