
import argparse

import torch

from diffusers import UNetaDConditionModel



def      __UpperCAmelCase    (  snake_case_							:		List[Any]				,					snake_case_							:		Tuple				,					snake_case_							:		Dict				,					snake_case_							:		List[Any]=None				,					snake_case_							:		List[Any]=None				,					snake_case_							:		int=None				):



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

			UpperCAmelCase:					int 						=     (-1, channels) if len(old_tensor.shape				) == 3 else (-1)

			UpperCAmelCase:					Optional[Any] 						=     old_tensor.shape[0] // config["num_head_channels"] // 3

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

			UpperCAmelCase:					Optional[int] 						=     query.reshape(snake_case_				)
			UpperCAmelCase:					int 						=     key.reshape(snake_case_				)
			UpperCAmelCase:					str 						=     value.reshape(snake_case_				)

	for path in paths:
		UpperCAmelCase:					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

		if additional_replacements is not None:
			for replacement in additional_replacements:
				UpperCAmelCase:					str 						=     new_path.replace(replacement["old"]				,					replacement["new"]				)

        # proj_attn.weight has to be converted from conv 1D to linear
		UpperCAmelCase:					Dict 						=     old_checkpoint[path["old"]]
		UpperCAmelCase:					int 						=     ["proj_attn.weight"]
		UpperCAmelCase:					Any 						=     ["proj_out.weight", "proj_in.weight"]
		if any(k in new_path for k in names				):
			UpperCAmelCase:					str 						=     weight[:, :, 0]
		elif any(k in new_path for k in names_a				) and len(weight.shape				) > 2 and ".attentions." not in new_path:
			UpperCAmelCase:					List[str] 						=     weight[:, :, 0]
		else:
			UpperCAmelCase:					Any 						=     weight



def      __UpperCAmelCase    (  snake_case_							:		Any				,					snake_case_							:		Dict=0				):



	'''simple docstring'''



	UpperCAmelCase:					str 						=     []
	for old_item in old_list:
		UpperCAmelCase:					Optional[int] 						=     old_item

		#         new_item = new_item.replace('norm.weight', 'group_norm.weight')
		#         new_item = new_item.replace('norm.bias', 'group_norm.bias')

		#         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
		#         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

		#         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

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

	return mapping



def      __UpperCAmelCase    (  snake_case_							:		Dict				,					snake_case_							:		Optional[Any]=1				):



	'''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    (  snake_case_							:		str				,					snake_case_							:		Tuple=0				):



	'''simple docstring'''



	UpperCAmelCase:					str 						=     []
	for old_item in old_list:
		mapping.append({"old": old_item, "new": old_item}				)

	return mapping



def      __UpperCAmelCase    (  snake_case_							:		Any				,					snake_case_							:		Union[str, Any]=0				):



	'''simple docstring'''



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

		UpperCAmelCase:					int 						=     new_item.replace("out_layers.0"				,					"norm2"				)
		UpperCAmelCase:					int 						=     new_item.replace("out_layers.3"				,					"conv2"				)

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

		UpperCAmelCase:					List[Any] 						=     shave_segments(snake_case_				,					n_shave_prefix_segments=snake_case_				)

		if "temopral_conv" not in old_item:
			mapping.append({"old": old_item, "new": new_item}				)

	return mapping



def      __UpperCAmelCase    (  snake_case_							:		Optional[Any]				,					snake_case_							:		Dict				,					snake_case_							:		Dict=None				,					snake_case_							:		Optional[Any]=False				):



	'''simple docstring'''



	UpperCAmelCase:					Tuple 						=     {}
	UpperCAmelCase:					Dict 						=     list(checkpoint.keys()				)

	UpperCAmelCase:					List[str] 						=     "model.diffusion_model."

	# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
	if sum(k.startswith("model_ema"				) for k in keys				) > 1_0_0 and extract_ema:
		print(F'Checkpoint {path} has both EMA and non-EMA weights.'				)
		print(
		    "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
		    " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."				)
		for key in keys:
			if key.startswith("model.diffusion_model"				):
				UpperCAmelCase:					Any 						=     "model_ema." + "".join(key.split("."				)[1:]				)
				UpperCAmelCase:					int 						=     checkpoint.pop(snake_case_				)
	else:
		if sum(k.startswith("model_ema"				) for k in keys				) > 1_0_0:
			print(
			    "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
			    " weights (usually better for inference), please make sure to add the `--extract_ema` flag."				)

		for key in keys:
			UpperCAmelCase:					int 						=     checkpoint.pop(snake_case_				)

	UpperCAmelCase:					str 						=     {}

	UpperCAmelCase:					Optional[Any] 						=     unet_state_dict["time_embed.0.weight"]
	UpperCAmelCase:					Any 						=     unet_state_dict["time_embed.0.bias"]
	UpperCAmelCase:					Optional[int] 						=     unet_state_dict["time_embed.2.weight"]
	UpperCAmelCase:					Optional[Any] 						=     unet_state_dict["time_embed.2.bias"]

	if config["class_embed_type"] is None:
		# No parameters to port
		...
	elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
		UpperCAmelCase:					Any 						=     unet_state_dict["label_emb.0.0.weight"]
		UpperCAmelCase:					List[str] 						=     unet_state_dict["label_emb.0.0.bias"]
		UpperCAmelCase:					int 						=     unet_state_dict["label_emb.0.2.weight"]
		UpperCAmelCase:					Union[str, Any] 						=     unet_state_dict["label_emb.0.2.bias"]
	else:
		raise NotImplementedError(F'Not implemented `class_embed_type`: {config["class_embed_type"]}'				)

	UpperCAmelCase:					List[str] 						=     unet_state_dict["input_blocks.0.0.weight"]
	UpperCAmelCase:					Dict 						=     unet_state_dict["input_blocks.0.0.bias"]

	UpperCAmelCase:					Union[str, Any] 						=     [v for v in unet_state_dict if v.startswith("input_blocks.0.1"				)]
	UpperCAmelCase:					Tuple 						=     renew_attention_paths(snake_case_				)
	UpperCAmelCase:					Optional[int] 						=     {"old": "input_blocks.0.1", "new": "transformer_in"}
	assign_to_checkpoint(snake_case_				,					snake_case_				,					snake_case_				,					additional_replacements=[meta_path]				,					config=snake_case_				)

	UpperCAmelCase:					str 						=     unet_state_dict["out.0.weight"]
	UpperCAmelCase:					Optional[int] 						=     unet_state_dict["out.0.bias"]
	UpperCAmelCase:					Dict 						=     unet_state_dict["out.2.weight"]
	UpperCAmelCase:					Any 						=     unet_state_dict["out.2.bias"]

	# Retrieves the keys for the input blocks only
	UpperCAmelCase:					Optional[int] 						=     len({".".join(layer.split("."				)[:2]				) for layer in unet_state_dict if "input_blocks" in layer}				)
	UpperCAmelCase:					str 						=     {
	    layer_id: [key for key in unet_state_dict if F'input_blocks.{layer_id}' in key]
	    for layer_id in range(snake_case_				)
	}

	# Retrieves the keys for the middle blocks only
	UpperCAmelCase:					List[Any] 						=     len({".".join(layer.split("."				)[:2]				) for layer in unet_state_dict if "middle_block" in layer}				)
	UpperCAmelCase:					Any 						=     {
	    layer_id: [key for key in unet_state_dict if F'middle_block.{layer_id}' in key]
	    for layer_id in range(snake_case_				)
	}

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

	for i in range(1				,					snake_case_				):
		UpperCAmelCase:					Optional[int] 						=     (i - 1) // (config["layers_per_block"] + 1)
		UpperCAmelCase:					List[str] 						=     (i - 1) % (config["layers_per_block"] + 1)

		UpperCAmelCase:					str 						=     [
		    key for key in input_blocks[i] if F'input_blocks.{i}.0' in key and F'input_blocks.{i}.0.op' not in key
		]
		UpperCAmelCase:					Optional[Any] 						=     [key for key in input_blocks[i] if F'input_blocks.{i}.1' in key]
		UpperCAmelCase:					List[Any] 						=     [key for key in input_blocks[i] if F'input_blocks.{i}.2' in key]

		if F'input_blocks.{i}.op.weight' in unet_state_dict:
			UpperCAmelCase:					Any 						=     unet_state_dict.pop(
			    F'input_blocks.{i}.op.weight'				)
			UpperCAmelCase:					Tuple 						=     unet_state_dict.pop(
			    F'input_blocks.{i}.op.bias'				)

		UpperCAmelCase:					List[str] 						=     renew_resnet_paths(snake_case_				)
		UpperCAmelCase:					Any 						=     {"old": F'input_blocks.{i}.0', "new": F'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
		assign_to_checkpoint(
		    snake_case_				,					snake_case_				,					snake_case_				,					additional_replacements=[meta_path]				,					config=snake_case_				)

		UpperCAmelCase:					str 						=     [key for key in resnets if "temopral_conv" in key]
		UpperCAmelCase:					List[str] 						=     renew_temp_conv_paths(snake_case_				)
		UpperCAmelCase:					Optional[int] 						=     {
		    "old": F'input_blocks.{i}.0.temopral_conv',
		    "new": F'down_blocks.{block_id}.temp_convs.{layer_in_block_id}',
		}
		assign_to_checkpoint(
		    snake_case_				,					snake_case_				,					snake_case_				,					additional_replacements=[meta_path]				,					config=snake_case_				)

		if len(snake_case_				):
			UpperCAmelCase:					Any 						=     renew_attention_paths(snake_case_				)
			UpperCAmelCase:					List[Any] 						=     {"old": F'input_blocks.{i}.1', "new": F'down_blocks.{block_id}.attentions.{layer_in_block_id}'}
			assign_to_checkpoint(
			    snake_case_				,					snake_case_				,					snake_case_				,					additional_replacements=[meta_path]				,					config=snake_case_				)

		if len(snake_case_				):
			UpperCAmelCase:					Any 						=     renew_attention_paths(snake_case_				)
			UpperCAmelCase:					List[Any] 						=     {
			    "old": F'input_blocks.{i}.2',
			    "new": F'down_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
			}
			assign_to_checkpoint(
			    snake_case_				,					snake_case_				,					snake_case_				,					additional_replacements=[meta_path]				,					config=snake_case_				)

	UpperCAmelCase:					Optional[Any] 						=     middle_blocks[0]
	UpperCAmelCase:					Optional[Any] 						=     [key for key in resnet_a if "temopral_conv" in key]
	UpperCAmelCase:					str 						=     middle_blocks[1]
	UpperCAmelCase:					Optional[int] 						=     middle_blocks[2]
	UpperCAmelCase:					List[str] 						=     middle_blocks[3]
	UpperCAmelCase:					Union[str, Any] 						=     [key for key in resnet_a if "temopral_conv" in key]

	UpperCAmelCase:					Optional[Any] 						=     renew_resnet_paths(snake_case_				)
	UpperCAmelCase:					List[Any] 						=     {"old": "middle_block.0", "new": "mid_block.resnets.0"}
	assign_to_checkpoint(
	    snake_case_				,					snake_case_				,					snake_case_				,					config=snake_case_				,					additional_replacements=[meta_path]				)

	UpperCAmelCase:					str 						=     renew_temp_conv_paths(snake_case_				)
	UpperCAmelCase:					Optional[int] 						=     {"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"}
	assign_to_checkpoint(
	    snake_case_				,					snake_case_				,					snake_case_				,					config=snake_case_				,					additional_replacements=[meta_path]				)

	UpperCAmelCase:					int 						=     renew_resnet_paths(snake_case_				)
	UpperCAmelCase:					List[Any] 						=     {"old": "middle_block.3", "new": "mid_block.resnets.1"}
	assign_to_checkpoint(
	    snake_case_				,					snake_case_				,					snake_case_				,					config=snake_case_				,					additional_replacements=[meta_path]				)

	UpperCAmelCase:					int 						=     renew_temp_conv_paths(snake_case_				)
	UpperCAmelCase:					List[str] 						=     {"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"}
	assign_to_checkpoint(
	    snake_case_				,					snake_case_				,					snake_case_				,					config=snake_case_				,					additional_replacements=[meta_path]				)

	UpperCAmelCase:					Any 						=     renew_attention_paths(snake_case_				)
	UpperCAmelCase:					int 						=     {"old": "middle_block.1", "new": "mid_block.attentions.0"}
	assign_to_checkpoint(
	    snake_case_				,					snake_case_				,					snake_case_				,					additional_replacements=[meta_path]				,					config=snake_case_				)

	UpperCAmelCase:					Any 						=     renew_attention_paths(snake_case_				)
	UpperCAmelCase:					List[str] 						=     {"old": "middle_block.2", "new": "mid_block.temp_attentions.0"}
	assign_to_checkpoint(
	    snake_case_				,					snake_case_				,					snake_case_				,					additional_replacements=[meta_path]				,					config=snake_case_				)

	for i in range(snake_case_				):
		UpperCAmelCase:					Any 						=     i // (config["layers_per_block"] + 1)
		UpperCAmelCase:					Union[str, Any] 						=     i % (config["layers_per_block"] + 1)
		UpperCAmelCase:					Optional[int] 						=     [shave_segments(snake_case_				,					2				) for name in output_blocks[i]]
		UpperCAmelCase:					Dict 						=     {}

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

		if len(snake_case_				) > 1:
			UpperCAmelCase:					List[str] 						=     [key for key in output_blocks[i] if F'output_blocks.{i}.0' in key]
			UpperCAmelCase:					List[str] 						=     [key for key in output_blocks[i] if F'output_blocks.{i}.1' in key]
			UpperCAmelCase:					Dict 						=     [key for key in output_blocks[i] if F'output_blocks.{i}.2' in key]

			UpperCAmelCase:					List[Any] 						=     renew_resnet_paths(snake_case_				)
			UpperCAmelCase:					int 						=     renew_resnet_paths(snake_case_				)

			UpperCAmelCase:					List[str] 						=     {"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_				)

			UpperCAmelCase:					List[str] 						=     [key for key in resnets if "temopral_conv" in key]
			UpperCAmelCase:					Dict 						=     renew_temp_conv_paths(snake_case_				)
			UpperCAmelCase:					Optional[int] 						=     {
			    "old": F'output_blocks.{i}.0.temopral_conv',
			    "new": F'up_blocks.{block_id}.temp_convs.{layer_in_block_id}',
			}
			assign_to_checkpoint(
			    snake_case_				,					snake_case_				,					snake_case_				,					additional_replacements=[meta_path]				,					config=snake_case_				)

			UpperCAmelCase:					Dict 						=     {k: sorted(snake_case_				) for k, v in output_block_list.items()}
			if ["conv.bias", "conv.weight"] in output_block_list.values():
				UpperCAmelCase:					str 						=     list(output_block_list.values()				).index(["conv.bias", "conv.weight"]				)
				UpperCAmelCase:					Dict 						=     unet_state_dict[
				    F'output_blocks.{i}.{index}.conv.weight'
				]
				UpperCAmelCase:					Optional[Any] 						=     unet_state_dict[
				    F'output_blocks.{i}.{index}.conv.bias'
				]

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

			if len(snake_case_				):
				UpperCAmelCase:					Optional[int] 						=     renew_attention_paths(snake_case_				)
				UpperCAmelCase:					Any 						=     {
				    "old": F'output_blocks.{i}.1',
				    "new": F'up_blocks.{block_id}.attentions.{layer_in_block_id}',
				}
				assign_to_checkpoint(
				    snake_case_				,					snake_case_				,					snake_case_				,					additional_replacements=[meta_path]				,					config=snake_case_				)

			if len(snake_case_				):
				UpperCAmelCase:					str 						=     renew_attention_paths(snake_case_				)
				UpperCAmelCase:					List[Any] 						=     {
				    "old": F'output_blocks.{i}.2',
				    "new": F'up_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
				}
				assign_to_checkpoint(
				    snake_case_				,					snake_case_				,					snake_case_				,					additional_replacements=[meta_path]				,					config=snake_case_				)
		else:
			UpperCAmelCase:					Tuple 						=     renew_resnet_paths(snake_case_				,					n_shave_prefix_segments=1				)
			for path in resnet_0_paths:
				UpperCAmelCase:					str 						=     ".".join(["output_blocks", str(snake_case_				), path["old"]]				)
				UpperCAmelCase:					int 						=     ".".join(["up_blocks", str(snake_case_				), "resnets", str(snake_case_				), path["new"]]				)
				UpperCAmelCase:					Dict 						=     unet_state_dict[old_path]

			UpperCAmelCase:					Tuple 						=     [l for l in output_block_layers if "temopral_conv" in l]
			for path in temopral_conv_paths:
				UpperCAmelCase:					Tuple 						=     path.split("temopral_conv."				)[-1]
				UpperCAmelCase:					str 						=     ".".join(["output_blocks", str(snake_case_				), str(snake_case_				), "temopral_conv", pruned_path]				)
				UpperCAmelCase:					Dict 						=     ".".join(["up_blocks", str(snake_case_				), "temp_convs", str(snake_case_				), pruned_path]				)
				UpperCAmelCase:					Any 						=     unet_state_dict[old_path]

	return new_checkpoint


if __name__ == "__main__":
	snake_case_      :					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('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
	snake_case_      :					str				      =     parser.parse_args()

	snake_case_      :					str				      =     torch.load(args.checkpoint_path, map_location='cpu')
	snake_case_      :					Tuple				      =     UNetaDConditionModel()

	snake_case_      :					List[Any]				      =     convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

	snake_case_      :					int				      =     set(unet.state_dict().keys()) - set(converted_ckpt.keys())
	snake_case_      :					Tuple				      =     set(converted_ckpt.keys()) - set(unet.state_dict().keys())

	assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

	# load state_dict
	unet.load_state_dict(converted_ckpt)

	unet.save_pretrained(args.dump_path)

	# -- finish converting the unet --


import torch
from torch import autograd





class 		__lowerCamelCase     (   autograd.Function		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case	,				__snake_case	,				__snake_case							)						->     Dict:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     inputs.numel()
								UpperCAmelCase:					Tuple 						=     int(0.0_05 * nb_elems							) + 1
								if sigmoid:
									UpperCAmelCase:					int 						=     (torch.sigmoid(__snake_case							) > threshold).type(inputs.type()							)
								else:
									UpperCAmelCase:					List[Any] 						=     (inputs > threshold).type(inputs.type()							)
								if mask.sum() < nb_min:
									# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
									UpperCAmelCase:					List[Any] 						=     inputs.flatten().kthvalue(max(nb_elems - nb_min	,				1							)							).values
									UpperCAmelCase:					Optional[int] 						=     (inputs > k_threshold).type(inputs.type()							)
								return mask




							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



								return gradOutput, None, None





class 		__lowerCamelCase     (   autograd.Function		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     inputs.clone()
								UpperCAmelCase						,   UpperCAmelCase:					List[Any] 						=     inputs.flatten().sort(descending=__snake_case							)
								UpperCAmelCase:					Optional[Any] 						=     int(threshold * inputs.numel()							)

								# flat_out and mask access the same memory.
								UpperCAmelCase:					Optional[int] 						=     mask.flatten()
								UpperCAmelCase:					Union[str, Any] 						=     0
								UpperCAmelCase:					int 						=     1
								return mask




							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								return gradOutput, None





class 		__lowerCamelCase     (   lowercase		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     str:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     inputs.clone()
								UpperCAmelCase						,   UpperCAmelCase:					Any 						=     inputs.abs().flatten().sort(descending=__snake_case							)
								UpperCAmelCase:					str 						=     int(threshold * inputs.numel()							)

								# flat_out and mask access the same memory.
								UpperCAmelCase:					Optional[int] 						=     mask.flatten()
								UpperCAmelCase:					Union[str, Any] 						=     0
								UpperCAmelCase:					List[str] 						=     1
								return mask
