






"""simple docstring"""





import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel



def     __lowercase					(    a :				Tuple       ,		a :				int=1 )					->					Union[str, Any]:
      if n_shave_prefix_segments >= 0:
            return ".".join(path.split('''.''' )[n_shave_prefix_segments:] )
      else:
            return ".".join(path.split('''.''' )[:n_shave_prefix_segments] )



def     __lowercase					(    a :				List[str]       ,		a :				Dict=0 )					->					Optional[Any]:
      __snake_case      :    int  			=[]
      for old_item in old_list:
            __snake_case      :    str  			=old_item.replace('''in_layers.0'''       ,		'''norm1''' )
            __snake_case      :    Dict  			=new_item.replace('''in_layers.2'''       ,		'''conv1''' )

            __snake_case      :    Dict  			=new_item.replace('''out_layers.0'''       ,		'''norm2''' )
            __snake_case      :    int  			=new_item.replace('''out_layers.3'''       ,		'''conv2''' )

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

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

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

      return mapping



def     __lowercase					(    a :				List[str]       ,		a :				str=0 )					->					List[str]:
      __snake_case      :    Optional[int]  			=[]
      for old_item in old_list:
            __snake_case      :    int  			=old_item

            __snake_case      :    str  			=new_item.replace('''norm.weight'''       ,		'''group_norm.weight''' )
            __snake_case      :    Tuple  			=new_item.replace('''norm.bias'''       ,		'''group_norm.bias''' )

            __snake_case      :    Optional[Any]  			=new_item.replace('''proj_out.weight'''       ,		'''proj_attn.weight''' )
            __snake_case      :    int  			=new_item.replace('''proj_out.bias'''       ,		'''proj_attn.bias''' )

            __snake_case      :    Any  			=shave_segments(a       ,		n_shave_prefix_segments=a )

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

      return mapping



def     __lowercase					(    a :				Optional[int]       ,		a :				Optional[Any]       ,		a :				Union[str, Any]       ,		a :				List[Any]=None       ,		a :				Dict=None       ,		a :				Union[str, Any]=None )					->					Optional[Any]:
      assert isinstance(a       ,		a ), "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():
                  __snake_case      :    Optional[Any]  			=old_checkpoint[path]
                  __snake_case      :    List[Any]  			=old_tensor.shape[0] // 3

                  __snake_case      :    Union[str, Any]  			=(-1, channels) if len(old_tensor.shape ) == 3 else (-1)

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

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

                  __snake_case      :    Optional[Any]  			=query.reshape(a )
                  __snake_case      :    List[Any]  			=key.reshape(a )
                  __snake_case      :    Dict  			=value.reshape(a )

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

            if additional_replacements is not None:
                  for replacement in additional_replacements:
                        __snake_case      :    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:
                  __snake_case      :    Dict  			=old_checkpoint[path['''old''']][:, :, 0]
            else:
                  __snake_case      :    Dict  			=old_checkpoint[path['''old''']]



def     __lowercase					(    a :				Optional[Any]       ,		a :				Tuple )					->					str:
      __snake_case      :    Tuple  			={}

      __snake_case      :    Union[str, Any]  			=checkpoint['''time_embed.0.weight''']
      __snake_case      :    List[str]  			=checkpoint['''time_embed.0.bias''']
      __snake_case      :    Dict  			=checkpoint['''time_embed.2.weight''']
      __snake_case      :    Union[str, Any]  			=checkpoint['''time_embed.2.bias''']

      __snake_case      :    Dict  			=checkpoint['''input_blocks.0.0.weight''']
      __snake_case      :    Dict  			=checkpoint['''input_blocks.0.0.bias''']

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

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

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

      # Retrieves the keys for the output blocks only
      __snake_case      :    List[str]  			=len({'''.'''.join(layer.split('''.''' )[:2] ) for layer in checkpoint if '''output_blocks''' in layer} )
      __snake_case      :    Union[str, Any]  			={
          layer_id: [key for key in checkpoint if f'''output_blocks.{layer_id}''' in key]
          for layer_id in range(a )
      }

      for i in range(1       ,		a ):
            __snake_case      :    Optional[Any]  			=(i - 1) // (config['''num_res_blocks'''] + 1)
            __snake_case      :    Dict  			=(i - 1) % (config['''num_res_blocks'''] + 1)

            __snake_case      :    List[str]  			=[key for key in input_blocks[i] if f'''input_blocks.{i}.0''' in key]
            __snake_case      :    str  			=[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:
                  __snake_case      :    int  			=checkpoint[
                      f'''input_blocks.{i}.0.op.weight'''
                  ]
                  __snake_case      :    str  			=checkpoint[
                      f'''input_blocks.{i}.0.op.bias'''
                  ]
                  continue

            __snake_case      :    Optional[int]  			=renew_resnet_paths(a )
            __snake_case      :    Tuple  			={'''old''': f'''input_blocks.{i}.0''', '''new''': f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
            __snake_case      :    int  			={'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
            assign_to_checkpoint(
                a       ,		a       ,		a       ,		additional_replacements=[meta_path, resnet_op]       ,		config=a )

            if len(a ):
                  __snake_case      :    str  			=renew_attention_paths(a )
                  __snake_case      :    Dict  			={
                      '''old''': f'''input_blocks.{i}.1''',
                      '''new''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
                  }
                  __snake_case      :    List[Any]  			={
                      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(
                      a       ,		a       ,		a       ,		additional_replacements=[meta_path]       ,		attention_paths_to_split=a       ,		config=a       ,		)

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

      __snake_case      :    Optional[Any]  			=renew_resnet_paths(a )
      assign_to_checkpoint(a       ,		a       ,		a       ,		config=a )

      __snake_case      :    Optional[Any]  			=renew_resnet_paths(a )
      assign_to_checkpoint(a       ,		a       ,		a       ,		config=a )

      __snake_case      :    List[str]  			=renew_attention_paths(a )
      __snake_case      :    Tuple  			={
          '''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(
          a       ,		a       ,		a       ,		attention_paths_to_split=a       ,		config=a )

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

            for layer in output_block_layers:
                  __snake_case			,				__snake_case      :    int  			=layer.split('''.''' )[0], shave_segments(a       ,		1 )
                  if layer_id in output_block_list:
                        output_block_list[layer_id].append(a )
                  else:
                        __snake_case      :    Any  			=[layer_name]

            if len(a ) > 1:
                  __snake_case      :    Optional[int]  			=[key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
                  __snake_case      :    int  			=[key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]

                  __snake_case      :    Optional[int]  			=renew_resnet_paths(a )
                  __snake_case      :    int  			=renew_resnet_paths(a )

                  __snake_case      :    Optional[int]  			={'''old''': f'''output_blocks.{i}.0''', '''new''': f'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
                  assign_to_checkpoint(a       ,		a       ,		a       ,		additional_replacements=[meta_path]       ,		config=a )

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

                        # Clear attentions as they have been attributed above.
                        if len(a ) == 2:
                              __snake_case      :    List[str]  			=[]

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

                        __snake_case      :    str  			=checkpoint[old_path]

      return new_checkpoint


if __name__ == "__main__":
    UpperCamelCase_	:				Optional[Any]										= 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.""")

    UpperCamelCase_	:				Tuple										= parser.parse_args()

    UpperCamelCase_	:				Optional[Any]										= torch.load(args.checkpoint_path)

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

    UpperCamelCase_	:				Dict										= convert_ldm_checkpoint(checkpoint, config)

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

    UpperCamelCase_	:				List[Any]										= UNetaDModel(**config)
    model.load_state_dict(converted_checkpoint)

    try:
        UpperCamelCase_	:				Dict										= DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
        UpperCamelCase_	:				Any										= VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

        UpperCamelCase_	:				Union[str, 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"""





from typing import TYPE_CHECKING

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


UpperCamelCase_	:				Any										= {"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_	:				List[str]										= ["""ReformerTokenizer"""]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_	:				List[str]										= ["""ReformerTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_	:				Optional[int]										= [
        """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ReformerAttention""",
        """ReformerForMaskedLM""",
        """ReformerForQuestionAnswering""",
        """ReformerForSequenceClassification""",
        """ReformerLayer""",
        """ReformerModel""",
        """ReformerModelWithLMHead""",
        """ReformerPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_reformer import ReformerTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_reformer_fast import ReformerTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_reformer import (
            REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            ReformerAttention,
            ReformerForMaskedLM,
            ReformerForQuestionAnswering,
            ReformerForSequenceClassification,
            ReformerLayer,
            ReformerModel,
            ReformerModelWithLMHead,
            ReformerPreTrainedModel,
        )

else:
    import sys

    UpperCamelCase_	:				Tuple										= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


