





"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 		SCREAMING_SNAKE_CASE_							(    snake_case__       ,  snake_case__       ,  unittest.TestCase   ):
				"""simple docstring"""
				__snake_case   :       Dict	 =		CycleDiffusionPipeline
				__snake_case   :       Optional[int]	 =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
				    """negative_prompt""",
				    """height""",
				    """width""",
				    """negative_prompt_embeds""",
				}
				__snake_case   :       Any	 =		PipelineTesterMixin.required_optional_params - {"""latents"""}
				__snake_case   :       int	 =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}   )
				__snake_case   :       Optional[int]	 =		IMAGE_TO_IMAGE_IMAGE_PARAMS
				__snake_case   :       str	 =		IMAGE_TO_IMAGE_IMAGE_PARAMS




				def 					__lowercase  (     self  :Dict			):
											torch.manual_seed(0			)
											__lowerCamelCase      :      List[str]         =UNetaDConditionModel(
											    block_out_channels=(32, 64)						,    layers_per_block=2						,    sample_size=32						,    in_channels=4						,    out_channels=4						,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')						,    cross_attention_dim=32						,    )
											__lowerCamelCase      :      List[Any]         =DDIMScheduler(
											    beta_start=0.00085						,    beta_end=0.012						,    beta_schedule='''scaled_linear'''						,    num_train_timesteps=1000						,    clip_sample=__lowercase						,    set_alpha_to_one=__lowercase						,    )
											torch.manual_seed(0			)
											__lowerCamelCase      :      Optional[int]         =AutoencoderKL(
											    block_out_channels=[32, 64]						,    in_channels=3						,    out_channels=3						,    down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']						,    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']						,    latent_channels=4						,    )
											torch.manual_seed(0			)
											__lowerCamelCase      :      int         =CLIPTextConfig(
											    bos_token_id=0						,    eos_token_id=2						,    hidden_size=32						,    intermediate_size=37						,    layer_norm_eps=1e-0_5						,    num_attention_heads=4						,    num_hidden_layers=5						,    pad_token_id=1						,    vocab_size=1000						,    )
											__lowerCamelCase      :      Optional[Any]         =CLIPTextModel(__lowercase			)
											__lowerCamelCase      :      Tuple         =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

											__lowerCamelCase      :      Optional[int]         ={
											    '''unet''': unet,
											    '''scheduler''': scheduler,
											    '''vae''': vae,
											    '''text_encoder''': text_encoder,
											    '''tokenizer''': tokenizer,
											    '''safety_checker''': None,
											    '''feature_extractor''': None,
											}
											return components




				def 					__lowercase  (     self  :Union[str, Any]						,    __lowercase  :Optional[int]						,    __lowercase  :str=0			):
											__lowerCamelCase      :      List[Any]         =floats_tensor((1, 3, 32, 32)						,    rng=random.Random(__lowercase			)			).to(__lowercase			)
											__lowerCamelCase      :      str         =image / 2 + 0.5
											if str(__lowercase			).startswith('''mps'''			):
																		__lowerCamelCase      :      Union[str, Any]         =torch.manual_seed(__lowercase			)
											else:
																		__lowerCamelCase      :      Any         =torch.Generator(device=__lowercase			).manual_seed(__lowercase			)
											__lowerCamelCase      :      Dict         ={
											    '''prompt''': '''An astronaut riding an elephant''',
											    '''source_prompt''': '''An astronaut riding a horse''',
											    '''image''': image,
											    '''generator''': generator,
											    '''num_inference_steps''': 2,
											    '''eta''': 0.1,
											    '''strength''': 0.8,
											    '''guidance_scale''': 3,
											    '''source_guidance_scale''': 1,
											    '''output_type''': '''numpy''',
											}
											return inputs




				def 					__lowercase  (     self  :Optional[int]			):
											__lowerCamelCase      :      int         ='''cpu'''  # ensure determinism for the device-dependent torch.Generator

											__lowerCamelCase      :      Tuple         =self.get_dummy_components()
											__lowerCamelCase      :      List[str]         =CycleDiffusionPipeline(**__lowercase			)
											__lowerCamelCase      :      Tuple         =pipe.to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)

											__lowerCamelCase      :      List[Any]         =self.get_dummy_inputs(__lowercase			)
											__lowerCamelCase      :      int         =pipe(**__lowercase			)
											__lowerCamelCase      :      Dict         =output.images

											__lowerCamelCase      :      Union[str, Any]         =images[0, -3:, -3:, -1]

											assert images.shape == (1, 32, 32, 3)
											__lowerCamelCase      :      Optional[Any]         =np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]			)

											assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2




				@unittest.skipIf(torch_device != '''cuda'''						,    '''This test requires a GPU'''			)
				def 					__lowercase  (     self  :str			):
											__lowerCamelCase      :      str         =self.get_dummy_components()
											for name, module in components.items():
																		if hasattr(__lowercase						,    '''half'''			):
																									__lowerCamelCase      :      Union[str, Any]         =module.half()
											__lowerCamelCase      :      int         =CycleDiffusionPipeline(**__lowercase			)
											__lowerCamelCase      :      List[str]         =pipe.to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)

											__lowerCamelCase      :      Optional[int]         =self.get_dummy_inputs(__lowercase			)
											__lowerCamelCase      :      Dict         =pipe(**__lowercase			)
											__lowerCamelCase      :      List[str]         =output.images

											__lowerCamelCase      :      Dict         =images[0, -3:, -3:, -1]

											assert images.shape == (1, 32, 32, 3)
											__lowerCamelCase      :      str         =np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]			)

											assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2




				@skip_mps
				def 					__lowercase  (     self  :Optional[Any]			):
											return super().test_save_load_local()




				@unittest.skip('''non-deterministic pipeline'''			)
				def 					__lowercase  (     self  :Dict			):
											return super().test_inference_batch_single_identical()




				@skip_mps
				def 					__lowercase  (     self  :Optional[Any]			):
											return super().test_dict_tuple_outputs_equivalent()




				@skip_mps
				def 					__lowercase  (     self  :str			):
											return super().test_save_load_optional_components()




				@skip_mps
				def 					__lowercase  (     self  :Dict			):
											return super().test_attention_slicing_forward_pass()

@slow
@require_torch_gpu
class 		SCREAMING_SNAKE_CASE_							(    unittest.TestCase   ):
				"""simple docstring"""




				def 					__lowercase  (     self  :Tuple			):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()




				def 					__lowercase  (     self  :Dict			):
											__lowerCamelCase      :      Dict         =load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/cycle-diffusion/black_colored_car.png'''			)
											__lowerCamelCase      :      Union[str, Any]         =load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''			)
											__lowerCamelCase      :      Any         =init_image.resize((512, 512)			)

											__lowerCamelCase      :      Optional[Any]         ='''CompVis/stable-diffusion-v1-4'''
											__lowerCamelCase      :      Optional[Any]         =DDIMScheduler.from_pretrained(__lowercase						,    subfolder='''scheduler'''			)
											__lowerCamelCase      :      Optional[Any]         =CycleDiffusionPipeline.from_pretrained(
											    __lowercase						,    scheduler=__lowercase						,    safety_checker=__lowercase						,    torch_dtype=torch.floataa						,    revision='''fp16'''			)

											pipe.to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)
											pipe.enable_attention_slicing()

											__lowerCamelCase      :      Dict         ='''A black colored car'''
											__lowerCamelCase      :      Union[str, Any]         ='''A blue colored car'''

											__lowerCamelCase      :      Dict         =torch.manual_seed(0			)
											__lowerCamelCase      :      Tuple         =pipe(
											    prompt=__lowercase						,    source_prompt=__lowercase						,    image=__lowercase						,    num_inference_steps=100						,    eta=0.1						,    strength=0.85						,    guidance_scale=3						,    source_guidance_scale=1						,    generator=__lowercase						,    output_type='''np'''						,    )
											__lowerCamelCase      :      Tuple         =output.images

											# the values aren't exactly equal, but the images look the same visually
											assert np.abs(image - expected_image			).max() < 5e-1




				def 					__lowercase  (     self  :Any			):
											__lowerCamelCase      :      Tuple         =load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/cycle-diffusion/black_colored_car.png'''			)
											__lowerCamelCase      :      List[Any]         =load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''			)
											__lowerCamelCase      :      Optional[Any]         =init_image.resize((512, 512)			)

											__lowerCamelCase      :      Any         ='''CompVis/stable-diffusion-v1-4'''
											__lowerCamelCase      :      List[Any]         =DDIMScheduler.from_pretrained(__lowercase						,    subfolder='''scheduler'''			)
											__lowerCamelCase      :      str         =CycleDiffusionPipeline.from_pretrained(__lowercase						,    scheduler=__lowercase						,    safety_checker=__lowercase			)

											pipe.to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)
											pipe.enable_attention_slicing()

											__lowerCamelCase      :      Any         ='''A black colored car'''
											__lowerCamelCase      :      int         ='''A blue colored car'''

											__lowerCamelCase      :      Tuple         =torch.manual_seed(0			)
											__lowerCamelCase      :      Union[str, Any]         =pipe(
											    prompt=__lowercase						,    source_prompt=__lowercase						,    image=__lowercase						,    num_inference_steps=100						,    eta=0.1						,    strength=0.85						,    guidance_scale=3						,    source_guidance_scale=1						,    generator=__lowercase						,    output_type='''np'''						,    )
											__lowerCamelCase      :      Dict         =output.images

											assert np.abs(image - expected_image			).max() < 2e-2











"""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							=      logging.get_logger(__name__)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Any   ):


							'''simple docstring'''







							__lowerCamelCase      :      Tuple         =DPTConfig()

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

							if "ade" in checkpoint_url:
														__lowerCamelCase      :      List[Any]         =True

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

							return config, expected_shape



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str   ):


							'''simple docstring'''







							__lowerCamelCase      :      List[Any]         =['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
							for k in ignore_keys:
														state_dict.pop(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							if (
							    "pretrained.model" in name
							    and "cls_token" not in name
							    and "pos_embed" not in name
							    and "patch_embed" not in name
							):
														__lowerCamelCase      :      str         =name.replace('''pretrained.model'''    ,						'''dpt.encoder'''   )
							if "pretrained.model" in name:
														__lowerCamelCase      :      str         =name.replace('''pretrained.model'''    ,						'''dpt.embeddings'''   )
							if "patch_embed" in name:
														__lowerCamelCase      :      List[str]         =name.replace('''patch_embed'''    ,						'''patch_embeddings'''   )
							if "pos_embed" in name:
														__lowerCamelCase      :      str         =name.replace('''pos_embed'''    ,						'''position_embeddings'''   )
							if "attn.proj" in name:
														__lowerCamelCase      :      Union[str, Any]         =name.replace('''attn.proj'''    ,						'''attention.output.dense'''   )
							if "proj" in name and "project" not in name:
														__lowerCamelCase      :      Any         =name.replace('''proj'''    ,						'''projection'''   )
							if "blocks" in name:
														__lowerCamelCase      :      Union[str, Any]         =name.replace('''blocks'''    ,						'''layer'''   )
							if "mlp.fc1" in name:
														__lowerCamelCase      :      Union[str, Any]         =name.replace('''mlp.fc1'''    ,						'''intermediate.dense'''   )
							if "mlp.fc2" in name:
														__lowerCamelCase      :      Optional[Any]         =name.replace('''mlp.fc2'''    ,						'''output.dense'''   )
							if "norm1" in name:
														__lowerCamelCase      :      List[str]         =name.replace('''norm1'''    ,						'''layernorm_before'''   )
							if "norm2" in name:
														__lowerCamelCase      :      Tuple         =name.replace('''norm2'''    ,						'''layernorm_after'''   )
							if "scratch.output_conv" in name:
														__lowerCamelCase      :      Tuple         =name.replace('''scratch.output_conv'''    ,						'''head'''   )
							if "scratch" in name:
														__lowerCamelCase      :      Optional[int]         =name.replace('''scratch'''    ,						'''neck'''   )
							if "layer1_rn" in name:
														__lowerCamelCase      :      str         =name.replace('''layer1_rn'''    ,						'''convs.0'''   )
							if "layer2_rn" in name:
														__lowerCamelCase      :      List[str]         =name.replace('''layer2_rn'''    ,						'''convs.1'''   )
							if "layer3_rn" in name:
														__lowerCamelCase      :      Any         =name.replace('''layer3_rn'''    ,						'''convs.2'''   )
							if "layer4_rn" in name:
														__lowerCamelCase      :      int         =name.replace('''layer4_rn'''    ,						'''convs.3'''   )
							if "refinenet" in name:
														__lowerCamelCase      :      int         =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
														__lowerCamelCase      :      Dict         =name.replace(F'refinenet{layer_idx}'    ,						F'fusion_stage.layers.{abs(layer_idx-4   )}'   )
							if "out_conv" in name:
														__lowerCamelCase      :      List[str]         =name.replace('''out_conv'''    ,						'''projection'''   )
							if "resConfUnit1" in name:
														__lowerCamelCase      :      Optional[Any]         =name.replace('''resConfUnit1'''    ,						'''residual_layer1'''   )
							if "resConfUnit2" in name:
														__lowerCamelCase      :      str         =name.replace('''resConfUnit2'''    ,						'''residual_layer2'''   )
							if "conv1" in name:
														__lowerCamelCase      :      Any         =name.replace('''conv1'''    ,						'''convolution1'''   )
							if "conv2" in name:
														__lowerCamelCase      :      Optional[int]         =name.replace('''conv2'''    ,						'''convolution2'''   )
							# readout blocks
							if "pretrained.act_postprocess1.0.project.0" in name:
														__lowerCamelCase      :      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:
														__lowerCamelCase      :      List[str]         =name.replace('''pretrained.act_postprocess2.0.project.0'''    ,						'''neck.reassemble_stage.readout_projects.1.0'''   )
							if "pretrained.act_postprocess3.0.project.0" in name:
														__lowerCamelCase      :      str         =name.replace('''pretrained.act_postprocess3.0.project.0'''    ,						'''neck.reassemble_stage.readout_projects.2.0'''   )
							if "pretrained.act_postprocess4.0.project.0" in name:
														__lowerCamelCase      :      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:
														__lowerCamelCase      :      List[Any]         =name.replace('''pretrained.act_postprocess1.3'''    ,						'''neck.reassemble_stage.layers.0.projection'''   )
							if "pretrained.act_postprocess1.4" in name:
														__lowerCamelCase      :      Tuple         =name.replace('''pretrained.act_postprocess1.4'''    ,						'''neck.reassemble_stage.layers.0.resize'''   )
							if "pretrained.act_postprocess2.3" in name:
														__lowerCamelCase      :      List[Any]         =name.replace('''pretrained.act_postprocess2.3'''    ,						'''neck.reassemble_stage.layers.1.projection'''   )
							if "pretrained.act_postprocess2.4" in name:
														__lowerCamelCase      :      List[Any]         =name.replace('''pretrained.act_postprocess2.4'''    ,						'''neck.reassemble_stage.layers.1.resize'''   )
							if "pretrained.act_postprocess3.3" in name:
														__lowerCamelCase      :      Tuple         =name.replace('''pretrained.act_postprocess3.3'''    ,						'''neck.reassemble_stage.layers.2.projection'''   )
							if "pretrained.act_postprocess4.3" in name:
														__lowerCamelCase      :      int         =name.replace('''pretrained.act_postprocess4.3'''    ,						'''neck.reassemble_stage.layers.3.projection'''   )
							if "pretrained.act_postprocess4.4" in name:
														__lowerCamelCase      :      int         =name.replace('''pretrained.act_postprocess4.4'''    ,						'''neck.reassemble_stage.layers.3.resize'''   )
							if "pretrained" in name:
														__lowerCamelCase      :      Dict         =name.replace('''pretrained'''    ,						'''dpt'''   )
							if "bn" in name:
														__lowerCamelCase      :      str         =name.replace('''bn'''    ,						'''batch_norm'''   )
							if "head" in name:
														__lowerCamelCase      :      str         =name.replace('''head'''    ,						'''head.head'''   )
							if "encoder.norm" in name:
														__lowerCamelCase      :      Union[str, Any]         =name.replace('''encoder.norm'''    ,						'''layernorm'''   )
							if "auxlayer" in name:
														__lowerCamelCase      :      List[str]         =name.replace('''auxlayer'''    ,						'''auxiliary_head.head'''   )

							return name



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[str]    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]   ):


							'''simple docstring'''







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



def 				lowerCAmelCase_			(      ):


							'''simple docstring'''







							__lowerCamelCase      :      List[Any]         ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
							__lowerCamelCase      :      Optional[Any]         =Image.open(requests.get(SCREAMING_SNAKE_CASE    ,						stream=SCREAMING_SNAKE_CASE   ).raw   )
							return im



@torch.no_grad()
def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Any    ,						SCREAMING_SNAKE_CASE       :							Any    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]   ):


							'''simple docstring'''







							__lowerCamelCase    ,						__lowerCamelCase      :      Tuple         =get_dpt_config(SCREAMING_SNAKE_CASE   )
							# load original state_dict from URL
							__lowerCamelCase      :      List[str]         =torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE    ,						map_location='''cpu'''   )
							# remove certain keys
							remove_ignore_keys_(SCREAMING_SNAKE_CASE   )
							# rename keys
							for key in state_dict.copy().keys():
														__lowerCamelCase      :      str         =state_dict.pop(SCREAMING_SNAKE_CASE   )
														__lowerCamelCase      :      Any         =val
							# read in qkv matrices
							read_in_q_k_v(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

							# load HuggingFace model
							__lowerCamelCase      :      Dict         =DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE   ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(SCREAMING_SNAKE_CASE   )
							model.load_state_dict(SCREAMING_SNAKE_CASE   )
							model.eval()

							# Check outputs on an image
							__lowerCamelCase      :      Union[str, Any]         =480 if '''ade''' in checkpoint_url else 384
							__lowerCamelCase      :      Dict         =DPTImageProcessor(size=SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      Optional[int]         =prepare_img()
							__lowerCamelCase      :      int         =image_processor(SCREAMING_SNAKE_CASE    ,						return_tensors='''pt'''   )

							# forward pass
							__lowerCamelCase      :      Tuple         =model(**SCREAMING_SNAKE_CASE   ).logits if '''ade''' in checkpoint_url else model(**SCREAMING_SNAKE_CASE   ).predicted_depth

							# Assert logits
							__lowerCamelCase      :      Optional[Any]         =torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]   )
							if "ade" in checkpoint_url:
														__lowerCamelCase      :      List[str]         =torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]   )
							assert outputs.shape == torch.Size(SCREAMING_SNAKE_CASE   )
							assert (
							    torch.allclose(outputs[0, 0, :3, :3]    ,						SCREAMING_SNAKE_CASE    ,						atol=1E-4   )
							    if "ade" in checkpoint_url
							    else torch.allclose(outputs[0, :3, :3]    ,						SCREAMING_SNAKE_CASE   )
							)

							Path(SCREAMING_SNAKE_CASE   ).mkdir(exist_ok=SCREAMING_SNAKE_CASE   )
							print(F'Saving model to {pytorch_dump_folder_path}'   )
							model.save_pretrained(SCREAMING_SNAKE_CASE   )
							print(F'Saving image processor to {pytorch_dump_folder_path}'   )
							image_processor.save_pretrained(SCREAMING_SNAKE_CASE   )

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


if __name__ == "__main__":
							_UpperCamelCase							=      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.',
							)

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




