


import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel






def 			UpperCamelCase (     __magic_name__					:							str   ,   __magic_name__					:							str=1					)       ->  Optional[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 			UpperCamelCase (     __magic_name__					:							Any   ,   __magic_name__					:							Optional[int]=0					)       ->  Optional[Any]:
		"""simple docstring"""




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

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

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

				lowercase__					      =		shave_segments(__magic_name__   ,   n_shave_prefix_segments=__magic_name__					)

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

		return mapping






def 			UpperCamelCase (     __magic_name__					:							List[str]   ,   __magic_name__					:							Optional[Any]=0					)       ->  Any:
		"""simple docstring"""




		lowercase__					      =		[]
		for old_item in old_list:
				lowercase__					      =		old_item

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

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

				lowercase__					      =		shave_segments(__magic_name__   ,   n_shave_prefix_segments=__magic_name__					)

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

		return mapping






def 			UpperCamelCase (     __magic_name__					:							str   ,   __magic_name__					:							List[str]   ,   __magic_name__					:							Union[str, Any]   ,   __magic_name__					:							Tuple=None   ,   __magic_name__					:							int=None   ,   __magic_name__					:							Optional[Any]=None					)       ->  int:
		"""simple docstring"""




		assert isinstance(__magic_name__   ,   __magic_name__					), "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():
						lowercase__					      =		old_checkpoint[path]
						lowercase__					      =		old_tensor.shape[0] // 3

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

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

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

						lowercase__					      =		query.reshape(__magic_name__					)
						lowercase__					      =		key.reshape(__magic_name__					)
						lowercase__					      =		value.reshape(__magic_name__					)

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

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






def 			UpperCamelCase (     __magic_name__					:							Any   ,   __magic_name__					:							Optional[Any]					)       ->  int:
		"""simple docstring"""




		lowercase__					      =		{}

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

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

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

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

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

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

		for i in range(1   ,   __magic_name__					):
				lowercase__					      =		(i - 1) // (config["""num_res_blocks"""] + 1)
				lowercase__					      =		(i - 1) % (config["""num_res_blocks"""] + 1)

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

				lowercase__					      =		renew_resnet_paths(__magic_name__					)
				lowercase__					      =		{"""old""": f'''input_blocks.{i}.0''', """new""": f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
				lowercase__					      =		{"""old""": """resnets.2.op""", """new""": """downsamplers.0.op"""}
				assign_to_checkpoint(
				    __magic_name__   ,   __magic_name__   ,   __magic_name__   ,   additional_replacements=[meta_path, resnet_op]   ,   config=__magic_name__					)

				if len(__magic_name__					):
						lowercase__					      =		renew_attention_paths(__magic_name__					)
						lowercase__					      =		{
						    """old""": f'''input_blocks.{i}.1''',
						    """new""": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
						}
						lowercase__					      =		{
						    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(
						    __magic_name__   ,   __magic_name__   ,   __magic_name__   ,   additional_replacements=[meta_path]   ,   attention_paths_to_split=__magic_name__   ,   config=__magic_name__   ,   )

		lowercase__					      =		middle_blocks[0]
		lowercase__					      =		middle_blocks[1]
		lowercase__					      =		middle_blocks[2]

		lowercase__					      =		renew_resnet_paths(__magic_name__					)
		assign_to_checkpoint(__magic_name__   ,   __magic_name__   ,   __magic_name__   ,   config=__magic_name__					)

		lowercase__					      =		renew_resnet_paths(__magic_name__					)
		assign_to_checkpoint(__magic_name__   ,   __magic_name__   ,   __magic_name__   ,   config=__magic_name__					)

		lowercase__					      =		renew_attention_paths(__magic_name__					)
		lowercase__					      =		{
		    """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(
		    __magic_name__   ,   __magic_name__   ,   __magic_name__   ,   attention_paths_to_split=__magic_name__   ,   config=__magic_name__					)

		for i in range(__magic_name__					):
				lowercase__					      =		i // (config["""num_res_blocks"""] + 1)
				lowercase__					      =		i % (config["""num_res_blocks"""] + 1)
				lowercase__					      =		[shave_segments(__magic_name__   ,   2					) for name in output_blocks[i]]
				lowercase__					      =		{}

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

				if len(__magic_name__					) > 1:
						lowercase__					      =		[key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
						lowercase__					      =		[key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]

						lowercase__					      =		renew_resnet_paths(__magic_name__					)
						lowercase__					      =		renew_resnet_paths(__magic_name__					)

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

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

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

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

								lowercase__					      =		checkpoint[old_path]

		return new_checkpoint


if __name__ == "__main__":
	A    =       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    =       parser.parse_args()

	A    =       torch.load(args.checkpoint_path)

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

	A    =       convert_ldm_checkpoint(checkpoint, config)

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

	A    =       UNetaDModel(**config)
	model.load_state_dict(converted_checkpoint)

	try:
		A    =       DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
		A    =       VQModel.from_pretrained('/'.join(args.checkpoint_path.split('/')[:-1]))

		A    =       LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
		pipe.save_pretrained(args.dump_path)
	except:  # noqa: E722
		model.save_pretrained(args.dump_path)





def 			UpperCamelCase (     __magic_name__					:							str   ,   __magic_name__					:							str					)       ->  list:
		"""simple docstring"""




		lowercase__					      =		len(__magic_name__					)
		lowercase__					      =		[]
		for i in range(len(__magic_name__					) - pat_len + 1					):
				lowercase__					      =		True
				for j in range(__magic_name__					):
						if s[i + j] != pattern[j]:
								lowercase__					      =		False
								break
				if match_found:
						position.append(__magic_name__					)
		return position


if __name__ == "__main__":
	assert naive_pattern_search('ABCDEFG', 'DE') == [3]
	print(naive_pattern_search('ABAAABCDBBABCDDEBCABC', 'ABC'))

