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 ChineseCLIPImageProcessor






class        _a(    unittest.TestCase    ):







		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 ,						)      ->      List[Any]:


							'''simple docstring'''

							_snake_case       :					Tuple				       =   size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
							_snake_case       :					Any				       =   crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
							_snake_case       :					Optional[int]				       =   parent
							_snake_case       :					Dict				       =   batch_size
							_snake_case       :					str				       =   num_channels
							_snake_case       :					Optional[Any]				       =   image_size
							_snake_case       :					Optional[int]				       =   min_resolution
							_snake_case       :					Tuple				       =   max_resolution
							_snake_case       :					Optional[int]				       =   do_resize
							_snake_case       :					Optional[int]				       =   size
							_snake_case       :					Union[str, Any]				       =   do_center_crop
							_snake_case       :					List[Any]				       =   crop_size
							_snake_case       :					int				       =   do_normalize
							_snake_case       :					Optional[Any]				       =   image_mean
							_snake_case       :					str				       =   image_std
							_snake_case       :					Optional[Any]				       =   do_convert_rgb







		def        lowercase       (    self				)      ->      Optional[int]:


							'''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        lowercase       (    self ,						__snake_case=False ,						__snake_case=False ,						__snake_case=False				)      ->      List[str]:


							'''simple docstring'''

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

							if equal_resolution:
												_snake_case       :					Optional[int]				       =   []
												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:
												_snake_case       :					Dict				       =   []
												for i in range(self.batch_size				):
																	_snake_case       :					Optional[Any]				       =   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
												_snake_case       :					int				       =   [Image.fromarray(np.moveaxis(__a ,						0 ,						-1				)				) for x in image_inputs]

							if torchify:
												_snake_case       :					List[Any]				       =   [torch.from_numpy(__a				) for x in image_inputs]

							return image_inputs






@require_torch
@require_vision
class        _a(    UpperCamelCase_	,   unittest.TestCase    ):
		lowerCamelCase__     :List[str]   				=		ChineseCLIPImageProcessor if is_vision_available() else None







		def        lowercase       (    self				)      ->      int:


							'''simple docstring'''

							_snake_case       :					Union[str, Any]				       =   ChineseCLIPImageProcessingTester(self ,						do_center_crop=__a				)







		@property
		def        lowercase       (    self				)      ->      List[str]:


							'''simple docstring'''

							return self.image_processor_tester.prepare_image_processor_dict()







		def        lowercase       (    self				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					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        lowercase       (    self				)      ->      int:


							'''simple docstring'''

							_snake_case       :					Optional[int]				       =   self.image_processing_class.from_dict(self.image_processor_dict				)
							self.assertEqual(image_processor.size ,						{"height": 2_2_4, "width": 2_2_4}				)
							self.assertEqual(image_processor.crop_size ,						{"height": 1_8, "width": 1_8}				)

							_snake_case       :					List[str]				       =   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        lowercase       (    self				)      ->      Union[str, Any]:


							'''simple docstring'''

							pass







		def        lowercase       (    self				)      ->      List[Any]:


							'''simple docstring'''

							_snake_case       :					List[Any]				       =   self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							_snake_case       :					Optional[Any]				       =   self.image_processor_tester.prepare_inputs(equal_resolution=__a				)
							for image in image_inputs:
												self.assertIsInstance(__a ,						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       :					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        lowercase       (    self				)      ->      List[Any]:


							'''simple docstring'''

							_snake_case       :					Union[str, Any]				       =   self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							_snake_case       :					Optional[int]				       =   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
							_snake_case       :					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
							_snake_case       :					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        lowercase       (    self				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					Any				       =   self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							_snake_case       :					Tuple				       =   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
							_snake_case       :					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
							_snake_case       :					Union[str, 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"],
							    ) ,						)






@require_torch
@require_vision
class        _a(    UpperCamelCase_	,   unittest.TestCase    ):
		lowerCamelCase__     :Union[str, Any]   				=		ChineseCLIPImageProcessor if is_vision_available() else None







		def        lowercase       (    self				)      ->      Optional[int]:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   ChineseCLIPImageProcessingTester(self ,						num_channels=4 ,						do_center_crop=__a				)
							_snake_case       :					List[Any]				       =   3







		@property
		def        lowercase       (    self				)      ->      List[str]:


							'''simple docstring'''

							return self.image_processor_tester.prepare_image_processor_dict()







		def        lowercase       (    self				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					Any				       =   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        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							pass







		def        lowercase       (    self				)      ->      int:


							'''simple docstring'''

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

							# Test not batched input
							_snake_case       :					Optional[int]				       =   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
							_snake_case       :					Optional[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_videomae import VideoMAEImageProcessor


__lowerCAmelCase :str     =    logging.get_logger(__name__)






class        _a(    __A    ):







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


							'''simple docstring'''

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