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, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import OwlViTImageProcessor
class 				lowerCamelCase			(							unittest.TestCase					):






      def __init__(      self  ,		__lowerCamelCase  ,		__lowerCamelCase=7  ,		__lowerCamelCase=3  ,		__lowerCamelCase=18  ,		__lowerCamelCase=30  ,		__lowerCamelCase=4_00  ,		__lowerCamelCase=True  ,		__lowerCamelCase=None  ,		__lowerCamelCase=True  ,		__lowerCamelCase=None  ,		__lowerCamelCase=True  ,		__lowerCamelCase=[0.48_145_466, 0.4_578_275, 0.40_821_073]  ,		__lowerCamelCase=[0.26_862_954, 0.26_130_258, 0.27_577_711]  ,		__lowerCamelCase=True  ,		)     ->				Tuple:


          '''simple docstring'''







          snake_case:						Tuple         =   parent
          snake_case:						Optional[Any]         =   batch_size
          snake_case:						str         =   num_channels
          snake_case:						List[Any]         =   image_size
          snake_case:						Dict         =   min_resolution
          snake_case:						Union[str, Any]         =   max_resolution
          snake_case:						Dict         =   do_resize
          snake_case:						str         =   size if size is not None else {"""height""": 18, """width""": 18}
          snake_case:						int         =   do_center_crop
          snake_case:						Optional[Any]         =   crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
          snake_case:						Optional[Any]         =   do_normalize
          snake_case:						List[str]         =   image_mean
          snake_case:						str         =   image_std
          snake_case:						str         =   do_convert_rgb






      def 			lowerCAmelCase_   (      self						)     ->				Dict:


          '''simple docstring'''







          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,
          }





@require_torch
@require_vision
class 				lowerCamelCase			(							__snake_case				,     unittest.TestCase					):
      __lowerCamelCase							=					OwlViTImageProcessor if is_vision_available() else None






      def 			lowerCAmelCase_   (      self						)     ->				Dict:


          '''simple docstring'''







          snake_case:						Union[str, Any]         =   OwlViTImageProcessingTester(self						)






      @property
      def 			lowerCAmelCase_   (      self						)     ->				Tuple:


          '''simple docstring'''







          return self.image_processor_tester.prepare_image_processor_dict()






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


          '''simple docstring'''







          snake_case:						Tuple         =   self.image_processing_class(**self.image_processor_dict						)
          self.assertTrue(hasattr(__lowerCamelCase  ,		"""do_resize"""						)						)
          self.assertTrue(hasattr(__lowerCamelCase  ,		"""size"""						)						)
          self.assertTrue(hasattr(__lowerCamelCase  ,		"""do_center_crop"""						)						)
          self.assertTrue(hasattr(__lowerCamelCase  ,		"""center_crop"""						)						)
          self.assertTrue(hasattr(__lowerCamelCase  ,		"""do_normalize"""						)						)
          self.assertTrue(hasattr(__lowerCamelCase  ,		"""image_mean"""						)						)
          self.assertTrue(hasattr(__lowerCamelCase  ,		"""image_std"""						)						)
          self.assertTrue(hasattr(__lowerCamelCase  ,		"""do_convert_rgb"""						)						)






      def 			lowerCAmelCase_   (      self						)     ->				int:


          '''simple docstring'''







          snake_case:						Optional[Any]         =   self.image_processing_class.from_dict(self.image_processor_dict						)
          self.assertEqual(image_processor.size  ,		{"""height""": 18, """width""": 18}						)
          self.assertEqual(image_processor.crop_size  ,		{"""height""": 18, """width""": 18}						)

          snake_case:						int         =   self.image_processing_class.from_dict(self.image_processor_dict  ,		size=42  ,		crop_size=84						)
          self.assertEqual(image_processor.size  ,		{"""height""": 42, """width""": 42}						)
          self.assertEqual(image_processor.crop_size  ,		{"""height""": 84, """width""": 84}						)






      def 			lowerCAmelCase_   (      self						)     ->				Optional[Any]:


          '''simple docstring'''







          snake_case:						Any         =   self.image_processing_class(**self.image_processor_dict						)
          # create random PIL images
          snake_case:						Optional[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:						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
          snake_case:						str         =   image_processing(__lowerCamelCase  ,		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						)     ->				Union[str, Any]:


          '''simple docstring'''







          snake_case:						Tuple         =   self.image_processing_class(**self.image_processor_dict						)
          # create random numpy tensors
          snake_case:						Optional[Any]         =   prepare_image_inputs(self.image_processor_tester  ,		equal_resolution=__lowerCamelCase  ,		numpify=__lowerCamelCase						)
          for image in image_inputs:
              self.assertIsInstance(__lowerCamelCase  ,		np.ndarray						)

          # Test not batched input
          snake_case:						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
          snake_case:						List[str]         =   image_processing(__lowerCamelCase  ,		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						)     ->				List[Any]:


          '''simple docstring'''







          snake_case:						str         =   self.image_processing_class(**self.image_processor_dict						)
          # create random PyTorch tensors
          snake_case:						Tuple         =   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:						Dict         =   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
          snake_case:						List[str]         =   image_processing(__lowerCamelCase  ,		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"""],
              )  ,		)



import warnings

from ...utils import logging
from .image_processing_conditional_detr import ConditionalDetrImageProcessor


__lowerCAmelCase    :  Tuple										= logging.get_logger(__name__)
class 				lowerCamelCase			(							__snake_case					):






      def __init__(      self  ,		*__lowerCamelCase  ,		**__lowerCamelCase						)     ->				None:


          '''simple docstring'''







          warnings.warn(
              """The class ConditionalDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
              """ Please use ConditionalDetrImageProcessor instead."""  ,		__lowerCamelCase  ,		)
          super().__init__(*__lowerCamelCase  ,		**__lowerCamelCase						)


