


import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase=1 ):
     if n_shave_prefix_segments >= 0:
          return ".".join(path.split(""".""" )[n_shave_prefix_segments:] )
     else:
          return ".".join(path.split(""".""" )[:n_shave_prefix_segments] )



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase=0 ):
     a_    :     List[str]										=    []
     for old_item in old_list:
          a_    :     Union[str, Any]										=    old_item.replace("""in_layers.0"""  ,  """norm1""" )
          a_    :     Optional[int]										=    new_item.replace("""in_layers.2"""  ,  """conv1""" )

          a_    :     Dict										=    new_item.replace("""out_layers.0"""  ,  """norm2""" )
          a_    :     Optional[int]										=    new_item.replace("""out_layers.3"""  ,  """conv2""" )

          a_    :     Optional[Any]										=    new_item.replace("""emb_layers.1"""  ,  """time_emb_proj""" )
          a_    :     List[str]										=    new_item.replace("""skip_connection"""  ,  """conv_shortcut""" )

          a_    :     Optional[int]										=    shave_segments(__UpperCamelCase  ,  n_shave_prefix_segments=__UpperCamelCase )

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

     return mapping



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase=0 ):
     a_    :     Optional[Any]										=    []
     for old_item in old_list:
          a_    :     Optional[int]										=    old_item

          a_    :     Optional[int]										=    new_item.replace("""norm.weight"""  ,  """group_norm.weight""" )
          a_    :     Any										=    new_item.replace("""norm.bias"""  ,  """group_norm.bias""" )

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

          a_    :     List[str]										=    shave_segments(__UpperCamelCase  ,  n_shave_prefix_segments=__UpperCamelCase )

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

     return mapping



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase=None  ,  __UpperCamelCase=None  ,  __UpperCamelCase=None ):
     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_    :     str										=    old_checkpoint[path]
               a_    :     List[str]										=    old_tensor.shape[0] // 3

               a_    :     Dict										=    (-1, channels) if len(old_tensor.shape ) == 3 else (-1)

               a_    :     List[str]										=    old_tensor.shape[0] // config["""num_head_channels"""] // 3

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

               a_    :     int										=    query.reshape(__UpperCamelCase )
               a_    :     Optional[int]										=    key.reshape(__UpperCamelCase )
               a_    :     Tuple										=    value.reshape(__UpperCamelCase )

     for path in paths:
          a_    :     Optional[int]										=    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_    :     List[str]										=    new_path.replace("""middle_block.0"""  ,  """mid_block.resnets.0""" )
          a_    :     int										=    new_path.replace("""middle_block.1"""  ,  """mid_block.attentions.0""" )
          a_    :     List[Any]										=    new_path.replace("""middle_block.2"""  ,  """mid_block.resnets.1""" )

          if additional_replacements is not None:
               for replacement in additional_replacements:
                    a_    :     Union[str, 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_    :     Union[str, Any]										=    old_checkpoint[path["""old"""]][:, :, 0]
          else:
               a_    :     Any										=    old_checkpoint[path["""old"""]]



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     Union[str, Any]										=    {}

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

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

     a_    :     List[Any]										=    checkpoint["""out.0.weight"""]
     a_    :     Optional[int]										=    checkpoint["""out.0.bias"""]
     a_    :     Any										=    checkpoint["""out.2.weight"""]
     a_    :     Dict										=    checkpoint["""out.2.bias"""]

     # Retrieves the keys for the input blocks only
     a_    :     Union[str, Any]										=    len({""".""".join(layer.split(""".""" )[:2] ) for layer in checkpoint if """input_blocks""" in layer} )
     a_    :     Dict										=    {
         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_    :     str										=    len({""".""".join(layer.split(""".""" )[:2] ) for layer in checkpoint if """middle_block""" in layer} )
     a_    :     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_    :     Optional[int]										=    len({""".""".join(layer.split(""".""" )[:2] ) for layer in checkpoint if """output_blocks""" in layer} )
     a_    :     Optional[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_    :     Dict										=    (i - 1) % (config["""num_res_blocks"""] + 1)

          a_    :     Tuple										=    [key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key]
          a_    :     Tuple										=    [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_    :     Tuple										=    checkpoint[
                   F'''input_blocks.{i}.0.op.weight'''
               ]
               a_    :     List[str]										=    checkpoint[
                   F'''input_blocks.{i}.0.op.bias'''
               ]
               continue

          a_    :     Dict										=    renew_resnet_paths(__UpperCamelCase )
          a_    :     Union[str, Any]										=    {"""old""": F'''input_blocks.{i}.0''', """new""": F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
          a_    :     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_    :     Dict										=    renew_attention_paths(__UpperCamelCase )
               a_    :     Any										=    {
                   """old""": F'''input_blocks.{i}.1''',
                   """new""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
               }
               a_    :     List[str]										=    {
                   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_    :     int										=    middle_blocks[0]
     a_    :     str										=    middle_blocks[1]
     a_    :     Any										=    middle_blocks[2]

     a_    :     List[Any]										=    renew_resnet_paths(__UpperCamelCase )
     assign_to_checkpoint(__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  config=__UpperCamelCase )

     a_    :     Tuple										=    renew_resnet_paths(__UpperCamelCase )
     assign_to_checkpoint(__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  config=__UpperCamelCase )

     a_    :     str										=    renew_attention_paths(__UpperCamelCase )
     a_    :     Optional[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(
         __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  attention_paths_to_split=__UpperCamelCase  ,  config=__UpperCamelCase )

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

          for layer in output_block_layers:
               a_			,			a_    :     int										=    layer.split(""".""" )[0], shave_segments(__UpperCamelCase  ,  1 )
               if layer_id in output_block_list:
                    output_block_list[layer_id].append(__UpperCamelCase )
               else:
                    a_    :     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_    :     Optional[int]										=    [key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]

               a_    :     Tuple										=    renew_resnet_paths(__UpperCamelCase )
               a_    :     Any										=    renew_resnet_paths(__UpperCamelCase )

               a_    :     Any										=    {"""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_    :     Tuple										=    list(output_block_list.values() ).index(["""conv.weight""", """conv.bias"""] )
                    a_    :     str										=    checkpoint[
                        F'''output_blocks.{i}.{index}.conv.weight'''
                    ]
                    a_    :     str										=    checkpoint[
                        F'''output_blocks.{i}.{index}.conv.bias'''
                    ]

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

               if len(__UpperCamelCase ):
                    a_    :     Optional[int]										=    renew_attention_paths(__UpperCamelCase )
                    a_    :     Any										=    {
                        """old""": F'''output_blocks.{i}.1''',
                        """new""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
                    }
                    a_    :     Optional[Any]										=    {
                        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_    :     List[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_    :     Dict										=    """.""".join(["""up_blocks""", str(__UpperCamelCase ), """resnets""", str(__UpperCamelCase ), path["""new"""]] )

                    a_    :     int										=    checkpoint[old_path]

     return new_checkpoint


if __name__ == "__main__":
       __lowerCamelCase        =     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.''')

       __lowerCamelCase        =     parser.parse_args()

       __lowerCamelCase        =     torch.load(args.checkpoint_path)

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

       __lowerCamelCase        =     convert_ldm_checkpoint(checkpoint, config)

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

       __lowerCamelCase        =     UNetaDModel(**config)
       model.load_state_dict(converted_checkpoint)

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

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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowerCamelCase        =     {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     ['''ViTFeatureExtractor''']
       __lowerCamelCase        =     ['''ViTImageProcessor''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''VIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''ViTForImageClassification''',
           '''ViTForMaskedImageModeling''',
           '''ViTModel''',
           '''ViTPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''TFViTForImageClassification''',
           '''TFViTModel''',
           '''TFViTPreTrainedModel''',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''FlaxViTForImageClassification''',
           '''FlaxViTModel''',
           '''FlaxViTPreTrainedModel''',
       ]

if TYPE_CHECKING:
       from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_vit import ViTFeatureExtractor
              from .image_processing_vit import ViTImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vit import (
                  VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ViTForImageClassification,
                  ViTForMaskedImageModeling,
                  ViTModel,
                  ViTPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
       import sys

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



