



'''simple docstring'''






import unittest

from diffusers.models.unet_ad_blocks import *  # noqa F403
from diffusers.utils import torch_device

from .test_unet_blocks_common import UNetBlockTesterMixin

class 				SCREAMING_SNAKE_CASE(							A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       DownBlockaD  # noqa F405
	lowerCamelCase__			 =       """down"""







	def      A	(						self			:     Dict		)			->		Union[str, Any]:
								UpperCAmelCase :       Any 							=		[-0.02_32, -0.98_69, 0.80_54, -0.06_37, -0.16_88, -1.42_64, 0.44_70, -1.33_94, 0.09_04]
								super().test_output(__snake_case		)

class 				SCREAMING_SNAKE_CASE(							A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       ResnetDownsampleBlockaD  # noqa F405
	lowerCamelCase__			 =       """down"""







	def      A	(						self			:     Optional[Any]		)			->		Optional[int]:
								UpperCAmelCase :       Dict 							=		[0.07_10, 0.24_10, -0.73_20, -1.07_57, -1.13_43, 0.35_40, -0.01_33, -0.25_76, 0.09_48]
								super().test_output(__snake_case		)

class 				SCREAMING_SNAKE_CASE(							A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       AttnDownBlockaD  # noqa F405
	lowerCamelCase__			 =       """down"""







	def      A	(						self			:     Optional[Any]		)			->		Union[str, Any]:
								UpperCAmelCase :       Union[str, Any] 							=		[0.06_36, 0.89_64, -0.62_34, -1.01_31, 0.08_44, 0.49_35, 0.34_37, 0.09_11, -0.29_57]
								super().test_output(__snake_case		)

class 				SCREAMING_SNAKE_CASE(							A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       CrossAttnDownBlockaD  # noqa F405
	lowerCamelCase__			 =       """down"""







	def      A	(						self			:     int		)			->		List[Any]:
								UpperCAmelCase							,		UpperCAmelCase :       Union[str, Any] 							=		super().prepare_init_args_and_inputs_for_common()
								UpperCAmelCase :       Dict 							=		32
								return init_dict, inputs_dict







	def      A	(						self			:     List[Any]		)			->		int:
								UpperCAmelCase :       str 							=		[0.22_38, -0.73_96, -0.22_55, -0.38_29, 0.19_25, 1.16_65, 0.06_03, -0.72_95, 0.19_83]
								super().test_output(__snake_case		)

class 				SCREAMING_SNAKE_CASE(							A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       SimpleCrossAttnDownBlockaD  # noqa F405
	lowerCamelCase__			 =       """down"""







	@property
	def      A	(						self			:     Optional[int]		)			->		str:
								return super().get_dummy_input(include_encoder_hidden_states=__snake_case		)







	def      A	(						self			:     Optional[Any]		)			->		str:
								UpperCAmelCase							,		UpperCAmelCase :       Optional[int] 							=		super().prepare_init_args_and_inputs_for_common()
								UpperCAmelCase :       int 							=		32
								return init_dict, inputs_dict







	@unittest.skipIf(torch_device == '''mps'''		,			'''MPS result is not consistent'''		)
	def      A	(						self			:     List[str]		)			->		Union[str, Any]:
								UpperCAmelCase :       Optional[int] 							=		[0.79_21, -0.09_92, -0.19_62, -0.76_95, -0.42_42, 0.78_04, 0.47_37, 0.27_65, 0.33_38]
								super().test_output(__snake_case		)

class 				SCREAMING_SNAKE_CASE(							A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       SkipDownBlockaD  # noqa F405
	lowerCamelCase__			 =       """down"""







	@property
	def      A	(						self			:     Any		)			->		Optional[Any]:
								return super().get_dummy_input(include_skip_sample=__snake_case		)







	def      A	(						self			:     Any		)			->		Optional[Any]:
								UpperCAmelCase :       Optional[Any] 							=		[-0.08_45, -0.20_87, -0.24_65, 0.09_71, 0.19_00, -0.04_84, 0.26_64, 0.41_79, 0.50_69]
								super().test_output(__snake_case		)

class 				SCREAMING_SNAKE_CASE(							A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       AttnSkipDownBlockaD  # noqa F405
	lowerCamelCase__			 =       """down"""







	@property
	def      A	(						self			:     List[Any]		)			->		List[str]:
								return super().get_dummy_input(include_skip_sample=__snake_case		)







	def      A	(						self			:     int		)			->		Dict:
								UpperCAmelCase :       Any 							=		[0.55_39, 0.16_09, 0.49_24, 0.05_37, -0.19_95, 0.40_50, 0.09_79, -0.27_21, -0.06_42]
								super().test_output(__snake_case		)

class 				SCREAMING_SNAKE_CASE(							A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       DownEncoderBlockaD  # noqa F405
	lowerCamelCase__			 =       """down"""







	@property
	def      A	(						self			:     Tuple		)			->		Union[str, Any]:
								return super().get_dummy_input(include_temb=__snake_case		)







	def      A	(						self			:     int		)			->		Union[str, Any]:
								UpperCAmelCase :       Any 							=		{
								    '''in_channels''': 32,
								    '''out_channels''': 32,
								}
								UpperCAmelCase :       Any 							=		self.dummy_input
								return init_dict, inputs_dict







	def      A	(						self			:     Optional[int]		)			->		Optional[int]:
								UpperCAmelCase :       Dict 							=		[1.11_02, 0.53_02, 0.48_72, -0.00_23, -0.80_42, 0.04_83, -0.34_89, -0.56_32, 0.76_26]
								super().test_output(__snake_case		)

class 				SCREAMING_SNAKE_CASE(							A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       AttnDownEncoderBlockaD  # noqa F405
	lowerCamelCase__			 =       """down"""







	@property
	def      A	(						self			:     str		)			->		Union[str, Any]:
								return super().get_dummy_input(include_temb=__snake_case		)







	def      A	(						self			:     Dict		)			->		Dict:
								UpperCAmelCase :       Optional[int] 							=		{
								    '''in_channels''': 32,
								    '''out_channels''': 32,
								}
								UpperCAmelCase :       List[Any] 							=		self.dummy_input
								return init_dict, inputs_dict







	def      A	(						self			:     int		)			->		Union[str, Any]:
								UpperCAmelCase :       List[Any] 							=		[0.89_66, -0.14_86, 0.85_68, 0.81_41, -0.90_46, -0.13_42, -0.09_72, -0.74_17, 0.15_38]
								super().test_output(__snake_case		)

class 				SCREAMING_SNAKE_CASE(							A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       UNetMidBlockaD  # noqa F405
	lowerCamelCase__			 =       """mid"""







	def      A	(						self			:     int		)			->		str:
								UpperCAmelCase :       List[Any] 							=		{
								    '''in_channels''': 32,
								    '''temb_channels''': 128,
								}
								UpperCAmelCase :       Optional[Any] 							=		self.dummy_input
								return init_dict, inputs_dict







	def      A	(						self			:     List[str]		)			->		Any:
								UpperCAmelCase :       Dict 							=		[-0.10_62, 1.72_48, 0.34_94, 1.45_69, -0.09_10, -1.24_21, -0.99_84, 0.67_36, 1.00_28]
								super().test_output(__snake_case		)

class 				SCREAMING_SNAKE_CASE(							A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       UNetMidBlockaDCrossAttn  # noqa F405
	lowerCamelCase__			 =       """mid"""







	def      A	(						self			:     Union[str, Any]		)			->		List[str]:
								UpperCAmelCase							,		UpperCAmelCase :       Optional[int] 							=		super().prepare_init_args_and_inputs_for_common()
								UpperCAmelCase :       Any 							=		32
								return init_dict, inputs_dict







	def      A	(						self			:     Any		)			->		List[Any]:
								UpperCAmelCase :       int 							=		[0.01_87, 2.42_20, 0.44_84, 1.12_03, -0.61_21, -1.51_22, -0.82_70, 0.78_51, 1.83_35]
								super().test_output(__snake_case		)

class 				SCREAMING_SNAKE_CASE(							A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       UNetMidBlockaDSimpleCrossAttn  # noqa F405
	lowerCamelCase__			 =       """mid"""







	@property
	def      A	(						self			:     Optional[int]		)			->		List[Any]:
								return super().get_dummy_input(include_encoder_hidden_states=__snake_case		)







	def      A	(						self			:     List[Any]		)			->		str:
								UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		super().prepare_init_args_and_inputs_for_common()
								UpperCAmelCase :       Union[str, Any] 							=		32
								return init_dict, inputs_dict







	def      A	(						self			:     int		)			->		str:
								UpperCAmelCase :       Optional[int] 							=		[0.71_43, 1.99_74, 0.54_48, 1.39_77, 0.12_82, -1.12_37, -1.42_38, 0.55_30, 0.88_80]
								super().test_output(__snake_case		)

class 				SCREAMING_SNAKE_CASE(							A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       UpBlockaD  # noqa F405
	lowerCamelCase__			 =       """up"""







	@property
	def      A	(						self			:     Optional[Any]		)			->		Optional[int]:
								return super().get_dummy_input(include_res_hidden_states_tuple=__snake_case		)







	def      A	(						self			:     Dict		)			->		List[str]:
								UpperCAmelCase :       Tuple 							=		[-0.20_41, -0.41_65, -0.30_22, 0.00_41, -0.66_28, -0.70_53, 0.19_28, -0.03_25, 0.05_23]
								super().test_output(__snake_case		)

class 				SCREAMING_SNAKE_CASE(							A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       ResnetUpsampleBlockaD  # noqa F405
	lowerCamelCase__			 =       """up"""







	@property
	def      A	(						self			:     Union[str, Any]		)			->		Optional[Any]:
								return super().get_dummy_input(include_res_hidden_states_tuple=__snake_case		)







	def      A	(						self			:     Union[str, Any]		)			->		Tuple:
								UpperCAmelCase :       Optional[Any] 							=		[0.22_87, 0.35_49, -0.13_46, 0.47_97, -0.17_15, -0.96_49, 0.73_05, -0.58_64, -0.62_44]
								super().test_output(__snake_case		)

class 				SCREAMING_SNAKE_CASE(							A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       CrossAttnUpBlockaD  # noqa F405
	lowerCamelCase__			 =       """up"""







	@property
	def      A	(						self			:     Optional[int]		)			->		List[Any]:
								return super().get_dummy_input(include_res_hidden_states_tuple=__snake_case		)







	def      A	(						self			:     Union[str, Any]		)			->		int:
								UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		super().prepare_init_args_and_inputs_for_common()
								UpperCAmelCase :       List[str] 							=		32
								return init_dict, inputs_dict







	def      A	(						self			:     Any		)			->		Optional[Any]:
								UpperCAmelCase :       List[str] 							=		[-0.14_03, -0.35_15, -0.04_20, -0.14_25, 0.31_67, 0.50_94, -0.21_81, 0.59_31, 0.55_82]
								super().test_output(__snake_case		)

class 				SCREAMING_SNAKE_CASE(							A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       SimpleCrossAttnUpBlockaD  # noqa F405
	lowerCamelCase__			 =       """up"""







	@property
	def      A	(						self			:     Dict		)			->		int:
								return super().get_dummy_input(include_res_hidden_states_tuple=__snake_case		,			include_encoder_hidden_states=__snake_case		)







	def      A	(						self			:     Tuple		)			->		Tuple:
								UpperCAmelCase							,		UpperCAmelCase :       List[str] 							=		super().prepare_init_args_and_inputs_for_common()
								UpperCAmelCase :       List[str] 							=		32
								return init_dict, inputs_dict







	def      A	(						self			:     int		)			->		int:
								UpperCAmelCase :       Optional[Any] 							=		[0.26_45, 0.14_80, 0.09_09, 0.80_44, -0.97_58, -0.90_83, 0.09_94, -1.14_53, -0.74_02]
								super().test_output(__snake_case		)

class 				SCREAMING_SNAKE_CASE(							A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       AttnUpBlockaD  # noqa F405
	lowerCamelCase__			 =       """up"""







	@property
	def      A	(						self			:     Optional[int]		)			->		Tuple:
								return super().get_dummy_input(include_res_hidden_states_tuple=__snake_case		)







	@unittest.skipIf(torch_device == '''mps'''		,			'''MPS result is not consistent'''		)
	def      A	(						self			:     str		)			->		Tuple:
								UpperCAmelCase :       Any 							=		[0.09_79, 0.13_26, 0.00_21, 0.06_59, 0.22_49, 0.00_59, 0.11_32, 0.59_52, 0.10_33]
								super().test_output(__snake_case		)

class 				SCREAMING_SNAKE_CASE(							A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       SkipUpBlockaD  # noqa F405
	lowerCamelCase__			 =       """up"""







	@property
	def      A	(						self			:     Optional[Any]		)			->		Optional[int]:
								return super().get_dummy_input(include_res_hidden_states_tuple=__snake_case		)







	def      A	(						self			:     Any		)			->		Optional[int]:
								UpperCAmelCase :       Optional[int] 							=		[-0.08_93, -0.12_34, -0.15_06, -0.03_32, 0.01_23, -0.02_11, 0.05_66, 0.01_43, 0.03_62]
								super().test_output(__snake_case		)

class 				SCREAMING_SNAKE_CASE(							A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       AttnSkipUpBlockaD  # noqa F405
	lowerCamelCase__			 =       """up"""







	@property
	def      A	(						self			:     Dict		)			->		List[Any]:
								return super().get_dummy_input(include_res_hidden_states_tuple=__snake_case		)







	def      A	(						self			:     Optional[Any]		)			->		Dict:
								UpperCAmelCase :       Optional[int] 							=		[0.03_61, 0.06_17, 0.27_87, -0.03_50, 0.03_42, 0.34_21, -0.08_43, 0.09_13, 0.30_15]
								super().test_output(__snake_case		)

class 				SCREAMING_SNAKE_CASE(							A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       UpDecoderBlockaD  # noqa F405
	lowerCamelCase__			 =       """up"""







	@property
	def      A	(						self			:     str		)			->		Optional[int]:
								return super().get_dummy_input(include_temb=__snake_case		)







	def      A	(						self			:     Union[str, Any]		)			->		Union[str, Any]:
								UpperCAmelCase :       Tuple 							=		{'''in_channels''': 32, '''out_channels''': 32}

								UpperCAmelCase :       Union[str, Any] 							=		self.dummy_input
								return init_dict, inputs_dict







	def      A	(						self			:     Optional[Any]		)			->		Any:
								UpperCAmelCase :       int 							=		[0.44_04, 0.19_98, -0.98_86, -0.33_20, -0.31_28, -0.70_34, -0.69_55, -0.23_38, -0.31_37]
								super().test_output(__snake_case		)

class 				SCREAMING_SNAKE_CASE(							A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       AttnUpDecoderBlockaD  # noqa F405
	lowerCamelCase__			 =       """up"""







	@property
	def      A	(						self			:     str		)			->		Tuple:
								return super().get_dummy_input(include_temb=__snake_case		)







	def      A	(						self			:     Optional[Any]		)			->		Dict:
								UpperCAmelCase :       Tuple 							=		{'''in_channels''': 32, '''out_channels''': 32}

								UpperCAmelCase :       Tuple 							=		self.dummy_input
								return init_dict, inputs_dict







	def      A	(						self			:     Union[str, Any]		)			->		Any:
								UpperCAmelCase :       Any 							=		[0.67_38, 0.44_91, 0.10_55, 1.07_10, 0.73_16, 0.33_39, 0.33_52, 0.10_23, 0.35_68]
								super().test_output(__snake_case		)










'''simple docstring'''






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__:   Optional[int]								=			logging.get_logger(__name__)


def  snake_case_     (					_lowerCAmelCase  :						Optional[int]  )      ->   Optional[int]:
							UpperCAmelCase :       Tuple 							=		DPTConfig(embedding_type='''hybrid'''  )

							if "large" in checkpoint_url:
														UpperCAmelCase :       Tuple 							=		1024
														UpperCAmelCase :       List[Any] 							=		4096
														UpperCAmelCase :       str 							=		24
														UpperCAmelCase :       List[Any] 							=		16
														UpperCAmelCase :       str 							=		[5, 11, 17, 23]
														UpperCAmelCase :       List[Any] 							=		[256, 512, 1024, 1024]
														UpperCAmelCase :       Tuple 							=		(1, 384, 384)

							if "nyu" or "midas" in checkpoint_url:
														UpperCAmelCase :       Optional[Any] 							=		768
														UpperCAmelCase :       Tuple 							=		[1, 1, 1, 0.5]
														UpperCAmelCase :       int 							=		[256, 512, 768, 768]
														UpperCAmelCase :       Any 							=		150
														UpperCAmelCase :       Tuple 							=		16
														UpperCAmelCase :       Any 							=		(1, 384, 384)
														UpperCAmelCase :       Optional[Any] 							=		False
														UpperCAmelCase :       Tuple 							=		'''project'''

							if "ade" in checkpoint_url:
														UpperCAmelCase :       Any 							=		True
														UpperCAmelCase :       str 							=		768
														UpperCAmelCase :       Optional[int] 							=		[1, 1, 1, 0.5]
														UpperCAmelCase :       List[Any] 							=		150
														UpperCAmelCase :       List[Any] 							=		16
														UpperCAmelCase :       str 							=		'''huggingface/label-files'''
														UpperCAmelCase :       Tuple 							=		'''ade20k-id2label.json'''
														UpperCAmelCase :       Any 							=		json.load(open(cached_download(hf_hub_url(_lowerCAmelCase   ,      _lowerCAmelCase   ,      repo_type='''dataset'''  )  )   ,      '''r'''  )  )
														UpperCAmelCase :       Optional[Any] 							=		{int(_lowerCAmelCase  ): v for k, v in idalabel.items()}
														UpperCAmelCase :       List[Any] 							=		idalabel
														UpperCAmelCase :       Optional[int] 							=		{v: k for k, v in idalabel.items()}
														UpperCAmelCase :       Union[str, Any] 							=		[1, 150, 480, 480]

							return config, expected_shape


def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]  )      ->   int:
							UpperCAmelCase :       List[str] 							=		['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
							for k in ignore_keys:
														state_dict.pop(_lowerCAmelCase   ,      _lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						Tuple  )      ->   Any:
							if (
							    "pretrained.model" in name
							    and "cls_token" not in name
							    and "pos_embed" not in name
							    and "patch_embed" not in name
							):
														UpperCAmelCase :       Tuple 							=		name.replace('''pretrained.model'''   ,      '''dpt.encoder'''  )
							if "pretrained.model" in name:
														UpperCAmelCase :       Union[str, Any] 							=		name.replace('''pretrained.model'''   ,      '''dpt.embeddings'''  )
							if "patch_embed" in name:
														UpperCAmelCase :       int 							=		name.replace('''patch_embed'''   ,      ''''''  )
							if "pos_embed" in name:
														UpperCAmelCase :       Tuple 							=		name.replace('''pos_embed'''   ,      '''position_embeddings'''  )
							if "attn.proj" in name:
														UpperCAmelCase :       Any 							=		name.replace('''attn.proj'''   ,      '''attention.output.dense'''  )
							if "proj" in name and "project" not in name:
														UpperCAmelCase :       str 							=		name.replace('''proj'''   ,      '''projection'''  )
							if "blocks" in name:
														UpperCAmelCase :       Any 							=		name.replace('''blocks'''   ,      '''layer'''  )
							if "mlp.fc1" in name:
														UpperCAmelCase :       Optional[int] 							=		name.replace('''mlp.fc1'''   ,      '''intermediate.dense'''  )
							if "mlp.fc2" in name:
														UpperCAmelCase :       Optional[Any] 							=		name.replace('''mlp.fc2'''   ,      '''output.dense'''  )
							if "norm1" in name and "backbone" not in name:
														UpperCAmelCase :       Dict 							=		name.replace('''norm1'''   ,      '''layernorm_before'''  )
							if "norm2" in name and "backbone" not in name:
														UpperCAmelCase :       Tuple 							=		name.replace('''norm2'''   ,      '''layernorm_after'''  )
							if "scratch.output_conv" in name:
														UpperCAmelCase :       Tuple 							=		name.replace('''scratch.output_conv'''   ,      '''head'''  )
							if "scratch" in name:
														UpperCAmelCase :       str 							=		name.replace('''scratch'''   ,      '''neck'''  )
							if "layer1_rn" in name:
														UpperCAmelCase :       Dict 							=		name.replace('''layer1_rn'''   ,      '''convs.0'''  )
							if "layer2_rn" in name:
														UpperCAmelCase :       int 							=		name.replace('''layer2_rn'''   ,      '''convs.1'''  )
							if "layer3_rn" in name:
														UpperCAmelCase :       Tuple 							=		name.replace('''layer3_rn'''   ,      '''convs.2'''  )
							if "layer4_rn" in name:
														UpperCAmelCase :       int 							=		name.replace('''layer4_rn'''   ,      '''convs.3'''  )
							if "refinenet" in name:
														UpperCAmelCase :       List[str] 							=		int(name[len('''neck.refinenet'''  ) : len('''neck.refinenet'''  ) + 1]  )
														# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
														UpperCAmelCase :       str 							=		name.replace(f"""refinenet{layer_idx}"""   ,      f"""fusion_stage.layers.{abs(layer_idx-4  )}"""  )
							if "out_conv" in name:
														UpperCAmelCase :       List[str] 							=		name.replace('''out_conv'''   ,      '''projection'''  )
							if "resConfUnit1" in name:
														UpperCAmelCase :       Union[str, Any] 							=		name.replace('''resConfUnit1'''   ,      '''residual_layer1'''  )
							if "resConfUnit2" in name:
														UpperCAmelCase :       Any 							=		name.replace('''resConfUnit2'''   ,      '''residual_layer2'''  )
							if "conv1" in name:
														UpperCAmelCase :       Optional[int] 							=		name.replace('''conv1'''   ,      '''convolution1'''  )
							if "conv2" in name:
														UpperCAmelCase :       Tuple 							=		name.replace('''conv2'''   ,      '''convolution2'''  )
							# readout blocks
							if "pretrained.act_postprocess1.0.project.0" in name:
														UpperCAmelCase :       Dict 							=		name.replace('''pretrained.act_postprocess1.0.project.0'''   ,      '''neck.reassemble_stage.readout_projects.0.0'''  )
							if "pretrained.act_postprocess2.0.project.0" in name:
														UpperCAmelCase :       int 							=		name.replace('''pretrained.act_postprocess2.0.project.0'''   ,      '''neck.reassemble_stage.readout_projects.1.0'''  )
							if "pretrained.act_postprocess3.0.project.0" in name:
														UpperCAmelCase :       Any 							=		name.replace('''pretrained.act_postprocess3.0.project.0'''   ,      '''neck.reassemble_stage.readout_projects.2.0'''  )
							if "pretrained.act_postprocess4.0.project.0" in name:
														UpperCAmelCase :       Optional[Any] 							=		name.replace('''pretrained.act_postprocess4.0.project.0'''   ,      '''neck.reassemble_stage.readout_projects.3.0'''  )

							# resize blocks
							if "pretrained.act_postprocess1.3" in name:
														UpperCAmelCase :       List[Any] 							=		name.replace('''pretrained.act_postprocess1.3'''   ,      '''neck.reassemble_stage.layers.0.projection'''  )
							if "pretrained.act_postprocess1.4" in name:
														UpperCAmelCase :       Any 							=		name.replace('''pretrained.act_postprocess1.4'''   ,      '''neck.reassemble_stage.layers.0.resize'''  )
							if "pretrained.act_postprocess2.3" in name:
														UpperCAmelCase :       Optional[int] 							=		name.replace('''pretrained.act_postprocess2.3'''   ,      '''neck.reassemble_stage.layers.1.projection'''  )
							if "pretrained.act_postprocess2.4" in name:
														UpperCAmelCase :       str 							=		name.replace('''pretrained.act_postprocess2.4'''   ,      '''neck.reassemble_stage.layers.1.resize'''  )
							if "pretrained.act_postprocess3.3" in name:
														UpperCAmelCase :       List[str] 							=		name.replace('''pretrained.act_postprocess3.3'''   ,      '''neck.reassemble_stage.layers.2.projection'''  )
							if "pretrained.act_postprocess4.3" in name:
														UpperCAmelCase :       Tuple 							=		name.replace('''pretrained.act_postprocess4.3'''   ,      '''neck.reassemble_stage.layers.3.projection'''  )
							if "pretrained.act_postprocess4.4" in name:
														UpperCAmelCase :       int 							=		name.replace('''pretrained.act_postprocess4.4'''   ,      '''neck.reassemble_stage.layers.3.resize'''  )
							if "pretrained" in name:
														UpperCAmelCase :       Optional[int] 							=		name.replace('''pretrained'''   ,      '''dpt'''  )
							if "bn" in name:
														UpperCAmelCase :       Dict 							=		name.replace('''bn'''   ,      '''batch_norm'''  )
							if "head" in name:
														UpperCAmelCase :       Any 							=		name.replace('''head'''   ,      '''head.head'''  )
							if "encoder.norm" in name:
														UpperCAmelCase :       Optional[int] 							=		name.replace('''encoder.norm'''   ,      '''layernorm'''  )
							if "auxlayer" in name:
														UpperCAmelCase :       Union[str, Any] 							=		name.replace('''auxlayer'''   ,      '''auxiliary_head.head'''  )
							if "backbone" in name:
														UpperCAmelCase :       List[Any] 							=		name.replace('''backbone'''   ,      '''backbone.bit.encoder'''  )

							if ".." in name:
														UpperCAmelCase :       Optional[int] 							=		name.replace('''..'''   ,      '''.'''  )

							if "stem.conv" in name:
														UpperCAmelCase :       Optional[Any] 							=		name.replace('''stem.conv'''   ,      '''bit.embedder.convolution'''  )
							if "blocks" in name:
														UpperCAmelCase :       Optional[int] 							=		name.replace('''blocks'''   ,      '''layers'''  )
							if "convolution" in name and "backbone" in name:
														UpperCAmelCase :       List[Any] 							=		name.replace('''convolution'''   ,      '''conv'''  )
							if "layer" in name and "backbone" in name:
														UpperCAmelCase :       List[str] 							=		name.replace('''layer'''   ,      '''layers'''  )
							if "backbone.bit.encoder.bit" in name:
														UpperCAmelCase :       List[Any] 							=		name.replace('''backbone.bit.encoder.bit'''   ,      '''backbone.bit'''  )
							if "embedder.conv" in name:
														UpperCAmelCase :       List[Any] 							=		name.replace('''embedder.conv'''   ,      '''embedder.convolution'''  )
							if "backbone.bit.encoder.stem.norm" in name:
														UpperCAmelCase :       Tuple 							=		name.replace('''backbone.bit.encoder.stem.norm'''   ,      '''backbone.bit.embedder.norm'''  )
							return name


def  snake_case_     (					_lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						List[Any]  )      ->   Optional[Any]:
							for i in range(config.num_hidden_layers  ):
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														UpperCAmelCase :       Optional[int] 							=		state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight"""  )
														UpperCAmelCase :       Tuple 							=		state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.bias"""  )
														# next, add query, keys and values (in that order) to the state dict
														UpperCAmelCase :       Tuple 							=		in_proj_weight[: config.hidden_size, :]
														UpperCAmelCase :       int 							=		in_proj_bias[: config.hidden_size]
														UpperCAmelCase :       List[str] 							=		in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														UpperCAmelCase :       List[str] 							=		in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														UpperCAmelCase :       str 							=		in_proj_weight[
														    -config.hidden_size :, :
														]
														UpperCAmelCase :       Union[str, Any] 							=		in_proj_bias[-config.hidden_size :]


def  snake_case_     (					)      ->   List[str]:
							UpperCAmelCase :       Optional[int] 							=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
							UpperCAmelCase :       Optional[int] 							=		Image.open(requests.get(_lowerCAmelCase   ,      stream=_lowerCAmelCase  ).raw  )
							return im


@torch.no_grad()
def  snake_case_     (					_lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						Union[str, Any]   ,      _lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						Union[str, Any]   ,      _lowerCAmelCase  :						List[str]  )      ->   Any:
							UpperCAmelCase							,		UpperCAmelCase :       int 							=		get_dpt_config(_lowerCAmelCase  )
							# load original state_dict from URL
							# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
							UpperCAmelCase :       List[Any] 							=		torch.load(_lowerCAmelCase   ,      map_location='''cpu'''  )
							# remove certain keys
							remove_ignore_keys_(_lowerCAmelCase  )
							# rename keys
							for key in state_dict.copy().keys():
														UpperCAmelCase :       Any 							=		state_dict.pop(_lowerCAmelCase  )
														UpperCAmelCase :       List[Any] 							=		val
							# read in qkv matrices
							read_in_q_k_v(_lowerCAmelCase   ,      _lowerCAmelCase  )

							# load HuggingFace model
							UpperCAmelCase :       Optional[Any] 							=		DPTForSemanticSegmentation(_lowerCAmelCase  ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(_lowerCAmelCase  )
							model.load_state_dict(_lowerCAmelCase  )
							model.eval()

							# Check outputs on an image
							UpperCAmelCase :       int 							=		480 if '''ade''' in checkpoint_url else 384
							UpperCAmelCase :       List[Any] 							=		DPTImageProcessor(size=_lowerCAmelCase  )

							UpperCAmelCase :       Dict 							=		prepare_img()
							UpperCAmelCase :       Optional[int] 							=		image_processor(_lowerCAmelCase   ,      return_tensors='''pt'''  )

							# forward pass
							UpperCAmelCase :       Any 							=		model(**_lowerCAmelCase  ).logits if '''ade''' in checkpoint_url else model(**_lowerCAmelCase  ).predicted_depth

							if show_prediction:
														UpperCAmelCase :       Dict 							=		(
														    torch.nn.functional.interpolate(
														        outputs.unsqueeze(1  )   ,      size=(image.size[1], image.size[0])   ,      mode='''bicubic'''   ,      align_corners=_lowerCAmelCase   ,      )
														    .squeeze()
														    .cpu()
														    .numpy()
														)

														Image.fromarray((prediction / prediction.max()) * 255  ).show()

							if pytorch_dump_folder_path is not None:
														Path(_lowerCAmelCase  ).mkdir(exist_ok=_lowerCAmelCase  )
														print(f"""Saving model to {pytorch_dump_folder_path}"""  )
														model.save_pretrained(_lowerCAmelCase  )
														print(f"""Saving image processor to {pytorch_dump_folder_path}"""  )
														image_processor.save_pretrained(_lowerCAmelCase  )

							if push_to_hub:
														model.push_to_hub('''ybelkada/dpt-hybrid-midas'''  )
														image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''  )


if __name__ == "__main__":
						UpperCamelCase__:   Dict								=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint_url",
						    default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
						    type=str,
						    help="URL of the original DPT checkpoint you'd like to convert.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default=None,
						    type=str,
						    required=False,
						    help="Path to the output PyTorch model directory.",
						)
						parser.add_argument(
						    "--push_to_hub",
						    action="store_true",
						)
						parser.add_argument(
						    "--model_name",
						    default="dpt-large",
						    type=str,
						    help="Name of the model, in case you're pushing to the hub.",
						)
						parser.add_argument(
						    "--show_prediction",
						    action="store_true",
						)

						UpperCamelCase__:   Tuple								=			parser.parse_args()
						convert_dpt_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
						)





