'''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 	__SCREAMING_SNAKE_CASE		(			unittest.TestCase			):







  '''simple docstring'''






  def __init__(					self ,						snake_case_ ,						snake_case_=7 ,						snake_case_=3 ,						snake_case_=1_8 ,						snake_case_=3_0 ,						snake_case_=4_0_0 ,						snake_case_=True ,						snake_case_=None ,						snake_case_=True ,						snake_case_=None ,						snake_case_=True ,						snake_case_=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73] ,						snake_case_=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11] ,						snake_case_=True ,						):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = size if size is not None else {'shortest_edge': 2_0}
       UpperCAmelCase_				:  str  = crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
       UpperCAmelCase_				:  List[Any]  = parent
       UpperCAmelCase_				:  Union[str, Any]  = batch_size
       UpperCAmelCase_				:  str  = num_channels
       UpperCAmelCase_				:  Optional[int]  = image_size
       UpperCAmelCase_				:  List[str]  = min_resolution
       UpperCAmelCase_				:  List[str]  = max_resolution
       UpperCAmelCase_				:  List[Any]  = do_resize
       UpperCAmelCase_				:  Any  = size
       UpperCAmelCase_				:  int  = do_center_crop
       UpperCAmelCase_				:  List[Any]  = crop_size
       UpperCAmelCase_				:  Optional[int]  = do_normalize
       UpperCAmelCase_				:  Dict  = image_mean
       UpperCAmelCase_				:  List[str]  = image_std
       UpperCAmelCase_				:  List[str]  = do_convert_rgb


  def _UpperCamelCase	(					self		):




       '''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,
       }






  def _UpperCamelCase	(					self ,						snake_case_=False ,						snake_case_=False ,						snake_case_=False		):




       '''simple docstring'''

       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       if equal_resolution:
            UpperCAmelCase_				:  Union[str, Any]  = []
            for i in range(self.batch_size		):
                 image_inputs.append(
                     np.random.randint(
                         2_5_5 ,						size=(self.num_channels, self.max_resolution, self.max_resolution) ,						dtype=np.uinta		)		)
       else:
            UpperCAmelCase_				:  List[str]  = []
            for i in range(self.batch_size		):
                 UpperCAmelCase_  ,     UpperCAmelCase_				:  Optional[int]  = np.random.choice(np.arange(self.min_resolution ,						self.max_resolution		) ,						2		)
                 image_inputs.append(np.random.randint(2_5_5 ,						size=(self.num_channels, width, height) ,						dtype=np.uinta		)		)

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

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

       return image_inputs







@require_torch
@require_vision
class 	__SCREAMING_SNAKE_CASE		(			__lowercase		,      unittest.TestCase			):







  '''simple docstring'''






  lowerCamelCase_						:str		       =						CLIPImageProcessor if is_vision_available() else None


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = CLIPImageProcessingTester(self		)


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return self.image_processor_tester.prepare_image_processor_dict()


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = self.image_processing_class(**self.image_processor_dict		)
       self.assertTrue(hasattr(A__ ,						'do_resize'		)		)
       self.assertTrue(hasattr(A__ ,						'size'		)		)
       self.assertTrue(hasattr(A__ ,						'do_center_crop'		)		)
       self.assertTrue(hasattr(A__ ,						'center_crop'		)		)
       self.assertTrue(hasattr(A__ ,						'do_normalize'		)		)
       self.assertTrue(hasattr(A__ ,						'image_mean'		)		)
       self.assertTrue(hasattr(A__ ,						'image_std'		)		)
       self.assertTrue(hasattr(A__ ,						'do_convert_rgb'		)		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = self.image_processing_class.from_dict(self.image_processor_dict		)
       self.assertEqual(image_processor.size ,						{'shortest_edge': 2_0}		)
       self.assertEqual(image_processor.crop_size ,						{'height': 1_8, 'width': 1_8}		)

       UpperCAmelCase_				:  Dict  = self.image_processing_class.from_dict(self.image_processor_dict ,						size=4_2 ,						crop_size=8_4		)
       self.assertEqual(image_processor.size ,						{'shortest_edge': 4_2}		)
       self.assertEqual(image_processor.crop_size ,						{'height': 8_4, 'width': 8_4}		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       pass


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = self.image_processing_class(**self.image_processor_dict		)
       # create random PIL images
       UpperCAmelCase_				:  Optional[int]  = self.image_processor_tester.prepare_inputs(equal_resolution=A__		)
       for image in image_inputs:
            self.assertIsInstance(A__ ,						Image.Image		)

       # Test not batched input
       UpperCAmelCase_				:  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
       UpperCAmelCase_				:  List[Any]  = image_processing(A__ ,						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 _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = self.image_processing_class(**self.image_processor_dict		)
       # create random numpy tensors
       UpperCAmelCase_				:  List[str]  = self.image_processor_tester.prepare_inputs(equal_resolution=A__ ,						numpify=A__		)
       for image in image_inputs:
            self.assertIsInstance(A__ ,						np.ndarray		)

       # Test not batched input
       UpperCAmelCase_				:  Optional[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
       UpperCAmelCase_				:  int  = image_processing(A__ ,						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 _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = self.image_processing_class(**self.image_processor_dict		)
       # create random PyTorch tensors
       UpperCAmelCase_				:  int  = self.image_processor_tester.prepare_inputs(equal_resolution=A__ ,						torchify=A__		)
       for image in image_inputs:
            self.assertIsInstance(A__ ,						torch.Tensor		)

       # Test not batched input
       UpperCAmelCase_				:  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
       UpperCAmelCase_				:  Dict  = image_processing(A__ ,						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 	__SCREAMING_SNAKE_CASE		(			__lowercase		,      unittest.TestCase			):







  '''simple docstring'''






  lowerCamelCase_						:Union[str, Any]		       =						CLIPImageProcessor if is_vision_available() else None


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = CLIPImageProcessingTester(self ,						num_channels=4		)
       UpperCAmelCase_				:  Optional[int]  = 3


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return self.image_processor_tester.prepare_image_processor_dict()


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = self.image_processing_class(**self.image_processor_dict		)
       self.assertTrue(hasattr(A__ ,						'do_resize'		)		)
       self.assertTrue(hasattr(A__ ,						'size'		)		)
       self.assertTrue(hasattr(A__ ,						'do_center_crop'		)		)
       self.assertTrue(hasattr(A__ ,						'center_crop'		)		)
       self.assertTrue(hasattr(A__ ,						'do_normalize'		)		)
       self.assertTrue(hasattr(A__ ,						'image_mean'		)		)
       self.assertTrue(hasattr(A__ ,						'image_std'		)		)
       self.assertTrue(hasattr(A__ ,						'do_convert_rgb'		)		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       pass






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = self.image_processing_class(**self.image_processor_dict		)
       # create random PIL images
       UpperCAmelCase_				:  str  = self.image_processor_tester.prepare_inputs(equal_resolution=A__		)
       for image in image_inputs:
            self.assertIsInstance(A__ ,						Image.Image		)

       # Test not batched input
       UpperCAmelCase_				:  Dict  = 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
       UpperCAmelCase_				:  int  = image_processing(A__ ,						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'],
           ) ,						)









import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


__A     						=      logging.get_logger(__name__)



class     lowercase_       (    __lowercase	):



      def __init__(     self :							List[str]     ,   *A__ :							Tuple     ,   **A__ :							int						)       ->	None:
        warnings.warn(
            '''The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
            ''' Please use DetrImageProcessor instead.'''     ,   A__     ,   )
        super().__init__(*A__     ,   **A__						)

