
import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_lowerCamelCase       :				Any       =			{
    '''sample_size''': 32,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1_000,
    '''block_out_channels''': [32, 64],
    '''attention_head_dim''': 8,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

_lowerCamelCase       :				Union[str, Any]       =			{
    '''sample_size''': 64,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1_000,
    '''block_out_channels''': [192, 192 * 2, 192 * 3, 192 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

_lowerCamelCase       :				Optional[int]       =			{
    '''sample_size''': 256,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''default''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

_lowerCamelCase       :				List[Any]       =			{
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

_lowerCamelCase       :				Tuple       =			{
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

_lowerCamelCase       :				List[Any]       =			{
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}






def        a_		(       __lowercase   :	Optional[Any] ) ->  str:
			if isinstance(__lowercase       ,					__lowercase ):
						return v
			if v.lower() in ("yes", "true", "t", "y", "1"):
						return True
			elif v.lower() in ("no", "false", "f", "n", "0"):
						return False
			else:
						raise argparse.ArgumentTypeError('boolean value expected' )






def        a_		(       __lowercase   :	Dict       ,					__lowercase   :	List[Any]       ,					__lowercase   :	int       ,					__lowercase   :	List[Any]       ,					__lowercase   :	Dict=False ) ->  Any:
			_snake_case		=			checkpoint[f'''{old_prefix}.in_layers.0.weight''']
			_snake_case		=			checkpoint[f'''{old_prefix}.in_layers.0.bias''']
			_snake_case		=			checkpoint[f'''{old_prefix}.in_layers.2.weight''']
			_snake_case		=			checkpoint[f'''{old_prefix}.in_layers.2.bias''']
			_snake_case		=			checkpoint[f'''{old_prefix}.emb_layers.1.weight''']
			_snake_case		=			checkpoint[f'''{old_prefix}.emb_layers.1.bias''']
			_snake_case		=			checkpoint[f'''{old_prefix}.out_layers.0.weight''']
			_snake_case		=			checkpoint[f'''{old_prefix}.out_layers.0.bias''']
			_snake_case		=			checkpoint[f'''{old_prefix}.out_layers.3.weight''']
			_snake_case		=			checkpoint[f'''{old_prefix}.out_layers.3.bias''']

			if has_skip:
						_snake_case		=			checkpoint[f'''{old_prefix}.skip_connection.weight''']
						_snake_case		=			checkpoint[f'''{old_prefix}.skip_connection.bias''']

			return new_checkpoint






def        a_		(       __lowercase   :	Optional[int]       ,					__lowercase   :	Any       ,					__lowercase   :	Dict       ,					__lowercase   :	Any       ,					__lowercase   :	Dict=None ) ->  Optional[Any]:
			_snake_case	,					_snake_case	,					_snake_case		=			checkpoint[f'''{old_prefix}.qkv.weight'''].chunk(3       ,					dim=0 )
			_snake_case	,					_snake_case	,					_snake_case		=			checkpoint[f'''{old_prefix}.qkv.bias'''].chunk(3       ,					dim=0 )

			_snake_case		=			checkpoint[f'''{old_prefix}.norm.weight''']
			_snake_case		=			checkpoint[f'''{old_prefix}.norm.bias''']

			_snake_case		=			weight_q.squeeze(-1 ).squeeze(-1 )
			_snake_case		=			bias_q.squeeze(-1 ).squeeze(-1 )
			_snake_case		=			weight_k.squeeze(-1 ).squeeze(-1 )
			_snake_case		=			bias_k.squeeze(-1 ).squeeze(-1 )
			_snake_case		=			weight_v.squeeze(-1 ).squeeze(-1 )
			_snake_case		=			bias_v.squeeze(-1 ).squeeze(-1 )

			_snake_case		=			(
			    checkpoint[f'''{old_prefix}.proj_out.weight'''].squeeze(-1 ).squeeze(-1 )
			)
			_snake_case		=			checkpoint[f'''{old_prefix}.proj_out.bias'''].squeeze(-1 ).squeeze(-1 )

			return new_checkpoint






def        a_		(       __lowercase   :	str       ,					__lowercase   :	Any ) ->  Optional[Any]:
			_snake_case		=			torch.load(__lowercase       ,					map_location='cpu' )
			_snake_case		=			{}

			_snake_case		=			checkpoint['time_embed.0.weight']
			_snake_case		=			checkpoint['time_embed.0.bias']
			_snake_case		=			checkpoint['time_embed.2.weight']
			_snake_case		=			checkpoint['time_embed.2.bias']

			if unet_config["num_class_embeds"] is not None:
						_snake_case		=			checkpoint['label_emb.weight']

			_snake_case		=			checkpoint['input_blocks.0.0.weight']
			_snake_case		=			checkpoint['input_blocks.0.0.bias']

			_snake_case		=			unet_config['down_block_types']
			_snake_case		=			unet_config['layers_per_block']
			_snake_case		=			unet_config['attention_head_dim']
			_snake_case		=			unet_config['block_out_channels']
			_snake_case		=			1
			_snake_case		=			channels_list[0]

			for i, layer_type in enumerate(__lowercase ):
						_snake_case		=			channels_list[i]
						_snake_case		=			current_channels != prev_channels
						if layer_type == "ResnetDownsampleBlock2D":
									for j in range(__lowercase ):
												_snake_case		=			f'''down_blocks.{i}.resnets.{j}'''
												_snake_case		=			f'''input_blocks.{current_layer}.0'''
												_snake_case		=			True if j == 0 and downsample_block_has_skip else False
												_snake_case		=			convert_resnet(__lowercase       ,					__lowercase       ,					__lowercase       ,					__lowercase       ,					has_skip=__lowercase )
												current_layer += 1

						elif layer_type == "AttnDownBlock2D":
									for j in range(__lowercase ):
												_snake_case		=			f'''down_blocks.{i}.resnets.{j}'''
												_snake_case		=			f'''input_blocks.{current_layer}.0'''
												_snake_case		=			True if j == 0 and downsample_block_has_skip else False
												_snake_case		=			convert_resnet(__lowercase       ,					__lowercase       ,					__lowercase       ,					__lowercase       ,					has_skip=__lowercase )
												_snake_case		=			f'''down_blocks.{i}.attentions.{j}'''
												_snake_case		=			f'''input_blocks.{current_layer}.1'''
												_snake_case		=			convert_attention(
												    __lowercase       ,					__lowercase       ,					__lowercase       ,					__lowercase       ,					__lowercase )
												current_layer += 1

						if i != len(__lowercase ) - 1:
									_snake_case		=			f'''down_blocks.{i}.downsamplers.0'''
									_snake_case		=			f'''input_blocks.{current_layer}.0'''
									_snake_case		=			convert_resnet(__lowercase       ,					__lowercase       ,					__lowercase       ,					__lowercase )
									current_layer += 1

						_snake_case		=			current_channels

			# hardcoded the mid-block for now
			_snake_case		=			'mid_block.resnets.0'
			_snake_case		=			'middle_block.0'
			_snake_case		=			convert_resnet(__lowercase       ,					__lowercase       ,					__lowercase       ,					__lowercase )
			_snake_case		=			'mid_block.attentions.0'
			_snake_case		=			'middle_block.1'
			_snake_case		=			convert_attention(__lowercase       ,					__lowercase       ,					__lowercase       ,					__lowercase       ,					__lowercase )
			_snake_case		=			'mid_block.resnets.1'
			_snake_case		=			'middle_block.2'
			_snake_case		=			convert_resnet(__lowercase       ,					__lowercase       ,					__lowercase       ,					__lowercase )

			_snake_case		=			0
			_snake_case		=			unet_config['up_block_types']

			for i, layer_type in enumerate(__lowercase ):
						if layer_type == "ResnetUpsampleBlock2D":
									for j in range(layers_per_block + 1 ):
												_snake_case		=			f'''up_blocks.{i}.resnets.{j}'''
												_snake_case		=			f'''output_blocks.{current_layer}.0'''
												_snake_case		=			convert_resnet(__lowercase       ,					__lowercase       ,					__lowercase       ,					__lowercase       ,					has_skip=__lowercase )
												current_layer += 1

									if i != len(__lowercase ) - 1:
												_snake_case		=			f'''up_blocks.{i}.upsamplers.0'''
												_snake_case		=			f'''output_blocks.{current_layer-1}.1'''
												_snake_case		=			convert_resnet(__lowercase       ,					__lowercase       ,					__lowercase       ,					__lowercase )
						elif layer_type == "AttnUpBlock2D":
									for j in range(layers_per_block + 1 ):
												_snake_case		=			f'''up_blocks.{i}.resnets.{j}'''
												_snake_case		=			f'''output_blocks.{current_layer}.0'''
												_snake_case		=			convert_resnet(__lowercase       ,					__lowercase       ,					__lowercase       ,					__lowercase       ,					has_skip=__lowercase )
												_snake_case		=			f'''up_blocks.{i}.attentions.{j}'''
												_snake_case		=			f'''output_blocks.{current_layer}.1'''
												_snake_case		=			convert_attention(
												    __lowercase       ,					__lowercase       ,					__lowercase       ,					__lowercase       ,					__lowercase )
												current_layer += 1

									if i != len(__lowercase ) - 1:
												_snake_case		=			f'''up_blocks.{i}.upsamplers.0'''
												_snake_case		=			f'''output_blocks.{current_layer-1}.2'''
												_snake_case		=			convert_resnet(__lowercase       ,					__lowercase       ,					__lowercase       ,					__lowercase )

			_snake_case		=			checkpoint['out.0.weight']
			_snake_case		=			checkpoint['out.0.bias']
			_snake_case		=			checkpoint['out.2.weight']
			_snake_case		=			checkpoint['out.2.bias']

			return new_checkpoint


if __name__ == "__main__":
			_lowerCamelCase       :				List[str]       =			argparse.ArgumentParser()

			parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
			parser.add_argument(
			    '''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
			)
			parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')

			_lowerCamelCase       :				int       =			parser.parse_args()
			_lowerCamelCase       :				Any       =			strabool(args.class_cond)

			_lowerCamelCase       :				List[Any]       =			os.path.basename(args.unet_path)
			print(F'Checkpoint: {ckpt_name}')

			# Get U-Net config
			if "imagenet64" in ckpt_name:
						_lowerCamelCase       :				Union[str, Any]       =			IMAGENET_64_UNET_CONFIG
			elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
						_lowerCamelCase       :				Optional[int]       =			LSUN_256_UNET_CONFIG
			elif "test" in ckpt_name:
						_lowerCamelCase       :				int       =			TEST_UNET_CONFIG
			else:
						raise ValueError(F'Checkpoint type {ckpt_name} is not currently supported.')

			if not args.class_cond:
						_lowerCamelCase       :				Optional[int]       =			None

			_lowerCamelCase       :				List[str]       =			con_pt_to_diffuser(args.unet_path, unet_config)

			_lowerCamelCase       :				Union[str, Any]       =			UNetaDModel(**unet_config)
			image_unet.load_state_dict(converted_unet_ckpt)

			# Get scheduler config
			if "cd" in ckpt_name or "test" in ckpt_name:
						_lowerCamelCase       :				Optional[int]       =			CD_SCHEDULER_CONFIG
			elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
						_lowerCamelCase       :				Optional[Any]       =			CT_IMAGENET_64_SCHEDULER_CONFIG
			elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
						_lowerCamelCase       :				Union[str, Any]       =			CT_LSUN_256_SCHEDULER_CONFIG
			else:
						raise ValueError(F'Checkpoint type {ckpt_name} is not currently supported.')

			_lowerCamelCase       :				Any       =			CMStochasticIterativeScheduler(**scheduler_config)

			_lowerCamelCase       :				Any       =			ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
			consistency_model.save_pretrained(args.dump_path)

from __future__ import annotations

from typing import Any




class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



	def __init__(  self	:    Tuple			,  lowercase	:    int			,  lowercase	:    int			,  lowercase	:    float = 0	):


				'''simple docstring'''


				_snake_case	,					_snake_case		=			row, column
				_snake_case		=			[[default_value for c in range(lowercase	)] for r in range(lowercase	)]



	def __str__(  self	:    int	):


				'''simple docstring'''


				_snake_case		=			f'''Matrix consist of {self.row} rows and {self.column} columns\n'''

				# Make string identifier
				_snake_case		=			0
				for row_vector in self.array:
							for obj in row_vector:
										_snake_case		=			max(lowercase			,  len(str(lowercase	)	)	)
				_snake_case		=			f'''%{max_element_length}s'''

				# Make string and return
				def single_line(lowercase	:    list[float]	) -> str:
							nonlocal string_format_identifier
							_snake_case		=			'['
							line += ", ".join(string_format_identifier % (obj,) for obj in row_vector	)
							line += "]"
							return line

				s += "\n".join(single_line(lowercase	) for row_vector in self.array	)
				return s



	def __repr__(  self	:    Dict	):


				'''simple docstring'''


				return str(self	)



	def  A							(  self	:    str			,  lowercase	:    tuple[int, int]	):


				'''simple docstring'''


				if not (isinstance(lowercase			,  (list, tuple)	) and len(lowercase	) == 2):
							return False
				elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
							return False
				else:
							return True



	def __getitem__(  self	:    Dict			,  lowercase	:    tuple[int, int]	):


				'''simple docstring'''


				assert self.validate_indicies(lowercase	)
				return self.array[loc[0]][loc[1]]



	def __setitem__(  self	:    str			,  lowercase	:    tuple[int, int]			,  lowercase	:    float	):


				'''simple docstring'''


				assert self.validate_indicies(lowercase	)
				_snake_case		=			value



	def __add__(  self	:    str			,  lowercase	:    Matrix	):


				'''simple docstring'''


				assert isinstance(lowercase			,  lowercase	)
				assert self.row == another.row and self.column == another.column

				# Add
				_snake_case		=			Matrix(self.row			,  self.column	)
				for r in range(self.row	):
							for c in range(self.column	):
										_snake_case		=			self[r, c] + another[r, c]
				return result



	def __neg__(  self	:    Tuple	):


				'''simple docstring'''


				_snake_case		=			Matrix(self.row			,  self.column	)
				for r in range(self.row	):
							for c in range(self.column	):
										_snake_case		=			-self[r, c]
				return result



	def __sub__(  self	:    List[str]			,  lowercase	:    Matrix	):


				'''simple docstring'''


				return self + (-another)



	def __mul__(  self	:    Dict			,  lowercase	:    int | float | Matrix	):


				'''simple docstring'''


				if isinstance(lowercase			,  (int, float)	):  # Scalar multiplication
							_snake_case		=			Matrix(self.row			,  self.column	)
							for r in range(self.row	):
										for c in range(self.column	):
													_snake_case		=			self[r, c] * another
							return result
				elif isinstance(lowercase			,  lowercase	):  # Matrix multiplication
							assert self.column == another.row
							_snake_case		=			Matrix(self.row			,  another.column	)
							for r in range(self.row	):
										for c in range(another.column	):
													for i in range(self.column	):
																result[r, c] += self[r, i] * another[i, c]
							return result
				else:
							_snake_case		=			f'''Unsupported type given for another ({type(lowercase	)})'''
							raise TypeError(lowercase	)



	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			Matrix(self.column			,  self.row	)
				for r in range(self.row	):
							for c in range(self.column	):
										_snake_case		=			self[r, c]
				return result





	def  A							(  self	:    List[Any]			,  lowercase	:    Matrix			,  lowercase	:    Matrix	):


				'''simple docstring'''


				assert isinstance(lowercase			,  lowercase	) and isinstance(lowercase			,  lowercase	)
				assert self.row == self.column == u.row == v.row  # u, v should be column vector
				assert u.column == v.column == 1  # u, v should be column vector

				# Calculate
				_snake_case		=			v.transpose()
				_snake_case		=			(v_t * self * u)[0, 0] + 1
				if numerator_factor == 0:
							return None  # It's not invertable
				return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":






			def        a_		(       ) ->  None:
						# a^(-1)
						_snake_case		=			Matrix(3       ,					3       ,					0 )
						for i in range(3 ):
									_snake_case		=			1
						print(f'''a^(-1) is {ainv}''' )
						# u, v
						_snake_case		=			Matrix(3       ,					1       ,					0 )
						_snake_case	,					_snake_case	,					_snake_case		=			1, 2, -3
						_snake_case		=			Matrix(3       ,					1       ,					0 )
						_snake_case	,					_snake_case	,					_snake_case		=			4, -2, 5
						print(f'''u is {u}''' )
						print(f'''v is {v}''' )
						print(f'''uv^T is {u * v.transpose()}''' )
						# Sherman Morrison
						print(f'''(a + uv^T)^(-1) is {ainv.sherman_morrison(__lowercase       ,					__lowercase )}''' )






			def        a_		(       ) ->  None:
						import doctest

						doctest.testmod()

			testa()