






"""simple docstring"""




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


UpperCamelCase			    =     logging.get_logger(__name__)

UpperCamelCase			    =     {
    'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.json',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}



class 				__UpperCAmelCase		(UpperCamelCase__   ):
			__snake_case    :				Union[str, Any]									=							"""dpt"""







			def __init__(     self:  Dict				,     UpperCAmelCase_:  Any=768				,     UpperCAmelCase_:  List[Any]=12				,     UpperCAmelCase_:  Any=12				,     UpperCAmelCase_:  Tuple=3_072				,     UpperCAmelCase_:  List[str]="gelu"				,     UpperCAmelCase_:  Any=0.0				,     UpperCAmelCase_:  Any=0.0				,     UpperCAmelCase_:  int=0.02				,     UpperCAmelCase_:  Optional[Any]=1E-12				,     UpperCAmelCase_:  Union[str, Any]=384				,     UpperCAmelCase_:  Optional[Any]=16				,     UpperCAmelCase_:  List[str]=3				,     UpperCAmelCase_:  Optional[int]=False				,     UpperCAmelCase_:  Tuple=True				,     UpperCAmelCase_:  int=[2, 5, 8, 11]				,     UpperCAmelCase_:  str="project"				,     UpperCAmelCase_:  List[str]=[4, 2, 1, 0.5]				,     UpperCAmelCase_:  List[Any]=[96, 192, 384, 768]				,     UpperCAmelCase_:  List[str]=256				,     UpperCAmelCase_:  Union[str, Any]=-1				,     UpperCAmelCase_:  Dict=False				,     UpperCAmelCase_:  str=True				,     UpperCAmelCase_:  Tuple=0.4				,     UpperCAmelCase_:  List[Any]=255				,     UpperCAmelCase_:  List[str]=0.1				,     UpperCAmelCase_:  int=[1, 1_024, 24, 24]				,     UpperCAmelCase_:  Any=[0, 1]				,     UpperCAmelCase_:  str=None				,     **UpperCAmelCase_:  Dict				,     ):







									'''simple docstring'''




									super().__init__(**__a					)

									_SCREAMING_SNAKE_CASE     				=  hidden_size
									_SCREAMING_SNAKE_CASE     				=  is_hybrid

									if self.is_hybrid:
															if backbone_config is None:
																					logger.info("""Initializing the config with a `BiT` backbone."""					)
																					_SCREAMING_SNAKE_CASE     				=  {
																					    """global_padding""": """same""",
																					    """layer_type""": """bottleneck""",
																					    """depths""": [3, 4, 9],
																					    """out_features""": ["""stage1""", """stage2""", """stage3"""],
																					    """embedding_dynamic_padding""": True,
																					}
																					_SCREAMING_SNAKE_CASE     				=  BitConfig(**__a					)
															elif isinstance(__a				,     __a					):
																					logger.info("""Initializing the config with a `BiT` backbone."""					)
																					_SCREAMING_SNAKE_CASE     				=  BitConfig(**__a					)
															elif isinstance(__a				,     __a					):
																					_SCREAMING_SNAKE_CASE     				=  backbone_config
															else:
																					raise ValueError(
																					    F'backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'					)

															_SCREAMING_SNAKE_CASE     				=  backbone_featmap_shape
															_SCREAMING_SNAKE_CASE     				=  neck_ignore_stages

															if readout_type != "project":
																					raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode."""					)
									else:
															_SCREAMING_SNAKE_CASE     				=  None
															_SCREAMING_SNAKE_CASE     				=  None
															_SCREAMING_SNAKE_CASE     				=  []

									_SCREAMING_SNAKE_CASE     				=  num_hidden_layers
									_SCREAMING_SNAKE_CASE     				=  num_attention_heads
									_SCREAMING_SNAKE_CASE     				=  intermediate_size
									_SCREAMING_SNAKE_CASE     				=  hidden_act
									_SCREAMING_SNAKE_CASE     				=  hidden_dropout_prob
									_SCREAMING_SNAKE_CASE     				=  attention_probs_dropout_prob
									_SCREAMING_SNAKE_CASE     				=  initializer_range
									_SCREAMING_SNAKE_CASE     				=  layer_norm_eps
									_SCREAMING_SNAKE_CASE     				=  image_size
									_SCREAMING_SNAKE_CASE     				=  patch_size
									_SCREAMING_SNAKE_CASE     				=  num_channels
									_SCREAMING_SNAKE_CASE     				=  qkv_bias
									_SCREAMING_SNAKE_CASE     				=  backbone_out_indices
									if readout_type not in ["ignore", "add", "project"]:
															raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']"""					)
									_SCREAMING_SNAKE_CASE     				=  readout_type
									_SCREAMING_SNAKE_CASE     				=  reassemble_factors
									_SCREAMING_SNAKE_CASE     				=  neck_hidden_sizes
									_SCREAMING_SNAKE_CASE     				=  fusion_hidden_size
									_SCREAMING_SNAKE_CASE     				=  head_in_index
									_SCREAMING_SNAKE_CASE     				=  use_batch_norm_in_fusion_residual
									# auxiliary head attributes (semantic segmentation)
									_SCREAMING_SNAKE_CASE     				=  use_auxiliary_head
									_SCREAMING_SNAKE_CASE     				=  auxiliary_loss_weight
									_SCREAMING_SNAKE_CASE     				=  semantic_loss_ignore_index
									_SCREAMING_SNAKE_CASE     				=  semantic_classifier_dropout







			def 							UpperCamelCase				(     self:  Dict					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  copy.deepcopy(self.__dict__					)

									if output["backbone_config"] is not None:
															_SCREAMING_SNAKE_CASE     				=  self.backbone_config.to_dict()

									_SCREAMING_SNAKE_CASE     				=  self.__class__.model_type
									return output









import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


UpperCamelCase			    =     {
    '''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''',
}

UpperCamelCase			    =     {
    '''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''',
}

UpperCamelCase			    =     {
    '''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''',
}

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

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

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

def 	__lowerCamelCase      (		snake_case__		)							->  Optional[Any]:






						"""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 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__=False		)							->  Optional[int]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.in_layers.0.weight']
						_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.in_layers.0.bias']
						_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.in_layers.2.weight']
						_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.in_layers.2.bias']
						_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.emb_layers.1.weight']
						_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.emb_layers.1.bias']
						_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.out_layers.0.weight']
						_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.out_layers.0.bias']
						_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.out_layers.3.weight']
						_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.out_layers.3.bias']

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

						return new_checkpoint

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__=None		)							->  Optional[int]:






						"""simple docstring"""





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

						_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.norm.weight']
						_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.norm.bias']

						_SCREAMING_SNAKE_CASE     				=  weight_q.squeeze(-1		).squeeze(-1		)
						_SCREAMING_SNAKE_CASE     				=  bias_q.squeeze(-1		).squeeze(-1		)
						_SCREAMING_SNAKE_CASE     				=  weight_k.squeeze(-1		).squeeze(-1		)
						_SCREAMING_SNAKE_CASE     				=  bias_k.squeeze(-1		).squeeze(-1		)
						_SCREAMING_SNAKE_CASE     				=  weight_v.squeeze(-1		).squeeze(-1		)
						_SCREAMING_SNAKE_CASE     				=  bias_v.squeeze(-1		).squeeze(-1		)

						_SCREAMING_SNAKE_CASE     				=  (
						    checkpoint[F'{old_prefix}.proj_out.weight'].squeeze(-1		).squeeze(-1		)
						)
						_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.proj_out.bias'].squeeze(-1		).squeeze(-1		)

						return new_checkpoint

def 	__lowerCamelCase      (		snake_case__	,snake_case__		)							->  int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  torch.load(snake_case__	,map_location="""cpu"""		)
						_SCREAMING_SNAKE_CASE     				=  {}

						_SCREAMING_SNAKE_CASE     				=  checkpoint["""time_embed.0.weight"""]
						_SCREAMING_SNAKE_CASE     				=  checkpoint["""time_embed.0.bias"""]
						_SCREAMING_SNAKE_CASE     				=  checkpoint["""time_embed.2.weight"""]
						_SCREAMING_SNAKE_CASE     				=  checkpoint["""time_embed.2.bias"""]

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

						_SCREAMING_SNAKE_CASE     				=  checkpoint["""input_blocks.0.0.weight"""]
						_SCREAMING_SNAKE_CASE     				=  checkpoint["""input_blocks.0.0.bias"""]

						_SCREAMING_SNAKE_CASE     				=  unet_config["""down_block_types"""]
						_SCREAMING_SNAKE_CASE     				=  unet_config["""layers_per_block"""]
						_SCREAMING_SNAKE_CASE     				=  unet_config["""attention_head_dim"""]
						_SCREAMING_SNAKE_CASE     				=  unet_config["""block_out_channels"""]
						_SCREAMING_SNAKE_CASE     				=  1
						_SCREAMING_SNAKE_CASE     				=  channels_list[0]

						for i, layer_type in enumerate(snake_case__		):
												_SCREAMING_SNAKE_CASE     				=  channels_list[i]
												_SCREAMING_SNAKE_CASE     				=  current_channels != prev_channels
												if layer_type == "ResnetDownsampleBlock2D":
																		for j in range(snake_case__		):
																								_SCREAMING_SNAKE_CASE     				=  F'down_blocks.{i}.resnets.{j}'
																								_SCREAMING_SNAKE_CASE     				=  F'input_blocks.{current_layer}.0'
																								_SCREAMING_SNAKE_CASE     				=  True if j == 0 and downsample_block_has_skip else False
																								_SCREAMING_SNAKE_CASE     				=  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__		):
																								_SCREAMING_SNAKE_CASE     				=  F'down_blocks.{i}.resnets.{j}'
																								_SCREAMING_SNAKE_CASE     				=  F'input_blocks.{current_layer}.0'
																								_SCREAMING_SNAKE_CASE     				=  True if j == 0 and downsample_block_has_skip else False
																								_SCREAMING_SNAKE_CASE     				=  convert_resnet(snake_case__	,snake_case__	,snake_case__	,snake_case__	,has_skip=snake_case__		)
																								_SCREAMING_SNAKE_CASE     				=  F'down_blocks.{i}.attentions.{j}'
																								_SCREAMING_SNAKE_CASE     				=  F'input_blocks.{current_layer}.1'
																								_SCREAMING_SNAKE_CASE     				=  convert_attention(
																								    snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__		)
																								current_layer += 1

												if i != len(snake_case__		) - 1:
																		_SCREAMING_SNAKE_CASE     				=  F'down_blocks.{i}.downsamplers.0'
																		_SCREAMING_SNAKE_CASE     				=  F'input_blocks.{current_layer}.0'
																		_SCREAMING_SNAKE_CASE     				=  convert_resnet(snake_case__	,snake_case__	,snake_case__	,snake_case__		)
																		current_layer += 1

												_SCREAMING_SNAKE_CASE     				=  current_channels

						# hardcoded the mid-block for now
						_SCREAMING_SNAKE_CASE     				=  """mid_block.resnets.0"""
						_SCREAMING_SNAKE_CASE     				=  """middle_block.0"""
						_SCREAMING_SNAKE_CASE     				=  convert_resnet(snake_case__	,snake_case__	,snake_case__	,snake_case__		)
						_SCREAMING_SNAKE_CASE     				=  """mid_block.attentions.0"""
						_SCREAMING_SNAKE_CASE     				=  """middle_block.1"""
						_SCREAMING_SNAKE_CASE     				=  convert_attention(snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__		)
						_SCREAMING_SNAKE_CASE     				=  """mid_block.resnets.1"""
						_SCREAMING_SNAKE_CASE     				=  """middle_block.2"""
						_SCREAMING_SNAKE_CASE     				=  convert_resnet(snake_case__	,snake_case__	,snake_case__	,snake_case__		)

						_SCREAMING_SNAKE_CASE     				=  0
						_SCREAMING_SNAKE_CASE     				=  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		):
																								_SCREAMING_SNAKE_CASE     				=  F'up_blocks.{i}.resnets.{j}'
																								_SCREAMING_SNAKE_CASE     				=  F'output_blocks.{current_layer}.0'
																								_SCREAMING_SNAKE_CASE     				=  convert_resnet(snake_case__	,snake_case__	,snake_case__	,snake_case__	,has_skip=snake_case__		)
																								current_layer += 1

																		if i != len(snake_case__		) - 1:
																								_SCREAMING_SNAKE_CASE     				=  F'up_blocks.{i}.upsamplers.0'
																								_SCREAMING_SNAKE_CASE     				=  F'output_blocks.{current_layer-1}.1'
																								_SCREAMING_SNAKE_CASE     				=  convert_resnet(snake_case__	,snake_case__	,snake_case__	,snake_case__		)
												elif layer_type == "AttnUpBlock2D":
																		for j in range(layers_per_block + 1		):
																								_SCREAMING_SNAKE_CASE     				=  F'up_blocks.{i}.resnets.{j}'
																								_SCREAMING_SNAKE_CASE     				=  F'output_blocks.{current_layer}.0'
																								_SCREAMING_SNAKE_CASE     				=  convert_resnet(snake_case__	,snake_case__	,snake_case__	,snake_case__	,has_skip=snake_case__		)
																								_SCREAMING_SNAKE_CASE     				=  F'up_blocks.{i}.attentions.{j}'
																								_SCREAMING_SNAKE_CASE     				=  F'output_blocks.{current_layer}.1'
																								_SCREAMING_SNAKE_CASE     				=  convert_attention(
																								    snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__		)
																								current_layer += 1

																		if i != len(snake_case__		) - 1:
																								_SCREAMING_SNAKE_CASE     				=  F'up_blocks.{i}.upsamplers.0'
																								_SCREAMING_SNAKE_CASE     				=  F'output_blocks.{current_layer-1}.2'
																								_SCREAMING_SNAKE_CASE     				=  convert_resnet(snake_case__	,snake_case__	,snake_case__	,snake_case__		)

						_SCREAMING_SNAKE_CASE     				=  checkpoint["""out.0.weight"""]
						_SCREAMING_SNAKE_CASE     				=  checkpoint["""out.0.bias"""]
						_SCREAMING_SNAKE_CASE     				=  checkpoint["""out.2.weight"""]
						_SCREAMING_SNAKE_CASE     				=  checkpoint["""out.2.bias"""]

						return new_checkpoint


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

		UpperCamelCase			    =     parser.parse_args()
		UpperCamelCase			    =     strabool(args.class_cond)

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

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

		if not args.class_cond:
				UpperCamelCase			    =     None

		UpperCamelCase			    =     con_pt_to_diffuser(args.unet_path, unet_config)

		UpperCamelCase			    =     UNetaDModel(**unet_config)
		image_unet.load_state_dict(converted_unet_ckpt)

		# Get scheduler config
		if "cd" in ckpt_name or "test" in ckpt_name:
				UpperCamelCase			    =     CD_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
				UpperCamelCase			    =     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)):
				UpperCamelCase			    =     CT_LSUN_256_SCHEDULER_CONFIG
		else:
				raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.")

		UpperCamelCase			    =     CMStochasticIterativeScheduler(**scheduler_config)

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

