'''simple docstring'''
import unittest

import numpy as np

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


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import CLIPImageProcessor




class _lowerCAmelCase (  unittest.TestCase				):







      def __init__(     self     ,						lowercase     ,						lowercase=7     ,						lowercase=3     ,						lowercase=18     ,						lowercase=30     ,						lowercase=400     ,						lowercase=True     ,						lowercase=None     ,						lowercase=True     ,						lowercase=None     ,						lowercase=True     ,						lowercase=[0.4814_5466, 0.457_8275, 0.4082_1073]     ,						lowercase=[0.2686_2954, 0.2613_0258, 0.2757_7711]     ,						lowercase=True     ,						):
        lowercase							:       int											=      size if size is not None else {'''shortest_edge''': 20}
        lowercase							:       Optional[Any]											=      crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
        lowercase							:       int											=      parent
        lowercase							:       Union[str, Any]											=      batch_size
        lowercase							:       int											=      num_channels
        lowercase							:       Optional[int]											=      image_size
        lowercase							:       int											=      min_resolution
        lowercase							:       List[str]											=      max_resolution
        lowercase							:       Optional[Any]											=      do_resize
        lowercase							:       List[str]											=      size
        lowercase							:       List[Any]											=      do_center_crop
        lowercase							:       Dict											=      crop_size
        lowercase							:       Optional[int]											=      do_normalize
        lowercase							:       Union[str, Any]											=      image_mean
        lowercase							:       Dict											=      image_std
        lowercase							:       Dict											=      do_convert_rgb







      def     lowerCamelCase__			(     self    ):
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
        }







      def     lowerCamelCase__			(     self     ,						lowercase=False     ,						lowercase=False     ,						lowercase=False    ):
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
          lowercase							:       Optional[Any]											=      []
          for i in range(self.batch_size    ):
            image_inputs.append(
                np.random.randint(
                    255     ,						size=(self.num_channels, self.max_resolution, self.max_resolution)     ,						dtype=np.uinta    )    )
        else:
          lowercase							:       Any											=      []
          for i in range(self.batch_size    ):
            lowercase							,			lowercase							:       List[str]											=      np.random.choice(np.arange(self.min_resolution     ,						self.max_resolution    )     ,						2    )
            image_inputs.append(np.random.randint(255     ,						size=(self.num_channels, width, height)     ,						dtype=np.uinta    )    )

        if not numpify and not torchify:
          # PIL expects the channel dimension as last dimension
          lowercase							:       Tuple											=      [Image.fromarray(np.moveaxis(lowercase     ,						0     ,						-1    )    ) for x in image_inputs]

        if torchify:
          lowercase							:       Tuple											=      [torch.from_numpy(lowercase    ) for x in image_inputs]

        return image_inputs





@require_torch
@require_vision
class _lowerCAmelCase (  snake_case       ,      unittest.TestCase				):
      UpperCAmelCase_  :	Tuple     =					CLIPImageProcessor if is_vision_available() else None







      def     lowerCamelCase__			(     self    ):
        lowercase							:       Optional[int]											=      CLIPImageProcessingTester(self    )







      @property
      def     lowerCamelCase__			(     self    ):
        return self.image_processor_tester.prepare_image_processor_dict()







      def     lowerCamelCase__			(     self    ):
        lowercase							:       Union[str, Any]											=      self.image_processing_class(**self.image_processor_dict    )
        self.assertTrue(hasattr(lowercase     ,						'''do_resize'''    )    )
        self.assertTrue(hasattr(lowercase     ,						'''size'''    )    )
        self.assertTrue(hasattr(lowercase     ,						'''do_center_crop'''    )    )
        self.assertTrue(hasattr(lowercase     ,						'''center_crop'''    )    )
        self.assertTrue(hasattr(lowercase     ,						'''do_normalize'''    )    )
        self.assertTrue(hasattr(lowercase     ,						'''image_mean'''    )    )
        self.assertTrue(hasattr(lowercase     ,						'''image_std'''    )    )
        self.assertTrue(hasattr(lowercase     ,						'''do_convert_rgb'''    )    )







      def     lowerCamelCase__			(     self    ):
        lowercase							:       int											=      self.image_processing_class.from_dict(self.image_processor_dict    )
        self.assertEqual(image_processor.size     ,						{'''shortest_edge''': 20}    )
        self.assertEqual(image_processor.crop_size     ,						{'''height''': 18, '''width''': 18}    )

        lowercase							:       Union[str, Any]											=      self.image_processing_class.from_dict(self.image_processor_dict     ,						size=42     ,						crop_size=84    )
        self.assertEqual(image_processor.size     ,						{'''shortest_edge''': 42}    )
        self.assertEqual(image_processor.crop_size     ,						{'''height''': 84, '''width''': 84}    )







      def     lowerCamelCase__			(     self    ):
        pass







      def     lowerCamelCase__			(     self    ):
        # Initialize image_processing
        lowercase							:       List[str]											=      self.image_processing_class(**self.image_processor_dict    )
        # create random PIL images
        lowercase							:       Dict											=      self.image_processor_tester.prepare_inputs(equal_resolution=lowercase    )
        for image in image_inputs:
          self.assertIsInstance(lowercase     ,						Image.Image    )

        # Test not batched input
        lowercase							:       List[Any]											=      image_processing(image_inputs[0]     ,						return_tensors='''pt'''    ).pixel_values
        self.assertEqual(
            encoded_images.shape     ,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )     ,						)

        # Test batched
        lowercase							:       Dict											=      image_processing(lowercase     ,						return_tensors='''pt'''    ).pixel_values
        self.assertEqual(
            encoded_images.shape     ,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )     ,						)







      def     lowerCamelCase__			(     self    ):
        # Initialize image_processing
        lowercase							:       Any											=      self.image_processing_class(**self.image_processor_dict    )
        # create random numpy tensors
        lowercase							:       List[str]											=      self.image_processor_tester.prepare_inputs(equal_resolution=lowercase     ,						numpify=lowercase    )
        for image in image_inputs:
          self.assertIsInstance(lowercase     ,						np.ndarray    )

        # Test not batched input
        lowercase							:       int											=      image_processing(image_inputs[0]     ,						return_tensors='''pt'''    ).pixel_values
        self.assertEqual(
            encoded_images.shape     ,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )     ,						)

        # Test batched
        lowercase							:       str											=      image_processing(lowercase     ,						return_tensors='''pt'''    ).pixel_values
        self.assertEqual(
            encoded_images.shape     ,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )     ,						)







      def     lowerCamelCase__			(     self    ):
        # Initialize image_processing
        lowercase							:       Dict											=      self.image_processing_class(**self.image_processor_dict    )
        # create random PyTorch tensors
        lowercase							:       int											=      self.image_processor_tester.prepare_inputs(equal_resolution=lowercase     ,						torchify=lowercase    )
        for image in image_inputs:
          self.assertIsInstance(lowercase     ,						torch.Tensor    )

        # Test not batched input
        lowercase							:       List[str]											=      image_processing(image_inputs[0]     ,						return_tensors='''pt'''    ).pixel_values
        self.assertEqual(
            encoded_images.shape     ,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )     ,						)

        # Test batched
        lowercase							:       Dict											=      image_processing(lowercase     ,						return_tensors='''pt'''    ).pixel_values
        self.assertEqual(
            encoded_images.shape     ,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )     ,						)





@require_torch
@require_vision
class _lowerCAmelCase (  snake_case       ,      unittest.TestCase				):
      UpperCAmelCase_  :	List[Any]     =					CLIPImageProcessor if is_vision_available() else None







      def     lowerCamelCase__			(     self    ):
        lowercase							:       Union[str, Any]											=      CLIPImageProcessingTester(self     ,						num_channels=4    )
        lowercase							:       Tuple											=      3







      @property
      def     lowerCamelCase__			(     self    ):
        return self.image_processor_tester.prepare_image_processor_dict()







      def     lowerCamelCase__			(     self    ):
        lowercase							:       str											=      self.image_processing_class(**self.image_processor_dict    )
        self.assertTrue(hasattr(lowercase     ,						'''do_resize'''    )    )
        self.assertTrue(hasattr(lowercase     ,						'''size'''    )    )
        self.assertTrue(hasattr(lowercase     ,						'''do_center_crop'''    )    )
        self.assertTrue(hasattr(lowercase     ,						'''center_crop'''    )    )
        self.assertTrue(hasattr(lowercase     ,						'''do_normalize'''    )    )
        self.assertTrue(hasattr(lowercase     ,						'''image_mean'''    )    )
        self.assertTrue(hasattr(lowercase     ,						'''image_std'''    )    )
        self.assertTrue(hasattr(lowercase     ,						'''do_convert_rgb'''    )    )







      def     lowerCamelCase__			(     self    ):
        pass







      def     lowerCamelCase__			(     self    ):
        # Initialize image_processing
        lowercase							:       Tuple											=      self.image_processing_class(**self.image_processor_dict    )
        # create random PIL images
        lowercase							:       List[Any]											=      self.image_processor_tester.prepare_inputs(equal_resolution=lowercase    )
        for image in image_inputs:
          self.assertIsInstance(lowercase     ,						Image.Image    )

        # Test not batched input
        lowercase							:       List[str]											=      image_processing(image_inputs[0]     ,						return_tensors='''pt'''    ).pixel_values
        self.assertEqual(
            encoded_images.shape     ,						(
                1,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )     ,						)

        # Test batched
        lowercase							:       Dict											=      image_processing(lowercase     ,						return_tensors='''pt'''    ).pixel_values
        self.assertEqual(
            encoded_images.shape     ,						(
                self.image_processor_tester.batch_size,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )     ,						)
'''simple docstring'''
import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


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




class _lowerCAmelCase (  snake_case				):







      def __init__(     self     ,						*lowercase     ,						**lowercase    ):
        warnings.warn(
            '''The class LayoutLMv3FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
            ''' Please use LayoutLMv3ImageProcessor instead.'''     ,						lowercase     ,						)
        super().__init__(*lowercase     ,						**lowercase    )