






"""simple docstring"""



import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


A_    =							{
    '''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''',
}

A_    =							{
    '''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''',
}

A_    =							{
    '''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''',
}

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

A_    =							{
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

A_    =							{
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Union[str, Any]      )		->Dict:
						if isinstance(UpperCAmelCase__,						UpperCAmelCase__      ):
												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      _lowerCAmelCase		(							UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					List[Any],						UpperCAmelCase__       :					str=False      )		->Optional[int]:
						A__						:  Dict		  =  checkpoint[f'{old_prefix}.in_layers.0.weight']
						A__						:  int		  =  checkpoint[f'{old_prefix}.in_layers.0.bias']
						A__						:  Union[str, Any]		  =  checkpoint[f'{old_prefix}.in_layers.2.weight']
						A__						:  Dict		  =  checkpoint[f'{old_prefix}.in_layers.2.bias']
						A__						:  Dict		  =  checkpoint[f'{old_prefix}.emb_layers.1.weight']
						A__						:  Optional[Any]		  =  checkpoint[f'{old_prefix}.emb_layers.1.bias']
						A__						:  Union[str, Any]		  =  checkpoint[f'{old_prefix}.out_layers.0.weight']
						A__						:  Optional[Any]		  =  checkpoint[f'{old_prefix}.out_layers.0.bias']
						A__						:  Optional[int]		  =  checkpoint[f'{old_prefix}.out_layers.3.weight']
						A__						:  List[Any]		  =  checkpoint[f'{old_prefix}.out_layers.3.bias']

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

						return new_checkpoint




def      _lowerCAmelCase		(							UpperCAmelCase__       :					int,						UpperCAmelCase__       :					str,						UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					List[Any],						UpperCAmelCase__       :					Tuple=None      )		->Union[str, Any]:
						A__  ,  A__  ,  A__						:  List[Any]		  =  checkpoint[f'{old_prefix}.qkv.weight'].chunk(3,						dim=0      )
						A__  ,  A__  ,  A__						:  List[Any]		  =  checkpoint[f'{old_prefix}.qkv.bias'].chunk(3,						dim=0      )

						A__						:  Optional[int]		  =  checkpoint[f'{old_prefix}.norm.weight']
						A__						:  Union[str, Any]		  =  checkpoint[f'{old_prefix}.norm.bias']

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

						A__						:  Optional[int]		  =  (
						    checkpoint[f'{old_prefix}.proj_out.weight'].squeeze(-1      ).squeeze(-1      )
						)
						A__						:  Optional[int]		  =  checkpoint[f'{old_prefix}.proj_out.bias'].squeeze(-1      ).squeeze(-1      )

						return new_checkpoint




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str,						UpperCAmelCase__       :					Any      )		->str:
						A__						:  Union[str, Any]		  =  torch.load(UpperCAmelCase__,						map_location="""cpu"""      )
						A__						:  Optional[Any]		  =  {}

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

						if unet_config["num_class_embeds"] is not None:
												A__						:  Tuple		  =  checkpoint["""label_emb.weight"""]

						A__						:  Dict		  =  checkpoint["""input_blocks.0.0.weight"""]
						A__						:  Optional[Any]		  =  checkpoint["""input_blocks.0.0.bias"""]

						A__						:  Optional[int]		  =  unet_config["""down_block_types"""]
						A__						:  List[str]		  =  unet_config["""layers_per_block"""]
						A__						:  Tuple		  =  unet_config["""attention_head_dim"""]
						A__						:  Any		  =  unet_config["""block_out_channels"""]
						A__						:  Dict		  =  1
						A__						:  Any		  =  channels_list[0]

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

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

												if i != len(UpperCAmelCase__      ) - 1:
																		A__						:  Optional[Any]		  =  f'down_blocks.{i}.downsamplers.0'
																		A__						:  Optional[Any]		  =  f'input_blocks.{current_layer}.0'
																		A__						:  List[Any]		  =  convert_resnet(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      )
																		current_layer += 1

												A__						:  int		  =  current_channels

						# hardcoded the mid-block for now
						A__						:  List[Any]		  =  """mid_block.resnets.0"""
						A__						:  List[str]		  =  """middle_block.0"""
						A__						:  Tuple		  =  convert_resnet(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      )
						A__						:  Dict		  =  """mid_block.attentions.0"""
						A__						:  Any		  =  """middle_block.1"""
						A__						:  int		  =  convert_attention(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      )
						A__						:  Union[str, Any]		  =  """mid_block.resnets.1"""
						A__						:  Union[str, Any]		  =  """middle_block.2"""
						A__						:  str		  =  convert_resnet(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      )

						A__						:  List[str]		  =  0
						A__						:  int		  =  unet_config["""up_block_types"""]

						for i, layer_type in enumerate(UpperCAmelCase__      ):
												if layer_type == "ResnetUpsampleBlock2D":
																		for j in range(layers_per_block + 1      ):
																								A__						:  Union[str, Any]		  =  f'up_blocks.{i}.resnets.{j}'
																								A__						:  Any		  =  f'output_blocks.{current_layer}.0'
																								A__						:  List[str]		  =  convert_resnet(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						has_skip=UpperCAmelCase__      )
																								current_layer += 1

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

																		if i != len(UpperCAmelCase__      ) - 1:
																								A__						:  str		  =  f'up_blocks.{i}.upsamplers.0'
																								A__						:  Optional[int]		  =  f'output_blocks.{current_layer-1}.2'
																								A__						:  Dict		  =  convert_resnet(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      )

						A__						:  List[str]		  =  checkpoint["""out.0.weight"""]
						A__						:  Optional[int]		  =  checkpoint["""out.0.bias"""]
						A__						:  Optional[Any]		  =  checkpoint["""out.2.weight"""]
						A__						:  Optional[Any]		  =  checkpoint["""out.2.bias"""]

						return new_checkpoint


if __name__ == "__main__":
						A_    =							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.''')

						A_    =							parser.parse_args()
						A_    =							strabool(args.class_cond)

						A_    =							os.path.basename(args.unet_path)
						print(F'Checkpoint: {ckpt_name}')

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

						if not args.class_cond:
												A_    =							None

						A_    =							con_pt_to_diffuser(args.unet_path, unet_config)

						A_    =							UNetaDModel(**unet_config)
						image_unet.load_state_dict(converted_unet_ckpt)

						# Get scheduler config
						if "cd" in ckpt_name or "test" in ckpt_name:
												A_    =							CD_SCHEDULER_CONFIG
						elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
												A_    =							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)):
												A_    =							CT_LSUN_256_SCHEDULER_CONFIG
						else:
												raise ValueError(F'Checkpoint type {ckpt_name} is not currently supported.')

						A_    =							CMStochasticIterativeScheduler(**scheduler_config)

						A_    =							ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
						consistency_model.save_pretrained(args.dump_path)














"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							42
							snake_case_     =							42


							def __init__(			self      :						Union[str, Any]			,    snake_case      :						UNetaDModel			,    snake_case      :						KarrasVeScheduler							):
													'''simple docstring'''





													super().__init__()
													self.register_modules(unet=snake_case			,    scheduler=snake_case							)


							@torch.no_grad()
							def __call__(			self      :						Any			,    snake_case      :						int = 1			,    snake_case      :						int = 50			,    snake_case      :						Optional[Union[torch.Generator, List[torch.Generator]]] = None			,    snake_case      :						Optional[str] = "pil"			,    snake_case      :						bool = True			,    **snake_case      :						Dict			,    ):
													'''simple docstring'''





													A__						:  Optional[int]		  =  self.unet.config.sample_size
													A__						:  Dict		  =  (batch_size, 3, img_size, img_size)

													A__						:  Tuple		  =  self.unet

													# sample x_0 ~ N(0, sigma_0^2 * I)
													A__						:  Union[str, Any]		  =  randn_tensor(snake_case			,    generator=snake_case			,    device=self.device							) * self.scheduler.init_noise_sigma

													self.scheduler.set_timesteps(snake_case							)

													for t in self.progress_bar(self.scheduler.timesteps							):
																			# here sigma_t == t_i from the paper
																			A__						:  Optional[Any]		  =  self.scheduler.schedule[t]
																			A__						:  Union[str, Any]		  =  self.scheduler.schedule[t - 1] if t > 0 else 0

																			# 1. Select temporarily increased noise level sigma_hat
																			# 2. Add new noise to move from sample_i to sample_hat
																			A__  ,  A__						:  Dict		  =  self.scheduler.add_noise_to_input(snake_case			,    snake_case			,    generator=snake_case							)

																			# 3. Predict the noise residual given the noise magnitude `sigma_hat`
																			# The model inputs and output are adjusted by following eq. (213) in [1].
																			A__						:  int		  =  (sigma_hat / 2) * model((sample_hat + 1) / 2			,    sigma_hat / 2							).sample

																			# 4. Evaluate dx/dt at sigma_hat
																			# 5. Take Euler step from sigma to sigma_prev
																			A__						:  Tuple		  =  self.scheduler.step(snake_case			,    snake_case			,    snake_case			,    snake_case							)

																			if sigma_prev != 0:
																									# 6. Apply 2nd order correction
																									# The model inputs and output are adjusted by following eq. (213) in [1].
																									A__						:  Dict		  =  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2			,    sigma_prev / 2							).sample
																									A__						:  Tuple		  =  self.scheduler.step_correct(
																									    snake_case			,    snake_case			,    snake_case			,    snake_case			,    step_output.prev_sample			,    step_output["""derivative"""]			,    )
																			A__						:  Tuple		  =  step_output.prev_sample

													A__						:  Dict		  =  (sample / 2 + 0.5).clamp(0			,    1							)
													A__						:  Dict		  =  sample.cpu().permute(0			,    2			,    3			,    1							).numpy()
													if output_type == "pil":
																			A__						:  Optional[Any]		  =  self.numpy_to_pil(snake_case							)

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=snake_case							)






