


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_						:			int													=							logging.get_logger(__name__)







def 						lowerCamelCase(   a__):
				_SCREAMING_SNAKE_CASE			  =DPTConfig()

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

				if "ade" in checkpoint_url:
								_SCREAMING_SNAKE_CASE			  =True

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

				return config, expected_shape







def 						lowerCamelCase(   a__):
				_SCREAMING_SNAKE_CASE			  =['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
				for k in ignore_keys:
								state_dict.pop(a__	,a__)







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

				return name







def 						lowerCamelCase(   a__	,a__):
				for i in range(config.num_hidden_layers):
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								_SCREAMING_SNAKE_CASE			  =state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight")
								_SCREAMING_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
								_SCREAMING_SNAKE_CASE			  =in_proj_weight[: config.hidden_size, :]
								_SCREAMING_SNAKE_CASE			  =in_proj_bias[: config.hidden_size]
								_SCREAMING_SNAKE_CASE			  =in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								_SCREAMING_SNAKE_CASE			  =in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								_SCREAMING_SNAKE_CASE			  =in_proj_weight[
								    -config.hidden_size :, :
								]
								_SCREAMING_SNAKE_CASE			  =in_proj_bias[-config.hidden_size :]







def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
				_SCREAMING_SNAKE_CASE			  =Image.open(requests.get(a__	,stream=a__).raw)
				return im







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

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

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

				_SCREAMING_SNAKE_CASE			  =prepare_img()
				_SCREAMING_SNAKE_CASE			  =image_processor(a__	,return_tensors='''pt''')

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

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

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

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


if __name__ == "__main__":
							snake_case_						:			List[str]													=							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_						:			Dict													=							parser.parse_args()
							convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)



import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

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

if is_vision_available():
							from PIL import Image

							from transformers import PixaStructImageProcessor

class 						A__							(		unittest.TestCase  ):




						def __init__(       self					:      Tuple				,    _a					:      str				,    _a					:      List[str]=7				,    _a					:      Union[str, Any]=3				,    _a					:      List[str]=18				,    _a					:      List[Any]=30				,    _a					:      Optional[Any]=400				,    _a					:      Dict=None				,    _a					:      Union[str, Any]=True				,    _a					:      List[str]=True				,    _a					:      Optional[Any]=None				,    )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =size if size is not None else {'''height''': 20, '''width''': 20}
										_SCREAMING_SNAKE_CASE			  =parent
										_SCREAMING_SNAKE_CASE			  =batch_size
										_SCREAMING_SNAKE_CASE			  =num_channels
										_SCREAMING_SNAKE_CASE			  =image_size
										_SCREAMING_SNAKE_CASE			  =min_resolution
										_SCREAMING_SNAKE_CASE			  =max_resolution
										_SCREAMING_SNAKE_CASE			  =size
										_SCREAMING_SNAKE_CASE			  =do_normalize
										_SCREAMING_SNAKE_CASE			  =do_convert_rgb
										_SCREAMING_SNAKE_CASE			  =[512, 1024, 2048, 4096]
										_SCREAMING_SNAKE_CASE			  =patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}




						def 							__UpperCamelCase				(       self					:      int  )							->     Any:


										"""simple docstring"""





										return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ='''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
										_SCREAMING_SNAKE_CASE			  =Image.open(requests.get(_a				,    stream=_a  ).raw  ).convert('''RGB'''  )
										return raw_image

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11   ,     reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."   ,     )
@require_torch
@require_vision
class 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						PixaStructImageProcessor if is_vision_available() else None




						def 							__UpperCamelCase				(       self					:      int  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =PixaStructImageProcessingTester(self  )




						@property
						def 							__UpperCamelCase				(       self					:      int  )							->     Any:


										"""simple docstring"""





										return self.image_processor_tester.prepare_image_processor_dict()




						def 							__UpperCamelCase				(       self					:      str  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										self.assertTrue(hasattr(_a				,    '''do_normalize'''  )  )
										self.assertTrue(hasattr(_a				,    '''do_convert_rgb'''  )  )




						def 							__UpperCamelCase				(       self					:      int  )							->     Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processor_tester.prepare_dummy_image()

										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										_SCREAMING_SNAKE_CASE			  =2048

										_SCREAMING_SNAKE_CASE			  =image_processor(_a				,    return_tensors='''pt'''				,    max_patches=_a  )
										self.assertTrue(torch.allclose(inputs.flattened_patches.mean()				,    torch.tensor(0.06_06  )				,    atol=1E-3				,    rtol=1E-3  )  )




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										# create random PIL images
										_SCREAMING_SNAKE_CASE			  =prepare_image_inputs(self.image_processor_tester				,    equal_resolution=_a  )
										for image in image_inputs:
														self.assertIsInstance(_a				,    Image.Image  )

										# Test not batched input
										_SCREAMING_SNAKE_CASE			  =(
										    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
										    * self.image_processor_tester.num_channels
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    image_inputs[0]				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (1, max_patch, expected_hidden_dim)				,    )

														# Test batched
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    _a				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,    )




						def 							__UpperCamelCase				(       self					:      Dict  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										# create random PIL images
										_SCREAMING_SNAKE_CASE			  =prepare_image_inputs(self.image_processor_tester				,    equal_resolution=_a  )
										for image in image_inputs:
														self.assertIsInstance(_a				,    Image.Image  )

										# Test not batched input
										_SCREAMING_SNAKE_CASE			  =(
										    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
										    * self.image_processor_tester.num_channels
										) + 2

										_SCREAMING_SNAKE_CASE			  =True

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														with self.assertRaises(_a  ):
																		_SCREAMING_SNAKE_CASE			  =image_processor(
																		    image_inputs[0]				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches

														_SCREAMING_SNAKE_CASE			  ='''Hello'''

														_SCREAMING_SNAKE_CASE			  =image_processor(
														    image_inputs[0]				,    return_tensors='''pt'''				,    max_patches=_a				,    header_text=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (1, max_patch, expected_hidden_dim)				,    )

														# Test batched
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    _a				,    return_tensors='''pt'''				,    max_patches=_a				,    header_text=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,    )




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										# create random numpy tensors
										_SCREAMING_SNAKE_CASE			  =prepare_image_inputs(self.image_processor_tester				,    equal_resolution=_a				,    numpify=_a  )
										for image in image_inputs:
														self.assertIsInstance(_a				,    np.ndarray  )

										_SCREAMING_SNAKE_CASE			  =(
										    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
										    * self.image_processor_tester.num_channels
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    image_inputs[0]				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (1, max_patch, expected_hidden_dim)				,    )

														# Test batched
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    _a				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,    )




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										# create random PyTorch tensors
										_SCREAMING_SNAKE_CASE			  =prepare_image_inputs(self.image_processor_tester				,    equal_resolution=_a				,    torchify=_a  )
										for image in image_inputs:
														self.assertIsInstance(_a				,    torch.Tensor  )

										# Test not batched input
										_SCREAMING_SNAKE_CASE			  =(
										    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
										    * self.image_processor_tester.num_channels
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    image_inputs[0]				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (1, max_patch, expected_hidden_dim)				,    )

														# Test batched
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    _a				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,    )

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11   ,     reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."   ,     )
@require_torch
@require_vision
class 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						PixaStructImageProcessor if is_vision_available() else None




						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =PixaStructImageProcessingTester(self				,    num_channels=4  )
										_SCREAMING_SNAKE_CASE			  =3




						@property
						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     List[str]:


										"""simple docstring"""





										return self.image_processor_tester.prepare_image_processor_dict()




						def 							__UpperCamelCase				(       self					:      Dict  )							->     Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										self.assertTrue(hasattr(_a				,    '''do_normalize'''  )  )
										self.assertTrue(hasattr(_a				,    '''do_convert_rgb'''  )  )




						def 							__UpperCamelCase				(       self					:      Dict  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										# create random PIL images
										_SCREAMING_SNAKE_CASE			  =prepare_image_inputs(self.image_processor_tester				,    equal_resolution=_a  )
										for image in image_inputs:
														self.assertIsInstance(_a				,    Image.Image  )

										# Test not batched input
										_SCREAMING_SNAKE_CASE			  =(
										    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
										    * (self.image_processor_tester.num_channels - 1)
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    image_inputs[0]				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (1, max_patch, expected_hidden_dim)				,    )

														# Test batched
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    _a				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,    )