






'''simple docstring'''





import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel






def    lowerCamelCase_     (    lowercase__				,     lowercase__=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    lowerCamelCase_     (    lowercase__				,     lowercase__=0):
 lowerCamelCase__  =     []
 for old_item in old_list:
  lowerCamelCase__  =     old_item.replace("in_layers.0"				,     "norm1")
  lowerCamelCase__  =     new_item.replace("in_layers.2"				,     "conv1")

  lowerCamelCase__  =     new_item.replace("out_layers.0"				,     "norm2")
  lowerCamelCase__  =     new_item.replace("out_layers.3"				,     "conv2")

  lowerCamelCase__  =     new_item.replace("emb_layers.1"				,     "time_emb_proj")
  lowerCamelCase__  =     new_item.replace("skip_connection"				,     "conv_shortcut")

  lowerCamelCase__  =     shave_segments(UpperCAmelCase__				,     n_shave_prefix_segments=UpperCAmelCase__)

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

 return mapping






def    lowerCamelCase_     (    lowercase__				,     lowercase__=0):
 lowerCamelCase__  =     []
 for old_item in old_list:
  lowerCamelCase__  =     old_item

  lowerCamelCase__  =     new_item.replace("norm.weight"				,     "group_norm.weight")
  lowerCamelCase__  =     new_item.replace("norm.bias"				,     "group_norm.bias")

  lowerCamelCase__  =     new_item.replace("proj_out.weight"				,     "proj_attn.weight")
  lowerCamelCase__  =     new_item.replace("proj_out.bias"				,     "proj_attn.bias")

  lowerCamelCase__  =     shave_segments(UpperCAmelCase__				,     n_shave_prefix_segments=UpperCAmelCase__)

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

 return mapping






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__=None				,     lowercase__=None				,     lowercase__=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():
   lowerCamelCase__  =     old_checkpoint[path]
   lowerCamelCase__  =     old_tensor.shape[0] // 3

   lowerCamelCase__  =     (-1, channels) if len(old_tensor.shape) == 3 else (-1)

   lowerCamelCase__  =     old_tensor.shape[0] // config["num_head_channels"] // 3

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

   lowerCamelCase__  =     query.reshape(UpperCAmelCase__)
   lowerCamelCase__  =     key.reshape(UpperCAmelCase__)
   lowerCamelCase__  =     value.reshape(UpperCAmelCase__)

 for path in paths:
  lowerCamelCase__  =     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__  =     new_path.replace("middle_block.0"				,     "mid_block.resnets.0")
  lowerCamelCase__  =     new_path.replace("middle_block.1"				,     "mid_block.attentions.0")
  lowerCamelCase__  =     new_path.replace("middle_block.2"				,     "mid_block.resnets.1")

  if additional_replacements is not None:
   for replacement in additional_replacements:
    lowerCamelCase__  =     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__  =     old_checkpoint[path["old"]][:, :, 0]
  else:
   lowerCamelCase__  =     old_checkpoint[path["old"]]






def    lowerCamelCase_     (    lowercase__				,     lowercase__):
 lowerCamelCase__  =     {}

 lowerCamelCase__  =     checkpoint["time_embed.0.weight"]
 lowerCamelCase__  =     checkpoint["time_embed.0.bias"]
 lowerCamelCase__  =     checkpoint["time_embed.2.weight"]
 lowerCamelCase__  =     checkpoint["time_embed.2.bias"]

 lowerCamelCase__  =     checkpoint["input_blocks.0.0.weight"]
 lowerCamelCase__  =     checkpoint["input_blocks.0.0.bias"]

 lowerCamelCase__  =     checkpoint["out.0.weight"]
 lowerCamelCase__  =     checkpoint["out.0.bias"]
 lowerCamelCase__  =     checkpoint["out.2.weight"]
 lowerCamelCase__  =     checkpoint["out.2.bias"]

 # Retrieves the keys for the input blocks only
 lowerCamelCase__  =     len({".".join(layer.split(".")[:2]) for layer in checkpoint if "input_blocks" in layer})
 lowerCamelCase__  =     {
     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
 lowerCamelCase__  =     len({".".join(layer.split(".")[:2]) for layer in checkpoint if "middle_block" in layer})
 lowerCamelCase__  =     {
     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
 lowerCamelCase__  =     len({".".join(layer.split(".")[:2]) for layer in checkpoint if "output_blocks" in layer})
 lowerCamelCase__  =     {
     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__):
  lowerCamelCase__  =     (i - 1) // (config["num_res_blocks"] + 1)
  lowerCamelCase__  =     (i - 1) % (config["num_res_blocks"] + 1)

  lowerCamelCase__  =     [key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key]
  lowerCamelCase__  =     [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__  =     checkpoint[
       F'''input_blocks.{i}.0.op.weight'''
   ]
   lowerCamelCase__  =     checkpoint[
       F'''input_blocks.{i}.0.op.bias'''
   ]
   continue

  lowerCamelCase__  =     renew_resnet_paths(UpperCAmelCase__)
  lowerCamelCase__  =     {"old": F'''input_blocks.{i}.0''', "new": F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
  lowerCamelCase__  =     {"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__):
   lowerCamelCase__  =     renew_attention_paths(UpperCAmelCase__)
   lowerCamelCase__  =     {
       "old": F'''input_blocks.{i}.1''',
       "new": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
   }
   lowerCamelCase__  =     {
       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__				,     )

 lowerCamelCase__  =     middle_blocks[0]
 lowerCamelCase__  =     middle_blocks[1]
 lowerCamelCase__  =     middle_blocks[2]

 lowerCamelCase__  =     renew_resnet_paths(UpperCAmelCase__)
 assign_to_checkpoint(UpperCAmelCase__				,     UpperCAmelCase__				,     UpperCAmelCase__				,     config=UpperCAmelCase__)

 lowerCamelCase__  =     renew_resnet_paths(UpperCAmelCase__)
 assign_to_checkpoint(UpperCAmelCase__				,     UpperCAmelCase__				,     UpperCAmelCase__				,     config=UpperCAmelCase__)

 lowerCamelCase__  =     renew_attention_paths(UpperCAmelCase__)
 lowerCamelCase__  =     {
     "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__):
  lowerCamelCase__  =     i // (config["num_res_blocks"] + 1)
  lowerCamelCase__  =     i % (config["num_res_blocks"] + 1)
  lowerCamelCase__  =     [shave_segments(UpperCAmelCase__				,     2) for name in output_blocks[i]]
  lowerCamelCase__  =     {}

  for layer in output_block_layers:
   lowerCamelCase__  ,	lowerCamelCase__  =     layer.split(".")[0], shave_segments(UpperCAmelCase__				,     1)
   if layer_id in output_block_list:
    output_block_list[layer_id].append(UpperCAmelCase__)
   else:
    lowerCamelCase__  =     [layer_name]

  if len(UpperCAmelCase__) > 1:
   lowerCamelCase__  =     [key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
   lowerCamelCase__  =     [key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]

   lowerCamelCase__  =     renew_resnet_paths(UpperCAmelCase__)
   lowerCamelCase__  =     renew_resnet_paths(UpperCAmelCase__)

   lowerCamelCase__  =     {"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():
    lowerCamelCase__  =     list(output_block_list.values()).index(["conv.weight", "conv.bias"])
    lowerCamelCase__  =     checkpoint[
        F'''output_blocks.{i}.{index}.conv.weight'''
    ]
    lowerCamelCase__  =     checkpoint[
        F'''output_blocks.{i}.{index}.conv.bias'''
    ]

    # Clear attentions as they have been attributed above.
    if len(UpperCAmelCase__) == 2:
     lowerCamelCase__  =     []

   if len(UpperCAmelCase__):
    lowerCamelCase__  =     renew_attention_paths(UpperCAmelCase__)
    lowerCamelCase__  =     {
        "old": F'''output_blocks.{i}.1''',
        "new": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
    }
    lowerCamelCase__  =     {
        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:
   lowerCamelCase__  =     renew_resnet_paths(UpperCAmelCase__				,     n_shave_prefix_segments=1)
   for path in resnet_0_paths:
    lowerCamelCase__  =     ".".join(["output_blocks", str(UpperCAmelCase__), path["old"]])
    lowerCamelCase__  =     ".".join(["up_blocks", str(UpperCAmelCase__), "resnets", str(UpperCAmelCase__), path["new"]])

    lowerCamelCase__  =     checkpoint[old_path]

 return new_checkpoint


if __name__ == "__main__":
 __A							:							Optional[int]  							=		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							:							Optional[int]  							=		parser.parse_args()

 __A							:							List[Any]  							=		torch.load(args.checkpoint_path)

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

 __A							:							Tuple  							=		convert_ldm_checkpoint(checkpoint, config)

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

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

 try:
  __A							:							int  							=		DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
  __A							:							List[str]  							=		VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

  __A							:							List[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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A							:							Tuple  							=		{
    """configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
    """tokenization_deberta""": ["""DebertaTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							int  							=		["""DebertaTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							str  							=		[
     """DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """DebertaForMaskedLM""",
     """DebertaForQuestionAnswering""",
     """DebertaForSequenceClassification""",
     """DebertaForTokenClassification""",
     """DebertaModel""",
     """DebertaPreTrainedModel""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							Union[str, Any]  							=		[
     """TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFDebertaForMaskedLM""",
     """TFDebertaForQuestionAnswering""",
     """TFDebertaForSequenceClassification""",
     """TFDebertaForTokenClassification""",
     """TFDebertaModel""",
     """TFDebertaPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
 from .tokenization_deberta import DebertaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_deberta_fast import DebertaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deberta import (
      DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      DebertaForMaskedLM,
      DebertaForQuestionAnswering,
      DebertaForSequenceClassification,
      DebertaForTokenClassification,
      DebertaModel,
      DebertaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deberta import (
      TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDebertaForMaskedLM,
      TFDebertaForQuestionAnswering,
      TFDebertaForSequenceClassification,
      TFDebertaForTokenClassification,
      TFDebertaModel,
      TFDebertaPreTrainedModel,
  )


else:
 import sys

 __A							:							List[Any]  							=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

