






'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A     :      str   					=logging.get_logger(__name__)

_A     :      str   					={
    '''facebook/levit-128S''': '''https://huggingface.co/facebook/levit-128S/resolve/main/config.json''',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}

class _lowercase     (    _lowercase		):
							a    				=	"""levit"""




							def __init__(							self:    str      ,							UpperCamelCase__:    str=224      ,							UpperCamelCase__:    str=3      ,							UpperCamelCase__:    Union[str, Any]=3      ,							UpperCamelCase__:    Tuple=2      ,							UpperCamelCase__:    str=1      ,							UpperCamelCase__:    List[Any]=16      ,							UpperCamelCase__:    Any=[128, 256, 384]      ,							UpperCamelCase__:    Any=[4, 8, 12]      ,							UpperCamelCase__:    List[Any]=[4, 4, 4]      ,							UpperCamelCase__:    List[str]=[16, 16, 16]      ,							UpperCamelCase__:    Dict=0      ,							UpperCamelCase__:    Dict=[2, 2, 2]      ,							UpperCamelCase__:    Tuple=[2, 2, 2]      ,							UpperCamelCase__:    int=0.02      ,							**UpperCamelCase__:    Tuple      ,							):
														super().__init__(**UpperCamelCase__					)
														lowerCamelCase__ :     Optional[int]	  =	image_size
														lowerCamelCase__ :     Tuple	  =	num_channels
														lowerCamelCase__ :     Dict	  =	kernel_size
														lowerCamelCase__ :     Union[str, Any]	  =	stride
														lowerCamelCase__ :     int	  =	padding
														lowerCamelCase__ :     Dict	  =	hidden_sizes
														lowerCamelCase__ :     int	  =	num_attention_heads
														lowerCamelCase__ :     Any	  =	depths
														lowerCamelCase__ :     List[Any]	  =	key_dim
														lowerCamelCase__ :     List[str]	  =	drop_path_rate
														lowerCamelCase__ :     Optional[Any]	  =	patch_size
														lowerCamelCase__ :     Tuple	  =	attention_ratio
														lowerCamelCase__ :     Dict	  =	mlp_ratio
														lowerCamelCase__ :     Tuple	  =	initializer_range
														lowerCamelCase__ :     Union[str, Any]	  =	[
														    ["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
														    ["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
														]



class _lowercase     (    _lowercase		):
							a    				=	version.parse("""1.11"""		)




							@property
							def 		lowerCamelCase_    (							self:    Any					):
														return OrderedDict(
														    [
														        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
														    ]					)




							@property
							def 		lowerCamelCase_    (							self:    Tuple					):
														return 1e-4









'''simple docstring'''







import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_A     :      str   					={
    '''sample_size''': 32,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1_000,
    '''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     :      Union[str, Any]   					={
    '''sample_size''': 64,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1_000,
    '''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     :      Dict   					={
    '''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     :      Dict   					={
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

_A     :      str   					={
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

_A     :      int   					={
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Dict:
							if isinstance(UpperCamelCase    ,      UpperCamelCase				):
														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_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase=False				)				->	Any:
							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.in_layers.0.weight''']
							lowerCamelCase__ :     int	  =	checkpoint[f'''{old_prefix}.in_layers.0.bias''']
							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.in_layers.2.weight''']
							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.in_layers.2.bias''']
							lowerCamelCase__ :     Optional[Any]	  =	checkpoint[f'''{old_prefix}.emb_layers.1.weight''']
							lowerCamelCase__ :     Optional[int]	  =	checkpoint[f'''{old_prefix}.emb_layers.1.bias''']
							lowerCamelCase__ :     Dict	  =	checkpoint[f'''{old_prefix}.out_layers.0.weight''']
							lowerCamelCase__ :     Tuple	  =	checkpoint[f'''{old_prefix}.out_layers.0.bias''']
							lowerCamelCase__ :     str	  =	checkpoint[f'''{old_prefix}.out_layers.3.weight''']
							lowerCamelCase__ :     int	  =	checkpoint[f'''{old_prefix}.out_layers.3.bias''']

							if has_skip:
														lowerCamelCase__ :     Tuple	  =	checkpoint[f'''{old_prefix}.skip_connection.weight''']
														lowerCamelCase__ :     List[Any]	  =	checkpoint[f'''{old_prefix}.skip_connection.bias''']

							return new_checkpoint





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase=None				)				->	str:
							lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__ :     Dict	  =	checkpoint[f'''{old_prefix}.qkv.weight'''].chunk(3    ,      dim=0				)
							lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__ :     str	  =	checkpoint[f'''{old_prefix}.qkv.bias'''].chunk(3    ,      dim=0				)

							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.norm.weight''']
							lowerCamelCase__ :     Optional[int]	  =	checkpoint[f'''{old_prefix}.norm.bias''']

							lowerCamelCase__ :     List[Any]	  =	weight_q.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     List[Any]	  =	bias_q.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     Any	  =	weight_k.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     Optional[Any]	  =	bias_k.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     Dict	  =	weight_v.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     Union[str, Any]	  =	bias_v.squeeze(-1				).squeeze(-1				)

							lowerCamelCase__ :     Optional[Any]	  =	(
							    checkpoint[f'''{old_prefix}.proj_out.weight'''].squeeze(-1				).squeeze(-1				)
							)
							lowerCamelCase__ :     Dict	  =	checkpoint[f'''{old_prefix}.proj_out.bias'''].squeeze(-1				).squeeze(-1				)

							return new_checkpoint





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	List[Any]:
							lowerCamelCase__ :     str	  =	torch.load(UpperCamelCase    ,      map_location="""cpu"""				)
							lowerCamelCase__ :     Optional[int]	  =	{}

							lowerCamelCase__ :     Optional[int]	  =	checkpoint["""time_embed.0.weight"""]
							lowerCamelCase__ :     List[Any]	  =	checkpoint["""time_embed.0.bias"""]
							lowerCamelCase__ :     int	  =	checkpoint["""time_embed.2.weight"""]
							lowerCamelCase__ :     Optional[Any]	  =	checkpoint["""time_embed.2.bias"""]

							if unet_config["num_class_embeds"] is not None:
														lowerCamelCase__ :     Optional[Any]	  =	checkpoint["""label_emb.weight"""]

							lowerCamelCase__ :     Tuple	  =	checkpoint["""input_blocks.0.0.weight"""]
							lowerCamelCase__ :     List[str]	  =	checkpoint["""input_blocks.0.0.bias"""]

							lowerCamelCase__ :     Optional[Any]	  =	unet_config["""down_block_types"""]
							lowerCamelCase__ :     Any	  =	unet_config["""layers_per_block"""]
							lowerCamelCase__ :     Any	  =	unet_config["""attention_head_dim"""]
							lowerCamelCase__ :     List[Any]	  =	unet_config["""block_out_channels"""]
							lowerCamelCase__ :     str	  =	1
							lowerCamelCase__ :     str	  =	channels_list[0]

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

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

														if i != len(UpperCamelCase				) - 1:
																					lowerCamelCase__ :     Tuple	  =	f'''down_blocks.{i}.downsamplers.0'''
																					lowerCamelCase__ :     str	  =	f'''input_blocks.{current_layer}.0'''
																					lowerCamelCase__ :     Union[str, Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
																					current_layer += 1

														lowerCamelCase__ :     Union[str, Any]	  =	current_channels

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

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

							for i, layer_type in enumerate(UpperCamelCase				):
														if layer_type == "ResnetUpsampleBlock2D":
																					for j in range(layers_per_block + 1				):
																												lowerCamelCase__ :     int	  =	f'''up_blocks.{i}.resnets.{j}'''
																												lowerCamelCase__ :     Optional[Any]	  =	f'''output_blocks.{current_layer}.0'''
																												lowerCamelCase__ :     Any	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      has_skip=UpperCamelCase				)
																												current_layer += 1

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

																					if i != len(UpperCamelCase				) - 1:
																												lowerCamelCase__ :     Tuple	  =	f'''up_blocks.{i}.upsamplers.0'''
																												lowerCamelCase__ :     Tuple	  =	f'''output_blocks.{current_layer-1}.2'''
																												lowerCamelCase__ :     List[str]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							lowerCamelCase__ :     Dict	  =	checkpoint["""out.0.weight"""]
							lowerCamelCase__ :     Dict	  =	checkpoint["""out.0.bias"""]
							lowerCamelCase__ :     Dict	  =	checkpoint["""out.2.weight"""]
							lowerCamelCase__ :     Tuple	  =	checkpoint["""out.2.bias"""]

							return new_checkpoint


if __name__ == "__main__":
			_A     :      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.''')

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

			_A     :      List[str]   					=os.path.basename(args.unet_path)
			print(F'Checkpoint: {ckpt_name}')

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

			if not args.class_cond:
						_A     :      str   					=None

			_A     :      Optional[int]   					=con_pt_to_diffuser(args.unet_path, unet_config)

			_A     :      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:
						_A     :      Tuple   					=CD_SCHEDULER_CONFIG
			elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
						_A     :      int   					=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     :      Union[str, Any]   					=CT_LSUN_256_SCHEDULER_CONFIG
			else:
						raise ValueError(F'Checkpoint type {ckpt_name} is not currently supported.')

			_A     :      str   					=CMStochasticIterativeScheduler(**scheduler_config)

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

