

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""": 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""",
}

a_      =     {
    """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""",
}

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""": 0.002,
    """sigma_max""": 80.0,
}

a_      =     {
    """num_train_timesteps""": 201,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}

a_      =     {
    """num_train_timesteps""": 151,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}

def 			lowerCamelCase__			(       _a):






	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 			lowerCamelCase__			(       _a				,      _a				,      _a				,      _a				,      _a=False):
	SCREAMING_SNAKE_CASE :   List[str]					  =							checkpoint[f"{old_prefix}.in_layers.0.weight"]
	SCREAMING_SNAKE_CASE :   Tuple					  =							checkpoint[f"{old_prefix}.in_layers.0.bias"]
	SCREAMING_SNAKE_CASE :   Any					  =							checkpoint[f"{old_prefix}.in_layers.2.weight"]
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							checkpoint[f"{old_prefix}.in_layers.2.bias"]
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							checkpoint[f"{old_prefix}.emb_layers.1.weight"]
	SCREAMING_SNAKE_CASE :   List[Any]					  =							checkpoint[f"{old_prefix}.emb_layers.1.bias"]
	SCREAMING_SNAKE_CASE :   List[Any]					  =							checkpoint[f"{old_prefix}.out_layers.0.weight"]
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							checkpoint[f"{old_prefix}.out_layers.0.bias"]
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							checkpoint[f"{old_prefix}.out_layers.3.weight"]
	SCREAMING_SNAKE_CASE :   Tuple					  =							checkpoint[f"{old_prefix}.out_layers.3.bias"]

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

	return new_checkpoint

def 			lowerCamelCase__			(       _a				,      _a				,      _a				,      _a				,      _a=None):
	SCREAMING_SNAKE_CASE :   str					  =							checkpoint[f"{old_prefix}.qkv.weight"].chunk(3				,      dim=0)
	SCREAMING_SNAKE_CASE :   Any					  =							checkpoint[f"{old_prefix}.qkv.bias"].chunk(3				,      dim=0)

	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							checkpoint[f"{old_prefix}.norm.weight"]
	SCREAMING_SNAKE_CASE :   Tuple					  =							checkpoint[f"{old_prefix}.norm.bias"]

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

	SCREAMING_SNAKE_CASE :   List[Any]					  =							(
	    checkpoint[f"{old_prefix}.proj_out.weight"].squeeze(-1).squeeze(-1)
	)
	SCREAMING_SNAKE_CASE :   List[str]					  =							checkpoint[f"{old_prefix}.proj_out.bias"].squeeze(-1).squeeze(-1)

	return new_checkpoint

def 			lowerCamelCase__			(       _a				,      _a):
	SCREAMING_SNAKE_CASE :   List[Any]					  =							torch.load(lowercase_				,      map_location="cpu")
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							{}

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

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

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

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

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

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

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

		SCREAMING_SNAKE_CASE :   Optional[Any]					  =							current_channels

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

	SCREAMING_SNAKE_CASE :   str					  =							0
	SCREAMING_SNAKE_CASE :   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):
				SCREAMING_SNAKE_CASE :   List[str]					  =							f"up_blocks.{i}.resnets.{j}"
				SCREAMING_SNAKE_CASE :   Dict					  =							f"output_blocks.{current_layer}.0"
				SCREAMING_SNAKE_CASE :   str					  =							convert_resnet(lowercase_				,      lowercase_				,      lowercase_				,      lowercase_				,      has_skip=lowercase_)
				current_layer += 1

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

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

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

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						List[str]:


  '''simple docstring'''






  global f  # a global dp table for knapsack
  if f[i][j] < 0:
    if j < wt[i - 1]:
      lowercase__	: str     		=   mf_knapsack(i - 1			,			lowercase_			,			lowercase_			,			lowercase_ )
    else:
      lowercase__	: List[str]     		=   max(
          mf_knapsack(i - 1			,			lowercase_			,			lowercase_			,			lowercase_ )			,			mf_knapsack(i - 1			,			lowercase_			,			lowercase_			,			j - wt[i - 1] ) + val[i - 1]			,			)
    lowercase__	: List[Any]     		=   val
  return f[i][j]

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						str:


  '''simple docstring'''






  lowercase__	: Any     		=   [[0] * (w + 1) for _ in range(n + 1 )]

  for i in range(1			,			n + 1 ):
    for w_ in range(1			,			w + 1 ):
      if wt[i - 1] <= w_:
        lowercase__	: List[Any]     		=   max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]			,			dp[i - 1][w_] )
      else:
        lowercase__	: Tuple     		=   dp[i - 1][w_]

  return dp[n][w_], dp

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						Optional[Any]:


  '''simple docstring'''






  if not (isinstance(lowercase_			,			(list, tuple) ) and isinstance(lowercase_			,			(list, tuple) )):
    raise ValueError(
        """Both the weights and values vectors must be either lists or tuples""" )

  lowercase__	: str     		=   len(lowercase_ )
  if num_items != len(lowercase_ ):
    lowercase__	: Optional[int]     		=   (
        """The number of weights must be the same as the number of values.\n"""
        F'But got {num_items} weights and {len(lowercase_ )} values'
    )
    raise ValueError(lowercase_ )
  for i in range(lowercase_ ):
    if not isinstance(wt[i]			,			lowercase_ ):
      lowercase__	: int     		=   (
          """All weights must be integers but got weight of """
          F'type {type(wt[i] )} at index {i}'
      )
      raise TypeError(lowercase_ )

  lowercase__		,     lowercase__	: Tuple     		=   knapsack(lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )
  lowercase__	: set     		=   set()
  _construct_solution(lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )

  return optimal_val, example_optional_set

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						Any:


  '''simple docstring'''






  if i > 0 and j > 0:
    if dp[i - 1][j] == dp[i][j]:
      _construct_solution(lowercase_			,			lowercase_			,			i - 1			,			lowercase_			,			lowercase_ )
    else:
      optimal_set.add(lowercase_ )
      _construct_solution(lowercase_			,			lowercase_			,			i - 1			,			j - wt[i - 1]			,			lowercase_ )


if __name__ == "__main__":
     lowerCamelCase__     :		Dict        = [3, 2, 4, 4]
     lowerCamelCase__     :		List[Any]        = [4, 3, 2, 3]
     lowerCamelCase__     :		Optional[int]        = 4
     lowerCamelCase__     :		Dict        = 6
     lowerCamelCase__     :		Optional[int]        = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
     lowerCamelCase__   ,    lowerCamelCase__     :		int        = knapsack(w, wt, val, n)
     print(optimal_solution)
     print(mf_knapsack(n, wt, val, w))  # switched the n and w

     # testing the dynamic programming problem with example
     # the optimal subset for the above example are items 3 and 4
     lowerCamelCase__   ,    lowerCamelCase__     :		Optional[int]        = knapsack_with_example_solution(w, wt, val)
     assert optimal_solution == 8
     assert optimal_subset == {3, 4}
     print("""optimal_value = """, optimal_solution)
     print("""An optimal subset corresponding to the optimal value""", optimal_subset)






