

'''simple docstring'''
import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def  a__       (    _SCREAMING_SNAKE_CASE   :       Optional[Any]	,				_SCREAMING_SNAKE_CASE   :       Optional[Any]=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  a__       (    _SCREAMING_SNAKE_CASE   :       List[Any]	,				_SCREAMING_SNAKE_CASE   :       List[Any]=0				)     ->					List[Any]:


	"""simple docstring"""





	UpperCAmelCase_     :      str						=  []
	for old_item in old_list:
		UpperCAmelCase_     :      Optional[Any]						=  old_item.replace("in_layers.0"	,				"norm1"				)
		UpperCAmelCase_     :      Union[str, Any]						=  new_item.replace("in_layers.2"	,				"conv1"				)

		UpperCAmelCase_     :      Any						=  new_item.replace("out_layers.0"	,				"norm2"				)
		UpperCAmelCase_     :      Optional[Any]						=  new_item.replace("out_layers.3"	,				"conv2"				)

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

		UpperCAmelCase_     :      str						=  shave_segments(__a	,				n_shave_prefix_segments=__a				)

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

	return mapping


def  a__       (    _SCREAMING_SNAKE_CASE   :       Any	,				_SCREAMING_SNAKE_CASE   :       Optional[Any]=0				)     ->					str:


	"""simple docstring"""





	UpperCAmelCase_     :      Any						=  []
	for old_item in old_list:
		UpperCAmelCase_     :      List[Any]						=  old_item

		UpperCAmelCase_     :      Dict						=  new_item.replace("norm.weight"	,				"group_norm.weight"				)
		UpperCAmelCase_     :      str						=  new_item.replace("norm.bias"	,				"group_norm.bias"				)

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

		UpperCAmelCase_     :      Dict						=  shave_segments(__a	,				n_shave_prefix_segments=__a				)

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

	return mapping


def  a__       (    _SCREAMING_SNAKE_CASE   :       Optional[int]	,				_SCREAMING_SNAKE_CASE   :       Optional[Any]	,				_SCREAMING_SNAKE_CASE   :       Dict	,				_SCREAMING_SNAKE_CASE   :       Tuple=None	,				_SCREAMING_SNAKE_CASE   :       Tuple=None	,				_SCREAMING_SNAKE_CASE   :       str=None				)     ->					Optional[int]:


	"""simple docstring"""





	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():
			UpperCAmelCase_     :      Optional[int]						=  old_checkpoint[path]
			UpperCAmelCase_     :      Tuple						=  old_tensor.shape[0] // 3

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

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

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

			UpperCAmelCase_     :      int						=  query.reshape(__a				)
			UpperCAmelCase_     :      Dict						=  key.reshape(__a				)
			UpperCAmelCase_     :      str						=  value.reshape(__a				)

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

		if additional_replacements is not None:
			for replacement in additional_replacements:
				UpperCAmelCase_     :      List[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_     :      Optional[Any]						=  old_checkpoint[path["old"]][:, :, 0]
		else:
			UpperCAmelCase_     :      List[Any]						=  old_checkpoint[path["old"]]


def  a__       (    _SCREAMING_SNAKE_CASE   :       Tuple	,				_SCREAMING_SNAKE_CASE   :       List[Any]				)     ->					Tuple:


	"""simple docstring"""





	UpperCAmelCase_     :      Dict						=  {}

	UpperCAmelCase_     :      str						=  checkpoint["time_embed.0.weight"]
	UpperCAmelCase_     :      Optional[Any]						=  checkpoint["time_embed.0.bias"]
	UpperCAmelCase_     :      int						=  checkpoint["time_embed.2.weight"]
	UpperCAmelCase_     :      Optional[Any]						=  checkpoint["time_embed.2.bias"]

	UpperCAmelCase_     :      Dict						=  checkpoint["input_blocks.0.0.weight"]
	UpperCAmelCase_     :      Any						=  checkpoint["input_blocks.0.0.bias"]

	UpperCAmelCase_     :      Optional[int]						=  checkpoint["out.0.weight"]
	UpperCAmelCase_     :      Union[str, Any]						=  checkpoint["out.0.bias"]
	UpperCAmelCase_     :      Dict						=  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_     :      Optional[int]						=  {
	    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
	UpperCAmelCase_     :      int						=  len({".".join(layer.split("."				)[:2]				) for layer in checkpoint if "middle_block" in layer}				)
	UpperCAmelCase_     :      List[str]						=  {
	    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
	UpperCAmelCase_     :      List[Any]						=  len({".".join(layer.split("."				)[:2]				) for layer in checkpoint if "output_blocks" in layer}				)
	UpperCAmelCase_     :      List[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				):
		UpperCAmelCase_     :      List[Any]						=  (i - 1) // (config["num_res_blocks"] + 1)
		UpperCAmelCase_     :      Dict						=  (i - 1) % (config["num_res_blocks"] + 1)

		UpperCAmelCase_     :      Dict						=  [key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key]
		UpperCAmelCase_     :      Dict						=  [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_     :      Union[str, Any]						=  checkpoint[
			    F'''input_blocks.{i}.0.op.weight'''
			]
			UpperCAmelCase_     :      Dict						=  checkpoint[
			    F'''input_blocks.{i}.0.op.bias'''
			]
			continue

		UpperCAmelCase_     :      Union[str, Any]						=  renew_resnet_paths(__a				)
		UpperCAmelCase_     :      List[Any]						=  {"old": F'''input_blocks.{i}.0''', "new": F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
		UpperCAmelCase_     :      str						=  {"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				):
			UpperCAmelCase_     :      str						=  renew_attention_paths(__a				)
			UpperCAmelCase_     :      List[Any]						=  {
			    "old": F'''input_blocks.{i}.1''',
			    "new": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
			}
			UpperCAmelCase_     :      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(
			    __a	,				__a	,				__a	,				additional_replacements=[meta_path]	,				attention_paths_to_split=__a	,				config=__a	,				)

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

	UpperCAmelCase_     :      Any						=  renew_resnet_paths(__a				)
	assign_to_checkpoint(__a	,				__a	,				__a	,				config=__a				)

	UpperCAmelCase_     :      List[str]						=  renew_resnet_paths(__a				)
	assign_to_checkpoint(__a	,				__a	,				__a	,				config=__a				)

	UpperCAmelCase_     :      Optional[int]						=  renew_attention_paths(__a				)
	UpperCAmelCase_     :      Union[str, 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(
	    __a	,				__a	,				__a	,				attention_paths_to_split=__a	,				config=__a				)

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

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

		if len(__a				) > 1:
			UpperCAmelCase_     :      Optional[Any]						=  [key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
			UpperCAmelCase_     :      Dict						=  [key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]

			UpperCAmelCase_     :      int						=  renew_resnet_paths(__a				)
			UpperCAmelCase_     :      Optional[int]						=  renew_resnet_paths(__a				)

			UpperCAmelCase_     :      Dict						=  {"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():
				UpperCAmelCase_     :      Dict						=  list(output_block_list.values()				).index(["conv.weight", "conv.bias"]				)
				UpperCAmelCase_     :      Union[str, Any]						=  checkpoint[
				    F'''output_blocks.{i}.{index}.conv.weight'''
				]
				UpperCAmelCase_     :      int						=  checkpoint[
				    F'''output_blocks.{i}.{index}.conv.bias'''
				]

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

			if len(__a				):
				UpperCAmelCase_     :      Any						=  renew_attention_paths(__a				)
				UpperCAmelCase_     :      Union[str, Any]						=  {
				    "old": F'''output_blocks.{i}.1''',
				    "new": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
				}
				UpperCAmelCase_     :      str						=  {
				    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:
			UpperCAmelCase_     :      int						=  renew_resnet_paths(__a	,				n_shave_prefix_segments=1				)
			for path in resnet_0_paths:
				UpperCAmelCase_     :      List[str]						=  ".".join(["output_blocks", str(__a				), path["old"]]				)
				UpperCAmelCase_     :      Dict						=  ".".join(["up_blocks", str(__a				), "resnets", str(__a				), path["new"]]				)

				UpperCAmelCase_     :      Tuple						=  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)






"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase_       :    List[str]						      =			{'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_       :    Tuple						      =			[
		    '''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SEWForCTC''',
		    '''SEWForSequenceClassification''',
		    '''SEWModel''',
		    '''SEWPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_sew import (
				    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SEWForCTC,
				    SEWForSequenceClassification,
				    SEWModel,
				    SEWPreTrainedModel,
				)


else:
		import sys

		UpperCamelCase_       :    List[str]						      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




