



"""simple docstring"""







import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel

def     UpperCamelCase_   (   lowerCAmelCase__				:    List[Any]   ,       lowerCAmelCase__				:    List[str]=1						)						->       Dict:

  """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     UpperCamelCase_   (   lowerCAmelCase__				:    List[str]   ,       lowerCAmelCase__				:    int=0						)						->       Any:

  """simple docstring"""




  lowerCAmelCase_       :    Optional[Any]								=			[]
  for old_item in old_list:
    lowerCAmelCase_       :    Dict								=			old_item.replace('in_layers.0'   ,       'norm1'						)
    lowerCAmelCase_       :    Optional[int]								=			new_item.replace('in_layers.2'   ,       'conv1'						)

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

    lowerCAmelCase_       :    Tuple								=			new_item.replace('emb_layers.1'   ,       'time_emb_proj'						)
    lowerCAmelCase_       :    Optional[int]								=			new_item.replace('skip_connection'   ,       'conv_shortcut'						)

    lowerCAmelCase_       :    List[str]								=			shave_segments(snake_case_   ,       n_shave_prefix_segments=snake_case_						)

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

  return mapping

def     UpperCamelCase_   (   lowerCAmelCase__				:    Optional[int]   ,       lowerCAmelCase__				:    List[str]=0						)						->       str:

  """simple docstring"""




  lowerCAmelCase_       :    Any								=			[]
  for old_item in old_list:
    lowerCAmelCase_       :    Union[str, Any]								=			old_item

    lowerCAmelCase_       :    List[Any]								=			new_item.replace('norm.weight'   ,       'group_norm.weight'						)
    lowerCAmelCase_       :    Tuple								=			new_item.replace('norm.bias'   ,       'group_norm.bias'						)

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

    lowerCAmelCase_       :    Tuple								=			shave_segments(snake_case_   ,       n_shave_prefix_segments=snake_case_						)

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

  return mapping

def     UpperCamelCase_   (   lowerCAmelCase__				:    Union[str, Any]   ,       lowerCAmelCase__				:    Optional[int]   ,       lowerCAmelCase__				:    Dict   ,       lowerCAmelCase__				:    List[Any]=None   ,       lowerCAmelCase__				:    List[str]=None   ,       lowerCAmelCase__				:    Union[str, Any]=None						)						->       Dict:

  """simple docstring"""




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

      lowerCAmelCase_       :    List[str]								=			(-1, channels) if len(old_tensor.shape						) == 3 else (-1)

      lowerCAmelCase_       :    Union[str, Any]								=			old_tensor.shape[0] // config['num_head_channels'] // 3

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

      lowerCAmelCase_       :    Optional[int]								=			query.reshape(snake_case_						)
      lowerCAmelCase_       :    Any								=			key.reshape(snake_case_						)
      lowerCAmelCase_       :    Dict								=			value.reshape(snake_case_						)

  for path in paths:
    lowerCAmelCase_       :    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
    lowerCAmelCase_       :    Tuple								=			new_path.replace('middle_block.0'   ,       'mid_block.resnets.0'						)
    lowerCAmelCase_       :    Any								=			new_path.replace('middle_block.1'   ,       'mid_block.attentions.0'						)
    lowerCAmelCase_       :    Optional[Any]								=			new_path.replace('middle_block.2'   ,       'mid_block.resnets.1'						)

    if additional_replacements is not None:
      for replacement in additional_replacements:
        lowerCAmelCase_       :    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:
      lowerCAmelCase_       :    Tuple								=			old_checkpoint[path['old']][:, :, 0]
    else:
      lowerCAmelCase_       :    List[str]								=			old_checkpoint[path['old']]

def     UpperCamelCase_   (   lowerCAmelCase__				:    Dict   ,       lowerCAmelCase__				:    Optional[int]						)						->       List[str]:

  """simple docstring"""




  lowerCAmelCase_       :    Any								=			{}

  lowerCAmelCase_       :    List[str]								=			checkpoint['time_embed.0.weight']
  lowerCAmelCase_       :    int								=			checkpoint['time_embed.0.bias']
  lowerCAmelCase_       :    Optional[int]								=			checkpoint['time_embed.2.weight']
  lowerCAmelCase_       :    Dict								=			checkpoint['time_embed.2.bias']

  lowerCAmelCase_       :    Tuple								=			checkpoint['input_blocks.0.0.weight']
  lowerCAmelCase_       :    str								=			checkpoint['input_blocks.0.0.bias']

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

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

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

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

  for i in range(1   ,       snake_case_						):
    lowerCAmelCase_       :    Any								=			(i - 1) // (config['num_res_blocks'] + 1)
    lowerCAmelCase_       :    List[str]								=			(i - 1) % (config['num_res_blocks'] + 1)

    lowerCAmelCase_       :    Dict								=			[key for key in input_blocks[i] if f"input_blocks.{i}.0" in key]
    lowerCAmelCase_       :    List[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:
      lowerCAmelCase_       :    List[str]								=			checkpoint[
          f"input_blocks.{i}.0.op.weight"
      ]
      lowerCAmelCase_       :    List[str]								=			checkpoint[
          f"input_blocks.{i}.0.op.bias"
      ]
      continue

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

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

  lowerCAmelCase_       :    List[str]								=			middle_blocks[0]
  lowerCAmelCase_       :    Optional[int]								=			middle_blocks[1]
  lowerCAmelCase_       :    Any								=			middle_blocks[2]

  lowerCAmelCase_       :    str								=			renew_resnet_paths(snake_case_						)
  assign_to_checkpoint(snake_case_   ,       snake_case_   ,       snake_case_   ,       config=snake_case_						)

  lowerCAmelCase_       :    Any								=			renew_resnet_paths(snake_case_						)
  assign_to_checkpoint(snake_case_   ,       snake_case_   ,       snake_case_   ,       config=snake_case_						)

  lowerCAmelCase_       :    Tuple								=			renew_attention_paths(snake_case_						)
  lowerCAmelCase_       :    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(
      snake_case_   ,       snake_case_   ,       snake_case_   ,       attention_paths_to_split=snake_case_   ,       config=snake_case_						)

  for i in range(snake_case_						):
    lowerCAmelCase_       :    List[Any]								=			i // (config['num_res_blocks'] + 1)
    lowerCAmelCase_       :    Dict								=			i % (config['num_res_blocks'] + 1)
    lowerCAmelCase_       :    Any								=			[shave_segments(snake_case_   ,       2						) for name in output_blocks[i]]
    lowerCAmelCase_       :    Dict								=			{}

    for layer in output_block_layers:
      lowerCAmelCase_						,lowerCAmelCase_       :    Any								=			layer.split('.'						)[0], shave_segments(snake_case_   ,       1						)
      if layer_id in output_block_list:
        output_block_list[layer_id].append(snake_case_						)
      else:
        lowerCAmelCase_       :    Union[str, Any]								=			[layer_name]

    if len(snake_case_						) > 1:
      lowerCAmelCase_       :    Any								=			[key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
      lowerCAmelCase_       :    Any								=			[key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]

      lowerCAmelCase_       :    Tuple								=			renew_resnet_paths(snake_case_						)
      lowerCAmelCase_       :    Tuple								=			renew_resnet_paths(snake_case_						)

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

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

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

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

        lowerCAmelCase_       :    int								=			checkpoint[old_path]

  return new_checkpoint


if __name__ == "__main__":
     lowercase__		:    Union[str, 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.""")

     lowercase__		:    Dict				   =     parser.parse_args()

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

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

     lowercase__		:    Union[str, Any]				   =     convert_ldm_checkpoint(checkpoint, config)

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

     lowercase__		:    Optional[Any]				   =     UNetaDModel(**config)
     model.load_state_dict(converted_checkpoint)

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

          lowercase__		:    Optional[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_torch_available,
)


lowercase__		:    Dict				   =     {
    """configuration_falcon""": ["""FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FalconConfig"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:    str				   =     [
         """FALCON_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """FalconForCausalLM""",
         """FalconModel""",
         """FalconPreTrainedModel""",
         """FalconForSequenceClassification""",
         """FalconForTokenClassification""",
         """FalconForQuestionAnswering""",
     ]


if TYPE_CHECKING:
     from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_falcon import (
              FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
              FalconForCausalLM,
              FalconForQuestionAnswering,
              FalconForSequenceClassification,
              FalconForTokenClassification,
              FalconModel,
              FalconPreTrainedModel,
          )


else:
     import sys

     lowercase__		:    Dict				   =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





