




import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowercase_	:				List[str]  				= {
    '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',
}

lowercase_	:				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',
}

lowercase_	:				Optional[Any]  				= {
    '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',
}

lowercase_	:				str  				= {
    'num_train_timesteps': 40,
    'sigma_min': 0.0_02,
    'sigma_max': 80.0,
}

lowercase_	:				Tuple  				= {
    'num_train_timesteps': 2_01,
    'sigma_min': 0.0_02,
    'sigma_max': 80.0,
}

lowercase_	:				int  				= {
    'num_train_timesteps': 1_51,
    'sigma_min': 0.0_02,
    'sigma_max': 80.0,
}


def 							__SCREAMING_SNAKE_CASE  (			snake_case_						):

						'''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 							__SCREAMING_SNAKE_CASE  (			snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_=False						):

						'''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 							__SCREAMING_SNAKE_CASE  (			snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_=None						):

						'''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 							__SCREAMING_SNAKE_CASE  (			snake_case_	,    snake_case_						):

						'''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__":
				lowercase_	:				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.')

				lowercase_	:				Optional[int]  				= parser.parse_args()
				lowercase_	:				int  				= strabool(args.class_cond)

				lowercase_	:				Tuple  				= os.path.basename(args.unet_path)
				print(f"""Checkpoint: {ckpt_name}""")

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

				if not args.class_cond:
								lowercase_	:				Dict  				= None

				lowercase_	:				List[Any]  				= con_pt_to_diffuser(args.unet_path, unet_config)

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

				lowercase_	:				Dict  				= CMStochasticIterativeScheduler(**scheduler_config)

				lowercase_	:				Any  				= ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
				consistency_model.save_pretrained(args.dump_path)











def _a				(	lowerCamelCase:   int = 2_00 )	->						int:







      '''simple docstring'''






      __A          =				[1, 2, 5, 10, 20, 50, 1_00, 2_00]
      __A          =				[0] * (pence + 1)
      __A          =				1  # base case: 1 way to make 0 pence

      for coin in coins:
            for i in range(lowerCamelCase					,				pence + 1					,				1 ):
                  number_of_ways[i] += number_of_ways[i - coin]
      return number_of_ways[pence]


if __name__ == "__main__":
       assert solution(200) == 73682





