


'''simple docstring'''



import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__=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       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__=0   ):
			UpperCAmelCase__					:   List[str]       				=       []
			for old_item in old_list:
						UpperCAmelCase__					:   Tuple       				=       old_item.replace("""in_layers.0"""	, """norm1"""   )
						UpperCAmelCase__					:   List[Any]       				=       new_item.replace("""in_layers.2"""	, """conv1"""   )

						UpperCAmelCase__					:   Tuple       				=       new_item.replace("""out_layers.0"""	, """norm2"""   )
						UpperCAmelCase__					:   List[Any]       				=       new_item.replace("""out_layers.3"""	, """conv2"""   )

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

						UpperCAmelCase__					:   Union[str, Any]       				=       shave_segments(UpperCamelCase__	, n_shave_prefix_segments=UpperCamelCase__   )

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

			return mapping







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__=0   ):
			UpperCAmelCase__					:   Tuple       				=       []
			for old_item in old_list:
						UpperCAmelCase__					:   List[Any]       				=       old_item

						UpperCAmelCase__					:   Any       				=       new_item.replace("""norm.weight"""	, """group_norm.weight"""   )
						UpperCAmelCase__					:   List[Any]       				=       new_item.replace("""norm.bias"""	, """group_norm.bias"""   )

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

						UpperCAmelCase__					:   List[str]       				=       shave_segments(UpperCamelCase__	, n_shave_prefix_segments=UpperCamelCase__   )

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

			return mapping







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__=None	, UpperCamelCase__=None	, UpperCamelCase__=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():
									UpperCAmelCase__					:   Any       				=       old_checkpoint[path]
									UpperCAmelCase__					:   Tuple       				=       old_tensor.shape[0] // 3

									UpperCAmelCase__					:   Dict       				=       (-1, channels) if len(old_tensor.shape   ) == 3 else (-1)

									UpperCAmelCase__					:   Optional[int]       				=       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__					:   Union[str, Any]       				=       old_tensor.split(channels // num_heads	, dim=1   )

									UpperCAmelCase__					:   List[str]       				=       query.reshape(UpperCamelCase__   )
									UpperCAmelCase__					:   int       				=       key.reshape(UpperCamelCase__   )
									UpperCAmelCase__					:   Optional[Any]       				=       value.reshape(UpperCamelCase__   )

			for path in paths:
						UpperCAmelCase__					:   Dict       				=       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__					:   int       				=       new_path.replace("""middle_block.0"""	, """mid_block.resnets.0"""   )
						UpperCAmelCase__					:   Union[str, Any]       				=       new_path.replace("""middle_block.1"""	, """mid_block.attentions.0"""   )
						UpperCAmelCase__					:   int       				=       new_path.replace("""middle_block.2"""	, """mid_block.resnets.1"""   )

						if additional_replacements is not None:
									for replacement in additional_replacements:
												UpperCAmelCase__					:   int       				=       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__					:   List[str]       				=       old_checkpoint[path["""old"""]][:, :, 0]
						else:
									UpperCAmelCase__					:   Optional[int]       				=       old_checkpoint[path["""old"""]]







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   str       				=       {}

			UpperCAmelCase__					:   str       				=       checkpoint["""time_embed.0.weight"""]
			UpperCAmelCase__					:   str       				=       checkpoint["""time_embed.0.bias"""]
			UpperCAmelCase__					:   List[str]       				=       checkpoint["""time_embed.2.weight"""]
			UpperCAmelCase__					:   Union[str, Any]       				=       checkpoint["""time_embed.2.bias"""]

			UpperCAmelCase__					:   Optional[Any]       				=       checkpoint["""input_blocks.0.0.weight"""]
			UpperCAmelCase__					:   Optional[Any]       				=       checkpoint["""input_blocks.0.0.bias"""]

			UpperCAmelCase__					:   List[Any]       				=       checkpoint["""out.0.weight"""]
			UpperCAmelCase__					:   int       				=       checkpoint["""out.0.bias"""]
			UpperCAmelCase__					:   Union[str, Any]       				=       checkpoint["""out.2.weight"""]
			UpperCAmelCase__					:   Any       				=       checkpoint["""out.2.bias"""]

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

						UpperCAmelCase__					:   Optional[Any]       				=       [key for key in input_blocks[i] if f'''input_blocks.{i}.0''' in key]
						UpperCAmelCase__					:   int       				=       [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__					:   List[Any]       				=       checkpoint[
									    f'''input_blocks.{i}.0.op.weight'''
									]
									UpperCAmelCase__					:   Tuple       				=       checkpoint[
									    f'''input_blocks.{i}.0.op.bias'''
									]
									continue

						UpperCAmelCase__					:   str       				=       renew_resnet_paths(UpperCamelCase__   )
						UpperCAmelCase__					:   Tuple       				=       {"""old""": f'''input_blocks.{i}.0''', """new""": f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
						UpperCAmelCase__					:   Optional[Any]       				=       {"""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__   ):
									UpperCAmelCase__					:   Any       				=       renew_attention_paths(UpperCamelCase__   )
									UpperCAmelCase__					:   int       				=       {
									    """old""": f'''input_blocks.{i}.1''',
									    """new""": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
									}
									UpperCAmelCase__					:   Optional[int]       				=       {
									    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__	, )

			UpperCAmelCase__					:   Optional[int]       				=       middle_blocks[0]
			UpperCAmelCase__					:   Union[str, Any]       				=       middle_blocks[1]
			UpperCAmelCase__					:   Optional[int]       				=       middle_blocks[2]

			UpperCAmelCase__					:   Tuple       				=       renew_resnet_paths(UpperCamelCase__   )
			assign_to_checkpoint(UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, config=UpperCamelCase__   )

			UpperCAmelCase__					:   Dict       				=       renew_resnet_paths(UpperCamelCase__   )
			assign_to_checkpoint(UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, config=UpperCamelCase__   )

			UpperCAmelCase__					:   List[Any]       				=       renew_attention_paths(UpperCamelCase__   )
			UpperCAmelCase__					:   Tuple       				=       {
			    """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__   ):
						UpperCAmelCase__					:   Union[str, Any]       				=       i // (config["""num_res_blocks"""] + 1)
						UpperCAmelCase__					:   Tuple       				=       i % (config["""num_res_blocks"""] + 1)
						UpperCAmelCase__					:   List[str]       				=       [shave_segments(UpperCamelCase__	, 2   ) for name in output_blocks[i]]
						UpperCAmelCase__					:   List[str]       				=       {}

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

						if len(UpperCamelCase__   ) > 1:
									UpperCAmelCase__					:   Any       				=       [key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
									UpperCAmelCase__					:   Optional[int]       				=       [key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]

									UpperCAmelCase__					:   Dict       				=       renew_resnet_paths(UpperCamelCase__   )
									UpperCAmelCase__					:   Optional[int]       				=       renew_resnet_paths(UpperCamelCase__   )

									UpperCAmelCase__					:   int       				=       {"""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():
												UpperCAmelCase__					:   Optional[int]       				=       list(output_block_list.values()   ).index(["""conv.weight""", """conv.bias"""]   )
												UpperCAmelCase__					:   List[str]       				=       checkpoint[
												    f'''output_blocks.{i}.{index}.conv.weight'''
												]
												UpperCAmelCase__					:   str       				=       checkpoint[
												    f'''output_blocks.{i}.{index}.conv.bias'''
												]

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

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

												UpperCAmelCase__					:   List[str]       				=       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)



'''simple docstring'''



import functools







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):

			# Validation
			if not isinstance(UpperCamelCase__	, UpperCamelCase__   ) or not all(isinstance(UpperCamelCase__	, UpperCamelCase__   ) for day in days   ):
						raise ValueError("""The parameter days should be a list of integers"""   )

			if len(UpperCamelCase__   ) != 3 or not all(isinstance(UpperCamelCase__	, UpperCamelCase__   ) for cost in costs   ):
						raise ValueError("""The parameter costs should be a list of three integers"""   )

			if len(UpperCamelCase__   ) == 0:
						return 0

			if min(UpperCamelCase__   ) <= 0:
						raise ValueError("""All days elements should be greater than 0"""   )

			if max(UpperCamelCase__   ) >= 3_6_6:
						raise ValueError("""All days elements should be less than 366"""   )

			UpperCAmelCase__					:   Union[str, Any]       				=       set(UpperCamelCase__   )

			@functools.cache
			def dynamic_programming(UpperCamelCase__   ) -> int:
						if index > 3_6_5:
									return 0

						if index not in days_set:
									return dynamic_programming(index + 1   )

						return min(
						    costs[0] + dynamic_programming(index + 1   )	, costs[1] + dynamic_programming(index + 7   )	, costs[2] + dynamic_programming(index + 3_0   )	, )

			return dynamic_programming(1   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()