


'''simple docstring'''





import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


__snake_case            =   logging.get_logger(__name__)  # pylint: disable=invalid-name




class    UpperCAmelCase					(       __snake_case    ):




			def __init__(						self							:		str       ,       __magic_name__							:		AutoencoderKL       ,       __magic_name__							:		CLIPTextModel       ,       __magic_name__							:		CLIPTokenizer       ,       __magic_name__							:		UNetaDConditionModel       ,       __magic_name__							:		Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]       ,       __magic_name__							:		StableDiffusionSafetyChecker       ,       __magic_name__							:		CLIPImageProcessor       ,       ):







						"""simple docstring"""







						super().__init__()
						self.register_modules(
						    vae=__magic_name__       ,       text_encoder=__magic_name__       ,       tokenizer=__magic_name__       ,       unet=__magic_name__       ,       scheduler=__magic_name__       ,       safety_checker=__magic_name__       ,       feature_extractor=__magic_name__       ,       )




			def   lowerCamelCase_					(						self							:		Dict       ,       __magic_name__							:		Optional[Union[str, int]] = "auto"						):







						"""simple docstring"""







						if slice_size == "auto":
									# half the attention head size is usually a good trade-off between
									# speed and memory
									UpperCamelCase         =					self.unet.config.attention_head_dim // 2
						self.unet.set_attention_slice(__magic_name__						)




			def   lowerCamelCase_					(						self							:		str						):







						"""simple docstring"""







						self.enable_attention_slicing(__magic_name__						)





			@torch.no_grad()
			def __call__(						self							:		Dict       ,       __magic_name__							:		Union[str, List[str]]       ,       __magic_name__							:		int = 5_1_2       ,       __magic_name__							:		int = 5_1_2       ,       __magic_name__							:		int = 5_0       ,       __magic_name__							:		float = 7.5       ,       __magic_name__							:		Optional[Union[str, List[str]]] = None       ,       __magic_name__							:		Optional[int] = 1       ,       __magic_name__							:		float = 0.0       ,       __magic_name__							:		Optional[torch.Generator] = None       ,       __magic_name__							:		Optional[torch.FloatTensor] = None       ,       __magic_name__							:		Optional[str] = "pil"       ,       __magic_name__							:		bool = True       ,       __magic_name__							:		Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,       __magic_name__							:		int = 1       ,       __magic_name__							:		Optional[torch.FloatTensor] = None       ,       **__magic_name__							:		Optional[int]       ,       ):







						"""simple docstring"""







						if isinstance(__magic_name__       ,       __magic_name__						):
									UpperCamelCase         =					1
						elif isinstance(__magic_name__       ,       __magic_name__						):
									UpperCamelCase         =					len(__magic_name__						)
						else:
									raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(__magic_name__						)}'						)

						if height % 8 != 0 or width % 8 != 0:
									raise ValueError(F'`height` and `width` have to be divisible by 8 but are {height} and {width}.'						)

						if (callback_steps is None) or (
						    callback_steps is not None and (not isinstance(__magic_name__       ,       __magic_name__						) or callback_steps <= 0)
						):
									raise ValueError(
									    F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
									    F' {type(__magic_name__						)}.'						)

						# get prompt text embeddings
						UpperCamelCase         =					self.tokenizer(
						    __magic_name__       ,       padding="""max_length"""       ,       max_length=self.tokenizer.model_max_length       ,       return_tensors="""pt"""       ,       )
						UpperCamelCase         =					text_inputs.input_ids

						if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
									UpperCamelCase         =					self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]						)
									logger.warning(
									    """The following part of your input was truncated because CLIP can only handle sequences up to"""
									    F' {self.tokenizer.model_max_length} tokens: {removed_text}'						)
									UpperCamelCase         =					text_input_ids[:, : self.tokenizer.model_max_length]

						if text_embeddings is None:
									UpperCamelCase         =					self.text_encoder(text_input_ids.to(self.device						)						)[0]

						# duplicate text embeddings for each generation per prompt, using mps friendly method
						UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					text_embeddings.shape
						UpperCamelCase         =					text_embeddings.repeat(1       ,       __magic_name__       ,       1						)
						UpperCamelCase         =					text_embeddings.view(bs_embed * num_images_per_prompt       ,       __magic_name__       ,       -1						)

						# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
						# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
						# corresponds to doing no classifier free guidance.
						UpperCamelCase         =					guidance_scale > 1.0
						# get unconditional embeddings for classifier free guidance
						if do_classifier_free_guidance:
									UpperCamelCase         =					4_2
									if negative_prompt is None:
												UpperCamelCase         =					[""""""]
									elif type(__magic_name__						) is not type(__magic_name__						):
												raise TypeError(
												    F'`negative_prompt` should be the same type to `prompt`, but got {type(__magic_name__						)} !='
												    F' {type(__magic_name__						)}.'						)
									elif isinstance(__magic_name__       ,       __magic_name__						):
												UpperCamelCase         =					[negative_prompt]
									elif batch_size != len(__magic_name__						):
												raise ValueError(
												    F'`negative_prompt`: {negative_prompt} has batch size {len(__magic_name__						)}, but `prompt`:'
												    F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
												    """ the batch size of `prompt`."""						)
									else:
												UpperCamelCase         =					negative_prompt

									UpperCamelCase         =					text_input_ids.shape[-1]
									UpperCamelCase         =					self.tokenizer(
									    __magic_name__       ,       padding="""max_length"""       ,       max_length=__magic_name__       ,       truncation=__magic_name__       ,       return_tensors="""pt"""       ,       )
									UpperCamelCase         =					self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]

									# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
									UpperCamelCase         =					uncond_embeddings.shape[1]
									UpperCamelCase         =					uncond_embeddings.repeat(__magic_name__       ,       __magic_name__       ,       1						)
									UpperCamelCase         =					uncond_embeddings.view(batch_size * num_images_per_prompt       ,       __magic_name__       ,       -1						)

									# For classifier free guidance, we need to do two forward passes.
									# Here we concatenate the unconditional and text embeddings into a single batch
									# to avoid doing two forward passes
									UpperCamelCase         =					torch.cat([uncond_embeddings, text_embeddings]						)

						# get the initial random noise unless the user supplied it

						# Unlike in other pipelines, latents need to be generated in the target device
						# for 1-to-1 results reproducibility with the CompVis implementation.
						# However this currently doesn't work in `mps`.
						UpperCamelCase         =					(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
						UpperCamelCase         =					(batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
						UpperCamelCase         =					text_embeddings.dtype
						if latents is None:
									if self.device.type == "mps":
												# randn does not exist on mps
												UpperCamelCase         =					torch.randn(
												    __magic_name__       ,       generator=__magic_name__       ,       device="""cpu"""       ,       dtype=__magic_name__						).to(self.device						)
												UpperCamelCase         =					torch.randn(__magic_name__       ,       generator=__magic_name__       ,       device="""cpu"""       ,       dtype=__magic_name__						).to(
												    self.device						)
									else:
												UpperCamelCase         =					torch.randn(
												    __magic_name__       ,       generator=__magic_name__       ,       device=self.device       ,       dtype=__magic_name__						)
												UpperCamelCase         =					torch.randn(__magic_name__       ,       generator=__magic_name__       ,       device=self.device       ,       dtype=__magic_name__						)
						else:
									if latents_reference.shape != latents_shape:
												raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'						)
									UpperCamelCase         =					latents_reference.to(self.device						)
									UpperCamelCase         =					latents.to(self.device						)

						# This is the key part of the pipeline where we
						# try to ensure that the generated images w/ the same seed
						# but different sizes actually result in similar images
						UpperCamelCase         =					(latents_shape[3] - latents_shape_reference[3]) // 2
						UpperCamelCase         =					(latents_shape[2] - latents_shape_reference[2]) // 2
						UpperCamelCase         =					latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
						UpperCamelCase         =					latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
						UpperCamelCase         =					0 if dx < 0 else dx
						UpperCamelCase         =					0 if dy < 0 else dy
						UpperCamelCase         =					max(-dx       ,       0						)
						UpperCamelCase         =					max(-dy       ,       0						)
						# import pdb
						# pdb.set_trace()
						UpperCamelCase         =					latents_reference[:, :, dy : dy + h, dx : dx + w]

						# set timesteps
						self.scheduler.set_timesteps(__magic_name__						)

						# Some schedulers like PNDM have timesteps as arrays
						# It's more optimized to move all timesteps to correct device beforehand
						UpperCamelCase         =					self.scheduler.timesteps.to(self.device						)

						# scale the initial noise by the standard deviation required by the scheduler
						UpperCamelCase         =					latents * self.scheduler.init_noise_sigma

						# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
						# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
						# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
						# and should be between [0, 1]
						UpperCamelCase         =					"""eta""" in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
						UpperCamelCase         =					{}
						if accepts_eta:
									UpperCamelCase         =					eta

						for i, t in enumerate(self.progress_bar(__magic_name__						)						):
									# expand the latents if we are doing classifier free guidance
									UpperCamelCase         =					torch.cat([latents] * 2						) if do_classifier_free_guidance else latents
									UpperCamelCase         =					self.scheduler.scale_model_input(__magic_name__       ,       __magic_name__						)

									# predict the noise residual
									UpperCamelCase         =					self.unet(__magic_name__       ,       __magic_name__       ,       encoder_hidden_states=__magic_name__						).sample

									# perform guidance
									if do_classifier_free_guidance:
												UpperCamelCase  ,  UpperCamelCase         =					noise_pred.chunk(2						)
												UpperCamelCase         =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

									# compute the previous noisy sample x_t -> x_t-1
									UpperCamelCase         =					self.scheduler.step(__magic_name__       ,       __magic_name__       ,       __magic_name__       ,       **__magic_name__						).prev_sample

									# call the callback, if provided
									if callback is not None and i % callback_steps == 0:
												callback(__magic_name__       ,       __magic_name__       ,       __magic_name__						)

						UpperCamelCase         =					1 / 0.18_215 * latents
						UpperCamelCase         =					self.vae.decode(__magic_name__						).sample

						UpperCamelCase         =					(image / 2 + 0.5).clamp(0       ,       1						)

						# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
						UpperCamelCase         =					image.cpu().permute(0       ,       2       ,       3       ,       1						).float().numpy()

						if self.safety_checker is not None:
									UpperCamelCase         =					self.feature_extractor(self.numpy_to_pil(__magic_name__						)       ,       return_tensors="""pt"""						).to(
									    self.device						)
									UpperCamelCase  ,  UpperCamelCase         =					self.safety_checker(
									    images=__magic_name__       ,       clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype						)						)
						else:
									UpperCamelCase         =					None

						if output_type == "pil":
									UpperCamelCase         =					self.numpy_to_pil(__magic_name__						)

						if not return_dict:
									return (image, has_nsfw_concept)

						return StableDiffusionPipelineOutput(images=__magic_name__       ,       nsfw_content_detected=__magic_name__						)








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()
__snake_case            =   logging.get_logger(__name__)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[int]	):





			"""simple docstring"""




			UpperCamelCase         =					DPTConfig()

			if "large" in checkpoint_url:
						UpperCamelCase         =					1_024
						UpperCamelCase         =					4_096
						UpperCamelCase         =					24
						UpperCamelCase         =					16
						UpperCamelCase         =					[5, 11, 17, 23]
						UpperCamelCase         =					[256, 512, 1_024, 1_024]
						UpperCamelCase         =					(1, 384, 384)

			if "ade" in checkpoint_url:
						UpperCamelCase         =					True

						UpperCamelCase         =					150
						UpperCamelCase         =					"""huggingface/label-files"""
						UpperCamelCase         =					"""ade20k-id2label.json"""
						UpperCamelCase         =					json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  repo_type="""dataset"""	)	)       ,  """r"""	)	)
						UpperCamelCase         =					{int(SCREAMING_SNAKE_CASE_	): v for k, v in idalabel.items()}
						UpperCamelCase         =					idalabel
						UpperCamelCase         =					{v: k for k, v in idalabel.items()}
						UpperCamelCase         =					[1, 150, 480, 480]

			return config, expected_shape






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Union[str, Any]	):





			"""simple docstring"""




			UpperCamelCase         =					["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
			for k in ignore_keys:
						state_dict.pop(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str	):





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

			return name






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		Tuple	):





			"""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)
						UpperCamelCase         =					state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight'	)
						UpperCamelCase         =					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         =					in_proj_weight[: config.hidden_size, :]
						UpperCamelCase         =					in_proj_bias[: config.hidden_size]
						UpperCamelCase         =					in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						UpperCamelCase         =					in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						UpperCamelCase         =					in_proj_weight[
						    -config.hidden_size :, :
						]
						UpperCamelCase         =					in_proj_bias[-config.hidden_size :]






def 							_lowercase    (		):





			"""simple docstring"""




			UpperCamelCase         =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
			UpperCamelCase         =					Image.open(requests.get(SCREAMING_SNAKE_CASE_       ,  stream=SCREAMING_SNAKE_CASE_	).raw	)
			return im






@torch.no_grad()
def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		Dict       ,  SCREAMING_SNAKE_CASE_   :		Tuple       ,  SCREAMING_SNAKE_CASE_   :		List[str]	):





			"""simple docstring"""




			UpperCamelCase  ,  UpperCamelCase         =					get_dpt_config(SCREAMING_SNAKE_CASE_	)
			# load original state_dict from URL
			UpperCamelCase         =					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():
						UpperCamelCase         =					state_dict.pop(SCREAMING_SNAKE_CASE_	)
						UpperCamelCase         =					val
			# read in qkv matrices
			read_in_q_k_v(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

			# load HuggingFace model
			UpperCamelCase         =					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
			UpperCamelCase         =					480 if """ade""" in checkpoint_url else 384
			UpperCamelCase         =					DPTImageProcessor(size=SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					prepare_img()
			UpperCamelCase         =					image_processor(SCREAMING_SNAKE_CASE_       ,  return_tensors="""pt"""	)

			# forward pass
			UpperCamelCase         =					model(**SCREAMING_SNAKE_CASE_	).logits if """ade""" in checkpoint_url else model(**SCREAMING_SNAKE_CASE_	).predicted_depth

			# Assert logits
			UpperCamelCase         =					torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]	)
			if "ade" in checkpoint_url:
						UpperCamelCase         =					torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]	)
			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__":
			__snake_case            =   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.",
			)

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




