'''simple docstring'''





import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


SCREAMING_SNAKE_CASE_:     Tuple  ={
    'sample_size': 32,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': 10_00,
    '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_:     Union[str, Any]  ={
    'sample_size': 64,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 3,
    'num_class_embeds': 10_00,
    'block_out_channels': [1_92, 1_92 * 2, 1_92 * 3, 1_92 * 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_:     Tuple  ={
    'sample_size': 2_56,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': None,
    'block_out_channels': [2_56, 2_56, 2_56 * 2, 2_56 * 2, 2_56 * 4, 2_56 * 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_:     Any  ={
    'num_train_timesteps': 40,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

SCREAMING_SNAKE_CASE_:     List[str]  ={
    'num_train_timesteps': 2_01,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

SCREAMING_SNAKE_CASE_:     List[Any]  ={
    'num_train_timesteps': 1_51,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}





def 	lowerCAmelCase_				(					snake_case_       : Optional[int]			)    ->      Optional[Any]:


     '''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 	lowerCAmelCase_				(					snake_case_       : Optional[Any]					,    snake_case_       : Optional[Any]					,    snake_case_       : Any					,    snake_case_       : Optional[int]					,    snake_case_       : Dict=False			)    ->      Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.in_layers.0.weight"""]
     UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.in_layers.0.bias"""]
     UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.in_layers.2.weight"""]
     UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.in_layers.2.bias"""]
     UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.emb_layers.1.weight"""]
     UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.emb_layers.1.bias"""]
     UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.out_layers.0.weight"""]
     UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.out_layers.0.bias"""]
     UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.out_layers.3.weight"""]
     UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.out_layers.3.bias"""]

     if has_skip:
          UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.skip_connection.weight"""]
          UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.skip_connection.bias"""]

     return new_checkpoint





def 	lowerCAmelCase_				(					snake_case_       : List[Any]					,    snake_case_       : Optional[int]					,    snake_case_       : int					,    snake_case_       : Any					,    snake_case_       : Any=None			)    ->      List[str]:


     '''simple docstring'''




     UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.qkv.weight"""].chunk(3					,    dim=0			)
     UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.qkv.bias"""].chunk(3					,    dim=0			)

     UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.norm.weight"""]
     UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.norm.bias"""]

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

     UpperCAmelCase_           =   (
         checkpoint[f"""{old_prefix}.proj_out.weight"""].squeeze(-1			).squeeze(-1			)
     )
     UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.proj_out.bias"""].squeeze(-1			).squeeze(-1			)

     return new_checkpoint





def 	lowerCAmelCase_				(					snake_case_       : str					,    snake_case_       : int			)    ->      Dict:


     '''simple docstring'''




     UpperCAmelCase_           =   torch.load(snake_case_					,    map_location="cpu"			)
     UpperCAmelCase_           =   {}

     UpperCAmelCase_           =   checkpoint["time_embed.0.weight"]
     UpperCAmelCase_           =   checkpoint["time_embed.0.bias"]
     UpperCAmelCase_           =   checkpoint["time_embed.2.weight"]
     UpperCAmelCase_           =   checkpoint["time_embed.2.bias"]

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

     UpperCAmelCase_           =   checkpoint["input_blocks.0.0.weight"]
     UpperCAmelCase_           =   checkpoint["input_blocks.0.0.bias"]

     UpperCAmelCase_           =   unet_config["down_block_types"]
     UpperCAmelCase_           =   unet_config["layers_per_block"]
     UpperCAmelCase_           =   unet_config["attention_head_dim"]
     UpperCAmelCase_           =   unet_config["block_out_channels"]
     UpperCAmelCase_           =   1
     UpperCAmelCase_           =   channels_list[0]

     for i, layer_type in enumerate(snake_case_			):
          UpperCAmelCase_           =   channels_list[i]
          UpperCAmelCase_           =   current_channels != prev_channels
          if layer_type == "ResnetDownsampleBlock2D":
               for j in range(snake_case_			):
                    UpperCAmelCase_           =   f"""down_blocks.{i}.resnets.{j}"""
                    UpperCAmelCase_           =   f"""input_blocks.{current_layer}.0"""
                    UpperCAmelCase_           =   True if j == 0 and downsample_block_has_skip else False
                    UpperCAmelCase_           =   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_			):
                    UpperCAmelCase_           =   f"""down_blocks.{i}.resnets.{j}"""
                    UpperCAmelCase_           =   f"""input_blocks.{current_layer}.0"""
                    UpperCAmelCase_           =   True if j == 0 and downsample_block_has_skip else False
                    UpperCAmelCase_           =   convert_resnet(snake_case_					,    snake_case_					,    snake_case_					,    snake_case_					,    has_skip=snake_case_			)
                    UpperCAmelCase_           =   f"""down_blocks.{i}.attentions.{j}"""
                    UpperCAmelCase_           =   f"""input_blocks.{current_layer}.1"""
                    UpperCAmelCase_           =   convert_attention(
                        snake_case_					,    snake_case_					,    snake_case_					,    snake_case_					,    snake_case_			)
                    current_layer += 1

          if i != len(snake_case_			) - 1:
               UpperCAmelCase_           =   f"""down_blocks.{i}.downsamplers.0"""
               UpperCAmelCase_           =   f"""input_blocks.{current_layer}.0"""
               UpperCAmelCase_           =   convert_resnet(snake_case_					,    snake_case_					,    snake_case_					,    snake_case_			)
               current_layer += 1

          UpperCAmelCase_           =   current_channels

     # hardcoded the mid-block for now
     UpperCAmelCase_           =   "mid_block.resnets.0"
     UpperCAmelCase_           =   "middle_block.0"
     UpperCAmelCase_           =   convert_resnet(snake_case_					,    snake_case_					,    snake_case_					,    snake_case_			)
     UpperCAmelCase_           =   "mid_block.attentions.0"
     UpperCAmelCase_           =   "middle_block.1"
     UpperCAmelCase_           =   convert_attention(snake_case_					,    snake_case_					,    snake_case_					,    snake_case_					,    snake_case_			)
     UpperCAmelCase_           =   "mid_block.resnets.1"
     UpperCAmelCase_           =   "middle_block.2"
     UpperCAmelCase_           =   convert_resnet(snake_case_					,    snake_case_					,    snake_case_					,    snake_case_			)

     UpperCAmelCase_           =   0
     UpperCAmelCase_           =   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			):
                    UpperCAmelCase_           =   f"""up_blocks.{i}.resnets.{j}"""
                    UpperCAmelCase_           =   f"""output_blocks.{current_layer}.0"""
                    UpperCAmelCase_           =   convert_resnet(snake_case_					,    snake_case_					,    snake_case_					,    snake_case_					,    has_skip=snake_case_			)
                    current_layer += 1

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

               if i != len(snake_case_			) - 1:
                    UpperCAmelCase_           =   f"""up_blocks.{i}.upsamplers.0"""
                    UpperCAmelCase_           =   f"""output_blocks.{current_layer-1}.2"""
                    UpperCAmelCase_           =   convert_resnet(snake_case_					,    snake_case_					,    snake_case_					,    snake_case_			)

     UpperCAmelCase_           =   checkpoint["out.0.weight"]
     UpperCAmelCase_           =   checkpoint["out.0.bias"]
     UpperCAmelCase_           =   checkpoint["out.2.weight"]
     UpperCAmelCase_           =   checkpoint["out.2.bias"]

     return new_checkpoint


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:     Tuple  =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_:     Union[str, Any]  =parser.parse_args()
    SCREAMING_SNAKE_CASE_:     Optional[Any]  =strabool(args.class_cond)

    SCREAMING_SNAKE_CASE_:     List[Any]  =os.path.basename(args.unet_path)
    print(f"Checkpoint: {ckpt_name}")

    # Get U-Net config
    if "imagenet64" in ckpt_name:
        SCREAMING_SNAKE_CASE_:     int  =IMAGENET_64_UNET_CONFIG
    elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
        SCREAMING_SNAKE_CASE_:     Tuple  =LSUN_256_UNET_CONFIG
    elif "test" in ckpt_name:
        SCREAMING_SNAKE_CASE_:     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_:     Any  =con_pt_to_diffuser(args.unet_path, unet_config)

    SCREAMING_SNAKE_CASE_:     str  =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_:     str  =CD_SCHEDULER_CONFIG
    elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
        SCREAMING_SNAKE_CASE_:     List[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_:     Optional[Any]  =CT_LSUN_256_SCHEDULER_CONFIG
    else:
        raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.")

    SCREAMING_SNAKE_CASE_:     Union[str, Any]  =CMStochasticIterativeScheduler(**scheduler_config)

    SCREAMING_SNAKE_CASE_:     Union[str, Any]  =ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
    consistency_model.save_pretrained(args.dump_path)





'''simple docstring'''





from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)




class        __A      ( UpperCamelCase__	):



    def __init__(self    :    int   ,			__a    :    Distribution   ,			__a    :    Dict=None   ,			__a    :    int=None   ,			__a    :    Any=0 ):
         UpperCAmelCase_           =   1.0 if scale is None else scale
         UpperCAmelCase_           =   0.0 if loc is None else loc

         super().__init__(__a   ,			[AffineTransform(loc=self.loc   ,			scale=self.scale   ,			event_dim=__a )] )



    @property
    def 					_lowercase						(self    :    Union[str, Any] ):
         return self.base_dist.mean * self.scale + self.loc



    @property
    def 					_lowercase						(self    :    List[Any] ):
         return self.base_dist.variance * self.scale**2






    @property
    def 					_lowercase						(self    :    List[Any] ):
         return self.variance.sqrt()




class        __A      ( nn.Module	):



    def __init__(self    :    Optional[int]   ,			__a    :    int   ,			__a    :    Dict[str, int]   ,			__a    :    Callable[..., Tuple[torch.Tensor]]   ,			**__a    :    List[str] ):
         super().__init__(**__a )
         UpperCAmelCase_           =   args_dim
         UpperCAmelCase_           =   nn.ModuleList([nn.Linear(__a   ,			__a ) for dim in args_dim.values()] )
         UpperCAmelCase_           =   domain_map






    def 					_lowercase						(self    :    List[str]   ,			__a    :    torch.Tensor ):
         UpperCAmelCase_           =   [proj(__a ) for proj in self.proj]

         return self.domain_map(*__a )




class        __A      ( nn.Module	):



    def __init__(self    :    Union[str, Any]   ,			__a    :    List[str] ):
         super().__init__()
         UpperCAmelCase_           =   function






    def 					_lowercase						(self    :    Optional[int]   ,			__a    :    List[str]   ,			*__a    :    Optional[int] ):
         return self.function(__a   ,			*__a )




class        __A      :
    a__       :						type
    a__       :						int
    a__       :						Dict[str, int]



    def __init__(self    :    List[Any]   ,			__a    :    int = 1 ):
         UpperCAmelCase_           =   dim
         UpperCAmelCase_           =   {k: dim * self.args_dim[k] for k in self.args_dim}



    def 					_lowercase						(self    :    Any   ,			__a    :    Any ):
         if self.dim == 1:
              return self.distribution_class(*__a )
         else:
              return Independent(self.distribution_class(*__a )   ,			1 )



    def 					_lowercase						(self    :    List[str]   ,			__a    :    Union[str, Any]   ,			__a    :    Optional[torch.Tensor] = None   ,			__a    :    Optional[torch.Tensor] = None   ,			):
         UpperCAmelCase_           =   self._base_distribution(__a )
         if loc is None and scale is None:
              return distr
         else:
              return AffineTransformed(__a   ,			loc=__a   ,			scale=__a   ,			event_dim=self.event_dim )



    @property
    def 					_lowercase						(self    :    Any ):
         return () if self.dim == 1 else (self.dim,)



    @property
    def 					_lowercase						(self    :    Dict ):
         return len(self.event_shape )



    @property
    def 					_lowercase						(self    :    Tuple ):
         return 0.0



    def 					_lowercase						(self    :    List[str]   ,			__a    :    int ):
         return ParameterProjection(
             in_features=__a   ,			args_dim=self.args_dim   ,			domain_map=LambdaLayer(self.domain_map )   ,			)



    def 					_lowercase						(self    :    Optional[int]   ,			*__a    :    torch.Tensor ):
         raise NotImplementedError()






    @staticmethod
    def 					_lowercase						(__a    :    torch.Tensor ):
         return (x + torch.sqrt(torch.square(__a ) + 4.0 )) / 2.0




class        __A      ( UpperCamelCase__	):
    a__       :						Dict[str, int]          =  {"df": 1, "loc": 1, "scale": 1}
    a__       :						type          =  StudentT



    @classmethod
    def 					_lowercase						(cls    :    Union[str, Any]   ,			__a    :    torch.Tensor   ,			__a    :    torch.Tensor   ,			__a    :    torch.Tensor ):
         UpperCAmelCase_           =   cls.squareplus(__a ).clamp_min(torch.finfo(scale.dtype ).eps )
         UpperCAmelCase_           =   2.0 + cls.squareplus(__a )
         return df.squeeze(-1 ), loc.squeeze(-1 ), scale.squeeze(-1 )




class        __A      ( UpperCamelCase__	):
    a__       :						Dict[str, int]          =  {"loc": 1, "scale": 1}
    a__       :						type          =  Normal



    @classmethod
    def 					_lowercase						(cls    :    Tuple   ,			__a    :    torch.Tensor   ,			__a    :    torch.Tensor ):
         UpperCAmelCase_           =   cls.squareplus(__a ).clamp_min(torch.finfo(scale.dtype ).eps )
         return loc.squeeze(-1 ), scale.squeeze(-1 )




class        __A      ( UpperCamelCase__	):
    a__       :						Dict[str, int]          =  {"total_count": 1, "logits": 1}
    a__       :						type          =  NegativeBinomial



    @classmethod
    def 					_lowercase						(cls    :    Optional[Any]   ,			__a    :    torch.Tensor   ,			__a    :    torch.Tensor ):
         UpperCAmelCase_           =   cls.squareplus(__a )
         return total_count.squeeze(-1 ), logits.squeeze(-1 )



    def 					_lowercase						(self    :    List[str]   ,			__a    :    str ):
         UpperCAmelCase_    , UpperCAmelCase_           =   distr_args
         if self.dim == 1:
              return self.distribution_class(total_count=__a   ,			logits=__a )
         else:
              return Independent(self.distribution_class(total_count=__a   ,			logits=__a )   ,			1 )






    def 					_lowercase						(self    :    Optional[Any]   ,			__a    :    int   ,			__a    :    Optional[torch.Tensor] = None   ,			__a    :    Optional[torch.Tensor] = None ):
         UpperCAmelCase_    , UpperCAmelCase_           =   distr_args

         if scale is not None:
              # See scaling property of Gamma.
              logits += scale.log()

         return self._base_distribution((total_count, logits) )




