

import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
					import jax

					from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
					from PIL import Image

					from transformers import BeitImageProcessor
class 		SCREAMING_SNAKE_CASE__       (		unittest.TestCase ):







						"""simple docstring"""




						def __init__(					self			,			__UpperCamelCase			,			__UpperCamelCase=1_00			,			__UpperCamelCase=13			,			__UpperCamelCase=30			,			__UpperCamelCase=2			,			__UpperCamelCase=3			,			__UpperCamelCase=True			,			__UpperCamelCase=True			,			__UpperCamelCase=32			,			__UpperCamelCase=5			,			__UpperCamelCase=4			,			__UpperCamelCase=37			,			__UpperCamelCase="gelu"			,			__UpperCamelCase=0.1			,			__UpperCamelCase=0.1			,			__UpperCamelCase=10			,			__UpperCamelCase=0.02			,			__UpperCamelCase=3			,			)				->						List[Any]:




									'''simple docstring'''


									__UpperCamelCase     :     List[Any]										=   parent
									__UpperCamelCase     :     List[Any]										=   vocab_size
									__UpperCamelCase     :     int										=   batch_size
									__UpperCamelCase     :     Optional[int]										=   image_size
									__UpperCamelCase     :     str										=   patch_size
									__UpperCamelCase     :     Dict										=   num_channels
									__UpperCamelCase     :     str										=   is_training
									__UpperCamelCase     :     int										=   use_labels
									__UpperCamelCase     :     Tuple										=   hidden_size
									__UpperCamelCase     :     Union[str, Any]										=   num_hidden_layers
									__UpperCamelCase     :     Optional[int]										=   num_attention_heads
									__UpperCamelCase     :     List[str]										=   intermediate_size
									__UpperCamelCase     :     str										=   hidden_act
									__UpperCamelCase     :     str										=   hidden_dropout_prob
									__UpperCamelCase     :     Dict										=   attention_probs_dropout_prob
									__UpperCamelCase     :     int										=   type_sequence_label_size
									__UpperCamelCase     :     Any										=   initializer_range

									# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
									__UpperCamelCase     :     List[Any]										=   (image_size // patch_size) ** 2
									__UpperCamelCase     :     Optional[int]										=   num_patches + 1

						def __lowerCamelCase       (					self     )				->						List[str]:




									'''simple docstring'''


									__UpperCamelCase     :     str										=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

									__UpperCamelCase     :     Optional[int]										=   None
									if self.use_labels:
												__UpperCamelCase     :     Optional[Any]										=   ids_tensor([self.batch_size]			,			self.type_sequence_label_size     )

									__UpperCamelCase     :     str										=   BeitConfig(
									    vocab_size=self.vocab_size			,			image_size=self.image_size			,			patch_size=self.patch_size			,			num_channels=self.num_channels			,			hidden_size=self.hidden_size			,			num_hidden_layers=self.num_hidden_layers			,			num_attention_heads=self.num_attention_heads			,			intermediate_size=self.intermediate_size			,			hidden_act=self.hidden_act			,			hidden_dropout_prob=self.hidden_dropout_prob			,			attention_probs_dropout_prob=self.attention_probs_dropout_prob			,			is_decoder=__UpperCAmelCase			,			initializer_range=self.initializer_range			,			)

									return config, pixel_values, labels

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




									'''simple docstring'''


									__UpperCamelCase     :     List[Any]										=   FlaxBeitModel(config=__UpperCAmelCase     )
									__UpperCamelCase     :     str										=   model(__UpperCAmelCase     )
									self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.seq_length, self.hidden_size)     )

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




									'''simple docstring'''


									__UpperCamelCase     :     Dict										=   FlaxBeitForMaskedImageModeling(config=__UpperCAmelCase     )
									__UpperCamelCase     :     Any										=   model(__UpperCAmelCase     )
									self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.seq_length - 1, self.vocab_size)     )

						def __lowerCamelCase       (					self			,			__UpperCamelCase			,			__UpperCamelCase			,			__UpperCamelCase     )				->						Dict:




									'''simple docstring'''


									__UpperCamelCase     :     Optional[Any]										=   self.type_sequence_label_size
									__UpperCamelCase     :     Optional[Any]										=   FlaxBeitForImageClassification(config=__UpperCAmelCase     )
									__UpperCamelCase     :     Any										=   model(__UpperCAmelCase     )
									self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.type_sequence_label_size)     )

									# test greyscale images
									__UpperCamelCase     :     List[Any]										=   1
									__UpperCamelCase     :     List[str]										=   FlaxBeitForImageClassification(__UpperCAmelCase     )

									__UpperCamelCase     :     Optional[int]										=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
									__UpperCamelCase     :     Optional[Any]										=   model(__UpperCAmelCase     )




						def __lowerCamelCase       (					self     )				->						Tuple:




									'''simple docstring'''


									__UpperCamelCase     :     str										=   self.prepare_config_and_inputs()
									(
									    __UpperCamelCase   
									)     :     str										=   config_and_inputs
									__UpperCamelCase     :     Any										=   {"pixel_values": pixel_values}
									return config, inputs_dict







@require_flax
class 		SCREAMING_SNAKE_CASE__       (		lowerCamelCase__    ,						unittest.TestCase ):







						"""simple docstring"""




						lowercase	:					Any           =				(
						    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
						)

						def __lowerCamelCase       (					self     )				->						Tuple:




									'''simple docstring'''


									__UpperCamelCase     :     Dict										=   FlaxBeitModelTester(self     )
									__UpperCamelCase     :     Optional[int]										=   ConfigTester(self			,			config_class=__UpperCAmelCase			,			has_text_modality=__UpperCAmelCase			,			hidden_size=37     )

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




									'''simple docstring'''


									self.config_tester.run_common_tests()

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




									'''simple docstring'''


									__UpperCamelCase     :     Dict										=   self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												__UpperCamelCase     :     Dict										=   model_class(__UpperCAmelCase     )
												__UpperCamelCase     :     Dict										=   inspect.signature(model.__call__     )
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												__UpperCamelCase     :     int										=   [*signature.parameters.keys()]

												__UpperCamelCase     :     Tuple										=   ["pixel_values"]
												self.assertListEqual(arg_names[:1]			,			__UpperCAmelCase     )

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




									'''simple docstring'''


									__UpperCamelCase     :     Tuple										=   self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__     ):
															__UpperCamelCase     :     List[str]										=   self._prepare_for_class(__UpperCAmelCase			,			__UpperCAmelCase     )
															__UpperCamelCase     :     List[str]										=   model_class(__UpperCAmelCase     )

															@jax.jit
															def model_jitted(__UpperCamelCase			,			**__UpperCamelCase     ):
																		return model(pixel_values=__UpperCAmelCase			,			**__UpperCAmelCase     )

															with self.subTest("JIT Enabled"     ):
																		__UpperCamelCase     :     Union[str, Any]										=   model_jitted(**__UpperCAmelCase     ).to_tuple()

															with self.subTest("JIT Disabled"     ):
																		with jax.disable_jit():
																					__UpperCamelCase     :     List[Any]										=   model_jitted(**__UpperCAmelCase     ).to_tuple()

															self.assertEqual(len(__UpperCAmelCase     )			,			len(__UpperCAmelCase     )     )
															for jitted_output, output in zip(__UpperCAmelCase			,			__UpperCAmelCase     ):
																		self.assertEqual(jitted_output.shape			,			output.shape     )

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




									'''simple docstring'''


									__UpperCamelCase     :     Optional[Any]										=   self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__UpperCAmelCase     )

						def __lowerCamelCase       (					self     )				->						Tuple:




									'''simple docstring'''


									__UpperCamelCase     :     Union[str, Any]										=   self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase     )

						def __lowerCamelCase       (					self     )				->						Tuple:




									'''simple docstring'''


									__UpperCamelCase     :     List[Any]										=   self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase     )




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




									'''simple docstring'''


									for model_class_name in self.all_model_classes:
												__UpperCamelCase     :     Tuple										=   model_class_name.from_pretrained("microsoft/beit-base-patch16-224"     )
												__UpperCamelCase     :     int										=   model(np.ones((1, 3, 2_24, 2_24)     )     )
												self.assertIsNotNone(__UpperCAmelCase     )







def 			UpperCAmelCase_ ():
			__UpperCamelCase     :     Optional[int]										=   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
			return image

@require_vision
@require_flax
class 		SCREAMING_SNAKE_CASE__       (		unittest.TestCase ):







						"""simple docstring"""




						@cached_property
						def __lowerCamelCase       (					self     )				->						Union[str, Any]:




									'''simple docstring'''


									return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"     ) if is_vision_available() else None

						@slow
						def __lowerCamelCase       (					self     )				->						Tuple:




									'''simple docstring'''


									__UpperCamelCase     :     Union[str, Any]										=   FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"     )

									__UpperCamelCase     :     Tuple										=   self.default_image_processor
									__UpperCamelCase     :     List[str]										=   prepare_img()
									__UpperCamelCase     :     Dict										=   image_processor(images=__UpperCAmelCase			,			return_tensors="np"     ).pixel_values

									# prepare bool_masked_pos
									__UpperCamelCase     :     Optional[int]										=   np.ones((1, 1_96)			,			dtype=__UpperCAmelCase     )

									# forward pass
									__UpperCamelCase     :     Any										=   model(pixel_values=__UpperCAmelCase			,			bool_masked_pos=__UpperCAmelCase     )
									__UpperCamelCase     :     List[Any]										=   outputs.logits

									# verify the logits
									__UpperCamelCase     :     Union[str, Any]										=   (1, 1_96, 81_92)
									self.assertEqual(logits.shape			,			__UpperCAmelCase     )

									__UpperCamelCase     :     Optional[int]										=   np.array(
									    [[-3.2437, 0.5072, -13.91_74], [-3.2456, 0.4948, -13.94_01], [-3.2033, 0.5121, -13.85_50]]     )

									self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]			,			__UpperCAmelCase			,			atol=1E-2     )     )

						@slow
						def __lowerCamelCase       (					self     )				->						Union[str, Any]:




									'''simple docstring'''


									__UpperCamelCase     :     Dict										=   FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"     )

									__UpperCamelCase     :     str										=   self.default_image_processor
									__UpperCamelCase     :     str										=   prepare_img()
									__UpperCamelCase     :     Optional[Any]										=   image_processor(images=__UpperCAmelCase			,			return_tensors="np"     )

									# forward pass
									__UpperCamelCase     :     List[Any]										=   model(**__UpperCAmelCase     )
									__UpperCamelCase     :     Optional[int]										=   outputs.logits

									# verify the logits
									__UpperCamelCase     :     int										=   (1, 10_00)
									self.assertEqual(logits.shape			,			__UpperCAmelCase     )

									__UpperCamelCase     :     Union[str, Any]										=   np.array([-1.2385, -1.0987, -1.0108]     )

									self.assertTrue(np.allclose(logits[0, :3]			,			__UpperCAmelCase			,			atol=1E-4     )     )

									__UpperCamelCase     :     Optional[Any]										=   2_81
									self.assertEqual(logits.argmax(-1     ).item()			,			__UpperCAmelCase     )




						@slow
						def __lowerCamelCase       (					self     )				->						Any:




									'''simple docstring'''


									__UpperCamelCase     :     Optional[Any]										=   FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"     )

									__UpperCamelCase     :     Any										=   self.default_image_processor
									__UpperCamelCase     :     List[Any]										=   prepare_img()
									__UpperCamelCase     :     Dict										=   image_processor(images=__UpperCAmelCase			,			return_tensors="np"     )

									# forward pass
									__UpperCamelCase     :     Optional[int]										=   model(**__UpperCAmelCase     )
									__UpperCamelCase     :     Union[str, Any]										=   outputs.logits

									# verify the logits
									__UpperCamelCase     :     Optional[Any]										=   (1, 2_18_41)
									self.assertEqual(logits.shape			,			__UpperCAmelCase     )

									__UpperCamelCase     :     str										=   np.array([1.6881, -0.2787, 0.5901]     )

									self.assertTrue(np.allclose(logits[0, :3]			,			__UpperCAmelCase			,			atol=1E-4     )     )

									__UpperCamelCase     :     Optional[int]										=   23_96
									self.assertEqual(logits.argmax(-1     ).item()			,			__UpperCAmelCase     )




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:
						lowerCamelCase		       =      False

if is_vision_available():
						from PIL import Image

						from transformers import PixaStructImageProcessor


class 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase=7 ,					__UpperCAmelCase=3 ,					__UpperCAmelCase=18 ,					__UpperCAmelCase=30 ,					__UpperCAmelCase=400 ,					__UpperCAmelCase=None ,					__UpperCAmelCase=True ,					__UpperCAmelCase=True ,					__UpperCAmelCase=None ,					):







											"""simple docstring"""



											a__	:							str      						= size if size is not None else {"height": 20, "width": 20}
											a__	:							Optional[int]      						= parent
											a__	:							Union[str, Any]      						= batch_size
											a__	:							List[Any]      						= num_channels
											a__	:							Union[str, Any]      						= image_size
											a__	:							List[str]      						= min_resolution
											a__	:							int      						= max_resolution
											a__	:							int      						= size
											a__	:							List[str]      						= do_normalize
											a__	:							Any      						= do_convert_rgb
											a__	:							Optional[Any]      						= [512, 1024, 2048, 4096]
											a__	:							Any      						= patch_size if patch_size is not None else {"height": 16, "width": 16}



							def 		_A	(							self				):







											"""simple docstring"""



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




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							str      						= "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
											a__	:							Union[str, Any]      						= Image.open(requests.get(__UpperCAmelCase ,					stream=__UpperCAmelCase				).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						(       SCREAMING_SNAKE_CASE       ,  unittest.TestCase			):





							'''simple docstring'''

							A				:List[str] =       PixaStructImageProcessor if is_vision_available() else None



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Any      						= PixaStructImageProcessingTester(self				)



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return self.image_processor_tester.prepare_image_processor_dict()



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= self.image_processing_class(**self.image_processor_dict				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_normalize"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_convert_rgb"				)				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Tuple      						= self.image_processor_tester.prepare_dummy_image()

											a__	:							Any      						= self.image_processing_class(**self.image_processor_dict				)
											a__	:							List[str]      						= 2048

											a__	:							Union[str, Any]      						= image_processor(__UpperCAmelCase ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				)
											self.assertTrue(torch.allclose(inputs.flattened_patches.mean() ,					torch.tensor(0.0_6_0_6				) ,					atol=1E-3 ,					rtol=1E-3				)				)



							def 		_A	(							self				):







											"""simple docstring"""



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

											# Test not batched input
											a__	:							List[Any]      						= (
											    (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
															a__	:							Optional[Any]      						= image_processor(
															    image_inputs[0] ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(1, max_patch, expected_hidden_dim) ,					)

															# Test batched
															a__	:							Optional[Any]      						= image_processor(
															    __UpperCAmelCase ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,					)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= self.image_processing_class(**self.image_processor_dict				)
											# create random PIL images
											a__	:							Any      						= prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__UpperCAmelCase				)
											for image in image_inputs:
															self.assertIsInstance(__UpperCAmelCase ,					Image.Image				)

											# Test not batched input
											a__	:							Optional[int]      						= (
											    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
											    * self.image_processor_tester.num_channels
											) + 2

											a__	:							int      						= True

											for max_patch in self.image_processor_tester.max_patches:
															# Test not batched input
															with self.assertRaises(__UpperCAmelCase				):
																			a__	:							List[str]      						= image_processor(
																			    image_inputs[0] ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).flattened_patches

															a__	:							Tuple      						= "Hello"

															a__	:							Union[str, Any]      						= image_processor(
															    image_inputs[0] ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase ,					header_text=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(1, max_patch, expected_hidden_dim) ,					)

															# Test batched
															a__	:							Optional[int]      						= image_processor(
															    __UpperCAmelCase ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase ,					header_text=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,					)



							def 		_A	(							self				):







											"""simple docstring"""



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

											a__	:							List[str]      						= (
											    (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
															a__	:							Any      						= image_processor(
															    image_inputs[0] ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(1, max_patch, expected_hidden_dim) ,					)

															# Test batched
															a__	:							str      						= image_processor(
															    __UpperCAmelCase ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,					)




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[Any]      						= self.image_processing_class(**self.image_processor_dict				)
											# create random PyTorch tensors
											a__	:							Any      						= prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__UpperCAmelCase ,					torchify=__UpperCAmelCase				)
											for image in image_inputs:
															self.assertIsInstance(__UpperCAmelCase ,					torch.Tensor				)

											# Test not batched input
											a__	:							Optional[Any]      						= (
											    (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
															a__	:							str      						= image_processor(
															    image_inputs[0] ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(1, max_patch, expected_hidden_dim) ,					)

															# Test batched
															a__	:							Optional[int]      						= image_processor(
															    __UpperCAmelCase ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).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						(       SCREAMING_SNAKE_CASE       ,  unittest.TestCase			):





							'''simple docstring'''

							A				:List[str] =       PixaStructImageProcessor if is_vision_available() else None



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= PixaStructImageProcessingTester(self ,					num_channels=4				)
											a__	:							Tuple      						= 3



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return self.image_processor_tester.prepare_image_processor_dict()



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[int]      						= self.image_processing_class(**self.image_processor_dict				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_normalize"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_convert_rgb"				)				)




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= self.image_processing_class(**self.image_processor_dict				)
											# create random PIL images
											a__	:							Union[str, Any]      						= prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__UpperCAmelCase				)
											for image in image_inputs:
															self.assertIsInstance(__UpperCAmelCase ,					Image.Image				)

											# Test not batched input
											a__	:							str      						= (
											    (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
															a__	:							List[str]      						= image_processor(
															    image_inputs[0] ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(1, max_patch, expected_hidden_dim) ,					)

															# Test batched
															a__	:							int      						= image_processor(
															    __UpperCAmelCase ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,					)


