


import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
							from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

class 						A__							:




						def __init__(       self					:      List[Any]				,    _a					:      Union[str, Any]				,    _a					:      List[str]=3				,    _a					:      Any=32				,    _a					:      List[str]=3				,    _a					:      List[str]=10				,    _a					:      int=[8, 16, 32, 64]				,    _a					:      Union[str, Any]=[1, 1, 2, 1]				,    _a					:      Optional[int]=True				,    _a					:      List[str]=True				,    _a					:      Tuple="relu"				,    _a					:      int=3				,    _a					:      int=None				,    _a					:      Dict=["stage2", "stage3", "stage4"]				,    _a					:      str=[2, 3, 4]				,    _a					:      Dict=1				,    )							->     Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =parent
										_SCREAMING_SNAKE_CASE			  =batch_size
										_SCREAMING_SNAKE_CASE			  =image_size
										_SCREAMING_SNAKE_CASE			  =num_channels
										_SCREAMING_SNAKE_CASE			  =embeddings_size
										_SCREAMING_SNAKE_CASE			  =hidden_sizes
										_SCREAMING_SNAKE_CASE			  =depths
										_SCREAMING_SNAKE_CASE			  =is_training
										_SCREAMING_SNAKE_CASE			  =use_labels
										_SCREAMING_SNAKE_CASE			  =hidden_act
										_SCREAMING_SNAKE_CASE			  =num_labels
										_SCREAMING_SNAKE_CASE			  =scope
										_SCREAMING_SNAKE_CASE			  =len(_a  )
										_SCREAMING_SNAKE_CASE			  =out_features
										_SCREAMING_SNAKE_CASE			  =out_indices
										_SCREAMING_SNAKE_CASE			  =num_groups




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

										_SCREAMING_SNAKE_CASE			  =None
										if self.use_labels:
														_SCREAMING_SNAKE_CASE			  =ids_tensor([self.batch_size]				,    self.num_labels  )

										_SCREAMING_SNAKE_CASE			  =self.get_config()

										return config, pixel_values, labels




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


										"""simple docstring"""





										return BitConfig(
										    num_channels=self.num_channels				,    embeddings_size=self.embeddings_size				,    hidden_sizes=self.hidden_sizes				,    depths=self.depths				,    hidden_act=self.hidden_act				,    num_labels=self.num_labels				,    out_features=self.out_features				,    out_indices=self.out_indices				,    num_groups=self.num_groups				,    )




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =BitModel(config=_a  )
										model.to(_a  )
										model.eval()
										_SCREAMING_SNAKE_CASE			  =model(_a  )
										self.parent.assertEqual(
										    result.last_hidden_state.shape				,    (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)				,    )




						def 							__UpperCamelCase				(       self					:      int				,    _a					:      int				,    _a					:      Dict				,    _a					:      Optional[int]  )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.num_labels
										_SCREAMING_SNAKE_CASE			  =BitForImageClassification(_a  )
										model.to(_a  )
										model.eval()
										_SCREAMING_SNAKE_CASE			  =model(_a				,    labels=_a  )
										self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_labels)  )




						def 							__UpperCamelCase				(       self					:      Tuple				,    _a					:      Optional[Any]				,    _a					:      List[Any]				,    _a					:      Union[str, Any]  )							->     Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =BitBackbone(config=_a  )
										model.to(_a  )
										model.eval()
										_SCREAMING_SNAKE_CASE			  =model(_a  )

										# verify feature maps
										self.parent.assertEqual(len(result.feature_maps  )				,    len(config.out_features  )  )
										self.parent.assertListEqual(list(result.feature_maps[0].shape  )				,    [self.batch_size, self.hidden_sizes[1], 4, 4]  )

										# verify channels
										self.parent.assertEqual(len(model.channels  )				,    len(config.out_features  )  )
										self.parent.assertListEqual(model.channels				,    config.hidden_sizes[1:]  )

										# verify backbone works with out_features=None
										_SCREAMING_SNAKE_CASE			  =None
										_SCREAMING_SNAKE_CASE			  =BitBackbone(config=_a  )
										model.to(_a  )
										model.eval()
										_SCREAMING_SNAKE_CASE			  =model(_a  )

										# verify feature maps
										self.parent.assertEqual(len(result.feature_maps  )				,    1  )
										self.parent.assertListEqual(list(result.feature_maps[0].shape  )				,    [self.batch_size, self.hidden_sizes[-1], 1, 1]  )

										# verify channels
										self.parent.assertEqual(len(model.channels  )				,    1  )
										self.parent.assertListEqual(model.channels				,    [config.hidden_sizes[-1]]  )




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.prepare_config_and_inputs()
										_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =config_and_inputs
										_SCREAMING_SNAKE_CASE			  ={'''pixel_values''': pixel_values}
										return config, inputs_dict

@require_torch
class 						A__							(		UpperCamelCase__   ,     UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
						UpperCAmelCase             =						(
						    {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
						    if is_torch_available()
						    else {}
						)

						UpperCAmelCase             =						False
						UpperCAmelCase             =						False
						UpperCAmelCase             =						False
						UpperCAmelCase             =						False
						UpperCAmelCase             =						False




						def 							__UpperCamelCase				(       self					:      Dict  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =BitModelTester(self  )
										_SCREAMING_SNAKE_CASE			  =ConfigTester(self				,    config_class=_a				,    has_text_modality=_a  )




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


										"""simple docstring"""





										self.create_and_test_config_common_properties()
										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()




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


										"""simple docstring"""





										return




						@unittest.skip(reason='''Bit does not output attentions'''  )
						def 							__UpperCamelCase				(       self					:      Any  )							->     Optional[int]:


										"""simple docstring"""





										pass




						@unittest.skip(reason='''Bit does not use inputs_embeds'''  )
						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     List[str]:


										"""simple docstring"""





										pass




						@unittest.skip(reason='''Bit does not support input and output embeddings'''  )
						def 							__UpperCamelCase				(       self					:      Union[str, Any]  )							->     str:


										"""simple docstring"""





										pass




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE			  =model_class(_a  )
														_SCREAMING_SNAKE_CASE			  =inspect.signature(model.forward  )
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														_SCREAMING_SNAKE_CASE			  =[*signature.parameters.keys()]

														_SCREAMING_SNAKE_CASE			  =['''pixel_values''']
														self.assertListEqual(arg_names[:1]				,    _a  )




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_a  )




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_backbone(*_a  )




						def 							__UpperCamelCase				(       self					:      int  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE			  =model_class(config=_a  )
														for name, module in model.named_modules():
																		if isinstance(_a				,    (nn.BatchNormad, nn.GroupNorm)  ):
																						self.assertTrue(
																						    torch.all(module.weight == 1  )				,    msg=f"Parameter {name} of model {model_class} seems not properly initialized"				,    )
																						self.assertTrue(
																						    torch.all(module.bias == 0  )				,    msg=f"Parameter {name} of model {model_class} seems not properly initialized"				,    )




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


										"""simple docstring"""





										def check_hidden_states_output(_a					:      Optional[Any]				,    _a					:      Optional[Any]				,    _a					:      Tuple  ):
														_SCREAMING_SNAKE_CASE			  =model_class(_a  )
														model.to(_a  )
														model.eval()

														with torch.no_grad():
																		_SCREAMING_SNAKE_CASE			  =model(**self._prepare_for_class(_a				,    _a  )  )

														_SCREAMING_SNAKE_CASE			  =outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

														_SCREAMING_SNAKE_CASE			  =self.model_tester.num_stages
														self.assertEqual(len(_a  )				,    expected_num_stages + 1  )

														# Bit's feature maps are of shape (batch_size, num_channels, height, width)
														self.assertListEqual(
														    list(hidden_states[0].shape[-2:]  )				,    [self.model_tester.image_size // 4, self.model_tester.image_size // 4]				,    )

										_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =self.model_tester.prepare_config_and_inputs_for_common()
										_SCREAMING_SNAKE_CASE			  =['''preactivation''', '''bottleneck''']
										for model_class in self.all_model_classes:
														for layer_type in layers_type:
																		_SCREAMING_SNAKE_CASE			  =layer_type
																		_SCREAMING_SNAKE_CASE			  =True
																		check_hidden_states_output(_a				,    _a				,    _a  )

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		_SCREAMING_SNAKE_CASE			  =True

																		check_hidden_states_output(_a				,    _a				,    _a  )




						@unittest.skip(reason='''Bit does not use feedforward chunking'''  )
						def 							__UpperCamelCase				(       self					:      Any  )							->     List[Any]:


										"""simple docstring"""





										pass




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*_a  )




						@slow
						def 							__UpperCamelCase				(       self					:      List[str]  )							->     List[str]:


										"""simple docstring"""





										for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_SCREAMING_SNAKE_CASE			  =BitModel.from_pretrained(_a  )
														self.assertIsNotNone(_a  )







def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
				return image

@require_torch
@require_vision
class 						A__							(		unittest.TestCase  ):




						@cached_property
						def 							__UpperCamelCase				(       self					:      Tuple  )							->     Optional[Any]:


										"""simple docstring"""





										return (
										    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ) if is_vision_available() else None
										)




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(_a  )

										_SCREAMING_SNAKE_CASE			  =self.default_image_processor
										_SCREAMING_SNAKE_CASE			  =prepare_img()
										_SCREAMING_SNAKE_CASE			  =image_processor(images=_a				,    return_tensors='''pt'''  ).to(_a  )

										# forward pass
										with torch.no_grad():
														_SCREAMING_SNAKE_CASE			  =model(**_a  )

										# verify the logits
										_SCREAMING_SNAKE_CASE			  =torch.Size((1, 1000)  )
										self.assertEqual(outputs.logits.shape				,    _a  )

										_SCREAMING_SNAKE_CASE			  =torch.tensor([[-0.65_26, -0.52_63, -1.43_98]]  ).to(_a  )

										self.assertTrue(torch.allclose(outputs.logits[0, :3]				,    _a				,    atol=1E-4  )  )

@require_torch
class 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						(BitBackbone,) if is_torch_available() else ()
						UpperCAmelCase             =						BitConfig

						UpperCAmelCase             =						False




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =BitModelTester(self  )



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)				,    )