




"""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				:   Optional[int]				)       ->   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				:   int							,    _snake_case				:   int							,    _snake_case				:   List[str]							,    _snake_case				:   List[str]							,    _snake_case				:   int=False				)       ->   Any:






      '''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				:   Union[str, Any]							,    _snake_case				:   Union[str, Any]							,    _snake_case				:   Optional[Any]							,    _snake_case				:   Any							,    _snake_case				:   Tuple=None				)       ->   Tuple:






      '''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				:   Optional[Any]							,    _snake_case				:   List[Any]				)       ->   List[Any]:






      '''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)







from __future__ import annotations

from collections.abc import MutableSequence



class       a_       :





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					None:




				"""simple docstring"""



				if len(SCREAMING_SNAKE_CASE      ) != degree + 1:
							raise ValueError(
							    'The number of coefficients should be equal to the degree + 1.'      )

				SCREAMING_SNAKE_CASE_								=						list(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						degree





	def __add__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Polynomial:




				"""simple docstring"""



				if self.degree > polynomial_a.degree:
							SCREAMING_SNAKE_CASE_								=						self.coefficients[:]
							for i in range(polynomial_a.degree + 1      ):
										coefficients[i] += polynomial_a.coefficients[i]
							return Polynomial(self.degree      ,     SCREAMING_SNAKE_CASE      )
				else:
							SCREAMING_SNAKE_CASE_								=						polynomial_a.coefficients[:]
							for i in range(self.degree + 1      ):
										coefficients[i] += self.coefficients[i]
							return Polynomial(polynomial_a.degree      ,     SCREAMING_SNAKE_CASE      )





	def __sub__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Polynomial:




				"""simple docstring"""



				return self + polynomial_a * Polynomial(0      ,     [-1]      )





	def __neg__(							self      )  ->					Polynomial:




				"""simple docstring"""



				return Polynomial(self.degree      ,     [-c for c in self.coefficients]      )





	def __mul__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Polynomial:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[0] * (self.degree + polynomial_a.degree + 1)
				for i in range(self.degree + 1      ):
							for j in range(polynomial_a.degree + 1      ):
										coefficients[i + j] += (
										    self.coefficients[i] * polynomial_a.coefficients[j]
										)

				return Polynomial(self.degree + polynomial_a.degree      ,     SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					int | float:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						0
				for i in range(self.degree + 1      ):
							result += self.coefficients[i] * (substitution**i)
				return result





	def __str__(							self      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						''
				for i in range(self.degree      ,     -1      ,     -1      ):
							if self.coefficients[i] == 0:
										continue
							elif self.coefficients[i] > 0:
										if polynomial:
													polynomial += " + "
							else:
										polynomial += " - "

							if i == 0:
										polynomial += str(abs(self.coefficients[i]      )      )
							elif i == 1:
										polynomial += str(abs(self.coefficients[i]      )      ) + "x"
							else:
										polynomial += str(abs(self.coefficients[i]      )      ) + "x^" + str(SCREAMING_SNAKE_CASE      )

				return polynomial





	def __repr__(							self      )  ->					str:




				"""simple docstring"""



				return self.__str__()





	def 						A_(							self      )  ->					Polynomial:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[0] * self.degree
				for i in range(self.degree      ):
							SCREAMING_SNAKE_CASE_								=						self.coefficients[i + 1] * (i + 1)
				return Polynomial(self.degree - 1      ,     SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE = 0      )  ->					Polynomial:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[0] * (self.degree + 2)
				SCREAMING_SNAKE_CASE_								=						constant
				for i in range(self.degree + 1      ):
							SCREAMING_SNAKE_CASE_								=						self.coefficients[i] / (i + 1)
				return Polynomial(self.degree + 1      ,     SCREAMING_SNAKE_CASE      )





	def __eq__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					bool:




				"""simple docstring"""



				if not isinstance(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ):
							return False

				if self.degree != polynomial_a.degree:
							return False

				for i in range(self.degree + 1      ):
							if self.coefficients[i] != polynomial_a.coefficients[i]:
										return False

				return True





	def __ne__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					bool:




				"""simple docstring"""



				return not self.__eq__(SCREAMING_SNAKE_CASE      )
