


import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__UpperCamelCase				:  List[Any]      					=							{
    'sample_size': 32,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': 1000,
    '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				:  str      					=							{
    'sample_size': 64,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 3,
    'num_class_embeds': 1000,
    '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',
}

__UpperCamelCase				:  List[str]      					=							{
    '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',
}

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

__UpperCamelCase				:  Union[str, Any]      					=							{
    'num_train_timesteps': 201,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

__UpperCamelCase				:  Optional[Any]      					=							{
    'num_train_timesteps': 151,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}
def      snake_case_						( __lowercase       ):
			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      snake_case_						( __lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       __lowercase=False       ):
			UpperCAmelCase_							:      Dict						=   checkpoint[F'''{old_prefix}.in_layers.0.weight''']
			UpperCAmelCase_							:      Union[str, Any]						=   checkpoint[F'''{old_prefix}.in_layers.0.bias''']
			UpperCAmelCase_							:      List[Any]						=   checkpoint[F'''{old_prefix}.in_layers.2.weight''']
			UpperCAmelCase_							:      Tuple						=   checkpoint[F'''{old_prefix}.in_layers.2.bias''']
			UpperCAmelCase_							:      Tuple						=   checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
			UpperCAmelCase_							:      Dict						=   checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
			UpperCAmelCase_							:      List[str]						=   checkpoint[F'''{old_prefix}.out_layers.0.weight''']
			UpperCAmelCase_							:      Any						=   checkpoint[F'''{old_prefix}.out_layers.0.bias''']
			UpperCAmelCase_							:      Optional[Any]						=   checkpoint[F'''{old_prefix}.out_layers.3.weight''']
			UpperCAmelCase_							:      int						=   checkpoint[F'''{old_prefix}.out_layers.3.bias''']

			if has_skip:
						UpperCAmelCase_							:      List[str]						=   checkpoint[F'''{old_prefix}.skip_connection.weight''']
						UpperCAmelCase_							:      int						=   checkpoint[F'''{old_prefix}.skip_connection.bias''']

			return new_checkpoint
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       __lowercase=None       ):
			UpperCAmelCase_							:      Optional[int]						=   checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3			,       dim=0       )
			UpperCAmelCase_							:      List[str]						=   checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3			,       dim=0       )

			UpperCAmelCase_							:      List[Any]						=   checkpoint[F'''{old_prefix}.norm.weight''']
			UpperCAmelCase_							:      Tuple						=   checkpoint[F'''{old_prefix}.norm.bias''']

			UpperCAmelCase_							:      Any						=   weight_q.squeeze(-1       ).squeeze(-1       )
			UpperCAmelCase_							:      Dict						=   bias_q.squeeze(-1       ).squeeze(-1       )
			UpperCAmelCase_							:      str						=   weight_k.squeeze(-1       ).squeeze(-1       )
			UpperCAmelCase_							:      List[Any]						=   bias_k.squeeze(-1       ).squeeze(-1       )
			UpperCAmelCase_							:      Union[str, Any]						=   weight_v.squeeze(-1       ).squeeze(-1       )
			UpperCAmelCase_							:      Optional[int]						=   bias_v.squeeze(-1       ).squeeze(-1       )

			UpperCAmelCase_							:      str						=   (
			    checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1       ).squeeze(-1       )
			)
			UpperCAmelCase_							:      Any						=   checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1       ).squeeze(-1       )

			return new_checkpoint
def      snake_case_						( __lowercase			,       __lowercase       ):
			UpperCAmelCase_							:      int						=   torch.load(__lowercase			,       map_location='''cpu'''       )
			UpperCAmelCase_							:      Optional[int]						=   {}

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

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

			UpperCAmelCase_							:      Optional[Any]						=   checkpoint['''input_blocks.0.0.weight''']
			UpperCAmelCase_							:      List[Any]						=   checkpoint['''input_blocks.0.0.bias''']

			UpperCAmelCase_							:      List[Any]						=   unet_config['''down_block_types''']
			UpperCAmelCase_							:      Tuple						=   unet_config['''layers_per_block''']
			UpperCAmelCase_							:      int						=   unet_config['''attention_head_dim''']
			UpperCAmelCase_							:      List[Any]						=   unet_config['''block_out_channels''']
			UpperCAmelCase_							:      List[Any]						=   1
			UpperCAmelCase_							:      Union[str, Any]						=   channels_list[0]

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

						elif layer_type == "AttnDownBlock2D":
									for j in range(__lowercase       ):
												UpperCAmelCase_							:      Dict						=   F'''down_blocks.{i}.resnets.{j}'''
												UpperCAmelCase_							:      int						=   F'''input_blocks.{current_layer}.0'''
												UpperCAmelCase_							:      Optional[int]						=   True if j == 0 and downsample_block_has_skip else False
												UpperCAmelCase_							:      Tuple						=   convert_resnet(__lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       has_skip=__lowercase       )
												UpperCAmelCase_							:      Optional[Any]						=   F'''down_blocks.{i}.attentions.{j}'''
												UpperCAmelCase_							:      Optional[Any]						=   F'''input_blocks.{current_layer}.1'''
												UpperCAmelCase_							:      List[Any]						=   convert_attention(
												    __lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       __lowercase       )
												current_layer += 1

						if i != len(__lowercase       ) - 1:
									UpperCAmelCase_							:      Any						=   F'''down_blocks.{i}.downsamplers.0'''
									UpperCAmelCase_							:      str						=   F'''input_blocks.{current_layer}.0'''
									UpperCAmelCase_							:      List[str]						=   convert_resnet(__lowercase			,       __lowercase			,       __lowercase			,       __lowercase       )
									current_layer += 1

						UpperCAmelCase_							:      str						=   current_channels

			# hardcoded the mid-block for now
			UpperCAmelCase_							:      Optional[int]						=   '''mid_block.resnets.0'''
			UpperCAmelCase_							:      List[str]						=   '''middle_block.0'''
			UpperCAmelCase_							:      List[Any]						=   convert_resnet(__lowercase			,       __lowercase			,       __lowercase			,       __lowercase       )
			UpperCAmelCase_							:      Optional[Any]						=   '''mid_block.attentions.0'''
			UpperCAmelCase_							:      int						=   '''middle_block.1'''
			UpperCAmelCase_							:      List[Any]						=   convert_attention(__lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       __lowercase       )
			UpperCAmelCase_							:      Optional[Any]						=   '''mid_block.resnets.1'''
			UpperCAmelCase_							:      Union[str, Any]						=   '''middle_block.2'''
			UpperCAmelCase_							:      Optional[Any]						=   convert_resnet(__lowercase			,       __lowercase			,       __lowercase			,       __lowercase       )

			UpperCAmelCase_							:      int						=   0
			UpperCAmelCase_							:      List[Any]						=   unet_config['''up_block_types''']

			for i, layer_type in enumerate(__lowercase       ):
						if layer_type == "ResnetUpsampleBlock2D":
									for j in range(layers_per_block + 1       ):
												UpperCAmelCase_							:      Tuple						=   F'''up_blocks.{i}.resnets.{j}'''
												UpperCAmelCase_							:      List[str]						=   F'''output_blocks.{current_layer}.0'''
												UpperCAmelCase_							:      Optional[int]						=   convert_resnet(__lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       has_skip=__lowercase       )
												current_layer += 1

									if i != len(__lowercase       ) - 1:
												UpperCAmelCase_							:      Optional[Any]						=   F'''up_blocks.{i}.upsamplers.0'''
												UpperCAmelCase_							:      Tuple						=   F'''output_blocks.{current_layer-1}.1'''
												UpperCAmelCase_							:      Tuple						=   convert_resnet(__lowercase			,       __lowercase			,       __lowercase			,       __lowercase       )
						elif layer_type == "AttnUpBlock2D":
									for j in range(layers_per_block + 1       ):
												UpperCAmelCase_							:      Tuple						=   F'''up_blocks.{i}.resnets.{j}'''
												UpperCAmelCase_							:      Tuple						=   F'''output_blocks.{current_layer}.0'''
												UpperCAmelCase_							:      Optional[Any]						=   convert_resnet(__lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       has_skip=__lowercase       )
												UpperCAmelCase_							:      Optional[Any]						=   F'''up_blocks.{i}.attentions.{j}'''
												UpperCAmelCase_							:      Union[str, Any]						=   F'''output_blocks.{current_layer}.1'''
												UpperCAmelCase_							:      Union[str, Any]						=   convert_attention(
												    __lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       __lowercase       )
												current_layer += 1

									if i != len(__lowercase       ) - 1:
												UpperCAmelCase_							:      List[Any]						=   F'''up_blocks.{i}.upsamplers.0'''
												UpperCAmelCase_							:      Tuple						=   F'''output_blocks.{current_layer-1}.2'''
												UpperCAmelCase_							:      Dict						=   convert_resnet(__lowercase			,       __lowercase			,       __lowercase			,       __lowercase       )

			UpperCAmelCase_							:      str						=   checkpoint['''out.0.weight''']
			UpperCAmelCase_							:      Optional[Any]						=   checkpoint['''out.0.bias''']
			UpperCAmelCase_							:      Union[str, Any]						=   checkpoint['''out.2.weight''']
			UpperCAmelCase_							:      int						=   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				:  str      					=							parser.parse_args()
							__UpperCamelCase				:  Optional[Any]      					=							strabool(args.class_cond)

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

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

							if not args.class_cond:
														__UpperCamelCase				:  List[Any]      					=							None

							__UpperCamelCase				:  Optional[Any]      					=							con_pt_to_diffuser(args.unet_path, unet_config)

							__UpperCamelCase				:  Optional[int]      					=							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				:  Optional[Any]      					=							CD_SCHEDULER_CONFIG
							elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
														__UpperCamelCase				:  List[str]      					=							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				:  List[str]      					=							CMStochasticIterativeScheduler(**scheduler_config)

							__UpperCamelCase				:  List[Any]      					=							ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
							consistency_model.save_pretrained(args.dump_path)



from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch





@dataclass
class  lowerCAmelCase__:






    '''simple docstring'''





    A_  :     torch.Tensor  # [batch_size x 3]
    A_  :     torch.Tensor  # [batch_size x 3]
    A_  :     torch.Tensor  # [batch_size x 3]
    A_  :     torch.Tensor  # [batch_size x 3]
    A_  :     int
    A_  :     int
    A_  :     float
    A_  :     float
    A_  :     Tuple[int]





    def 					_lowerCamelCase (	self				:   List[Any]	):





       '''simple docstring'''
       assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
       assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
       assert len(self.x.shape	) == len(self.y.shape	) == len(self.z.shape	) == len(self.origin.shape	) == 2





    def 					_lowerCamelCase (	self				:   Tuple	):





       '''simple docstring'''
       return torch.from_numpy(np.array([self.width, self.height]       ,   dtype=np.floataa	)	)





    def 					_lowerCamelCase (	self				:   Tuple	):





       '''simple docstring'''
       return torch.from_numpy(np.array([self.x_fov, self.y_fov]       ,   dtype=np.floataa	)	)





    def 					_lowerCamelCase (	self				:   Any	):





       '''simple docstring'''
       UpperCAmelCase_							:      Optional[int]						=   torch.arange(self.height * self.width	)
       UpperCAmelCase_							:      Any						=   torch.stack(
           [
               pixel_indices % self.width,
               torch.div(__snake_case       ,   self.width       ,   rounding_mode='''trunc'''	),
           ]       ,   axis=1       ,   )
       return coords





    @property
    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       UpperCAmelCase_		,      *UpperCAmelCase_							:      List[Any]						=   self.shape
       UpperCAmelCase_							:      Optional[Any]						=   int(np.prod(__snake_case	)	)

       UpperCAmelCase_							:      str						=   self.get_image_coords()
       UpperCAmelCase_							:      List[str]						=   torch.broadcast_to(coords.unsqueeze(0	)       ,   [batch_size * inner_batch_size, *coords.shape]	)
       UpperCAmelCase_							:      Tuple						=   self.get_camera_rays(__snake_case	)

       UpperCAmelCase_							:      Union[str, Any]						=   rays.view(__snake_case       ,   inner_batch_size * self.height * self.width       ,   2       ,   3	)

       return rays





    def 					_lowerCamelCase (	self				:   Dict       ,   __snake_case				:   torch.Tensor	):





       '''simple docstring'''
       UpperCAmelCase_		,      *UpperCAmelCase_		,      UpperCAmelCase_							:      int						=   coords.shape
       assert n_coords == 2
       assert batch_size == self.origin.shape[0]

       UpperCAmelCase_							:      str						=   coords.view(__snake_case       ,   -1       ,   2	)

       UpperCAmelCase_							:      List[Any]						=   self.resolution()
       UpperCAmelCase_							:      Optional[Any]						=   self.fov()

       UpperCAmelCase_							:      int						=   (flat.float() / (res - 1)) * 2 - 1
       UpperCAmelCase_							:      Optional[Any]						=   fracs * torch.tan(fov / 2	)

       UpperCAmelCase_							:      Optional[Any]						=   fracs.view(__snake_case       ,   -1       ,   2	)
       UpperCAmelCase_							:      List[Any]						=   (
           self.z.view(__snake_case       ,   1       ,   3	)
           + self.x.view(__snake_case       ,   1       ,   3	) * fracs[:, :, :1]
           + self.y.view(__snake_case       ,   1       ,   3	) * fracs[:, :, 1:]
       )
       UpperCAmelCase_							:      Union[str, Any]						=   directions / directions.norm(dim=-1       ,   keepdim=__snake_case	)
       UpperCAmelCase_							:      Optional[int]						=   torch.stack(
           [
               torch.broadcast_to(self.origin.view(__snake_case       ,   1       ,   3	)       ,   [batch_size, directions.shape[1], 3]	),
               directions,
           ]       ,   dim=2       ,   )
       return rays.view(__snake_case       ,   *__snake_case       ,   2       ,   3	)





    def 					_lowerCamelCase (	self				:   Any       ,   __snake_case				:   int       ,   __snake_case				:   int	):





       '''simple docstring'''
       assert width * self.height == height * self.width, "The aspect ratio should not change."
       return DifferentiableProjectiveCamera(
           origin=self.origin       ,   x=self.x       ,   y=self.y       ,   z=self.z       ,   width=__snake_case       ,   height=__snake_case       ,   x_fov=self.x_fov       ,   y_fov=self.y_fov       ,   )





def      snake_case_						( __lowercase       ):
   UpperCAmelCase_							:      List[Any]						=   []
   UpperCAmelCase_							:      int						=   []
   UpperCAmelCase_							:      Any						=   []
   UpperCAmelCase_							:      Tuple						=   []
   for theta in np.linspace(0			,       2 * np.pi			,       num=2_0       ):
      UpperCAmelCase_							:      Tuple						=   np.array([np.sin(__lowercase       ), np.cos(__lowercase       ), -0.5]       )
      z /= np.sqrt(np.sum(z**2       )       )
      UpperCAmelCase_							:      str						=   -z * 4
      UpperCAmelCase_							:      List[Any]						=   np.array([np.cos(__lowercase       ), -np.sin(__lowercase       ), 0.0]       )
      UpperCAmelCase_							:      Tuple						=   np.cross(__lowercase			,       __lowercase       )
      origins.append(__lowercase       )
      xs.append(__lowercase       )
      ys.append(__lowercase       )
      zs.append(__lowercase       )
   return DifferentiableProjectiveCamera(
       origin=torch.from_numpy(np.stack(__lowercase			,       axis=0       )       ).float()			,       x=torch.from_numpy(np.stack(__lowercase			,       axis=0       )       ).float()			,       y=torch.from_numpy(np.stack(__lowercase			,       axis=0       )       ).float()			,       z=torch.from_numpy(np.stack(__lowercase			,       axis=0       )       ).float()			,       width=__lowercase			,       height=__lowercase			,       x_fov=0.7			,       y_fov=0.7			,       shape=(1, len(__lowercase       ))			,       )