

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


_SCREAMING_SNAKE_CASE					:  List[Any]             =      logging.get_logger(__name__)






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= ["pixel_values"]






  def __init__(							self						:		str     ,       __lowerCamelCase						:		bool = True     ,       __lowerCamelCase						:		Dict[str, int] = None     ,       __lowerCamelCase						:		int = 0.9     ,       __lowerCamelCase						:		PILImageResampling = PILImageResampling.BICUBIC     ,       __lowerCamelCase						:		bool = True     ,       __lowerCamelCase						:		Dict[str, int] = None     ,       __lowerCamelCase						:		Union[int, float] = 1 / 255     ,       __lowerCamelCase						:		bool = True     ,       __lowerCamelCase						:		bool = True     ,       __lowerCamelCase						:		Optional[Union[float, List[float]]] = None     ,       __lowerCamelCase						:		Optional[Union[float, List[float]]] = None     ,       **__lowerCamelCase						:		Tuple     ,       )		->							None:
       super().__init__(**__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   size if size is not None else {'''shortest_edge''': 224}
       SCREAMING_SNAKE_CASE__   =   get_size_dict(__lowerCamelCase     ,       default_to_square=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       SCREAMING_SNAKE_CASE__   =   get_size_dict(__lowerCamelCase     ,       param_name='''crop_size''' )

       SCREAMING_SNAKE_CASE__   =   do_resize
       SCREAMING_SNAKE_CASE__   =   size
       SCREAMING_SNAKE_CASE__   =   crop_pct
       SCREAMING_SNAKE_CASE__   =   resample
       SCREAMING_SNAKE_CASE__   =   do_center_crop
       SCREAMING_SNAKE_CASE__   =   crop_size
       SCREAMING_SNAKE_CASE__   =   do_rescale
       SCREAMING_SNAKE_CASE__   =   rescale_factor
       SCREAMING_SNAKE_CASE__   =   do_normalize
       SCREAMING_SNAKE_CASE__   =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
       SCREAMING_SNAKE_CASE__   =   image_std if image_std is not None else IMAGENET_DEFAULT_STD






  def 				lowercase_		(							self						:		int     ,       __lowerCamelCase						:		np.ndarray     ,       __lowerCamelCase						:		Dict[str, int]     ,       __lowerCamelCase						:		Optional[float] = None     ,       __lowerCamelCase						:		PILImageResampling = PILImageResampling.BICUBIC     ,       __lowerCamelCase						:		Optional[Union[str, ChannelDimension]] = None     ,       **__lowerCamelCase						:		str     ,       )		->							np.ndarray:
       SCREAMING_SNAKE_CASE__   =   get_size_dict(__lowerCamelCase     ,       default_to_square=__lowerCamelCase )
       if "shortest_edge" not in size and ("height" not in size or "width" not in size):
            raise ValueError(f'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''' )
       if crop_pct is not None:
            if "shortest_edge" in size:
                 SCREAMING_SNAKE_CASE__   =   int(size['''shortest_edge'''] / crop_pct )
            elif "height" in size and "width" in size:
                 if size["height"] == size["width"]:
                      SCREAMING_SNAKE_CASE__   =   int(size['''height'''] / crop_pct )
                 else:
                      SCREAMING_SNAKE_CASE__   =   (int(size['''height'''] / crop_pct ), int(size['''width'''] / crop_pct ))
            else:
                 raise ValueError('''Invalid size for resize: {}'''.format(__lowerCamelCase ) )

            SCREAMING_SNAKE_CASE__   =   get_resize_output_image_size(__lowerCamelCase     ,       size=__lowerCamelCase     ,       default_to_square=__lowerCamelCase )
       else:
            if "shortest_edge" in size:
                 SCREAMING_SNAKE_CASE__   =   get_resize_output_image_size(__lowerCamelCase     ,       size=size['''shortest_edge''']     ,       default_to_square=__lowerCamelCase )
            elif "height" in size and "width" in size:
                 SCREAMING_SNAKE_CASE__   =   (size['''height'''], size['''width'''])
            else:
                 raise ValueError('''Invalid size for resize: {}'''.format(__lowerCamelCase ) )

       return resize(__lowerCamelCase     ,       size=__lowerCamelCase     ,       resample=__lowerCamelCase     ,       data_format=__lowerCamelCase     ,       **__lowerCamelCase )






  def 				lowercase_		(							self						:		Tuple     ,       __lowerCamelCase						:		np.ndarray     ,       __lowerCamelCase						:		Dict[str, int]     ,       __lowerCamelCase						:		Optional[Union[str, ChannelDimension]] = None     ,       **__lowerCamelCase						:		str     ,       )		->							np.ndarray:
       SCREAMING_SNAKE_CASE__   =   get_size_dict(__lowerCamelCase )
       if "height" not in size or "width" not in size:
            raise ValueError(f'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}''' )
       return center_crop(__lowerCamelCase     ,       size=(size['''height'''], size['''width'''])     ,       data_format=__lowerCamelCase     ,       **__lowerCamelCase )






  def 				lowercase_		(							self						:		int     ,       __lowerCamelCase						:		np.ndarray     ,       __lowerCamelCase						:		Union[int, float]     ,       __lowerCamelCase						:		Optional[Union[str, ChannelDimension]] = None     ,       **__lowerCamelCase						:		Optional[int]     ,       )		->							str:
       return rescale(__lowerCamelCase     ,       scale=__lowerCamelCase     ,       data_format=__lowerCamelCase     ,       **__lowerCamelCase )






  def 				lowercase_		(							self						:		Tuple     ,       __lowerCamelCase						:		np.ndarray     ,       __lowerCamelCase						:		Union[float, List[float]]     ,       __lowerCamelCase						:		Union[float, List[float]]     ,       __lowerCamelCase						:		Optional[Union[str, ChannelDimension]] = None     ,       **__lowerCamelCase						:		Optional[int]     ,       )		->							np.ndarray:
       return normalize(__lowerCamelCase     ,       mean=__lowerCamelCase     ,       std=__lowerCamelCase     ,       data_format=__lowerCamelCase     ,       **__lowerCamelCase )







  def 				lowercase_		(							self						:		Optional[Any]     ,       __lowerCamelCase						:		ImageInput     ,       __lowerCamelCase						:		bool = None     ,       __lowerCamelCase						:		Dict[str, int] = None     ,       __lowerCamelCase						:		int = None     ,       __lowerCamelCase						:		PILImageResampling = None     ,       __lowerCamelCase						:		bool = None     ,       __lowerCamelCase						:		Dict[str, int] = None     ,       __lowerCamelCase						:		bool = None     ,       __lowerCamelCase						:		float = None     ,       __lowerCamelCase						:		bool = None     ,       __lowerCamelCase						:		Optional[Union[float, List[float]]] = None     ,       __lowerCamelCase						:		Optional[Union[float, List[float]]] = None     ,       __lowerCamelCase						:		Optional[Union[str, TensorType]] = None     ,       __lowerCamelCase						:		ChannelDimension = ChannelDimension.FIRST     ,       **__lowerCamelCase						:		List[str]     ,       )		->							PIL.Image.Image:
       SCREAMING_SNAKE_CASE__   =   do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE__   =   crop_pct if crop_pct is not None else self.crop_pct
       SCREAMING_SNAKE_CASE__   =   resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE__   =   do_center_crop if do_center_crop is not None else self.do_center_crop
       SCREAMING_SNAKE_CASE__   =   do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE__   =   rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE__   =   do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE__   =   image_mean if image_mean is not None else self.image_mean
       SCREAMING_SNAKE_CASE__   =   image_std if image_std is not None else self.image_std

       SCREAMING_SNAKE_CASE__   =   size if size is not None else self.size
       SCREAMING_SNAKE_CASE__   =   get_size_dict(__lowerCamelCase     ,       default_to_square=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   crop_size if crop_size is not None else self.crop_size
       SCREAMING_SNAKE_CASE__   =   get_size_dict(__lowerCamelCase     ,       param_name='''crop_size''' )

       SCREAMING_SNAKE_CASE__   =   make_list_of_images(__lowerCamelCase )

       if not valid_images(__lowerCamelCase ):
            raise ValueError(
                '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                '''torch.Tensor, tf.Tensor or jax.ndarray.''' )

       if do_resize and size is None or resample is None:
            raise ValueError('''Size and resample must be specified if do_resize is True.''' )

       if do_center_crop and crop_pct is None:
            raise ValueError('''Crop_pct must be specified if do_center_crop is True.''' )

       if do_rescale and rescale_factor is None:
            raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )

       if do_normalize and (image_mean is None or image_std is None):
            raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )

       # All transformations expect numpy arrays.
       SCREAMING_SNAKE_CASE__   =   [to_numpy_array(__lowerCamelCase ) for image in images]

       if do_resize:
            SCREAMING_SNAKE_CASE__   =   [self.resize(image=__lowerCamelCase     ,       size=__lowerCamelCase     ,       crop_pct=__lowerCamelCase     ,       resample=__lowerCamelCase ) for image in images]

       if do_center_crop:
            SCREAMING_SNAKE_CASE__   =   [self.center_crop(image=__lowerCamelCase     ,       size=__lowerCamelCase ) for image in images]

       if do_rescale:
            SCREAMING_SNAKE_CASE__   =   [self.rescale(image=__lowerCamelCase     ,       scale=__lowerCamelCase ) for image in images]

       if do_normalize:
            SCREAMING_SNAKE_CASE__   =   [self.normalize(image=__lowerCamelCase     ,       mean=__lowerCamelCase     ,       std=__lowerCamelCase ) for image in images]

       SCREAMING_SNAKE_CASE__   =   [to_channel_dimension_format(__lowerCamelCase     ,       __lowerCamelCase ) for image in images]

       SCREAMING_SNAKE_CASE__   =   {'''pixel_values''': images}
       return BatchFeature(data=__lowerCamelCase     ,       tensor_type=__lowerCamelCase )





import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_SCREAMING_SNAKE_CASE					:  str             =      {
    '''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''',
}

_SCREAMING_SNAKE_CASE					:  Dict             =      {
    '''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''',
}

_SCREAMING_SNAKE_CASE					:  int             =      {
    '''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''',
}

_SCREAMING_SNAKE_CASE					:  int             =      {
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

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

_SCREAMING_SNAKE_CASE					:  Tuple             =      {
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     if isinstance(_A				,		_A    ):
          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     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A				,		_A=False    ):





     '''simple docstring'''




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

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

     return new_checkpoint
def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A				,		_A=None    ):





     '''simple docstring'''




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

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

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

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

     return new_checkpoint
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




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

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

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

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

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

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

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

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

          SCREAMING_SNAKE_CASE__   =   current_channels

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

     SCREAMING_SNAKE_CASE__   =   0
     SCREAMING_SNAKE_CASE__   =   unet_config['''up_block_types''']

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

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

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

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

     return new_checkpoint


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE					:  Optional[Any]             =      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.''')

       _SCREAMING_SNAKE_CASE					:  Tuple             =      parser.parse_args()
       _SCREAMING_SNAKE_CASE					:  List[str]             =      strabool(args.class_cond)

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

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

       if not args.class_cond:
              _SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      None

       _SCREAMING_SNAKE_CASE					:  int             =      con_pt_to_diffuser(args.unet_path, unet_config)

       _SCREAMING_SNAKE_CASE					:  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:
              _SCREAMING_SNAKE_CASE					:  Optional[Any]             =      CD_SCHEDULER_CONFIG
       elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
              _SCREAMING_SNAKE_CASE					:  Any             =      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)):
              _SCREAMING_SNAKE_CASE					:  int             =      CT_LSUN_256_SCHEDULER_CONFIG
       else:
              raise ValueError(F"Checkpoint type {ckpt_name} is not currently supported.")

       _SCREAMING_SNAKE_CASE					:  int             =      CMStochasticIterativeScheduler(**scheduler_config)

       _SCREAMING_SNAKE_CASE					:  str             =      ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
       consistency_model.save_pretrained(args.dump_path)


