





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 	UpperCamelCase_     (     unittest.TestCase       ):



					def __init__(		self    :    int					,			lowerCamelCase    :    List[Any]					,			lowerCamelCase    :    int=7					,			lowerCamelCase    :    Dict=3					,			lowerCamelCase    :    List[str]=18					,			lowerCamelCase    :    List[Any]=30					,			lowerCamelCase    :    Any=4_00					,			lowerCamelCase    :    List[Any]=True					,			lowerCamelCase    :    Dict=None					,			lowerCamelCase    :    str=0.875					,			lowerCamelCase    :    int=True					,			lowerCamelCase    :    str=[0.5, 0.5, 0.5]					,			lowerCamelCase    :    Tuple=[0.5, 0.5, 0.5]					,			):
							lowerCamelCase_  :    Tuple   	=			size if size is not None else {'shortest_edge': 20}
							lowerCamelCase_  :    Tuple   	=			parent
							lowerCamelCase_  :    Optional[int]   	=			batch_size
							lowerCamelCase_  :    Dict   	=			num_channels
							lowerCamelCase_  :    Union[str, Any]   	=			image_size
							lowerCamelCase_  :    str   	=			min_resolution
							lowerCamelCase_  :    int   	=			max_resolution
							lowerCamelCase_  :    List[Any]   	=			do_resize
							lowerCamelCase_  :    Tuple   	=			size
							lowerCamelCase_  :    str   	=			crop_pct
							lowerCamelCase_  :    List[str]   	=			do_normalize
							lowerCamelCase_  :    List[str]   	=			image_mean
							lowerCamelCase_  :    Any   	=			image_std





					def      __a    (		self    :    int	):
							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 	UpperCamelCase_     (     snake_case__     ,    unittest.TestCase       ):
					_a			:	Any							=				ConvNextImageProcessor if is_vision_available() else None



					def      __a    (		self    :    Dict	):
							lowerCamelCase_  :    Union[str, Any]   	=			ConvNextImageProcessingTester(self	)



					@property
					def      __a    (		self    :    Union[str, Any]	):
							return self.image_processor_tester.prepare_image_processor_dict()



					def      __a    (		self    :    List[Any]	):
							lowerCamelCase_  :    Optional[int]   	=			self.image_processing_class(**self.image_processor_dict	)
							self.assertTrue(hasattr(lowerCamelCase					,			'do_resize'	)	)
							self.assertTrue(hasattr(lowerCamelCase					,			'size'	)	)
							self.assertTrue(hasattr(lowerCamelCase					,			'crop_pct'	)	)
							self.assertTrue(hasattr(lowerCamelCase					,			'do_normalize'	)	)
							self.assertTrue(hasattr(lowerCamelCase					,			'image_mean'	)	)
							self.assertTrue(hasattr(lowerCamelCase					,			'image_std'	)	)



					def      __a    (		self    :    Union[str, Any]	):
							lowerCamelCase_  :    List[Any]   	=			self.image_processing_class.from_dict(self.image_processor_dict	)
							self.assertEqual(image_processor.size					,			{'shortest_edge': 20}	)

							lowerCamelCase_  :    List[str]   	=			self.image_processing_class.from_dict(self.image_processor_dict					,			size=42	)
							self.assertEqual(image_processor.size					,			{'shortest_edge': 42}	)



					def      __a    (		self    :    Tuple	):
							pass



					def      __a    (		self    :    Optional[Any]	):
							# Initialize image_processing
							lowerCamelCase_  :    Tuple   	=			self.image_processing_class(**self.image_processor_dict	)
							# create random PIL images
							lowerCamelCase_  :    List[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
							lowerCamelCase_  :    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.size['shortest_edge'],
							        self.image_processor_tester.size['shortest_edge'],
							    )					,			)

							# Test batched
							lowerCamelCase_  :    Optional[Any]   	=			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.size['shortest_edge'],
							        self.image_processor_tester.size['shortest_edge'],
							    )					,			)



					def      __a    (		self    :    Any	):
							# Initialize image_processing
							lowerCamelCase_  :    Optional[int]   	=			self.image_processing_class(**self.image_processor_dict	)
							# create random numpy tensors
							lowerCamelCase_  :    str   	=			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
							lowerCamelCase_  :    Union[str, 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.size['shortest_edge'],
							        self.image_processor_tester.size['shortest_edge'],
							    )					,			)

							# Test batched
							lowerCamelCase_  :    Tuple   	=			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.size['shortest_edge'],
							        self.image_processor_tester.size['shortest_edge'],
							    )					,			)





					def      __a    (		self    :    Optional[int]	):
							# Initialize image_processing
							lowerCamelCase_  :    int   	=			self.image_processing_class(**self.image_processor_dict	)
							# create random PyTorch tensors
							lowerCamelCase_  :    Optional[int]   	=			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
							lowerCamelCase_  :    Tuple   	=			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
							lowerCamelCase_  :    Any   	=			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.size['shortest_edge'],
							        self.image_processor_tester.size['shortest_edge'],
							    )					,			)







import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

_lowercase		:     Optional[Any] 					=[(1,)]


def 		_SCREAMING_SNAKE_CASE    (   ):
		lowerCamelCase_  :    Union[str, Any]   	=			bool(os.getenv('RUN_SLOW'       ,0	)	)
		if run_slow:
				assert True
		else:
				assert False, "should have been skipped"


def 		_SCREAMING_SNAKE_CASE    (   ):
		lowerCamelCase_  :    List[str]   	=			bool(os.getenv('RUN_SLOW'       ,0	)	)
		if run_slow and torch_device == "cuda":
				assert True
		else:
				assert False, "should have been skipped"






@require_torch
class 	UpperCamelCase_     (     unittest.TestCase       ):



					@slow
					@require_torch_gpu
					def      __a    (		self    :    Tuple	):
							check_slow_torch_cuda()



					@require_torch_gpu
					@slow
					def      __a    (		self    :    Optional[int]	):
							check_slow_torch_cuda()





					@parameterized.expand(lowerCamelCase	)
					@slow
					def      __a    (		self    :    List[str]					,			lowerCamelCase    :    Optional[Any]=None	):
							check_slow()


@slow
@require_torch_gpu
def 		_SCREAMING_SNAKE_CASE    (   ):
		check_slow_torch_cuda()


@require_torch_gpu
@slow
def 		_SCREAMING_SNAKE_CASE    (   ):
		check_slow_torch_cuda()


@slow
@pytest.mark.parametrize('param'       ,[1]	)
def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
		check_slow()





@pytest.mark.parametrize('param'       ,[1]	)
@slow
def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
		check_slow()
