
import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowerCamelCase__     :		Optional[Any]        = {
    """sample_size""": 3_2,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": 1_0_0_0,
    """block_out_channels""": [3_2, 6_4],
    """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""",
}

lowerCamelCase__     :		Union[str, Any]        = {
    """sample_size""": 6_4,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 3,
    """num_class_embeds""": 1_0_0_0,
    """block_out_channels""": [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
    """attention_head_dim""": 6_4,
    """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""",
}

lowerCamelCase__     :		List[str]        = {
    """sample_size""": 2_5_6,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": None,
    """block_out_channels""": [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
    """attention_head_dim""": 6_4,
    """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""",
}

lowerCamelCase__     :		Any        = {
    """num_train_timesteps""": 4_0,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}

lowerCamelCase__     :		int        = {
    """num_train_timesteps""": 2_0_1,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}

lowerCamelCase__     :		List[Any]        = {
    """num_train_timesteps""": 1_5_1,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}

def 		UpperCamelCase						(   lowercase_ )		->						Tuple:


  '''simple docstring'''






  if isinstance(lowercase_			,			lowercase_ ):
    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						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_=False )		->						Any:


  '''simple docstring'''






  lowercase__	: List[str]     		=   checkpoint[F'{old_prefix}.in_layers.0.weight']
  lowercase__	: Tuple     		=   checkpoint[F'{old_prefix}.in_layers.0.bias']
  lowercase__	: Any     		=   checkpoint[F'{old_prefix}.in_layers.2.weight']
  lowercase__	: Union[str, Any]     		=   checkpoint[F'{old_prefix}.in_layers.2.bias']
  lowercase__	: Optional[int]     		=   checkpoint[F'{old_prefix}.emb_layers.1.weight']
  lowercase__	: List[Any]     		=   checkpoint[F'{old_prefix}.emb_layers.1.bias']
  lowercase__	: List[Any]     		=   checkpoint[F'{old_prefix}.out_layers.0.weight']
  lowercase__	: Optional[int]     		=   checkpoint[F'{old_prefix}.out_layers.0.bias']
  lowercase__	: Union[str, Any]     		=   checkpoint[F'{old_prefix}.out_layers.3.weight']
  lowercase__	: Tuple     		=   checkpoint[F'{old_prefix}.out_layers.3.bias']

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

  return new_checkpoint

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_=None )		->						List[str]:


  '''simple docstring'''






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

  lowercase__	: Optional[Any]     		=   checkpoint[F'{old_prefix}.norm.weight']
  lowercase__	: Tuple     		=   checkpoint[F'{old_prefix}.norm.bias']

  lowercase__	: int     		=   weight_q.squeeze(-1 ).squeeze(-1 )
  lowercase__	: int     		=   bias_q.squeeze(-1 ).squeeze(-1 )
  lowercase__	: str     		=   weight_k.squeeze(-1 ).squeeze(-1 )
  lowercase__	: Dict     		=   bias_k.squeeze(-1 ).squeeze(-1 )
  lowercase__	: List[Any]     		=   weight_v.squeeze(-1 ).squeeze(-1 )
  lowercase__	: Dict     		=   bias_v.squeeze(-1 ).squeeze(-1 )

  lowercase__	: List[Any]     		=   (
      checkpoint[F'{old_prefix}.proj_out.weight'].squeeze(-1 ).squeeze(-1 )
  )
  lowercase__	: List[str]     		=   checkpoint[F'{old_prefix}.proj_out.bias'].squeeze(-1 ).squeeze(-1 )

  return new_checkpoint

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Tuple:


  '''simple docstring'''






  lowercase__	: List[Any]     		=   torch.load(lowercase_			,			map_location="""cpu""" )
  lowercase__	: Optional[Any]     		=   {}

  lowercase__	: Tuple     		=   checkpoint["""time_embed.0.weight"""]
  lowercase__	: Any     		=   checkpoint["""time_embed.0.bias"""]
  lowercase__	: Dict     		=   checkpoint["""time_embed.2.weight"""]
  lowercase__	: str     		=   checkpoint["""time_embed.2.bias"""]

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

  lowercase__	: Optional[int]     		=   checkpoint["""input_blocks.0.0.weight"""]
  lowercase__	: Union[str, Any]     		=   checkpoint["""input_blocks.0.0.bias"""]

  lowercase__	: Union[str, Any]     		=   unet_config["""down_block_types"""]
  lowercase__	: str     		=   unet_config["""layers_per_block"""]
  lowercase__	: Optional[int]     		=   unet_config["""attention_head_dim"""]
  lowercase__	: Optional[Any]     		=   unet_config["""block_out_channels"""]
  lowercase__	: Tuple     		=   1
  lowercase__	: Optional[int]     		=   channels_list[0]

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

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

    if i != len(lowercase_ ) - 1:
      lowercase__	: Dict     		=   F'down_blocks.{i}.downsamplers.0'
      lowercase__	: Union[str, Any]     		=   F'input_blocks.{current_layer}.0'
      lowercase__	: List[str]     		=   convert_resnet(lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )
      current_layer += 1

    lowercase__	: Optional[Any]     		=   current_channels

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

  lowercase__	: str     		=   0
  lowercase__	: Union[str, Any]     		=   unet_config["""up_block_types"""]

  for i, layer_type in enumerate(lowercase_ ):
    if layer_type == "ResnetUpsampleBlock2D":
      for j in range(layers_per_block + 1 ):
        lowercase__	: List[str]     		=   F'up_blocks.{i}.resnets.{j}'
        lowercase__	: Dict     		=   F'output_blocks.{current_layer}.0'
        lowercase__	: str     		=   convert_resnet(lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			has_skip=lowercase_ )
        current_layer += 1

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

      if i != len(lowercase_ ) - 1:
        lowercase__	: Any     		=   F'up_blocks.{i}.upsamplers.0'
        lowercase__	: Optional[int]     		=   F'output_blocks.{current_layer-1}.2'
        lowercase__	: int     		=   convert_resnet(lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )

  lowercase__	: str     		=   checkpoint["""out.0.weight"""]
  lowercase__	: Any     		=   checkpoint["""out.0.bias"""]
  lowercase__	: Any     		=   checkpoint["""out.2.weight"""]
  lowercase__	: Any     		=   checkpoint["""out.2.bias"""]

  return new_checkpoint


if __name__ == "__main__":
     lowerCamelCase__     :		Dict        = 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.""")

     lowerCamelCase__     :		Tuple        = parser.parse_args()
     lowerCamelCase__     :		Dict        = strabool(args.class_cond)

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

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

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

     lowerCamelCase__     :		Optional[Any]        = con_pt_to_diffuser(args.unet_path, unet_config)

     lowerCamelCase__     :		List[Any]        = UNetaDModel(**unet_config)
     image_unet.load_state_dict(converted_unet_ckpt)

     # Get scheduler config
     if "cd" in ckpt_name or "test" in ckpt_name:
          lowerCamelCase__     :		Union[str, Any]        = CD_SCHEDULER_CONFIG
     elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
          lowerCamelCase__     :		List[str]        = 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)):
          lowerCamelCase__     :		List[Any]        = CT_LSUN_256_SCHEDULER_CONFIG
     else:
          raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

     lowerCamelCase__     :		str        = CMStochasticIterativeScheduler(**scheduler_config)

     lowerCamelCase__     :		Optional[int]        = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
     consistency_model.save_pretrained(args.dump_path)








'''simple docstring'''
from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						42







class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		@register_to_config
		def __init__(      self				:			List[str]  ,	UpperCamelCase__				:			int = 16  ,	UpperCamelCase__				:			int = 88  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			int = 1  ,	UpperCamelCase__				:			float = 0.0  ,	UpperCamelCase__				:			int = 32  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			bool = False  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			str = "geglu"  ,	UpperCamelCase__				:			bool = True  ,	UpperCamelCase__				:			bool = True  ,	)						->			List[Any]:






				"""simple docstring"""







				super().__init__()
				snake_case  :       Optional[int]     			=						num_attention_heads
				snake_case  :       int     			=						attention_head_dim
				snake_case  :       Dict     			=						num_attention_heads * attention_head_dim

				snake_case  :       Tuple     			=						in_channels

				snake_case  :       Optional[int]     			=						torch.nn.GroupNorm(num_groups=UpperCamelCase__  ,	num_channels=UpperCamelCase__  ,	eps=1e-6  ,	affine=UpperCamelCase__      )
				snake_case  :       List[Any]     			=						nn.Linear(UpperCamelCase__  ,	UpperCamelCase__      )

				# 3. Define transformers blocks
				snake_case  :       Union[str, Any]     			=						nn.ModuleList(
				    [
				        BasicTransformerBlock(
				            UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	dropout=UpperCamelCase__  ,	cross_attention_dim=UpperCamelCase__  ,	activation_fn=UpperCamelCase__  ,	attention_bias=UpperCamelCase__  ,	double_self_attention=UpperCamelCase__  ,	norm_elementwise_affine=UpperCamelCase__  ,	)
				        for d in range(UpperCamelCase__      )
				    ]      )

				snake_case  :       Any     			=						nn.Linear(UpperCamelCase__  ,	UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			List[str]  ,	UpperCamelCase__				:			Tuple  ,	UpperCamelCase__				:			Any=None  ,	UpperCamelCase__				:			Dict=None  ,	UpperCamelCase__				:			Dict=None  ,	UpperCamelCase__				:			Union[str, Any]=1  ,	UpperCamelCase__				:			Optional[int]=None  ,	UpperCamelCase__				:			bool = True  ,	)						->			Any:






				"""simple docstring"""







				snake_case			,snake_case			,snake_case			,snake_case  :       Tuple     			=						hidden_states.shape
				snake_case  :       List[Any]     			=						batch_frames // num_frames

				snake_case  :       Union[str, Any]     			=						hidden_states

				snake_case  :       Optional[Any]     			=						hidden_states[None, :].reshape(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )
				snake_case  :       int     			=						hidden_states.permute(0  ,	2  ,	1  ,	3  ,	4      )

				snake_case  :       Tuple     			=						self.norm(UpperCamelCase__      )
				snake_case  :       Union[str, Any]     			=						hidden_states.permute(0  ,	3  ,	4  ,	2  ,	1      ).reshape(batch_size * height * width  ,	UpperCamelCase__  ,	UpperCamelCase__      )

				snake_case  :       Optional[Any]     			=						self.proj_in(UpperCamelCase__      )

				# 2. Blocks
				for block in self.transformer_blocks:
						snake_case  :       Union[str, Any]     			=						block(
						    UpperCamelCase__  ,	encoder_hidden_states=UpperCamelCase__  ,	timestep=UpperCamelCase__  ,	cross_attention_kwargs=UpperCamelCase__  ,	class_labels=UpperCamelCase__  ,	)

				# 3. Output
				snake_case  :       Dict     			=						self.proj_out(UpperCamelCase__      )
				snake_case  :       Tuple     			=						(
				    hidden_states[None, None, :]
				    .reshape(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )
				    .permute(0  ,	3  ,	4  ,	1  ,	2      )
				    .contiguous()
				)
				snake_case  :       List[Any]     			=						hidden_states.reshape(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )

				snake_case  :       Any     			=						hidden_states + residual

				if not return_dict:
						return (output,)

				return TransformerTemporalModelOutput(sample=UpperCamelCase__      )


