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    _UpperCamelCase      (						unittest.TestCase ):


			"""simple docstring"""






			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__=[0.4814_5466, 0.457_8275, 0.4082_1073]      ,   lowerCAmelCase__=[0.2686_2954, 0.2613_0258, 0.2757_7711]      ,   lowerCAmelCase__=True      ,   )     ->					int:

							'''simple docstring'''


							__lowercase 			=							size if size is not None else {'height': 2_24, 'width': 2_24}
							__lowercase 			=							crop_size if crop_size is not None else {'height': 18, 'width': 18}
							__lowercase 			=							parent
							__lowercase 			=							batch_size
							__lowercase 			=							num_channels
							__lowercase 			=							image_size
							__lowercase 			=							min_resolution
							__lowercase 			=							max_resolution
							__lowercase 			=							do_resize
							__lowercase 			=							size
							__lowercase 			=							do_center_crop
							__lowercase 			=							crop_size
							__lowercase 			=							do_normalize
							__lowercase 			=							image_mean
							__lowercase 			=							image_std
							__lowercase 			=							do_convert_rgb





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''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        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=False						)     ->					Union[str, Any]:

							'''simple docstring'''


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

							if equal_resolution:
											__lowercase 			=							[]
											for i in range(self.batch_size						):
															image_inputs.append(
															    np.random.randint(
															        2_55      ,   size=(self.num_channels, self.max_resolution, self.max_resolution)      ,   dtype=np.uinta						)						)
							else:
											__lowercase 			=							[]
											for i in range(self.batch_size						):
															__lowercase 			=							np.random.choice(np.arange(self.min_resolution      ,   self.max_resolution						)      ,   2						)
															image_inputs.append(np.random.randint(2_55      ,   size=(self.num_channels, width, height)      ,   dtype=np.uinta						)						)

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

							if torchify:
											__lowercase 			=							[torch.from_numpy(__UpperCamelCase						) for x in image_inputs]

							return image_inputs




@require_torch
@require_vision
class    _UpperCamelCase      (						lowercase_						,unittest.TestCase ):


			"""simple docstring"""






			__a  : int			 =						ChineseCLIPImageProcessor if is_vision_available() else None





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							__lowercase 			=							ChineseCLIPImageProcessingTester(self      ,   do_center_crop=__UpperCamelCase						)





			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Union[str, Any]:

							'''simple docstring'''


							return self.image_processor_tester.prepare_image_processor_dict()





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

							'''simple docstring'''


							__lowercase 			=							self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(__UpperCamelCase      ,   '''do_resize'''						)						)
							self.assertTrue(hasattr(__UpperCamelCase      ,   '''size'''						)						)
							self.assertTrue(hasattr(__UpperCamelCase      ,   '''do_center_crop'''						)						)
							self.assertTrue(hasattr(__UpperCamelCase      ,   '''center_crop'''						)						)
							self.assertTrue(hasattr(__UpperCamelCase      ,   '''do_normalize'''						)						)
							self.assertTrue(hasattr(__UpperCamelCase      ,   '''image_mean'''						)						)
							self.assertTrue(hasattr(__UpperCamelCase      ,   '''image_std'''						)						)
							self.assertTrue(hasattr(__UpperCamelCase      ,   '''do_convert_rgb'''						)						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


							__lowercase 			=							self.image_processing_class.from_dict(self.image_processor_dict						)
							self.assertEqual(image_processor.size      ,   {'''height''': 2_24, '''width''': 2_24}						)
							self.assertEqual(image_processor.crop_size      ,   {'''height''': 18, '''width''': 18}						)

							__lowercase 			=							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        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							pass





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

							'''simple docstring'''


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

							# Test not batched input
							__lowercase 			=							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 			=							image_processing(__UpperCamelCase      ,   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        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[Any]:

							'''simple docstring'''


							__lowercase 			=							self.image_processing_class(**self.image_processor_dict						)
							# create random numpy tensors
							__lowercase 			=							self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase      ,   numpify=__UpperCamelCase						)
							for image in image_inputs:
											self.assertIsInstance(__UpperCamelCase      ,   np.ndarray						)

							# Test not batched input
							__lowercase 			=							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 			=							image_processing(__UpperCamelCase      ,   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        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							__lowercase 			=							self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase      ,   torchify=__UpperCamelCase						)
							for image in image_inputs:
											self.assertIsInstance(__UpperCamelCase      ,   torch.Tensor						)

							# Test not batched input
							__lowercase 			=							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 			=							image_processing(__UpperCamelCase      ,   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    _UpperCamelCase      (						lowercase_						,unittest.TestCase ):


			"""simple docstring"""






			__a  : Optional[Any]			 =						ChineseCLIPImageProcessor if is_vision_available() else None





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

							'''simple docstring'''


							__lowercase 			=							ChineseCLIPImageProcessingTester(self      ,   num_channels=4      ,   do_center_crop=__UpperCamelCase						)
							__lowercase 			=							3





			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[Any]:

							'''simple docstring'''


							return self.image_processor_tester.prepare_image_processor_dict()





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							__lowercase 			=							self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(__UpperCamelCase      ,   '''do_resize'''						)						)
							self.assertTrue(hasattr(__UpperCamelCase      ,   '''size'''						)						)
							self.assertTrue(hasattr(__UpperCamelCase      ,   '''do_center_crop'''						)						)
							self.assertTrue(hasattr(__UpperCamelCase      ,   '''center_crop'''						)						)
							self.assertTrue(hasattr(__UpperCamelCase      ,   '''do_normalize'''						)						)
							self.assertTrue(hasattr(__UpperCamelCase      ,   '''image_mean'''						)						)
							self.assertTrue(hasattr(__UpperCamelCase      ,   '''image_std'''						)						)
							self.assertTrue(hasattr(__UpperCamelCase      ,   '''do_convert_rgb'''						)						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


							pass





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


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

							# Test not batched input
							__lowercase 			=							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 			=							image_processing(__UpperCamelCase      ,   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_beit import BeitImageProcessor


__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)







class        UpperCamelCase		(			lowercase_  ):
   def __init__(			self     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      None:


        '''simple docstring'''


        warnings.warn(
            'The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
            ' use BeitImageProcessor instead.'     ,__UpperCamelCase     ,)
        super().__init__(*__UpperCamelCase     ,**__UpperCamelCase			)

