


"""simple docstring"""






import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel





def 				A_       ( snake_case__       ,					snake_case__=1		)					->  Optional[int]:
			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_       ( snake_case__       ,					snake_case__=0		)					->  Any:
			_UpperCamelCase					:str					     =	[]
			for old_item in old_list:
						_UpperCamelCase					:Tuple					     =	old_item.replace('''in_layers.0'''       ,					'''norm1'''		)
						_UpperCamelCase					:List[str]					     =	new_item.replace('''in_layers.2'''       ,					'''conv1'''		)

						_UpperCamelCase					:List[str]					     =	new_item.replace('''out_layers.0'''       ,					'''norm2'''		)
						_UpperCamelCase					:List[Any]					     =	new_item.replace('''out_layers.3'''       ,					'''conv2'''		)

						_UpperCamelCase					:int					     =	new_item.replace('''emb_layers.1'''       ,					'''time_emb_proj'''		)
						_UpperCamelCase					:Dict					     =	new_item.replace('''skip_connection'''       ,					'''conv_shortcut'''		)

						_UpperCamelCase					:str					     =	shave_segments(snake_case__       ,					n_shave_prefix_segments=snake_case__		)

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

			return mapping





def 				A_       ( snake_case__       ,					snake_case__=0		)					->  Optional[Any]:
			_UpperCamelCase					:Optional[Any]					     =	[]
			for old_item in old_list:
						_UpperCamelCase					:Optional[Any]					     =	old_item

						_UpperCamelCase					:Optional[Any]					     =	new_item.replace('''norm.weight'''       ,					'''group_norm.weight'''		)
						_UpperCamelCase					:Dict					     =	new_item.replace('''norm.bias'''       ,					'''group_norm.bias'''		)

						_UpperCamelCase					:List[Any]					     =	new_item.replace('''proj_out.weight'''       ,					'''proj_attn.weight'''		)
						_UpperCamelCase					:Optional[int]					     =	new_item.replace('''proj_out.bias'''       ,					'''proj_attn.bias'''		)

						_UpperCamelCase					:List[Any]					     =	shave_segments(snake_case__       ,					n_shave_prefix_segments=snake_case__		)

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

			return mapping





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__=None       ,					snake_case__=None       ,					snake_case__=None		)					->  Any:
			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					:Union[str, Any]					     =	old_checkpoint[path]
									_UpperCamelCase					:List[Any]					     =	old_tensor.shape[0] // 3

									_UpperCamelCase					:Tuple					     =	(-1, channels) if len(old_tensor.shape		) == 3 else (-1)

									_UpperCamelCase					:Optional[Any]					     =	old_tensor.shape[0] // config['''num_head_channels'''] // 3

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

									_UpperCamelCase					:Optional[int]					     =	query.reshape(snake_case__		)
									_UpperCamelCase					:Dict					     =	key.reshape(snake_case__		)
									_UpperCamelCase					:Optional[Any]					     =	value.reshape(snake_case__		)

			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					:Optional[int]					     =	new_path.replace('''middle_block.0'''       ,					'''mid_block.resnets.0'''		)
						_UpperCamelCase					:str					     =	new_path.replace('''middle_block.1'''       ,					'''mid_block.attentions.0'''		)
						_UpperCamelCase					:Optional[int]					     =	new_path.replace('''middle_block.2'''       ,					'''mid_block.resnets.1'''		)

						if additional_replacements is not None:
									for replacement in additional_replacements:
												_UpperCamelCase					:Dict					     =	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					:Union[str, Any]					     =	old_checkpoint[path['''old''']]





def 				A_       ( snake_case__       ,					snake_case__		)					->  Optional[int]:
			_UpperCamelCase					:Optional[Any]					     =	{}

			_UpperCamelCase					:int					     =	checkpoint['''time_embed.0.weight''']
			_UpperCamelCase					:Optional[Any]					     =	checkpoint['''time_embed.0.bias''']
			_UpperCamelCase					:str					     =	checkpoint['''time_embed.2.weight''']
			_UpperCamelCase					:List[Any]					     =	checkpoint['''time_embed.2.bias''']

			_UpperCamelCase					:Optional[int]					     =	checkpoint['''input_blocks.0.0.weight''']
			_UpperCamelCase					:Any					     =	checkpoint['''input_blocks.0.0.bias''']

			_UpperCamelCase					:int					     =	checkpoint['''out.0.weight''']
			_UpperCamelCase					:Union[str, Any]					     =	checkpoint['''out.0.bias''']
			_UpperCamelCase					:Dict					     =	checkpoint['''out.2.weight''']
			_UpperCamelCase					:List[str]					     =	checkpoint['''out.2.bias''']

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

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

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

			for i in range(1       ,					snake_case__		):
						_UpperCamelCase					:Optional[Any]					     =	(i - 1) // (config['''num_res_blocks'''] + 1)
						_UpperCamelCase					:str					     =	(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					:Union[str, Any]					     =	[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					:Tuple					     =	checkpoint[
									    f"input_blocks.{i}.0.op.weight"
									]
									_UpperCamelCase					:int					     =	checkpoint[
									    f"input_blocks.{i}.0.op.bias"
									]
									continue

						_UpperCamelCase					:Any					     =	renew_resnet_paths(snake_case__		)
						_UpperCamelCase					:Optional[Any]					     =	{'''old''': f"input_blocks.{i}.0", '''new''': f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
						_UpperCamelCase					:List[str]					     =	{'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
						assign_to_checkpoint(
						    snake_case__       ,					snake_case__       ,					snake_case__       ,					additional_replacements=[meta_path, resnet_op]       ,					config=snake_case__		)

						if len(snake_case__		):
									_UpperCamelCase					:List[str]					     =	renew_attention_paths(snake_case__		)
									_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(
									    snake_case__       ,					snake_case__       ,					snake_case__       ,					additional_replacements=[meta_path]       ,					attention_paths_to_split=snake_case__       ,					config=snake_case__       ,					)

			_UpperCamelCase					:str					     =	middle_blocks[0]
			_UpperCamelCase					:int					     =	middle_blocks[1]
			_UpperCamelCase					:Tuple					     =	middle_blocks[2]

			_UpperCamelCase					:Any					     =	renew_resnet_paths(snake_case__		)
			assign_to_checkpoint(snake_case__       ,					snake_case__       ,					snake_case__       ,					config=snake_case__		)

			_UpperCamelCase					:Union[str, Any]					     =	renew_resnet_paths(snake_case__		)
			assign_to_checkpoint(snake_case__       ,					snake_case__       ,					snake_case__       ,					config=snake_case__		)

			_UpperCamelCase					:Any					     =	renew_attention_paths(snake_case__		)
			_UpperCamelCase					:List[str]					     =	{
			    '''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(
			    snake_case__       ,					snake_case__       ,					snake_case__       ,					attention_paths_to_split=snake_case__       ,					config=snake_case__		)

			for i in range(snake_case__		):
						_UpperCamelCase					:Optional[Any]					     =	i // (config['''num_res_blocks'''] + 1)
						_UpperCamelCase					:Any					     =	i % (config['''num_res_blocks'''] + 1)
						_UpperCamelCase					:Dict					     =	[shave_segments(snake_case__       ,					2		) for name in output_blocks[i]]
						_UpperCamelCase					:int					     =	{}

						for layer in output_block_layers:
									_UpperCamelCase     ,      _UpperCamelCase					:Union[str, Any]					     =	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					:Tuple					     =	[key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
									_UpperCamelCase					:Union[str, Any]					     =	[key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]

									_UpperCamelCase					:Optional[int]					     =	renew_resnet_paths(snake_case__		)
									_UpperCamelCase					:int					     =	renew_resnet_paths(snake_case__		)

									_UpperCamelCase					:Tuple					     =	{'''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__		)

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

												# Clear attentions as they have been attributed above.
												if len(snake_case__		) == 2:
															_UpperCamelCase					:Tuple					     =	[]

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

												_UpperCamelCase					:Any					     =	checkpoint[old_path]

			return new_checkpoint


if __name__ == "__main__":
					UpperCamelCase__				:Optional[Any]			   =	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.""")

					UpperCamelCase__				:Dict			   =	parser.parse_args()

					UpperCamelCase__				:List[Any]			   =	torch.load(args.checkpoint_path)

					with open(args.config_file) as f:
										UpperCamelCase__				:List[str]			   =	json.loads(f.read())

					UpperCamelCase__				:List[Any]			   =	convert_ldm_checkpoint(checkpoint, config)

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

					UpperCamelCase__				:Optional[Any]			   =	UNetaDModel(**config)
					model.load_state_dict(converted_checkpoint)

					try:
										UpperCamelCase__				:Optional[int]			   =	DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
										UpperCamelCase__				:List[Any]			   =	VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

										UpperCamelCase__				:List[str]			   =	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

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	float:

					"""simple docstring"""



					return 0.0





def 				A_       ( snake_case__       ,					snake_case__		)					->  tuple[int | float, int | float]:
			_UpperCamelCase					:Union[str, Any]					     =	min([-20, np.min(fft_results[1 : samplerate // 2 - 1]		)]		)
			_UpperCamelCase					:Optional[int]					     =	max([20, np.max(fft_results[1 : samplerate // 2 - 1]		)]		)
			return lowest, highest





def 				A_       ( snake_case__       ,					snake_case__		)					->  None:
			_UpperCamelCase					:List[str]					     =	5_12
			_UpperCamelCase					:int					     =	[1] + [0] * (size - 1)
			_UpperCamelCase					:Union[str, Any]					     =	[filter_type.process(snake_case__		) for item in inputs]

			_UpperCamelCase					:Any					     =	[0] * (samplerate - size)  # zero-padding
			outputs += filler
			_UpperCamelCase					:int					     =	np.abs(np.fft.fft(snake_case__		)		)
			_UpperCamelCase					:str					     =	20 * np.logaa(snake_case__		)

			# Frequencies on log scale from 24 to nyquist frequency
			plt.xlim(24       ,					samplerate / 2 - 1		)
			plt.xlabel('''Frequency (Hz)'''		)
			plt.xscale('''log'''		)

			# Display within reasonable bounds
			_UpperCamelCase					:Tuple					     =	get_bounds(snake_case__       ,					snake_case__		)
			plt.ylim(max([-80, bounds[0]]		)       ,					min([80, bounds[1]]		)		)
			plt.ylabel('''Gain (dB)'''		)

			plt.plot(snake_case__		)
			plt.show()





def 				A_       ( snake_case__       ,					snake_case__		)					->  None:
			_UpperCamelCase					:Tuple					     =	5_12
			_UpperCamelCase					:Union[str, Any]					     =	[1] + [0] * (size - 1)
			_UpperCamelCase					:List[Any]					     =	[filter_type.process(snake_case__		) for item in inputs]

			_UpperCamelCase					:str					     =	[0] * (samplerate - size)  # zero-padding
			outputs += filler
			_UpperCamelCase					:Tuple					     =	np.angle(np.fft.fft(snake_case__		)		)

			# Frequencies on log scale from 24 to nyquist frequency
			plt.xlim(24       ,					samplerate / 2 - 1		)
			plt.xlabel('''Frequency (Hz)'''		)
			plt.xscale('''log'''		)

			plt.ylim(-2 * pi       ,					2 * pi		)
			plt.ylabel('''Phase shift (Radians)'''		)
			plt.plot(np.unwrap(snake_case__       ,					-2 * pi		)		)
			plt.show()

