



"""simple docstring"""


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 PoolFormerImageProcessor
class 			_a (					unittest.TestCase):
			"""simple docstring"""



			def __init__(		self     :    Optional[Any]				,   __UpperCamelCase     :    Tuple				,   __UpperCamelCase     :    Union[str, Any]=7				,   __UpperCamelCase     :    Optional[int]=3				,   __UpperCamelCase     :    str=3_0				,   __UpperCamelCase     :    List[str]=4_0_0				,   __UpperCamelCase     :    Optional[Any]=True				,   __UpperCamelCase     :    str=None				,   __UpperCamelCase     :    Tuple=0.9				,   __UpperCamelCase     :    Union[str, Any]=None				,   __UpperCamelCase     :    int=True				,   __UpperCamelCase     :    List[Any]=[0.5, 0.5, 0.5]				,   __UpperCamelCase     :    str=[0.5, 0.5, 0.5]				,   )->Dict:
							_UpperCAmelCase					=    size if size is not None else {'''shortest_edge''': 3_0}
							_UpperCAmelCase					=    crop_size if crop_size is not None else {'''height''': 3_0, '''width''': 3_0}
							_UpperCAmelCase					=    parent
							_UpperCAmelCase					=    batch_size
							_UpperCAmelCase					=    num_channels
							_UpperCAmelCase					=    min_resolution
							_UpperCAmelCase					=    max_resolution
							_UpperCAmelCase					=    do_resize_and_center_crop
							_UpperCAmelCase					=    size
							_UpperCAmelCase					=    crop_pct
							_UpperCAmelCase					=    crop_size
							_UpperCAmelCase					=    do_normalize
							_UpperCAmelCase					=    image_mean
							_UpperCAmelCase					=    image_std



			def  lowercase__						(		self     :    Optional[Any]   )->Union[str, Any]:
							return {
							    "size": self.size,
							    "do_resize_and_center_crop": self.do_resize_and_center_crop,
							    "crop_pct": self.crop_pct,
							    "crop_size": self.crop_size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							}



@require_torch
@require_vision
class 			_a (					lowerCAmelCase			,							unittest.TestCase):
			"""simple docstring"""
			UpperCamelCase__			      =	PoolFormerImageProcessor if is_vision_available() else None



			def  lowercase__						(		self     :    Any   )->str:
							_UpperCAmelCase					=    PoolFormerImageProcessingTester(self   )



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



			def  lowercase__						(		self     :    str   )->List[Any]:
							_UpperCAmelCase					=    self.image_processing_class(**self.image_processor_dict   )
							self.assertTrue(hasattr(__UpperCamelCase				,   '''do_resize_and_center_crop'''   )   )
							self.assertTrue(hasattr(__UpperCamelCase				,   '''size'''   )   )
							self.assertTrue(hasattr(__UpperCamelCase				,   '''crop_pct'''   )   )
							self.assertTrue(hasattr(__UpperCamelCase				,   '''do_normalize'''   )   )
							self.assertTrue(hasattr(__UpperCamelCase				,   '''image_mean'''   )   )
							self.assertTrue(hasattr(__UpperCamelCase				,   '''image_std'''   )   )



			def  lowercase__						(		self     :    List[Any]   )->Union[str, Any]:
							_UpperCAmelCase					=    self.image_processing_class.from_dict(self.image_processor_dict   )
							self.assertEqual(image_processor.size				,   {'''shortest_edge''': 3_0}   )
							self.assertEqual(image_processor.crop_size				,   {'''height''': 3_0, '''width''': 3_0}   )

							_UpperCAmelCase					=    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     :    Dict   )->List[str]:
							pass



			def  lowercase__						(		self     :    List[Any]   )->List[str]:
							# 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=__UpperCamelCase   )
							for image in image_inputs:
											self.assertIsInstance(__UpperCamelCase				,   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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )				,   )

							# Test batched
							_UpperCAmelCase					=    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  lowercase__						(		self     :    Optional[int]   )->List[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=__UpperCamelCase				,   numpify=__UpperCamelCase   )
							for image in image_inputs:
											self.assertIsInstance(__UpperCamelCase				,   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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )				,   )

							# Test batched
							_UpperCAmelCase					=    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  lowercase__						(		self     :    List[Any]   )->List[Any]:
							# 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=__UpperCamelCase				,   torchify=__UpperCamelCase   )
							for image in image_inputs:
											self.assertIsInstance(__UpperCamelCase				,   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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )				,   )

							# Test batched
							_UpperCAmelCase					=    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'''],
							    )				,   )








"""simple docstring"""


from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				def is_in_circle(_SCREAMING_SNAKE_CASE   :						float					,     _SCREAMING_SNAKE_CASE   :						float				) -> bool:
								_UpperCAmelCase					=    sqrt((x**2) + (y**2)				)
								# Our circle has a radius of 1, so a distance
								# greater than 1 would land outside the circle.
								return distance_from_centre <= 1

				# The proportion of guesses that landed in the circle
				_UpperCAmelCase					=    mean(
				    int(is_in_circle(uniform(-1.0					,     1.0				)					,     uniform(-1.0					,     1.0				)				)				)
				    for _ in range(_SCREAMING_SNAKE_CASE				)				)
				# The ratio of the area for circle to square is pi/4.
				_UpperCAmelCase					=    proportion * 4
				print(f'The estimated value of pi is {pi_estimate}'				)
				print(f'The numpy value of pi is {pi}'				)
				print(f'The total error is {abs(pi - pi_estimate				)}'				)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						Callable[[float], float]					,     _SCREAMING_SNAKE_CASE   :						float = 0.0					,     _SCREAMING_SNAKE_CASE   :						float = 1.0					,     ):






				'''simple docstring'''



				return mean(
				    function_to_integrate(uniform(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)				) for _ in range(_SCREAMING_SNAKE_CASE				)				) * (max_value - min_value)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						float = 0.0					,     _SCREAMING_SNAKE_CASE   :						float = 1.0				):






				'''simple docstring'''



				def identity_function(_SCREAMING_SNAKE_CASE   :						float				) -> float:
								return x

				_UpperCAmelCase					=    area_under_curve_estimator(
				    _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    (max_value * max_value - min_value * min_value) / 2

				print('''******************'''				)
				print(f'Estimating area under y=x where x varies from {min_value} to {max_value}'				)
				print(f'Estimated value is {estimated_value}'				)
				print(f'Expected value is {expected_value}'				)
				print(f'Total error is {abs(estimated_value - expected_value				)}'				)
				print('''******************'''				)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				def function_to_integrate(_SCREAMING_SNAKE_CASE   :						float				) -> float:
								return sqrt(4.0 - x * x				)

				_UpperCAmelCase					=    area_under_curve_estimator(
				    _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     0.0					,     2.0				)

				print('''******************'''				)
				print('''Estimating pi using area_under_curve_estimator'''				)
				print(f'Estimated value is {estimated_value}'				)
				print(f'Expected value is {pi}'				)
				print(f'Total error is {abs(estimated_value - pi				)}'				)
				print('''******************'''				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



