



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 ConvNextImageProcessor







class 							a_  (		unittest.TestCase				):

						def __init__(    self	:int   ,						_lowercase	:Optional[int]   ,						_lowercase	:Tuple=7   ,						_lowercase	:Union[str, Any]=3   ,						_lowercase	:Optional[Any]=18   ,						_lowercase	:str=30   ,						_lowercase	:Optional[int]=400   ,						_lowercase	:Optional[Any]=True   ,						_lowercase	:Optional[Any]=None   ,						_lowercase	:Dict=0.875   ,						_lowercase	:Optional[int]=True   ,						_lowercase	:Dict=[0.5, 0.5, 0.5]   ,						_lowercase	:Union[str, Any]=[0.5, 0.5, 0.5]   ,						)		->					str:
												UpperCAmelCase_     							=     size if size is not None else {"shortest_edge": 20}
												UpperCAmelCase_     							=     parent
												UpperCAmelCase_     							=     batch_size
												UpperCAmelCase_     							=     num_channels
												UpperCAmelCase_     							=     image_size
												UpperCAmelCase_     							=     min_resolution
												UpperCAmelCase_     							=     max_resolution
												UpperCAmelCase_     							=     do_resize
												UpperCAmelCase_     							=     size
												UpperCAmelCase_     							=     crop_pct
												UpperCAmelCase_     							=     do_normalize
												UpperCAmelCase_     							=     image_mean
												UpperCAmelCase_     							=     image_std




						def 	__a       (    self	:int)		->					Union[str, Any]:
												return {
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_normalize": self.do_normalize,
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "crop_pct": self.crop_pct,
												}







@require_torch
@require_vision
class 							a_  (		__SCREAMING_SNAKE_CASE					, unittest.TestCase				):
						UpperCamelCase__      :						Union[str, Any]				       =ConvNextImageProcessor if is_vision_available() else None

						def 	__a       (    self	:Dict)		->					Tuple:
												UpperCAmelCase_     							=     ConvNextImageProcessingTester(self)

						@property
						def 	__a       (    self	:Optional[int])		->					List[str]:
												return self.image_processor_tester.prepare_image_processor_dict()

						def 	__a       (    self	:Tuple)		->					str:
												UpperCAmelCase_     							=     self.image_processing_class(**self.image_processor_dict)
												self.assertTrue(hasattr(_snake_case   ,						'''do_resize'''))
												self.assertTrue(hasattr(_snake_case   ,						'''size'''))
												self.assertTrue(hasattr(_snake_case   ,						'''crop_pct'''))
												self.assertTrue(hasattr(_snake_case   ,						'''do_normalize'''))
												self.assertTrue(hasattr(_snake_case   ,						'''image_mean'''))
												self.assertTrue(hasattr(_snake_case   ,						'''image_std'''))

						def 	__a       (    self	:List[Any])		->					Tuple:
												UpperCAmelCase_     							=     self.image_processing_class.from_dict(self.image_processor_dict)
												self.assertEqual(image_processor.size   ,						{'''shortest_edge''': 20})

												UpperCAmelCase_     							=     self.image_processing_class.from_dict(self.image_processor_dict   ,						size=42)
												self.assertEqual(image_processor.size   ,						{'''shortest_edge''': 42})

						def 	__a       (    self	:int)		->					Optional[int]:
												pass

						def 	__a       (    self	:Union[str, Any])		->					Tuple:
												# Initialize image_processing
												UpperCAmelCase_     							=     self.image_processing_class(**self.image_processor_dict)
												# create random PIL images
												UpperCAmelCase_     							=     prepare_image_inputs(self.image_processor_tester   ,						equal_resolution=_snake_case)
												for image in image_inputs:
																		self.assertIsInstance(_snake_case   ,						Image.Image)

												# Test not batched input
												UpperCAmelCase_     							=     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.size['''shortest_edge'''],
												        self.image_processor_tester.size['''shortest_edge'''],
												    )   ,						)

												# Test batched
												UpperCAmelCase_     							=     image_processing(_snake_case   ,						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.size['''shortest_edge'''],
												        self.image_processor_tester.size['''shortest_edge'''],
												    )   ,						)

						def 	__a       (    self	:Optional[Any])		->					Any:
												# Initialize image_processing
												UpperCAmelCase_     							=     self.image_processing_class(**self.image_processor_dict)
												# create random numpy tensors
												UpperCAmelCase_     							=     prepare_image_inputs(self.image_processor_tester   ,						equal_resolution=_snake_case   ,						numpify=_snake_case)
												for image in image_inputs:
																		self.assertIsInstance(_snake_case   ,						np.ndarray)

												# Test not batched input
												UpperCAmelCase_     							=     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.size['''shortest_edge'''],
												        self.image_processor_tester.size['''shortest_edge'''],
												    )   ,						)

												# Test batched
												UpperCAmelCase_     							=     image_processing(_snake_case   ,						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.size['''shortest_edge'''],
												        self.image_processor_tester.size['''shortest_edge'''],
												    )   ,						)




						def 	__a       (    self	:Optional[int])		->					int:
												# Initialize image_processing
												UpperCAmelCase_     							=     self.image_processing_class(**self.image_processor_dict)
												# create random PyTorch tensors
												UpperCAmelCase_     							=     prepare_image_inputs(self.image_processor_tester   ,						equal_resolution=_snake_case   ,						torchify=_snake_case)
												for image in image_inputs:
																		self.assertIsInstance(_snake_case   ,						torch.Tensor)

												# Test not batched input
												UpperCAmelCase_     							=     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.size['''shortest_edge'''],
												        self.image_processor_tester.size['''shortest_edge'''],
												    )   ,						)

												# Test batched
												UpperCAmelCase_     							=     image_processing(_snake_case   ,						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.size['''shortest_edge'''],
												        self.image_processor_tester.size['''shortest_edge'''],
												    )   ,						)








'''simple docstring'''
from transformers import PretrainedConfig
class  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     Optional[Any]          ="custom"






			def __init__(   self       ,_snake_case=1       ,**_snake_case			):
				UpperCAmelCase_			:	Optional[int]      =   attribute
				super().__init__(**_snake_case			)




class  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     Dict          ="custom"






			def __init__(   self       ,_snake_case=1       ,**_snake_case			):
				UpperCAmelCase_			:	Optional[int]      =   attribute




