





"""simple docstring"""




import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


UpperCAmelCase__  :     Dict   			={
    '''sample_size''': 32,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 10_00,
    '''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''',
}

UpperCAmelCase__  :     Dict   			={
    '''sample_size''': 64,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 10_00,
    '''block_out_channels''': [1_92, 1_92 * 2, 1_92 * 3, 1_92 * 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''',
}

UpperCAmelCase__  :     Optional[Any]   			={
    '''sample_size''': 2_56,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [2_56, 2_56, 2_56 * 2, 2_56 * 2, 2_56 * 4, 2_56 * 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''',
}

UpperCAmelCase__  :     str   			={
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

UpperCAmelCase__  :     Optional[Any]   			={
    '''num_train_timesteps''': 2_01,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

UpperCAmelCase__  :     Dict   			={
    '''num_train_timesteps''': 1_51,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}





def        _lowercase   (  _UpperCAmelCase			)  ->					str:
				if isinstance(lowerCamelCase__     , lowerCamelCase__			):
								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        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase=False			)  ->					Any:
				lowerCamelCase			=checkpoint[F"""{old_prefix}.in_layers.0.weight"""]
				lowerCamelCase			=checkpoint[F"""{old_prefix}.in_layers.0.bias"""]
				lowerCamelCase			=checkpoint[F"""{old_prefix}.in_layers.2.weight"""]
				lowerCamelCase			=checkpoint[F"""{old_prefix}.in_layers.2.bias"""]
				lowerCamelCase			=checkpoint[F"""{old_prefix}.emb_layers.1.weight"""]
				lowerCamelCase			=checkpoint[F"""{old_prefix}.emb_layers.1.bias"""]
				lowerCamelCase			=checkpoint[F"""{old_prefix}.out_layers.0.weight"""]
				lowerCamelCase			=checkpoint[F"""{old_prefix}.out_layers.0.bias"""]
				lowerCamelCase			=checkpoint[F"""{old_prefix}.out_layers.3.weight"""]
				lowerCamelCase			=checkpoint[F"""{old_prefix}.out_layers.3.bias"""]

				if has_skip:
								lowerCamelCase			=checkpoint[F"""{old_prefix}.skip_connection.weight"""]
								lowerCamelCase			=checkpoint[F"""{old_prefix}.skip_connection.bias"""]

				return new_checkpoint





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase=None			)  ->					Union[str, Any]:
				lowerCamelCase			=checkpoint[F"""{old_prefix}.qkv.weight"""].chunk(3     , dim=0			)
				lowerCamelCase			=checkpoint[F"""{old_prefix}.qkv.bias"""].chunk(3     , dim=0			)

				lowerCamelCase			=checkpoint[F"""{old_prefix}.norm.weight"""]
				lowerCamelCase			=checkpoint[F"""{old_prefix}.norm.bias"""]

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

				lowerCamelCase			=(
				    checkpoint[F"""{old_prefix}.proj_out.weight"""].squeeze(-1			).squeeze(-1			)
				)
				lowerCamelCase			=checkpoint[F"""{old_prefix}.proj_out.bias"""].squeeze(-1			).squeeze(-1			)

				return new_checkpoint





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					str:
				lowerCamelCase			=torch.load(lowerCamelCase__     , map_location="""cpu"""			)
				lowerCamelCase			={}

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

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

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

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

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

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

								if i != len(lowerCamelCase__			) - 1:
												lowerCamelCase			=F"""down_blocks.{i}.downsamplers.0"""
												lowerCamelCase			=F"""input_blocks.{current_layer}.0"""
												lowerCamelCase			=convert_resnet(lowerCamelCase__     , lowerCamelCase__     , lowerCamelCase__     , lowerCamelCase__			)
												current_layer += 1

								lowerCamelCase			=current_channels

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

				lowerCamelCase			=0
				lowerCamelCase			=unet_config['''up_block_types''']

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

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

												if i != len(lowerCamelCase__			) - 1:
																lowerCamelCase			=F"""up_blocks.{i}.upsamplers.0"""
																lowerCamelCase			=F"""output_blocks.{current_layer-1}.2"""
																lowerCamelCase			=convert_resnet(lowerCamelCase__     , lowerCamelCase__     , lowerCamelCase__     , lowerCamelCase__			)

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

				return new_checkpoint


if __name__ == "__main__":
							UpperCAmelCase__  :     Dict   			=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.''')

							UpperCAmelCase__  :     Optional[int]   			=parser.parse_args()
							UpperCAmelCase__  :     Dict   			=strabool(args.class_cond)

							UpperCAmelCase__  :     Tuple   			=os.path.basename(args.unet_path)
							print(F"Checkpoint: {ckpt_name}")

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

							if not args.class_cond:
														UpperCAmelCase__  :     Any   			=None

							UpperCAmelCase__  :     str   			=con_pt_to_diffuser(args.unet_path, unet_config)

							UpperCAmelCase__  :     Tuple   			=UNetaDModel(**unet_config)
							image_unet.load_state_dict(converted_unet_ckpt)

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

							UpperCAmelCase__  :     Tuple   			=CMStochasticIterativeScheduler(**scheduler_config)

							UpperCAmelCase__  :     Optional[int]   			=ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
							consistency_model.save_pretrained(args.dump_path)












import unittest

from .lib import (
    Matrix,
    Vector,
    axpy,
    square_zero_matrix,
    unit_basis_vector,
    zero_vector,
)

class  __A       (  unittest.TestCase      ):


				def _snake_case    (						self							):
								lowerCamelCase			=Vector([1, 2, 3]							)
								self.assertEqual(x.component(0							)				,	1							)
								self.assertEqual(x.component(2							)				,	3							)
								lowerCamelCase			=Vector()


				def _snake_case    (						self							):
								lowerCamelCase			=Vector([0, 0, 0, 0, 0, 1]							)
								self.assertEqual(str(UpperCAmelCase_							)				,	"""(0,0,0,0,0,1)"""							)


				def _snake_case    (						self							):
								lowerCamelCase			=Vector([1, 2, 3, 4]							)
								self.assertEqual(len(UpperCAmelCase_							)				,	4							)


				def _snake_case    (						self							):
								lowerCamelCase			=Vector([1, 2]							)
								lowerCamelCase			=Vector([1, 2, 3, 4, 5]							)
								lowerCamelCase			=Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]							)
								lowerCamelCase			=Vector([1, -1, 1, -1, 2, -3, 4, -5]							)
								self.assertAlmostEqual(x.euclidean_length()				,	2.2_3_6				,	3							)
								self.assertAlmostEqual(y.euclidean_length()				,	7.4_1_6				,	3							)
								self.assertEqual(z.euclidean_length()				,	0							)
								self.assertAlmostEqual(w.euclidean_length()				,	7.6_1_6				,	3							)


				def _snake_case    (						self							):
								lowerCamelCase			=Vector([1, 2, 3]							)
								lowerCamelCase			=Vector([1, 1, 1]							)
								self.assertEqual((x + y).component(0							)				,	2							)
								self.assertEqual((x + y).component(1							)				,	3							)
								self.assertEqual((x + y).component(2							)				,	4							)


				def _snake_case    (						self							):
								lowerCamelCase			=Vector([1, 2, 3]							)
								lowerCamelCase			=Vector([1, 1, 1]							)
								self.assertEqual((x - y).component(0							)				,	0							)
								self.assertEqual((x - y).component(1							)				,	1							)
								self.assertEqual((x - y).component(2							)				,	2							)


				def _snake_case    (						self							):
								lowerCamelCase			=Vector([1, 2, 3]							)
								lowerCamelCase			=Vector([2, -1, 4]							)  # for test of dot product
								lowerCamelCase			=Vector([1, -2, -1]							)
								self.assertEqual(str(x * 3.0							)				,	"""(3.0,6.0,9.0)"""							)
								self.assertEqual((a * b)				,	0							)


				def _snake_case    (						self							):
								self.assertEqual(str(zero_vector(10							)							).count("""0"""							)				,	10							)


				def _snake_case    (						self							):
								self.assertEqual(str(unit_basis_vector(3				,	1							)							)				,	"""(0,1,0)"""							)


				def _snake_case    (						self							):
								lowerCamelCase			=Vector([1, 2, 3]							)
								lowerCamelCase			=Vector([1, 0, 1]							)
								self.assertEqual(str(axpy(2				,	UpperCAmelCase_				,	UpperCAmelCase_							)							)				,	"""(3,4,7)"""							)


				def _snake_case    (						self							):
								lowerCamelCase			=Vector([1, 0, 0, 0, 0, 0]							)
								lowerCamelCase			=x.copy()
								self.assertEqual(str(UpperCAmelCase_							)				,	str(UpperCAmelCase_							)							)


				def _snake_case    (						self							):
								lowerCamelCase			=Vector([1, 0, 0]							)
								x.change_component(0				,	0							)
								x.change_component(1				,	1							)
								self.assertEqual(str(UpperCAmelCase_							)				,	"""(0,1,0)"""							)


				def _snake_case    (						self							):
								lowerCamelCase			=Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]				,	3				,	3							)
								self.assertEqual("""|1,2,3|\n|2,4,5|\n|6,7,8|\n"""				,	str(UpperCAmelCase_							)							)


				def _snake_case    (						self							):
								lowerCamelCase			=Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]				,	3				,	3							)
								lowerCamelCase			=[[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
								for x in range(a.height()							):
												for y in range(a.width()							):
																self.assertEqual(minors[x][y]				,	a.minor(UpperCAmelCase_				,	UpperCAmelCase_							)							)


				def _snake_case    (						self							):
								lowerCamelCase			=Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]				,	3				,	3							)
								lowerCamelCase			=[[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
								for x in range(a.height()							):
												for y in range(a.width()							):
																self.assertEqual(cofactors[x][y]				,	a.cofactor(UpperCAmelCase_				,	UpperCAmelCase_							)							)


				def _snake_case    (						self							):
								lowerCamelCase			=Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]				,	3				,	3							)
								self.assertEqual(-5				,	a.determinant()							)


				def _snake_case    (						self							):
								lowerCamelCase			=Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]				,	3				,	3							)
								lowerCamelCase			=Vector([1, 2, 3]							)
								self.assertEqual("""(14,32,50)"""				,	str(a * x							)							)
								self.assertEqual("""|2,4,6|\n|8,10,12|\n|14,16,18|\n"""				,	str(a * 2							)							)


				def _snake_case    (						self							):
								lowerCamelCase			=Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]				,	3				,	3							)
								a.change_component(0				,	2				,	5							)
								self.assertEqual("""|1,2,5|\n|2,4,5|\n|6,7,8|\n"""				,	str(UpperCAmelCase_							)							)


				def _snake_case    (						self							):
								lowerCamelCase			=Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]				,	3				,	3							)
								self.assertEqual(7				,	a.component(2				,	1							)				,	0.0_1							)


				def _snake_case    (						self							):
								lowerCamelCase			=Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]				,	3				,	3							)
								lowerCamelCase			=Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]				,	3				,	3							)
								self.assertEqual("""|2,4,10|\n|4,8,10|\n|12,14,18|\n"""				,	str(a + b							)							)


				def _snake_case    (						self							):
								lowerCamelCase			=Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]				,	3				,	3							)
								lowerCamelCase			=Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]				,	3				,	3							)
								self.assertEqual("""|0,0,-4|\n|0,0,0|\n|0,0,-2|\n"""				,	str(a - b							)							)


				def _snake_case    (						self							):
								self.assertEqual(
								    """|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n"""				,	str(square_zero_matrix(5							)							)				,	)


if __name__ == "__main__":
							unittest.main()





