



'''simple docstring'''

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       __magic_name__		(       unittest.TestCase  ):







							def __init__(				self   :			List[str]      ,_UpperCAmelCase   :			List[str]      ,_UpperCAmelCase   :			Dict=7      ,_UpperCAmelCase   :			Dict=3      ,_UpperCAmelCase   :			Union[str, Any]=18      ,_UpperCAmelCase   :			Union[str, Any]=30      ,_UpperCAmelCase   :			Union[str, Any]=400      ,_UpperCAmelCase   :			List[Any]=None      ,_UpperCAmelCase   :			int=True      ,_UpperCAmelCase   :			Tuple=True      ,_UpperCAmelCase   :			Optional[Any]=None      ,):
									_a	: List[Any]								=				size if size is not None else {'height': 20, 'width': 20}
									_a	: List[str]								=				parent
									_a	: str								=				batch_size
									_a	: List[str]								=				num_channels
									_a	: List[Any]								=				image_size
									_a	: List[Any]								=				min_resolution
									_a	: Any								=				max_resolution
									_a	: List[Any]								=				size
									_a	: Union[str, Any]								=				do_normalize
									_a	: List[str]								=				do_convert_rgb
									_a	: Any								=				[512, 1024, 2048, 4096]
									_a	: Optional[Any]								=				patch_size if patch_size is not None else {'height': 16, 'width': 16}







							def 				__lowercase     (				self   :			int						):
									return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







							def 				__lowercase     (				self   :			Optional[int]						):
									_a	: Optional[int]								=				'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
									_a	: str								=				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       __magic_name__		(       _UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       List[Any]  							=   PixaStructImageProcessor if is_vision_available() else None







							def 				__lowercase     (				self   :			List[Any]						):
									_a	: int								=				PixaStructImageProcessingTester(self						)







							@property
							def 				__lowercase     (				self   :			List[Any]						):
									return self.image_processor_tester.prepare_image_processor_dict()







							def 				__lowercase     (				self   :			Tuple						):
									_a	: Tuple								=				self.image_processing_class(**self.image_processor_dict						)
									self.assertTrue(hasattr(_UpperCAmelCase      ,'do_normalize'						)						)
									self.assertTrue(hasattr(_UpperCAmelCase      ,'do_convert_rgb'						)						)







							def 				__lowercase     (				self   :			Dict						):
									_a	: Optional[int]								=				self.image_processor_tester.prepare_dummy_image()

									_a	: Optional[int]								=				self.image_processing_class(**self.image_processor_dict						)
									_a	: Optional[int]								=				2048

									_a	: Union[str, Any]								=				image_processor(_UpperCAmelCase      ,return_tensors='pt'      ,max_patches=_UpperCAmelCase						)
									self.assertTrue(torch.allclose(inputs.flattened_patches.mean()      ,torch.tensor(0.06_06						)      ,atol=1E-3      ,rtol=1E-3						)						)







							def 				__lowercase     (				self   :			int						):
									# Initialize image_processor
									_a	: List[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[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[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)      ,)







							def 				__lowercase     (				self   :			Any						):
									# Initialize image_processor
									_a	: Dict								=				self.image_processing_class(**self.image_processor_dict						)
									# create random PIL images
									_a	: Tuple								=				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	: int								=				(
									    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
									    * self.image_processor_tester.num_channels
									) + 2

									_a	: Optional[Any]								=				True

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

											_a	: int								=				'Hello'

											_a	: Optional[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	: List[str]								=				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 				__lowercase     (				self   :			Union[str, Any]						):
									# Initialize image_processor
									_a	: Tuple								=				self.image_processing_class(**self.image_processor_dict						)
									# create random numpy tensors
									_a	: Union[str, Any]								=				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	: Tuple								=				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	: List[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 				__lowercase     (				self   :			Optional[Any]						):
									# Initialize image_processor
									_a	: List[Any]								=				self.image_processing_class(**self.image_processor_dict						)
									# create random PyTorch tensors
									_a	: str								=				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	: 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	: Optional[int]								=				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	: Tuple								=				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       __magic_name__		(       _UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       Optional[Any]  							=   PixaStructImageProcessor if is_vision_available() else None







							def 				__lowercase     (				self   :			int						):
									_a	: str								=				PixaStructImageProcessingTester(self      ,num_channels=4						)
									_a	: List[Any]								=				3







							@property
							def 				__lowercase     (				self   :			Union[str, Any]						):
									return self.image_processor_tester.prepare_image_processor_dict()







							def 				__lowercase     (				self   :			Optional[int]						):
									_a	: str								=				self.image_processing_class(**self.image_processor_dict						)
									self.assertTrue(hasattr(_UpperCAmelCase      ,'do_normalize'						)						)
									self.assertTrue(hasattr(_UpperCAmelCase      ,'do_convert_rgb'						)						)







							def 				__lowercase     (				self   :			Any						):
									# Initialize image_processor
									_a	: Dict								=				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	: Any								=				(
									    (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)      ,)









'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


__lowerCAmelCase		     =  logging.get_logger(__name__)

class       __magic_name__		(       _UpperCamelCase  ):







							def __init__(				self   :			Tuple      ,*_UpperCAmelCase   :			str      ,**_UpperCAmelCase   :			int						):
									warnings.warn(
									    'The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
									    ' use DeiTImageProcessor instead.'      ,_UpperCAmelCase      ,)
									super().__init__(*_UpperCAmelCase      ,**_UpperCAmelCase						)




