



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_video_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import VideoMAEImageProcessor



class     __snake_case						(					unittest.TestCase						):




							def __init__(	self					:	str    ,    A_					:	str    ,    A_					:	int=7    ,    A_					:	List[Any]=3    ,    A_					:	Dict=1_0    ,    A_					:	List[str]=1_8    ,    A_					:	List[Any]=3_0    ,    A_					:	str=4_0_0    ,    A_					:	str=True    ,    A_					:	Tuple=None    ,    A_					:	Optional[Any]=True    ,    A_					:	Tuple=[0.5, 0.5, 0.5]    ,    A_					:	Any=[0.5, 0.5, 0.5]    ,    A_					:	List[str]=None    ,    ):
									lowerCAmelCase_      :					Optional[Any]     =			size if size is not None else {'''shortest_edge''': 1_8}
									lowerCAmelCase_      :					Optional[int]     =			crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}

									lowerCAmelCase_      :					Any     =			parent
									lowerCAmelCase_      :					Optional[Any]     =			batch_size
									lowerCAmelCase_      :					List[Any]     =			num_channels
									lowerCAmelCase_      :					Optional[int]     =			num_frames
									lowerCAmelCase_      :					Optional[int]     =			image_size
									lowerCAmelCase_      :					List[Any]     =			min_resolution
									lowerCAmelCase_      :					str     =			max_resolution
									lowerCAmelCase_      :					List[str]     =			do_resize
									lowerCAmelCase_      :					int     =			size
									lowerCAmelCase_      :					Union[str, Any]     =			do_normalize
									lowerCAmelCase_      :					Union[str, Any]     =			image_mean
									lowerCAmelCase_      :					List[str]     =			image_std
									lowerCAmelCase_      :					Any     =			crop_size







							def        UpperCAmelCase__      (	self					:	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_size": self.crop_size,
									}





@require_torch
@require_vision
class     __snake_case						(					UpperCamelCase_						,unittest.TestCase						):
							_a      				=			VideoMAEImageProcessor if is_vision_available() else None




							def        UpperCAmelCase__      (	self					:	Tuple):
									lowerCAmelCase_      :					int     =			VideoMAEImageProcessingTester(self)




							@property
							def        UpperCAmelCase__      (	self					:	Tuple):
									return self.image_processor_tester.prepare_image_processor_dict()




							def        UpperCAmelCase__      (	self					:	Optional[int]):
									lowerCAmelCase_      :					int     =			self.image_processing_class(**self.image_processor_dict)
									self.assertTrue(hasattr(A_    ,    '''image_mean'''))
									self.assertTrue(hasattr(A_    ,    '''image_std'''))
									self.assertTrue(hasattr(A_    ,    '''do_normalize'''))
									self.assertTrue(hasattr(A_    ,    '''do_resize'''))
									self.assertTrue(hasattr(A_    ,    '''do_center_crop'''))
									self.assertTrue(hasattr(A_    ,    '''size'''))




							def        UpperCAmelCase__      (	self					:	Dict):
									lowerCAmelCase_      :					int     =			self.image_processing_class.from_dict(self.image_processor_dict)
									self.assertEqual(image_processor.size    ,    {'''shortest_edge''': 1_8})
									self.assertEqual(image_processor.crop_size    ,    {'''height''': 1_8, '''width''': 1_8})

									lowerCAmelCase_      :					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        UpperCAmelCase__      (	self					:	Dict):
									pass




							def        UpperCAmelCase__      (	self					:	str):
									# Initialize image_processing
									lowerCAmelCase_      :					int     =			self.image_processing_class(**self.image_processor_dict)
									# create random PIL videos
									lowerCAmelCase_      :					Optional[int]     =			prepare_video_inputs(self.image_processor_tester    ,    equal_resolution=A_)
									for video in video_inputs:
											self.assertIsInstance(A_    ,    A_)
											self.assertIsInstance(video[0]    ,    Image.Image)

									# Test not batched input
									lowerCAmelCase_      :					int     =			image_processing(video_inputs[0]    ,    return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,    (
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,    )

									# Test batched
									lowerCAmelCase_      :					Tuple     =			image_processing(A_    ,    return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,    (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,    )




							def        UpperCAmelCase__      (	self					:	int):
									# Initialize image_processing
									lowerCAmelCase_      :					Dict     =			self.image_processing_class(**self.image_processor_dict)
									# create random numpy tensors
									lowerCAmelCase_      :					str     =			prepare_video_inputs(self.image_processor_tester    ,    equal_resolution=A_    ,    numpify=A_)
									for video in video_inputs:
											self.assertIsInstance(A_    ,    A_)
											self.assertIsInstance(video[0]    ,    np.ndarray)

									# Test not batched input
									lowerCAmelCase_      :					Tuple     =			image_processing(video_inputs[0]    ,    return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,    (
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,    )

									# Test batched
									lowerCAmelCase_      :					Union[str, Any]     =			image_processing(A_    ,    return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,    (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,    )







							def        UpperCAmelCase__      (	self					:	List[str]):
									# Initialize image_processing
									lowerCAmelCase_      :					Tuple     =			self.image_processing_class(**self.image_processor_dict)
									# create random PyTorch tensors
									lowerCAmelCase_      :					Tuple     =			prepare_video_inputs(self.image_processor_tester    ,    equal_resolution=A_    ,    torchify=A_)
									for video in video_inputs:
											self.assertIsInstance(A_    ,    A_)
											self.assertIsInstance(video[0]    ,    torch.Tensor)

									# Test not batched input
									lowerCAmelCase_      :					List[str]     =			image_processing(video_inputs[0]    ,    return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,    (
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,    )

									# Test batched
									lowerCAmelCase_      :					List[Any]     =			image_processing(A_    ,    return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,    (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,    )










A__  :  str       = 1000


def       UpperCamelCase(  __UpperCamelCase							:		float			):
		return x * x


def       UpperCamelCase(  __UpperCamelCase							:		Optional[int]					,__UpperCamelCase							:		float					,__UpperCamelCase							:		float					,__UpperCamelCase							:		int = 4			):
		assert callable(
		    __UpperCamelCase			), f"""the function(object) passed should be callable your input : {function}"""
		assert isinstance(__UpperCamelCase					,(float, int)			), f"""a should be float or integer your input : {a}"""
		assert isinstance(function(__UpperCamelCase			)					,(float, int)			), (
		    "the function should return integer or float return type of your function, "
		    f"""{type(__UpperCamelCase			)}"""
		)
		assert isinstance(__UpperCamelCase					,(float, int)			), f"""b should be float or integer your input : {b}"""
		assert (
		    isinstance(__UpperCamelCase					,__UpperCamelCase			) and precision > 0
		), f"""precision should be positive integer your input : {precision}"""

		# just applying the formula of simpson for approximate integration written in
		# mentioned article in first comment of this file and above this function

		lowerCAmelCase_      :					List[str]     =			(b - a) / N_STEPS
		lowerCAmelCase_      :					str     =			function(__UpperCamelCase			) + function(__UpperCamelCase			)

		for i in range(1					,__UpperCamelCase			):
				lowerCAmelCase_      :					Optional[Any]     =			a + h * i
				result += function(__UpperCamelCase			) * (4 if i % 2 else 2)

		result *= h / 3
		return round(__UpperCamelCase					,__UpperCamelCase			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()





