


'''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__  :					int        =     old_item.replace("""in_layers.0""" ,      """norm1"""					)
						UpperCAmelCase__  :					Dict        =     new_item.replace("""in_layers.2""" ,      """conv1"""					)

						UpperCAmelCase__  :					Union[str, Any]        =     new_item.replace("""out_layers.0""" ,      """norm2"""					)
						UpperCAmelCase__  :					Dict        =     new_item.replace("""out_layers.3""" ,      """conv2"""					)

						UpperCAmelCase__  :					Any        =     new_item.replace("""emb_layers.1""" ,      """time_emb_proj"""					)
						UpperCAmelCase__  :					Dict        =     new_item.replace("""skip_connection""" ,      """conv_shortcut"""					)

						UpperCAmelCase__  :					Optional[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__  :					Optional[int]        =     []
			for old_item in old_list:
						UpperCAmelCase__  :					Any        =     old_item

						UpperCAmelCase__  :					str        =     new_item.replace("""norm.weight""" ,      """group_norm.weight"""					)
						UpperCAmelCase__  :					Any        =     new_item.replace("""norm.bias""" ,      """group_norm.bias"""					)

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

						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__ ,      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__  :					Optional[Any]        =     old_checkpoint[path]
									UpperCAmelCase__  :					int        =     old_tensor.shape[0] // 3

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

									UpperCAmelCase__  :					Dict        =     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__  :					List[Any]        =     old_tensor.split(channels // num_heads ,      dim=1					)

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

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

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







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

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

			UpperCAmelCase__  :					int        =     checkpoint["""input_blocks.0.0.weight"""]
			UpperCAmelCase__  :					Tuple        =     checkpoint["""input_blocks.0.0.bias"""]

			UpperCAmelCase__  :					Dict        =     checkpoint["""out.0.weight"""]
			UpperCAmelCase__  :					Optional[int]        =     checkpoint["""out.0.bias"""]
			UpperCAmelCase__  :					int        =     checkpoint["""out.2.weight"""]
			UpperCAmelCase__  :					int        =     checkpoint["""out.2.bias"""]

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

						UpperCAmelCase__  :					Union[str, Any]        =     [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__  :					Optional[int]        =     checkpoint[
									    f'''input_blocks.{i}.0.op.weight'''
									]
									UpperCAmelCase__  :					List[str]        =     checkpoint[
									    f'''input_blocks.{i}.0.op.bias'''
									]
									continue

						UpperCAmelCase__  :					int        =     renew_resnet_paths(UpperCamelCase__					)
						UpperCAmelCase__  :					Optional[Any]        =     {"""old""": f'''input_blocks.{i}.0''', """new""": f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
						UpperCAmelCase__  :					List[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__  :					Optional[int]        =     renew_attention_paths(UpperCamelCase__					)
									UpperCAmelCase__  :					Any        =     {
									    """old""": f'''input_blocks.{i}.1''',
									    """new""": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
									}
									UpperCAmelCase__  :					Optional[Any]        =     {
									    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__  :					Dict        =     middle_blocks[0]
			UpperCAmelCase__  :					Any        =     middle_blocks[1]
			UpperCAmelCase__  :					Optional[int]        =     middle_blocks[2]

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

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

			UpperCAmelCase__  :					Union[str, Any]        =     renew_attention_paths(UpperCamelCase__					)
			UpperCAmelCase__  :					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(
			    UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      attention_paths_to_split=UpperCamelCase__ ,      config=UpperCamelCase__					)

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

						for layer in output_block_layers:
									UpperCAmelCase__  :					Union[str, Any]        =     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__  :					int        =     [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__  :					Optional[Any]        =     renew_resnet_paths(UpperCamelCase__					)
									UpperCAmelCase__  :					Tuple        =     renew_resnet_paths(UpperCamelCase__					)

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

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

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

												UpperCAmelCase__  :					Any        =     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'''



from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
__A	=numpy.array([0, 0])
__A	=numpy.array([0.5, 0.8_6_6_0_2_5_4])
__A	=numpy.array([1, 0])
__A	=[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Union[str, Any]        =     initial_vectors
			for _ in range(UpperCamelCase__					):
						UpperCAmelCase__  :					Dict        =     iteration_step(UpperCamelCase__					)
			return vectors







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					Optional[Any]        =     []
			for i, start_vector in enumerate(vectors[:-1]					):
						UpperCAmelCase__  :					Tuple        =     vectors[i + 1]
						new_vectors.append(UpperCamelCase__					)
						UpperCAmelCase__  :					Optional[Any]        =     end_vector - start_vector
						new_vectors.append(start_vector + difference_vector / 3					)
						new_vectors.append(
						    start_vector + difference_vector / 3 + rotate(difference_vector / 3 ,      6_0					)					)
						new_vectors.append(start_vector + difference_vector * 2 / 3					)
			new_vectors.append(vectors[-1]					)
			return new_vectors







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Any        =     numpy.radians(UpperCamelCase__					)
			UpperCAmelCase__   ,       UpperCAmelCase__  :					int        =     numpy.cos(UpperCamelCase__					), numpy.sin(UpperCamelCase__					)
			UpperCAmelCase__  :					Dict        =     numpy.array(((c, -s), (s, c))					)
			return numpy.dot(UpperCamelCase__ ,      UpperCamelCase__					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					Any        =     plt.gca()
			axes.set_aspect("""equal"""					)

			# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
			# y-coordinates as inputs, which are constructed from the vector-list using
			# zip()
			UpperCAmelCase__   ,       UpperCAmelCase__  :					Tuple        =     zip(*UpperCamelCase__					)
			plt.plot(UpperCamelCase__ ,      UpperCamelCase__					)
			plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		__A	=iterate(INITIAL_VECTORS, 5)
		plot(processed_vectors)