



import io
import math
from typing import Dict, Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    get_image_size,
    infer_channel_dimension_format,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends


if is_vision_available():
 import textwrap

 from PIL import Image, ImageDraw, ImageFont

if is_torch_available():
 import torch

 from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
 __snake_case						:Any      		=     False


__snake_case						:List[str]      		=     logging.get_logger(__name__)
__snake_case						:int      		=     '''ybelkada/fonts'''





def      __snake_case	(			):
    if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
        raise ImportError(
            f'You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use '
            '''Pix2StructImageProcessor. Please upgrade torch.'''				)





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    requires_backends(_UpperCAmelCase   , ['''torch''']				)
    _check_torch_version()

    __a						       =				image_tensor.unsqueeze(0				)
    __a						       =				torch.nn.functional.unfold(_UpperCAmelCase   , (patch_height, patch_width)   , stride=(patch_height, patch_width)				)
    __a						       =				patches.reshape(image_tensor.size(0				)   , image_tensor.size(1				)   , _UpperCAmelCase   , _UpperCAmelCase   , -1				)
    __a						       =				patches.permute(0   , 4   , 2   , 3   , 1				).reshape(
        image_tensor.size(2				) // patch_height   , image_tensor.size(3				) // patch_width   , image_tensor.size(1				) * patch_height * patch_width   , )
    return patches.unsqueeze(0				)





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase = 36   , _UpperCAmelCase = "black"   , _UpperCAmelCase = "white"   , _UpperCAmelCase = 5   , _UpperCAmelCase = 5   , _UpperCAmelCase = 5   , _UpperCAmelCase = 5   , _UpperCAmelCase = None   , _UpperCAmelCase = None   , ):
    requires_backends(_UpperCAmelCase   , '''vision'''				)
    # Add new lines so that each line is no more than 80 characters.

    __a						       =				textwrap.TextWrapper(width=80				)
    __a						       =				wrapper.wrap(text=_UpperCAmelCase				)
    __a						       =				'''\n'''.join(_UpperCAmelCase				)

    if font_bytes is not None and font_path is None:
        __a						       =				io.BytesIO(_UpperCAmelCase				)
    elif font_path is not None:
        __a						       =				font_path
    else:
        __a						       =				hf_hub_download(_UpperCAmelCase   , '''Arial.TTF'''				)
    __a						       =				ImageFont.truetype(_UpperCAmelCase   , encoding='''UTF-8'''   , size=_UpperCAmelCase				)

    # Use a temporary canvas to determine the width and height in pixels when
    # rendering the text.
    __a						       =				ImageDraw.Draw(Image.new('''RGB'''   , (1, 1)   , _UpperCAmelCase				)				)
    __a       ,   __a       ,   __a       ,   __a						       =				temp_draw.textbbox((0, 0)   , _UpperCAmelCase   , _UpperCAmelCase				)

    # Create the actual image with a bit of padding around the text.
    __a						       =				text_width + left_padding + right_padding
    __a						       =				text_height + top_padding + bottom_padding
    __a						       =				Image.new('''RGB'''   , (image_width, image_height)   , _UpperCAmelCase				)
    __a						       =				ImageDraw.Draw(_UpperCAmelCase				)
    draw.text(xy=(left_padding, top_padding)   , text=_UpperCAmelCase   , fill=_UpperCAmelCase   , font=_UpperCAmelCase				)
    return image





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , **_UpperCAmelCase				):
    requires_backends(_UpperCAmelCase   , '''vision'''				)

    # Convert to PIL image if necessary
    __a						       =				to_pil_image(_UpperCAmelCase				)

    __a						       =				render_text(_UpperCAmelCase   , **_UpperCAmelCase				)
    __a						       =				max(header_image.width   , image.width				)

    __a						       =				int(image.height * (new_width / image.width)				)
    __a						       =				int(header_image.height * (new_width / header_image.width)				)

    __a						       =				Image.new('''RGB'''   , (new_width, new_height + new_header_height)   , '''white'''				)
    new_image.paste(header_image.resize((new_width, new_header_height)				)   , (0, 0)				)
    new_image.paste(image.resize((new_width, new_height)				)   , (0, new_header_height)				)

    # Convert back to the original framework if necessary
    __a						       =				to_numpy_array(_UpperCAmelCase				)

    if infer_channel_dimension_format(_UpperCAmelCase				) == ChannelDimension.LAST:
        __a						       =				to_channel_dimension_format(_UpperCAmelCase   , ChannelDimension.LAST				)

    return new_image

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    List[str]											=						['''flattened_patches''']



       def __init__(		self							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     bool = True     ,		__SCREAMING_SNAKE_CASE							:     bool = True     ,		__SCREAMING_SNAKE_CASE							:     Dict[str, int] = None     ,		__SCREAMING_SNAKE_CASE							:     int = 2_048     ,		__SCREAMING_SNAKE_CASE							:     bool = False     ,		**__SCREAMING_SNAKE_CASE							:     Dict     ,		):


           '''simple docstring'''

           super().__init__(**__SCREAMING_SNAKE_CASE)
           __a						       =				patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}
           __a						       =				do_normalize
           __a						       =				do_convert_rgb
           __a						       =				max_patches
           __a						       =				is_vqa



       def 		_lowerCamelCase			(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     np.ndarray     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     dict     ,		**__SCREAMING_SNAKE_CASE							:     str):


           '''simple docstring'''

           requires_backends(self.extract_flattened_patches     ,		'''torch''')
           _check_torch_version()

           # convert to torch
           __a						       =				to_channel_dimension_format(__SCREAMING_SNAKE_CASE     ,		ChannelDimension.FIRST)
           __a						       =				torch.from_numpy(__SCREAMING_SNAKE_CASE)

           __a       ,   __a						       =				patch_size['''height'''], patch_size['''width''']
           __a       ,   __a						       =				get_image_size(__SCREAMING_SNAKE_CASE)

           # maximize scale s.t.
           __a						       =				math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width))
           __a						       =				max(min(math.floor(scale * image_height / patch_height)     ,		__SCREAMING_SNAKE_CASE)     ,		1)
           __a						       =				max(min(math.floor(scale * image_width / patch_width)     ,		__SCREAMING_SNAKE_CASE)     ,		1)
           __a						       =				max(num_feasible_rows * patch_height     ,		1)
           __a						       =				max(num_feasible_cols * patch_width     ,		1)

           __a						       =				torch.nn.functional.interpolate(
               image.unsqueeze(0)     ,		size=(resized_height, resized_width)     ,		mode='''bilinear'''     ,		align_corners=__SCREAMING_SNAKE_CASE     ,		antialias=__SCREAMING_SNAKE_CASE     ,		).squeeze(0)

           # [1, rows, columns, patch_height * patch_width * image_channels]
           __a						       =				torch_extract_patches(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

           __a						       =				patches.shape
           __a						       =				patches_shape[1]
           __a						       =				patches_shape[2]
           __a						       =				patches_shape[3]

           # [rows * columns, patch_height * patch_width * image_channels]
           __a						       =				patches.reshape([rows * columns, depth])

           # [rows * columns, 1]
           __a						       =				torch.arange(__SCREAMING_SNAKE_CASE).reshape([rows, 1]).repeat(1     ,		__SCREAMING_SNAKE_CASE).reshape([rows * columns, 1])
           __a						       =				torch.arange(__SCREAMING_SNAKE_CASE).reshape([1, columns]).repeat(__SCREAMING_SNAKE_CASE     ,		1).reshape([rows * columns, 1])

           # Offset by 1 so the ids do not contain zeros, which represent padding.
           row_ids += 1
           col_ids += 1

           # Prepare additional patch features.
           # [rows * columns, 1]
           __a						       =				row_ids.to(torch.floataa)
           __a						       =				col_ids.to(torch.floataa)

           # [rows * columns, 2 + patch_height * patch_width * image_channels]
           __a						       =				torch.cat([row_ids, col_ids, patches]     ,		-1)

           # [max_patches, 2 + patch_height * patch_width * image_channels]
           __a						       =				torch.nn.functional.pad(__SCREAMING_SNAKE_CASE     ,		[0, 0, 0, max_patches - (rows * columns)]).float()

           __a						       =				to_numpy_array(__SCREAMING_SNAKE_CASE)

           return result



       def 		_lowerCamelCase			(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     np.ndarray     ,		__SCREAMING_SNAKE_CASE							:     Optional[Union[str, ChannelDimension]] = None     ,		**__SCREAMING_SNAKE_CASE							:     Any):


           '''simple docstring'''

           if image.dtype == np.uinta:
               __a						       =				image.astype(np.floataa)

           # take mean across the whole `image`
           __a						       =				np.mean(__SCREAMING_SNAKE_CASE)
           __a						       =				np.std(__SCREAMING_SNAKE_CASE)
           __a						       =				max(__SCREAMING_SNAKE_CASE     ,		1.0 / math.sqrt(np.prod(image.shape)))

           return normalize(__SCREAMING_SNAKE_CASE     ,		mean=__SCREAMING_SNAKE_CASE     ,		std=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase			(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     ImageInput     ,		__SCREAMING_SNAKE_CASE							:     Optional[str] = None     ,		__SCREAMING_SNAKE_CASE							:     bool = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[bool] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[int] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[Dict[str, int]] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[Union[str, TensorType]] = None     ,		__SCREAMING_SNAKE_CASE							:     ChannelDimension = ChannelDimension.FIRST     ,		**__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		):


           '''simple docstring'''

           __a						       =				do_normalize if do_normalize is not None else self.do_normalize
           __a						       =				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
           __a						       =				patch_size if patch_size is not None else self.patch_size
           __a						       =				max_patches if max_patches is not None else self.max_patches
           __a						       =				self.is_vqa

           if kwargs.get('''data_format'''     ,		__SCREAMING_SNAKE_CASE) is not None:
               raise ValueError('''data_format is not an accepted input as the outputs are ''')

           __a						       =				make_list_of_images(__SCREAMING_SNAKE_CASE)

           if not valid_images(__SCREAMING_SNAKE_CASE):
               raise ValueError(
                   '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                   '''torch.Tensor, tf.Tensor or jax.ndarray.''')

           # PIL RGBA images are converted to RGB
           if do_convert_rgb:
               __a						       =				[convert_to_rgb(__SCREAMING_SNAKE_CASE) for image in images]

           # All transformations expect numpy arrays.
           __a						       =				[to_numpy_array(__SCREAMING_SNAKE_CASE) for image in images]

           if is_vqa:
               if header_text is None:
                   raise ValueError('''A header text must be provided for VQA models.''')
               __a						       =				kwargs.pop('''font_bytes'''     ,		__SCREAMING_SNAKE_CASE)
               __a						       =				kwargs.pop('''font_path'''     ,		__SCREAMING_SNAKE_CASE)

               if isinstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE):
                   __a						       =				[header_text] * len(__SCREAMING_SNAKE_CASE)

               __a						       =				[
                   render_header(__SCREAMING_SNAKE_CASE     ,		header_text[i]     ,		font_bytes=__SCREAMING_SNAKE_CASE     ,		font_path=__SCREAMING_SNAKE_CASE)
                   for i, image in enumerate(__SCREAMING_SNAKE_CASE)
               ]

           if do_normalize:
               __a						       =				[self.normalize(image=__SCREAMING_SNAKE_CASE) for image in images]

           # convert to torch tensor and permute
           __a						       =				[
               self.extract_flattened_patches(image=__SCREAMING_SNAKE_CASE     ,		max_patches=__SCREAMING_SNAKE_CASE     ,		patch_size=__SCREAMING_SNAKE_CASE)
               for image in images
           ]

           # create attention mask in numpy
           __a						       =				[(image.sum(axis=-1) != 0).astype(np.floataa) for image in images]

           __a						       =				BatchFeature(
               data={'''flattened_patches''': images, '''attention_mask''': attention_masks}     ,		tensor_type=__SCREAMING_SNAKE_CASE)

           return encoded_outputs











'''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()
a  :				Any									=  logging.get_logger(__name__)


def 		__magic_name__    (	__UpperCAmelCase	)		->					Optional[Any]:

					'''simple docstring'''

					snake_case_									=				DPTConfig()

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

					if "ade" in checkpoint_url:
										snake_case_									=				True

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

					return config, expected_shape


def 		__magic_name__    (	__UpperCAmelCase	)		->					Union[str, Any]:

					'''simple docstring'''

					snake_case_									=				['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
					for k in ignore_keys:
										state_dict.pop(__UpperCAmelCase,      __UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					List[Any]:

					'''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
					):
										snake_case_									=				name.replace('''pretrained.model''',      '''dpt.encoder'''	)
					if "pretrained.model" in name:
										snake_case_									=				name.replace('''pretrained.model''',      '''dpt.embeddings'''	)
					if "patch_embed" in name:
										snake_case_									=				name.replace('''patch_embed''',      '''patch_embeddings'''	)
					if "pos_embed" in name:
										snake_case_									=				name.replace('''pos_embed''',      '''position_embeddings'''	)
					if "attn.proj" in name:
										snake_case_									=				name.replace('''attn.proj''',      '''attention.output.dense'''	)
					if "proj" in name and "project" not in name:
										snake_case_									=				name.replace('''proj''',      '''projection'''	)
					if "blocks" in name:
										snake_case_									=				name.replace('''blocks''',      '''layer'''	)
					if "mlp.fc1" in name:
										snake_case_									=				name.replace('''mlp.fc1''',      '''intermediate.dense'''	)
					if "mlp.fc2" in name:
										snake_case_									=				name.replace('''mlp.fc2''',      '''output.dense'''	)
					if "norm1" in name:
										snake_case_									=				name.replace('''norm1''',      '''layernorm_before'''	)
					if "norm2" in name:
										snake_case_									=				name.replace('''norm2''',      '''layernorm_after'''	)
					if "scratch.output_conv" in name:
										snake_case_									=				name.replace('''scratch.output_conv''',      '''head'''	)
					if "scratch" in name:
										snake_case_									=				name.replace('''scratch''',      '''neck'''	)
					if "layer1_rn" in name:
										snake_case_									=				name.replace('''layer1_rn''',      '''convs.0'''	)
					if "layer2_rn" in name:
										snake_case_									=				name.replace('''layer2_rn''',      '''convs.1'''	)
					if "layer3_rn" in name:
										snake_case_									=				name.replace('''layer3_rn''',      '''convs.2'''	)
					if "layer4_rn" in name:
										snake_case_									=				name.replace('''layer4_rn''',      '''convs.3'''	)
					if "refinenet" in name:
										snake_case_									=				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
										snake_case_									=				name.replace(F"refinenet{layer_idx}",      F"fusion_stage.layers.{abs(layer_idx-4	)}"	)
					if "out_conv" in name:
										snake_case_									=				name.replace('''out_conv''',      '''projection'''	)
					if "resConfUnit1" in name:
										snake_case_									=				name.replace('''resConfUnit1''',      '''residual_layer1'''	)
					if "resConfUnit2" in name:
										snake_case_									=				name.replace('''resConfUnit2''',      '''residual_layer2'''	)
					if "conv1" in name:
										snake_case_									=				name.replace('''conv1''',      '''convolution1'''	)
					if "conv2" in name:
										snake_case_									=				name.replace('''conv2''',      '''convolution2'''	)
					# readout blocks
					if "pretrained.act_postprocess1.0.project.0" in name:
										snake_case_									=				name.replace('''pretrained.act_postprocess1.0.project.0''',      '''neck.reassemble_stage.readout_projects.0.0'''	)
					if "pretrained.act_postprocess2.0.project.0" in name:
										snake_case_									=				name.replace('''pretrained.act_postprocess2.0.project.0''',      '''neck.reassemble_stage.readout_projects.1.0'''	)
					if "pretrained.act_postprocess3.0.project.0" in name:
										snake_case_									=				name.replace('''pretrained.act_postprocess3.0.project.0''',      '''neck.reassemble_stage.readout_projects.2.0'''	)
					if "pretrained.act_postprocess4.0.project.0" in name:
										snake_case_									=				name.replace('''pretrained.act_postprocess4.0.project.0''',      '''neck.reassemble_stage.readout_projects.3.0'''	)
					# resize blocks
					if "pretrained.act_postprocess1.3" in name:
										snake_case_									=				name.replace('''pretrained.act_postprocess1.3''',      '''neck.reassemble_stage.layers.0.projection'''	)
					if "pretrained.act_postprocess1.4" in name:
										snake_case_									=				name.replace('''pretrained.act_postprocess1.4''',      '''neck.reassemble_stage.layers.0.resize'''	)
					if "pretrained.act_postprocess2.3" in name:
										snake_case_									=				name.replace('''pretrained.act_postprocess2.3''',      '''neck.reassemble_stage.layers.1.projection'''	)
					if "pretrained.act_postprocess2.4" in name:
										snake_case_									=				name.replace('''pretrained.act_postprocess2.4''',      '''neck.reassemble_stage.layers.1.resize'''	)
					if "pretrained.act_postprocess3.3" in name:
										snake_case_									=				name.replace('''pretrained.act_postprocess3.3''',      '''neck.reassemble_stage.layers.2.projection'''	)
					if "pretrained.act_postprocess4.3" in name:
										snake_case_									=				name.replace('''pretrained.act_postprocess4.3''',      '''neck.reassemble_stage.layers.3.projection'''	)
					if "pretrained.act_postprocess4.4" in name:
										snake_case_									=				name.replace('''pretrained.act_postprocess4.4''',      '''neck.reassemble_stage.layers.3.resize'''	)
					if "pretrained" in name:
										snake_case_									=				name.replace('''pretrained''',      '''dpt'''	)
					if "bn" in name:
										snake_case_									=				name.replace('''bn''',      '''batch_norm'''	)
					if "head" in name:
										snake_case_									=				name.replace('''head''',      '''head.head'''	)
					if "encoder.norm" in name:
										snake_case_									=				name.replace('''encoder.norm''',      '''layernorm'''	)
					if "auxlayer" in name:
										snake_case_									=				name.replace('''auxlayer''',      '''auxiliary_head.head'''	)

					return name


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Dict:

					'''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)
										snake_case_									=				state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight"	)
										snake_case_									=				state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.bias"	)
										# next, add query, keys and values (in that order) to the state dict
										snake_case_									=				in_proj_weight[: config.hidden_size, :]
										snake_case_									=				in_proj_bias[: config.hidden_size]
										snake_case_									=				in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										snake_case_									=				in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										snake_case_									=				in_proj_weight[
										    -config.hidden_size :, :
										]
										snake_case_									=				in_proj_bias[-config.hidden_size :]


def 		__magic_name__    (	)		->					Any:

					'''simple docstring'''

					snake_case_									=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					snake_case_									=				Image.open(requests.get(__UpperCAmelCase,      stream=__UpperCAmelCase	).raw	)
					return im


@torch.no_grad()
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					Union[str, Any]:

					'''simple docstring'''

					snake_case_			,snake_case_									=				get_dpt_config(__UpperCAmelCase	)
					# load original state_dict from URL
					snake_case_									=				torch.hub.load_state_dict_from_url(__UpperCAmelCase,      map_location='''cpu'''	)
					# remove certain keys
					remove_ignore_keys_(__UpperCAmelCase	)
					# rename keys
					for key in state_dict.copy().keys():
										snake_case_									=				state_dict.pop(__UpperCAmelCase	)
										snake_case_									=				val
					# read in qkv matrices
					read_in_q_k_v(__UpperCAmelCase,      __UpperCAmelCase	)

					# load HuggingFace model
					snake_case_									=				DPTForSemanticSegmentation(__UpperCAmelCase	) if '''ade''' in checkpoint_url else DPTForDepthEstimation(__UpperCAmelCase	)
					model.load_state_dict(__UpperCAmelCase	)
					model.eval()

					# Check outputs on an image
					snake_case_									=				480 if '''ade''' in checkpoint_url else 384
					snake_case_									=				DPTImageProcessor(size=__UpperCAmelCase	)

					snake_case_									=				prepare_img()
					snake_case_									=				image_processor(__UpperCAmelCase,      return_tensors='''pt'''	)

					# forward pass
					snake_case_									=				model(**__UpperCAmelCase	).logits if '''ade''' in checkpoint_url else model(**__UpperCAmelCase	).predicted_depth

					# Assert logits
					snake_case_									=				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:
										snake_case_									=				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(__UpperCAmelCase	)
					assert (
					    torch.allclose(outputs[0, 0, :3, :3],      __UpperCAmelCase,      atol=1e-4	)
					    if "ade" in checkpoint_url
					    else torch.allclose(outputs[0, :3, :3],      __UpperCAmelCase	)
					)

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

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


if __name__ == "__main__":
							a  :				Tuple									=  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.',
							)

							a  :				List[Any]									=  parser.parse_args()
							convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)

