



'''simple docstring'''

import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__lowerCAmelCase									=   {
    '''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''',
}

__lowerCAmelCase									=   {
    '''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''',
}

__lowerCAmelCase									=   {
    '''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''',
}

__lowerCAmelCase									=   {
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

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

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



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      Dict:
		if isinstance(lowerCAmelCase_						,			lowerCAmelCase_  ):
				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     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=False  )			->      int:
		_a     :      Tuple        =							checkpoint[f"""{old_prefix}.in_layers.0.weight"""]
		_a     :      int        =							checkpoint[f"""{old_prefix}.in_layers.0.bias"""]
		_a     :      Tuple        =							checkpoint[f"""{old_prefix}.in_layers.2.weight"""]
		_a     :      List[str]        =							checkpoint[f"""{old_prefix}.in_layers.2.bias"""]
		_a     :      Optional[int]        =							checkpoint[f"""{old_prefix}.emb_layers.1.weight"""]
		_a     :      Optional[Any]        =							checkpoint[f"""{old_prefix}.emb_layers.1.bias"""]
		_a     :      Optional[Any]        =							checkpoint[f"""{old_prefix}.out_layers.0.weight"""]
		_a     :      List[str]        =							checkpoint[f"""{old_prefix}.out_layers.0.bias"""]
		_a     :      int        =							checkpoint[f"""{old_prefix}.out_layers.3.weight"""]
		_a     :      int        =							checkpoint[f"""{old_prefix}.out_layers.3.bias"""]

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

		return new_checkpoint



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=None  )			->      Optional[int]:
		_a       ,					_a       ,					_a     :      Optional[Any]        =							checkpoint[f"""{old_prefix}.qkv.weight"""].chunk(3						,			dim=0  )
		_a       ,					_a       ,					_a     :      Optional[Any]        =							checkpoint[f"""{old_prefix}.qkv.bias"""].chunk(3						,			dim=0  )

		_a     :      int        =							checkpoint[f"""{old_prefix}.norm.weight"""]
		_a     :      Dict        =							checkpoint[f"""{old_prefix}.norm.bias"""]

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

		_a     :      Union[str, Any]        =							(
		    checkpoint[f"""{old_prefix}.proj_out.weight"""].squeeze(-1  ).squeeze(-1  )
		)
		_a     :      int        =							checkpoint[f"""{old_prefix}.proj_out.bias"""].squeeze(-1  ).squeeze(-1  )

		return new_checkpoint



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      Tuple:
		_a     :      List[Any]        =							torch.load(lowerCAmelCase_						,			map_location='cpu'  )
		_a     :      List[Any]        =							{}

		_a     :      List[str]        =							checkpoint['time_embed.0.weight']
		_a     :      Union[str, Any]        =							checkpoint['time_embed.0.bias']
		_a     :      Optional[Any]        =							checkpoint['time_embed.2.weight']
		_a     :      str        =							checkpoint['time_embed.2.bias']

		if unet_config["num_class_embeds"] is not None:
				_a     :      List[str]        =							checkpoint['label_emb.weight']

		_a     :      Optional[Any]        =							checkpoint['input_blocks.0.0.weight']
		_a     :      List[Any]        =							checkpoint['input_blocks.0.0.bias']

		_a     :      Optional[int]        =							unet_config['down_block_types']
		_a     :      Union[str, Any]        =							unet_config['layers_per_block']
		_a     :      Tuple        =							unet_config['attention_head_dim']
		_a     :      Dict        =							unet_config['block_out_channels']
		_a     :      int        =							1
		_a     :      Dict        =							channels_list[0]

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

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

				if i != len(lowerCAmelCase_  ) - 1:
						_a     :      Optional[Any]        =							f"""down_blocks.{i}.downsamplers.0"""
						_a     :      Tuple        =							f"""input_blocks.{current_layer}.0"""
						_a     :      str        =							convert_resnet(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )
						current_layer += 1

				_a     :      List[str]        =							current_channels

		# hardcoded the mid-block for now
		_a     :      Optional[Any]        =							'mid_block.resnets.0'
		_a     :      str        =							'middle_block.0'
		_a     :      Optional[int]        =							convert_resnet(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )
		_a     :      Optional[Any]        =							'mid_block.attentions.0'
		_a     :      Any        =							'middle_block.1'
		_a     :      Optional[int]        =							convert_attention(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )
		_a     :      int        =							'mid_block.resnets.1'
		_a     :      Any        =							'middle_block.2'
		_a     :      Union[str, Any]        =							convert_resnet(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )

		_a     :      Union[str, Any]        =							0
		_a     :      str        =							unet_config['up_block_types']

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

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

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

		_a     :      List[str]        =							checkpoint['out.0.weight']
		_a     :      int        =							checkpoint['out.0.bias']
		_a     :      Tuple        =							checkpoint['out.2.weight']
		_a     :      Tuple        =							checkpoint['out.2.bias']

		return new_checkpoint


if __name__ == "__main__":
							__lowerCAmelCase									=   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									=   parser.parse_args()
							__lowerCAmelCase									=   strabool(args.class_cond)

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

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

							if not args.class_cond:
														__lowerCAmelCase									=   None

							__lowerCAmelCase									=   con_pt_to_diffuser(args.unet_path, unet_config)

							__lowerCAmelCase									=   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									=   CD_SCHEDULER_CONFIG
							elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
														__lowerCAmelCase									=   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									=   CT_LSUN_256_SCHEDULER_CONFIG
							else:
														raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

							__lowerCAmelCase									=   CMStochasticIterativeScheduler(**scheduler_config)

							__lowerCAmelCase									=   ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
							consistency_model.save_pretrained(args.dump_path)









'''simple docstring'''

import copy
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
from ..auto import CONFIG_MAPPING


__lowerCAmelCase									=   logging.get_logger(__name__)

__lowerCAmelCase									=   {
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    Any   				=					'conditional_detr'
							lowerCAmelCase       :    List[str]   				=					['past_key_values']
							lowerCAmelCase       :    Optional[int]   				=					{
							    'hidden_size': 'd_model',
							    'num_attention_heads': 'encoder_attention_heads',
							}







							def __init__(							self					:       Optional[int]	,_UpperCAmelCase					:       Optional[int]=True	,_UpperCAmelCase					:       Union[str, Any]=None	,_UpperCAmelCase					:       List[Any]=3	,_UpperCAmelCase					:       List[Any]=300	,_UpperCAmelCase					:       Dict=6	,_UpperCAmelCase					:       List[str]=2048	,_UpperCAmelCase					:       Optional[int]=8	,_UpperCAmelCase					:       List[Any]=6	,_UpperCAmelCase					:       Optional[int]=2048	,_UpperCAmelCase					:       Dict=8	,_UpperCAmelCase					:       int=0.0	,_UpperCAmelCase					:       Optional[Any]=0.0	,_UpperCAmelCase					:       Optional[Any]=True	,_UpperCAmelCase					:       str="relu"	,_UpperCAmelCase					:       Tuple=256	,_UpperCAmelCase					:       Optional[int]=0.1	,_UpperCAmelCase					:       str=0.0	,_UpperCAmelCase					:       Optional[int]=0.0	,_UpperCAmelCase					:       Union[str, Any]=0.02	,_UpperCAmelCase					:       List[str]=1.0	,_UpperCAmelCase					:       Any=False	,_UpperCAmelCase					:       int="sine"	,_UpperCAmelCase					:       List[str]="resnet50"	,_UpperCAmelCase					:       Optional[int]=True	,_UpperCAmelCase					:       str=False	,_UpperCAmelCase					:       str=2	,_UpperCAmelCase					:       int=5	,_UpperCAmelCase					:       Optional[int]=2	,_UpperCAmelCase					:       str=1	,_UpperCAmelCase					:       Union[str, Any]=1	,_UpperCAmelCase					:       List[str]=2	,_UpperCAmelCase					:       Union[str, Any]=5	,_UpperCAmelCase					:       List[Any]=2	,_UpperCAmelCase					:       Optional[int]=0.25	,**_UpperCAmelCase					:       Tuple	,):
									if backbone_config is not None and use_timm_backbone:
											raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'		)

									if not use_timm_backbone:
											if backbone_config is None:
													logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'		)
													_a     :      Optional[Any]        =							CONFIG_MAPPING['resnet'](out_features=['stage4']		)
											elif isinstance(_UpperCAmelCase	,_UpperCAmelCase		):
													_a     :      str        =							backbone_config.get('model_type'		)
													_a     :      Union[str, Any]        =							CONFIG_MAPPING[backbone_model_type]
													_a     :      List[Any]        =							config_class.from_dict(_UpperCAmelCase		)

									_a     :      Tuple        =							use_timm_backbone
									_a     :      Union[str, Any]        =							backbone_config
									_a     :      List[Any]        =							num_channels
									_a     :      Union[str, Any]        =							num_queries
									_a     :      Optional[Any]        =							d_model
									_a     :      Tuple        =							encoder_ffn_dim
									_a     :      Dict        =							encoder_layers
									_a     :      List[str]        =							encoder_attention_heads
									_a     :      Union[str, Any]        =							decoder_ffn_dim
									_a     :      Optional[int]        =							decoder_layers
									_a     :      int        =							decoder_attention_heads
									_a     :      Optional[int]        =							dropout
									_a     :      Tuple        =							attention_dropout
									_a     :      List[Any]        =							activation_dropout
									_a     :      str        =							activation_function
									_a     :      Optional[Any]        =							init_std
									_a     :      Union[str, Any]        =							init_xavier_std
									_a     :      List[Any]        =							encoder_layerdrop
									_a     :      List[Any]        =							decoder_layerdrop
									_a     :      Dict        =							encoder_layers
									_a     :      List[Any]        =							auxiliary_loss
									_a     :      Optional[int]        =							position_embedding_type
									_a     :      List[Any]        =							backbone
									_a     :      Optional[int]        =							use_pretrained_backbone
									_a     :      Optional[int]        =							dilation
									# Hungarian matcher
									_a     :      Tuple        =							class_cost
									_a     :      str        =							bbox_cost
									_a     :      Any        =							giou_cost
									# Loss coefficients
									_a     :      Tuple        =							mask_loss_coefficient
									_a     :      Dict        =							dice_loss_coefficient
									_a     :      Tuple        =							cls_loss_coefficient
									_a     :      Any        =							bbox_loss_coefficient
									_a     :      Dict        =							giou_loss_coefficient
									_a     :      Union[str, Any]        =							focal_alpha
									super().__init__(is_encoder_decoder=_UpperCAmelCase	,**_UpperCAmelCase		)







							@property
							def 							__lowercase			(							self					:       Dict		):
									return self.encoder_attention_heads







							@property
							def 							__lowercase			(							self					:       str		):
									return self.d_model







							def 							__lowercase			(							self					:       int		):
									_a     :      List[str]        =							copy.deepcopy(self.__dict__		)
									if self.backbone_config is not None:
											_a     :      Dict        =							self.backbone_config.to_dict()
									_a     :      Union[str, Any]        =							self.__class__.model_type
									return output

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    str   				=					version.parse('1.11'   )







							@property
							def 							__lowercase			(							self					:       Dict		):
									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									        ('pixel_mask', {0: 'batch'}),
									    ]		)







							@property
							def 							__lowercase			(							self					:       Any		):
									return 1E-5







							@property
							def 							__lowercase			(							self					:       List[Any]		):
									return 12




