




'''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:
      A_			      =		False

if is_vision_available():
      from PIL import Image

      from transformers import PixaStructImageProcessor






class        lowercase_      (						unittest.TestCase							):





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





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}





     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           snake_case__       :   List[Any]	      =    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
           snake_case__       :   Union[str, Any]	      =    Image.open(requests.get(_lowerCAmelCase     ,							stream=_lowerCAmelCase			).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        lowercase_      (						_SCREAMING_SNAKE_CASE						,       unittest.TestCase							):
     A_											=			PixaStructImageProcessor if is_vision_available() else None





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           snake_case__       :   List[str]	      =    PixaStructImageProcessingTester(self			)





     @property
     def 			_lowerCAmelCase      (     self     :					int			):
           return self.image_processor_tester.prepare_image_processor_dict()





     def 			_lowerCAmelCase      (     self     :					Any			):
           snake_case__       :   Tuple	      =    self.image_processing_class(**self.image_processor_dict			)
           self.assertTrue(hasattr(_lowerCAmelCase     ,							'do_normalize'			)			)
           self.assertTrue(hasattr(_lowerCAmelCase     ,							'do_convert_rgb'			)			)





     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           snake_case__       :   str	      =    self.image_processor_tester.prepare_dummy_image()

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

           snake_case__       :   Any	      =    image_processor(_lowerCAmelCase     ,							return_tensors='pt'     ,							max_patches=_lowerCAmelCase			)
           self.assertTrue(torch.allclose(inputs.flattened_patches.mean()     ,							torch.tensor(0.0_6_0_6			)     ,							atol=1E-3     ,							rtol=1E-3			)			)





     def 			_lowerCAmelCase      (     self     :					str			):
           # Initialize image_processor
           snake_case__       :   int	      =    self.image_processing_class(**self.image_processor_dict			)
           # create random PIL images
           snake_case__       :   List[Any]	      =    prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=_lowerCAmelCase			)
           for image in image_inputs:
                 self.assertIsInstance(_lowerCAmelCase     ,							Image.Image			)

           # Test not batched input
           snake_case__       :   Union[str, 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
                 snake_case__       :   List[Any]	      =    image_processor(
                     image_inputs[0]     ,							return_tensors='pt'     ,							max_patches=_lowerCAmelCase			).flattened_patches
                 self.assertEqual(
                     encoded_images.shape     ,							(1, max_patch, expected_hidden_dim)     ,							)

                 # Test batched
                 snake_case__       :   List[str]	      =    image_processor(
                     _lowerCAmelCase     ,							return_tensors='pt'     ,							max_patches=_lowerCAmelCase			).flattened_patches
                 self.assertEqual(
                     encoded_images.shape     ,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,							)





     def 			_lowerCAmelCase      (     self     :					int			):
           # Initialize image_processor
           snake_case__       :   Any	      =    self.image_processing_class(**self.image_processor_dict			)
           # create random PIL images
           snake_case__       :   Optional[int]	      =    prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=_lowerCAmelCase			)
           for image in image_inputs:
                 self.assertIsInstance(_lowerCAmelCase     ,							Image.Image			)

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

           snake_case__       :   Optional[int]	      =    True

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

                 snake_case__       :   int	      =    'Hello'

                 snake_case__       :   int	      =    image_processor(
                     image_inputs[0]     ,							return_tensors='pt'     ,							max_patches=_lowerCAmelCase     ,							header_text=_lowerCAmelCase			).flattened_patches
                 self.assertEqual(
                     encoded_images.shape     ,							(1, max_patch, expected_hidden_dim)     ,							)

                 # Test batched
                 snake_case__       :   List[str]	      =    image_processor(
                     _lowerCAmelCase     ,							return_tensors='pt'     ,							max_patches=_lowerCAmelCase     ,							header_text=_lowerCAmelCase			).flattened_patches
                 self.assertEqual(
                     encoded_images.shape     ,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,							)





     def 			_lowerCAmelCase      (     self     :					str			):
           # Initialize image_processor
           snake_case__       :   int	      =    self.image_processing_class(**self.image_processor_dict			)
           # create random numpy tensors
           snake_case__       :   Any	      =    prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=_lowerCAmelCase     ,							numpify=_lowerCAmelCase			)
           for image in image_inputs:
                 self.assertIsInstance(_lowerCAmelCase     ,							np.ndarray			)

           snake_case__       :   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
                 snake_case__       :   str	      =    image_processor(
                     image_inputs[0]     ,							return_tensors='pt'     ,							max_patches=_lowerCAmelCase			).flattened_patches
                 self.assertEqual(
                     encoded_images.shape     ,							(1, max_patch, expected_hidden_dim)     ,							)

                 # Test batched
                 snake_case__       :   str	      =    image_processor(
                     _lowerCAmelCase     ,							return_tensors='pt'     ,							max_patches=_lowerCAmelCase			).flattened_patches
                 self.assertEqual(
                     encoded_images.shape     ,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,							)





     def 			_lowerCAmelCase      (     self     :					str			):
           # Initialize image_processor
           snake_case__       :   Tuple	      =    self.image_processing_class(**self.image_processor_dict			)
           # create random PyTorch tensors
           snake_case__       :   Union[str, Any]	      =    prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=_lowerCAmelCase     ,							torchify=_lowerCAmelCase			)
           for image in image_inputs:
                 self.assertIsInstance(_lowerCAmelCase     ,							torch.Tensor			)

           # Test not batched input
           snake_case__       :   Tuple	      =    (
               (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
                 snake_case__       :   int	      =    image_processor(
                     image_inputs[0]     ,							return_tensors='pt'     ,							max_patches=_lowerCAmelCase			).flattened_patches
                 self.assertEqual(
                     encoded_images.shape     ,							(1, max_patch, expected_hidden_dim)     ,							)

                 # Test batched
                 snake_case__       :   Any	      =    image_processor(
                     _lowerCAmelCase     ,							return_tensors='pt'     ,							max_patches=_lowerCAmelCase			).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        lowercase_      (						_SCREAMING_SNAKE_CASE						,       unittest.TestCase							):
     A_											=			PixaStructImageProcessor if is_vision_available() else None





     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           snake_case__       :   List[Any]	      =    PixaStructImageProcessingTester(self     ,							num_channels=4			)
           snake_case__       :   Dict	      =    3





     @property
     def 			_lowerCAmelCase      (     self     :					Any			):
           return self.image_processor_tester.prepare_image_processor_dict()





     def 			_lowerCAmelCase      (     self     :					str			):
           snake_case__       :   Dict	      =    self.image_processing_class(**self.image_processor_dict			)
           self.assertTrue(hasattr(_lowerCAmelCase     ,							'do_normalize'			)			)
           self.assertTrue(hasattr(_lowerCAmelCase     ,							'do_convert_rgb'			)			)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           # Initialize image_processor
           snake_case__       :   Any	      =    self.image_processing_class(**self.image_processor_dict			)
           # create random PIL images
           snake_case__       :   int	      =    prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=_lowerCAmelCase			)
           for image in image_inputs:
                 self.assertIsInstance(_lowerCAmelCase     ,							Image.Image			)

           # Test not batched input
           snake_case__       :   Tuple	      =    (
               (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
                 snake_case__       :   str	      =    image_processor(
                     image_inputs[0]     ,							return_tensors='pt'     ,							max_patches=_lowerCAmelCase			).flattened_patches
                 self.assertEqual(
                     encoded_images.shape     ,							(1, max_patch, expected_hidden_dim)     ,							)

                 # Test batched
                 snake_case__       :   List[Any]	      =    image_processor(
                     _lowerCAmelCase     ,							return_tensors='pt'     ,							max_patches=_lowerCAmelCase			).flattened_patches
                 self.assertEqual(
                     encoded_images.shape     ,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,							)






import warnings

from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor


lowerCamelCase__					:  Optional[int]      							=				logging.get_logger(__name__)





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







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