
import gc
import unittest

import numpy as np
import torch
from torch.backends.cuda import sdp_kernel

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class   UpperCAmelCase		(						A_       ,unittest.TestCase ):
   A__					:		Union[str, Any]						     =     ConsistencyModelPipeline
   A__					:		Union[str, Any]						     =     UNCONDITIONAL_IMAGE_GENERATION_PARAMS
   A__					:		Any						     =     UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS

   # Override required_optional_params to remove num_images_per_prompt
   A__					:		List[str]						     =     frozenset(
       [
           "num_inference_steps",
           "generator",
           "latents",
           "output_type",
           "return_dict",
           "callback",
           "callback_steps",
       ] )



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      UNetaDModel.from_pretrained(
              "diffusers/consistency-models-test"    ,						subfolder="test_unet"    ,						)
          return unet



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   List[Any]							=      UNetaDModel.from_pretrained(
              "diffusers/consistency-models-test"    ,						subfolder="test_unet_class_cond"    ,						)
          return unet



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					Union[str, Any]=False )		->	List[Any]:


          '''simple docstring'''


          if class_cond:
                 snake_case       :   List[Any]							=      self.dummy_cond_unet
          else:
                 snake_case       :   Optional[int]							=      self.dummy_uncond_unet

          # Default to CM multistep sampler
          snake_case       :   Any							=      CMStochasticIterativeScheduler(
              num_train_timesteps=40    ,						sigma_min=0.002    ,						sigma_max=80.0    ,						)

          snake_case       :   List[Any]							=      {
              "unet": unet,
              "scheduler": scheduler,
          }

          return components



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Any    ,						snake_case__		:					Optional[Any]=0 )		->	Optional[Any]:


          '''simple docstring'''


          if str(snake_case__ ).startswith("mps" ):
                 snake_case       :   List[str]							=      torch.manual_seed(snake_case__ )
          else:
                 snake_case       :   Any							=      torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )

          snake_case       :   Optional[int]							=      {
              "batch_size": 1,
              "num_inference_steps": None,
              "timesteps": [22, 0],
              "generator": generator,
              "output_type": "np",
          }

          return inputs



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	str:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      "cpu"  # ensure determinism for the device-dependent torch.Generator
          snake_case       :   Dict							=      self.get_dummy_components()
          snake_case       :   Optional[Any]							=      ConsistencyModelPipeline(**snake_case__ )
          snake_case       :   str							=      pipe.to(snake_case__ )
          pipe.set_progress_bar_config(disable=snake_case__ )

          snake_case       :   Union[str, Any]							=      self.get_dummy_inputs(snake_case__ )
          snake_case       :   Optional[int]							=      pipe(**snake_case__ ).images
          assert image.shape == (1, 32, 32, 3)

          snake_case       :   Union[str, Any]							=      image[0, -3:, -3:, -1]
          snake_case       :   Dict							=      np.array([0.3572, 0.6273, 0.4031, 0.3961, 0.4321, 0.5730, 0.5266, 0.4780, 0.5004] )

          assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	Any:


          '''simple docstring'''


          snake_case       :   List[Any]							=      "cpu"  # ensure determinism for the device-dependent torch.Generator
          snake_case       :   int							=      self.get_dummy_components(class_cond=snake_case__ )
          snake_case       :   Any							=      ConsistencyModelPipeline(**snake_case__ )
          snake_case       :   List[Any]							=      pipe.to(snake_case__ )
          pipe.set_progress_bar_config(disable=snake_case__ )

          snake_case       :   Union[str, Any]							=      self.get_dummy_inputs(snake_case__ )
          snake_case       :   Dict							=      0
          snake_case       :   List[Any]							=      pipe(**snake_case__ ).images
          assert image.shape == (1, 32, 32, 3)

          snake_case       :   Optional[Any]							=      image[0, -3:, -3:, -1]
          snake_case       :   Optional[Any]							=      np.array([0.3572, 0.6273, 0.4031, 0.3961, 0.4321, 0.5730, 0.5266, 0.4780, 0.5004] )

          assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   List[Any]							=      "cpu"  # ensure determinism for the device-dependent torch.Generator
          snake_case       :   Union[str, Any]							=      self.get_dummy_components()
          snake_case       :   Optional[Any]							=      ConsistencyModelPipeline(**snake_case__ )
          snake_case       :   Optional[int]							=      pipe.to(snake_case__ )
          pipe.set_progress_bar_config(disable=snake_case__ )

          snake_case       :   Dict							=      self.get_dummy_inputs(snake_case__ )
          snake_case       :   List[Any]							=      1
          snake_case       :   Dict							=      None
          snake_case       :   Tuple							=      pipe(**snake_case__ ).images
          assert image.shape == (1, 32, 32, 3)

          snake_case       :   Any							=      image[0, -3:, -3:, -1]
          snake_case       :   List[str]							=      np.array([0.5004, 0.5004, 0.4994, 0.5008, 0.4976, 0.5018, 0.4990, 0.4982, 0.4987] )

          assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any] )		->	Any:


          '''simple docstring'''


          snake_case       :   List[Any]							=      "cpu"  # ensure determinism for the device-dependent torch.Generator
          snake_case       :   str							=      self.get_dummy_components(class_cond=snake_case__ )
          snake_case       :   List[Any]							=      ConsistencyModelPipeline(**snake_case__ )
          snake_case       :   Union[str, Any]							=      pipe.to(snake_case__ )
          pipe.set_progress_bar_config(disable=snake_case__ )

          snake_case       :   List[str]							=      self.get_dummy_inputs(snake_case__ )
          snake_case       :   Any							=      1
          snake_case       :   Optional[int]							=      None
          snake_case       :   List[Any]							=      0
          snake_case       :   int							=      pipe(**snake_case__ ).images
          assert image.shape == (1, 32, 32, 3)

          snake_case       :   Union[str, Any]							=      image[0, -3:, -3:, -1]
          snake_case       :   Dict							=      np.array([0.5004, 0.5004, 0.4994, 0.5008, 0.4976, 0.5018, 0.4990, 0.4982, 0.4987] )

          assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3






@slow
@require_torch_gpu
class   UpperCAmelCase		(						unittest.TestCase ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Tuple:


          '''simple docstring'''


          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any    ,						snake_case__		:					Union[str, Any]=0    ,						snake_case__		:					Optional[Any]=False    ,						snake_case__		:					str="cpu"    ,						snake_case__		:					Optional[int]=torch.floataa    ,						snake_case__		:					Optional[Any]=(1, 3, 64, 64) )		->	Tuple:


          '''simple docstring'''


          snake_case       :   List[str]							=      torch.manual_seed(snake_case__ )

          snake_case       :   List[str]							=      {
              "num_inference_steps": None,
              "timesteps": [22, 0],
              "class_labels": 0,
              "generator": generator,
              "output_type": "np",
          }

          if get_fixed_latents:
                 snake_case       :   Tuple							=      self.get_fixed_latents(seed=snake_case__    ,						device=snake_case__    ,						dtype=snake_case__    ,						shape=snake_case__ )
                 snake_case       :   int							=      latents

          return inputs



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any    ,						snake_case__		:					List[Any]=0    ,						snake_case__		:					Union[str, Any]="cpu"    ,						snake_case__		:					int=torch.floataa    ,						snake_case__		:					Any=(1, 3, 64, 64) )		->	int:


          '''simple docstring'''


          if type(snake_case__ ) == str:
                 snake_case       :   Optional[Any]							=      torch.device(snake_case__ )
          snake_case       :   List[Any]							=      torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
          snake_case       :   List[Any]							=      randn_tensor(snake_case__    ,						generator=snake_case__    ,						device=snake_case__    ,						dtype=snake_case__ )
          return latents



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      UNetaDModel.from_pretrained("diffusers/consistency_models"    ,						subfolder="diffusers_cd_imagenet64_l2" )
          snake_case       :   Tuple							=      CMStochasticIterativeScheduler(
              num_train_timesteps=40    ,						sigma_min=0.002    ,						sigma_max=80.0    ,						)
          snake_case       :   List[Any]							=      ConsistencyModelPipeline(unet=snake_case__    ,						scheduler=snake_case__ )
          pipe.to(torch_device=snake_case__ )
          pipe.set_progress_bar_config(disable=snake_case__ )

          snake_case       :   str							=      self.get_inputs()
          snake_case       :   Optional[int]							=      pipe(**snake_case__ ).images
          assert image.shape == (1, 64, 64, 3)

          snake_case       :   Optional[Any]							=      image[0, -3:, -3:, -1]

          snake_case       :   List[str]							=      np.array([0.0888, 0.0881, 0.0666, 0.0479, 0.0292, 0.0195, 0.0201, 0.0163, 0.0254] )

          assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      UNetaDModel.from_pretrained("diffusers/consistency_models"    ,						subfolder="diffusers_cd_imagenet64_l2" )
          snake_case       :   Tuple							=      CMStochasticIterativeScheduler(
              num_train_timesteps=40    ,						sigma_min=0.002    ,						sigma_max=80.0    ,						)
          snake_case       :   Dict							=      ConsistencyModelPipeline(unet=snake_case__    ,						scheduler=snake_case__ )
          pipe.to(torch_device=snake_case__ )
          pipe.set_progress_bar_config(disable=snake_case__ )

          snake_case       :   int							=      self.get_inputs()
          snake_case       :   Union[str, Any]							=      1
          snake_case       :   Optional[int]							=      None
          snake_case       :   int							=      pipe(**snake_case__ ).images
          assert image.shape == (1, 64, 64, 3)

          snake_case       :   Dict							=      image[0, -3:, -3:, -1]

          snake_case       :   Union[str, Any]							=      np.array([0.0340, 0.0152, 0.0063, 0.0267, 0.0221, 0.0107, 0.0416, 0.0186, 0.0217] )

          assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2



   @require_torch_a
   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   str							=      UNetaDModel.from_pretrained("diffusers/consistency_models"    ,						subfolder="diffusers_cd_imagenet64_l2" )
          snake_case       :   Optional[int]							=      CMStochasticIterativeScheduler(
              num_train_timesteps=40    ,						sigma_min=0.002    ,						sigma_max=80.0    ,						)
          snake_case       :   Optional[int]							=      ConsistencyModelPipeline(unet=snake_case__    ,						scheduler=snake_case__ )
          pipe.to(torch_device=snake_case__    ,						torch_dtype=torch.floataa )
          pipe.set_progress_bar_config(disable=snake_case__ )

          snake_case       :   str							=      self.get_inputs(get_fixed_latents=snake_case__    ,						device=snake_case__ )
          # Ensure usage of flash attention in torch 2.0
          with sdp_kernel(enable_flash=snake_case__    ,						enable_math=snake_case__    ,						enable_mem_efficient=snake_case__ ):
                 snake_case       :   List[Any]							=      pipe(**snake_case__ ).images
          assert image.shape == (1, 64, 64, 3)

          snake_case       :   Optional[Any]							=      image[0, -3:, -3:, -1]

          snake_case       :   Optional[Any]							=      np.array([0.1875, 0.1428, 0.1289, 0.2151, 0.2092, 0.1477, 0.1877, 0.1641, 0.1353] )

          assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3



   @require_torch_a
   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	List[Any]:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      UNetaDModel.from_pretrained("diffusers/consistency_models"    ,						subfolder="diffusers_cd_imagenet64_l2" )
          snake_case       :   str							=      CMStochasticIterativeScheduler(
              num_train_timesteps=40    ,						sigma_min=0.002    ,						sigma_max=80.0    ,						)
          snake_case       :   str							=      ConsistencyModelPipeline(unet=snake_case__    ,						scheduler=snake_case__ )
          pipe.to(torch_device=snake_case__    ,						torch_dtype=torch.floataa )
          pipe.set_progress_bar_config(disable=snake_case__ )

          snake_case       :   Dict							=      self.get_inputs(get_fixed_latents=snake_case__    ,						device=snake_case__ )
          snake_case       :   Any							=      1
          snake_case       :   Union[str, Any]							=      None
          # Ensure usage of flash attention in torch 2.0
          with sdp_kernel(enable_flash=snake_case__    ,						enable_math=snake_case__    ,						enable_mem_efficient=snake_case__ ):
                 snake_case       :   Union[str, Any]							=      pipe(**snake_case__ ).images
          assert image.shape == (1, 64, 64, 3)

          snake_case       :   int							=      image[0, -3:, -3:, -1]

          snake_case       :   List[Any]							=      np.array([0.1663, 0.1948, 0.2275, 0.1680, 0.1204, 0.1245, 0.1858, 0.1338, 0.2095] )

          assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3









'''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()
lowerCamelCase__					=   logging.get_logger(__name__)






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				List[Any]	    =					DPTConfig()

		if "large" in checkpoint_url:
				_UpperCAmelCase						:				List[str]	    =					1_024
				_UpperCAmelCase						:				Optional[int]	    =					4_096
				_UpperCAmelCase						:				Union[str, Any]	    =					24
				_UpperCAmelCase						:				List[Any]	    =					16
				_UpperCAmelCase						:				List[Any]	    =					[5, 11, 17, 23]
				_UpperCAmelCase						:				int	    =					[256, 512, 1_024, 1_024]
				_UpperCAmelCase						:				Optional[Any]	    =					(1, 384, 384)

		if "ade" in checkpoint_url:
				_UpperCAmelCase						:				Optional[int]	    =					True

				_UpperCAmelCase						:				List[Any]	    =					150
				_UpperCAmelCase						:				Optional[Any]	    =					"huggingface/label-files"
				_UpperCAmelCase						:				Optional[int]	    =					"ade20k-id2label.json"
				_UpperCAmelCase						:				Tuple	    =					json.load(open(cached_download(hf_hub_url(__lowerCAmelCase					,       __lowerCAmelCase					,       repo_type="dataset"      )      )					,       "r"      )      )
				_UpperCAmelCase						:				str	    =					{int(__lowerCAmelCase      ): v for k, v in idalabel.items()}
				_UpperCAmelCase						:				int	    =					idalabel
				_UpperCAmelCase						:				Union[str, Any]	    =					{v: k for k, v in idalabel.items()}
				_UpperCAmelCase						:				int	    =					[1, 150, 480, 480]

		return config, expected_shape






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				Union[str, Any]	    =					["pretrained.model.head.weight", "pretrained.model.head.bias"]
		for k in ignore_keys:
				state_dict.pop(__lowerCAmelCase					,       __lowerCAmelCase      )






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		if (
		    "pretrained.model" in name
		    and "cls_token" not in name
		    and "pos_embed" not in name
		    and "patch_embed" not in name
		):
				_UpperCAmelCase						:				str	    =					name.replace("pretrained.model"					,       "dpt.encoder"      )
		if "pretrained.model" in name:
				_UpperCAmelCase						:				List[str]	    =					name.replace("pretrained.model"					,       "dpt.embeddings"      )
		if "patch_embed" in name:
				_UpperCAmelCase						:				Dict	    =					name.replace("patch_embed"					,       "patch_embeddings"      )
		if "pos_embed" in name:
				_UpperCAmelCase						:				int	    =					name.replace("pos_embed"					,       "position_embeddings"      )
		if "attn.proj" in name:
				_UpperCAmelCase						:				int	    =					name.replace("attn.proj"					,       "attention.output.dense"      )
		if "proj" in name and "project" not in name:
				_UpperCAmelCase						:				int	    =					name.replace("proj"					,       "projection"      )
		if "blocks" in name:
				_UpperCAmelCase						:				Tuple	    =					name.replace("blocks"					,       "layer"      )
		if "mlp.fc1" in name:
				_UpperCAmelCase						:				Union[str, Any]	    =					name.replace("mlp.fc1"					,       "intermediate.dense"      )
		if "mlp.fc2" in name:
				_UpperCAmelCase						:				Union[str, Any]	    =					name.replace("mlp.fc2"					,       "output.dense"      )
		if "norm1" in name:
				_UpperCAmelCase						:				Optional[Any]	    =					name.replace("norm1"					,       "layernorm_before"      )
		if "norm2" in name:
				_UpperCAmelCase						:				int	    =					name.replace("norm2"					,       "layernorm_after"      )
		if "scratch.output_conv" in name:
				_UpperCAmelCase						:				List[Any]	    =					name.replace("scratch.output_conv"					,       "head"      )
		if "scratch" in name:
				_UpperCAmelCase						:				List[str]	    =					name.replace("scratch"					,       "neck"      )
		if "layer1_rn" in name:
				_UpperCAmelCase						:				Union[str, Any]	    =					name.replace("layer1_rn"					,       "convs.0"      )
		if "layer2_rn" in name:
				_UpperCAmelCase						:				str	    =					name.replace("layer2_rn"					,       "convs.1"      )
		if "layer3_rn" in name:
				_UpperCAmelCase						:				int	    =					name.replace("layer3_rn"					,       "convs.2"      )
		if "layer4_rn" in name:
				_UpperCAmelCase						:				Tuple	    =					name.replace("layer4_rn"					,       "convs.3"      )
		if "refinenet" in name:
				_UpperCAmelCase						:				Optional[Any]	    =					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						:				List[str]	    =					name.replace(F"""refinenet{layer_idx}"""					,       F"""fusion_stage.layers.{abs(layer_idx-4      )}"""      )
		if "out_conv" in name:
				_UpperCAmelCase						:				Tuple	    =					name.replace("out_conv"					,       "projection"      )
		if "resConfUnit1" in name:
				_UpperCAmelCase						:				Optional[int]	    =					name.replace("resConfUnit1"					,       "residual_layer1"      )
		if "resConfUnit2" in name:
				_UpperCAmelCase						:				Optional[int]	    =					name.replace("resConfUnit2"					,       "residual_layer2"      )
		if "conv1" in name:
				_UpperCAmelCase						:				Optional[Any]	    =					name.replace("conv1"					,       "convolution1"      )
		if "conv2" in name:
				_UpperCAmelCase						:				Optional[Any]	    =					name.replace("conv2"					,       "convolution2"      )
		# readout blocks
		if "pretrained.act_postprocess1.0.project.0" in name:
				_UpperCAmelCase						:				int	    =					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						:				Optional[Any]	    =					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						:				Dict	    =					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						:				int	    =					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						:				int	    =					name.replace("pretrained.act_postprocess1.3"					,       "neck.reassemble_stage.layers.0.projection"      )
		if "pretrained.act_postprocess1.4" in name:
				_UpperCAmelCase						:				Dict	    =					name.replace("pretrained.act_postprocess1.4"					,       "neck.reassemble_stage.layers.0.resize"      )
		if "pretrained.act_postprocess2.3" in name:
				_UpperCAmelCase						:				Tuple	    =					name.replace("pretrained.act_postprocess2.3"					,       "neck.reassemble_stage.layers.1.projection"      )
		if "pretrained.act_postprocess2.4" in name:
				_UpperCAmelCase						:				Optional[int]	    =					name.replace("pretrained.act_postprocess2.4"					,       "neck.reassemble_stage.layers.1.resize"      )
		if "pretrained.act_postprocess3.3" in name:
				_UpperCAmelCase						:				Union[str, Any]	    =					name.replace("pretrained.act_postprocess3.3"					,       "neck.reassemble_stage.layers.2.projection"      )
		if "pretrained.act_postprocess4.3" in name:
				_UpperCAmelCase						:				Union[str, Any]	    =					name.replace("pretrained.act_postprocess4.3"					,       "neck.reassemble_stage.layers.3.projection"      )
		if "pretrained.act_postprocess4.4" in name:
				_UpperCAmelCase						:				Dict	    =					name.replace("pretrained.act_postprocess4.4"					,       "neck.reassemble_stage.layers.3.resize"      )
		if "pretrained" in name:
				_UpperCAmelCase						:				List[str]	    =					name.replace("pretrained"					,       "dpt"      )
		if "bn" in name:
				_UpperCAmelCase						:				Dict	    =					name.replace("bn"					,       "batch_norm"      )
		if "head" in name:
				_UpperCAmelCase						:				Tuple	    =					name.replace("head"					,       "head.head"      )
		if "encoder.norm" in name:
				_UpperCAmelCase						:				Optional[Any]	    =					name.replace("encoder.norm"					,       "layernorm"      )
		if "auxlayer" in name:
				_UpperCAmelCase						:				Dict	    =					name.replace("auxlayer"					,       "auxiliary_head.head"      )

		return name






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		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						:				int	    =					state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight"""      )
				_UpperCAmelCase						:				str	    =					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						:				List[str]	    =					in_proj_weight[: config.hidden_size, :]
				_UpperCAmelCase						:				Dict	    =					in_proj_bias[: config.hidden_size]
				_UpperCAmelCase						:				List[str]	    =					in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				_UpperCAmelCase						:				int	    =					in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				_UpperCAmelCase						:				Union[str, Any]	    =					in_proj_weight[
				    -config.hidden_size :, :
				]
				_UpperCAmelCase						:				str	    =					in_proj_bias[-config.hidden_size :]






def        __lowerCAmelCase       ():
		_UpperCAmelCase						:				Optional[int]	    =					"http://images.cocodataset.org/val2017/000000039769.jpg"
		_UpperCAmelCase						:				Any	    =					Image.open(requests.get(__lowerCAmelCase					,       stream=__lowerCAmelCase      ).raw      )
		return im






@torch.no_grad()
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase    ,			_UpperCAmelCase						:				Dict	    =					get_dpt_config(__lowerCAmelCase      )
		# load original state_dict from URL
		_UpperCAmelCase						:				List[Any]	    =					torch.hub.load_state_dict_from_url(__lowerCAmelCase					,       map_location="cpu"      )
		# remove certain keys
		remove_ignore_keys_(__lowerCAmelCase      )
		# rename keys
		for key in state_dict.copy().keys():
				_UpperCAmelCase						:				Tuple	    =					state_dict.pop(__lowerCAmelCase      )
				_UpperCAmelCase						:				List[Any]	    =					val
		# read in qkv matrices
		read_in_q_k_v(__lowerCAmelCase					,       __lowerCAmelCase      )

		# load HuggingFace model
		_UpperCAmelCase						:				Any	    =					DPTForSemanticSegmentation(__lowerCAmelCase      ) if "ade" in checkpoint_url else DPTForDepthEstimation(__lowerCAmelCase      )
		model.load_state_dict(__lowerCAmelCase      )
		model.eval()

		# Check outputs on an image
		_UpperCAmelCase						:				Any	    =					480 if "ade" in checkpoint_url else 384
		_UpperCAmelCase						:				List[str]	    =					DPTImageProcessor(size=__lowerCAmelCase      )

		_UpperCAmelCase						:				Any	    =					prepare_img()
		_UpperCAmelCase						:				Dict	    =					image_processor(__lowerCAmelCase					,       return_tensors="pt"      )

		# forward pass
		_UpperCAmelCase						:				Tuple	    =					model(**__lowerCAmelCase      ).logits if "ade" in checkpoint_url else model(**__lowerCAmelCase      ).predicted_depth

		# Assert logits
		_UpperCAmelCase						:				Dict	    =					torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]      )
		if "ade" in checkpoint_url:
				_UpperCAmelCase						:				str	    =					torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]      )
		assert outputs.shape == torch.Size(__lowerCAmelCase      )
		assert (
		    torch.allclose(outputs[0, 0, :3, :3]					,       __lowerCAmelCase					,       atol=1e-4      )
		    if "ade" in checkpoint_url
		    else torch.allclose(outputs[0, :3, :3]					,       __lowerCAmelCase      )
		)

		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:
				print("Pushing model to hub..."      )
				model.push_to_hub(
				    repo_path_or_name=Path(__lowerCAmelCase					,       __lowerCAmelCase      )					,       organization="nielsr"					,       commit_message="Add model"					,       use_temp_dir=__lowerCAmelCase					,       )
				image_processor.push_to_hub(
				    repo_path_or_name=Path(__lowerCAmelCase					,       __lowerCAmelCase      )					,       organization="nielsr"					,       commit_message="Add image processor"					,       use_temp_dir=__lowerCAmelCase					,       )


if __name__ == "__main__":
						lowerCamelCase__					=   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=True,
						    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.',
						)

						lowerCamelCase__					=   parser.parse_args()
						convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)



