




"""simple docstring"""

# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					torch.FloatTensor
 UpperCAmelCase   :					Optional[torch.FloatTensor]				  =     None






def 						_snake_case					( _snake_case							: Union[str, Any]    ,  _snake_case							: Optional[int]=0.999    ,  _snake_case							: Any="cosine"    ,  )   -> Optional[int]:






      '''simple docstring'''


      if alpha_transform_type == "cosine":

            def alpha_bar_fn(_snake_case							: Union[str, Any]							):
                  return math.cos((t + 0.008) / 1.008 * math.pi / 2							) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(_snake_case							: Optional[int]							):
                  return math.exp(t * -12.0							)

      else:
            raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''							)

      _A        =       []
      for i in range(_snake_case							):
            _A        =       i / num_diffusion_timesteps
            _A        =       (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(_snake_case							) / alpha_bar_fn(_snake_case							)    ,  _snake_case							)							)
      return torch.tensor(_snake_case    ,  dtype=torch.floataa							)







class     lowercase_	(					__lowerCAmelCase      ,     __lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Any				  =     1

 @register_to_config
 def __init__(			self	:	int   ,							_UpperCAmelCase	:	int = 1_000   ,							_UpperCAmelCase	:	float = 0.0001   ,							_UpperCAmelCase	:	float = 0.02   ,							_UpperCAmelCase	:	str = "linear"   ,							_UpperCAmelCase	:	Optional[Union[np.ndarray, List[float]]] = None   ,							_UpperCAmelCase	:	bool = True   ,							_UpperCAmelCase	:	bool = True   ,							_UpperCAmelCase	:	int = 0   ,							_UpperCAmelCase	:	str = "epsilon"   ,							_UpperCAmelCase	:	float = 1.0   ,							**_UpperCAmelCase	:	Tuple   ,							):
       if kwargs.get('set_alpha_to_one'   ,							_UpperCAmelCase		) is not None:
             _A        =       (
                 'The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'
             )
             deprecate('set_alpha_to_one'   ,							'1.0.0'   ,							_UpperCAmelCase   ,							standard_warn=_UpperCAmelCase		)
             _A        =       kwargs['set_alpha_to_one']
       if trained_betas is not None:
             _A        =       torch.tensor(_UpperCAmelCase   ,							dtype=torch.floataa		)
       elif beta_schedule == "linear":
             _A        =       torch.linspace(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							dtype=torch.floataa		)
       elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             _A        =       (
                 torch.linspace(beta_start**0.5   ,							beta_end**0.5   ,							_UpperCAmelCase   ,							dtype=torch.floataa		) ** 2
             )
       elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             _A        =       betas_for_alpha_bar(_UpperCAmelCase		)
       else:
             raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''		)

       _A        =       1.0 - self.betas
       _A        =       torch.cumprod(self.alphas   ,							dim=0		)

       # At every step in inverted ddim, we are looking into the next alphas_cumprod
       # For the final step, there is no next alphas_cumprod, and the index is out of bounds
       # `set_alpha_to_zero` decides whether we set this parameter simply to zero
       # in this case, self.step() just output the predicted noise
       # or whether we use the final alpha of the "non-previous" one.
       _A        =       torch.tensor(0.0		) if set_alpha_to_zero else self.alphas_cumprod[-1]

       # standard deviation of the initial noise distribution
       _A        =       1.0

       # setable values
       _A        =       None
       _A        =       torch.from_numpy(np.arange(0   ,							_UpperCAmelCase		).copy().astype(np.intaa		)		)

 def       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	torch.FloatTensor   ,							_UpperCAmelCase	:	Optional[int] = None		):
       return sample

 def       lowerCAmelCase_							(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	Union[str, torch.device] = None		):

       if num_inference_steps > self.config.num_train_timesteps:
             raise ValueError(
                 F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
                 F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
                 F''' maximal {self.config.num_train_timesteps} timesteps.'''		)

       _A        =       num_inference_steps
       _A        =       self.config.num_train_timesteps // self.num_inference_steps
       # creates integer timesteps by multiplying by ratio
       # casting to int to avoid issues when num_inference_step is power of 3
       _A        =       (np.arange(0   ,							_UpperCAmelCase		) * step_ratio).round().copy().astype(np.intaa		)
       _A        =       torch.from_numpy(_UpperCAmelCase		).to(_UpperCAmelCase		)
       self.timesteps += self.config.steps_offset

 def       lowerCAmelCase_							(			self	:	List[Any]   ,							_UpperCAmelCase	:	torch.FloatTensor   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	torch.FloatTensor   ,							_UpperCAmelCase	:	float = 0.0   ,							_UpperCAmelCase	:	bool = False   ,							_UpperCAmelCase	:	Optional[torch.FloatTensor] = None   ,							_UpperCAmelCase	:	bool = True   ,							):
       # 1. get previous step value (=t+1)
       _A        =       timestep + self.config.num_train_timesteps // self.num_inference_steps

       # 2. compute alphas, betas
       # change original implementation to exactly match noise levels for analogous forward process
       _A        =       self.alphas_cumprod[timestep]
       _A        =       (
           self.alphas_cumprod[prev_timestep]
           if prev_timestep < self.config.num_train_timesteps
           else self.final_alpha_cumprod
       )

       _A        =       1 - alpha_prod_t

       # 3. compute predicted original sample from predicted noise also called
       # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       if self.config.prediction_type == "epsilon":
             _A        =       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
             _A        =       model_output
       elif self.config.prediction_type == "sample":
             _A        =       model_output
             _A        =       (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
       elif self.config.prediction_type == "v_prediction":
             _A        =       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
             _A        =       (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
       else:
             raise ValueError(
                 F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
                 ' `v_prediction`'		)

       # 4. Clip or threshold "predicted x_0"
       if self.config.clip_sample:
             _A        =       pred_original_sample.clamp(
                 -self.config.clip_sample_range   ,							self.config.clip_sample_range		)

       # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       _A        =       (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

       # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       _A        =       alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

       if not return_dict:
             return (prev_sample, pred_original_sample)
       return DDIMSchedulerOutput(prev_sample=_UpperCAmelCase   ,							pred_original_sample=_UpperCAmelCase		)



 def __len__(			self	:	Optional[Any]		):
       return self.config.num_train_timesteps







"""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''': 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''',
}

a         =      {
    '''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''',
}

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''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

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

a         =      {
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}






def 						_snake_case					( _snake_case							: Dict							)   -> int:






      '''simple docstring'''


      if isinstance(_snake_case    ,  _snake_case							):
            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					( _snake_case							: Optional[int]    ,  _snake_case							: Tuple    ,  _snake_case							: Dict    ,  _snake_case							: Dict    ,  _snake_case							: Optional[Any]=False							)   -> List[str]:






      '''simple docstring'''


      _A        =       checkpoint[F'''{old_prefix}.in_layers.0.weight''']
      _A        =       checkpoint[F'''{old_prefix}.in_layers.0.bias''']
      _A        =       checkpoint[F'''{old_prefix}.in_layers.2.weight''']
      _A        =       checkpoint[F'''{old_prefix}.in_layers.2.bias''']
      _A        =       checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
      _A        =       checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
      _A        =       checkpoint[F'''{old_prefix}.out_layers.0.weight''']
      _A        =       checkpoint[F'''{old_prefix}.out_layers.0.bias''']
      _A        =       checkpoint[F'''{old_prefix}.out_layers.3.weight''']
      _A        =       checkpoint[F'''{old_prefix}.out_layers.3.bias''']

      if has_skip:
            _A        =       checkpoint[F'''{old_prefix}.skip_connection.weight''']
            _A        =       checkpoint[F'''{old_prefix}.skip_connection.bias''']

      return new_checkpoint






def 						_snake_case					( _snake_case							: List[Any]    ,  _snake_case							: int    ,  _snake_case							: Optional[int]    ,  _snake_case							: List[Any]    ,  _snake_case							: int=None							)   -> Optional[int]:






      '''simple docstring'''


      _A       ,   _A       ,   _A        =       checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3    ,  dim=0							)
      _A       ,   _A       ,   _A        =       checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3    ,  dim=0							)

      _A        =       checkpoint[F'''{old_prefix}.norm.weight''']
      _A        =       checkpoint[F'''{old_prefix}.norm.bias''']

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

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

      return new_checkpoint






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: Any							)   -> str:






      '''simple docstring'''


      _A        =       torch.load(_snake_case    ,  map_location='cpu'							)
      _A        =       {}

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

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

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

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

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

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

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

            _A        =       current_channels

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

      _A        =       0
      _A        =       unet_config['up_block_types']

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

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

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

      _A        =       checkpoint['out.0.weight']
      _A        =       checkpoint['out.0.bias']
      _A        =       checkpoint['out.2.weight']
      _A        =       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)

