




"""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 MobileNetVaImageProcessor

class        UpperCAmelCase_  (	unittest.TestCase						):
  def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_=7							,		UpperCamelCase_=3							,		UpperCamelCase_=18							,		UpperCamelCase_=30							,		UpperCamelCase_=4_00							,		UpperCamelCase_=True							,		UpperCamelCase_=None							,		UpperCamelCase_=True							,		UpperCamelCase_=None							,		)		->   Optional[Any]:
     __lowercase	:       Tuple				=   size if size is not None else {'''shortest_edge''': 20}
     __lowercase	:       Optional[int]				=   crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
     __lowercase	:       Any				=   parent
     __lowercase	:       List[str]				=   batch_size
     __lowercase	:       Optional[Any]				=   num_channels
     __lowercase	:       List[Any]				=   image_size
     __lowercase	:       List[Any]				=   min_resolution
     __lowercase	:       List[str]				=   max_resolution
     __lowercase	:       List[Any]				=   do_resize
     __lowercase	:       Union[str, Any]				=   size
     __lowercase	:       Union[str, Any]				=   do_center_crop
     __lowercase	:       int				=   crop_size
  def 					_lowerCamelCase    (		self   )		->   List[Any]:
     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_center_crop": self.do_center_crop,
         "crop_size": self.crop_size,
     }


@require_torch
@require_vision
class        UpperCAmelCase_  (	snake_case ,			unittest.TestCase						):
  UpperCamelCase									=MobileNetVaImageProcessor if is_vision_available() else None
  def 					_lowerCamelCase    (		self   )		->   List[Any]:
     __lowercase	:       Any				=   MobileNetVaImageProcessingTester(self   )
  @property
  def 					_lowerCamelCase    (		self   )		->   int:
     return self.image_processor_tester.prepare_image_processor_dict()
  def 					_lowerCamelCase    (		self   )		->   int:
     __lowercase	:       Optional[int]				=   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'''   )   )
  def 					_lowerCamelCase    (		self   )		->   Union[str, Any]:
     __lowercase	:       Optional[int]				=   self.image_processing_class.from_dict(self.image_processor_dict   )
     self.assertEqual(image_processor.size							,		{'''shortest_edge''': 20}   )
     self.assertEqual(image_processor.crop_size							,		{'''height''': 18, '''width''': 18}   )

     __lowercase	:       Any				=   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 					_lowerCamelCase    (		self   )		->   Union[str, Any]:
     pass
  def 					_lowerCamelCase    (		self   )		->   Union[str, Any]:
     # Initialize image_processing
     __lowercase	:       Optional[int]				=   self.image_processing_class(**self.image_processor_dict   )
     # create random PIL images
     __lowercase	:       str				=   prepare_image_inputs(self.image_processor_tester							,		equal_resolution=UpperCamelCase_   )
     for image in image_inputs:
        self.assertIsInstance(UpperCamelCase_							,		Image.Image   )

     # Test not batched input
     __lowercase	:       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.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )							,		)

     # Test batched
     __lowercase	:       Any				=   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 					_lowerCamelCase    (		self   )		->   int:
     # Initialize image_processing
     __lowercase	:       Optional[Any]				=   self.image_processing_class(**self.image_processor_dict   )
     # create random numpy tensors
     __lowercase	:       Union[str, Any]				=   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
     __lowercase	:       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.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )							,		)

     # Test batched
     __lowercase	:       Tuple				=   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 					_lowerCamelCase    (		self   )		->   Optional[int]:
     # Initialize image_processing
     __lowercase	:       Optional[Any]				=   self.image_processing_class(**self.image_processor_dict   )
     # create random PyTorch tensors
     __lowercase	:       List[str]				=   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
     __lowercase	:       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.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )							,		)

     # Test batched
     __lowercase	:       Optional[int]				=   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"""




a_            =   1_0_0_0







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return x * x







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase = 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

			__lowercase	:       Optional[Any]				=   (b - a) / N_STEPS
			__lowercase	:       Optional[int]				=   function(__UpperCamelCase					) + function(__UpperCamelCase					)

			for i in range(1					,							__UpperCamelCase					):
						__lowercase	:       Union[str, 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()



