






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 EfficientNetImageProcessor
class   UpperCamelCase			(					unittest.TestCase			):







		"""simple docstring"""



		def __init__(	self		:							Dict    ,_SCREAMING_SNAKE_CASE		:							List[Any]    ,_SCREAMING_SNAKE_CASE		:							Optional[int]=1_3    ,_SCREAMING_SNAKE_CASE		:							List[str]=3    ,_SCREAMING_SNAKE_CASE		:							Optional[int]=1_8    ,_SCREAMING_SNAKE_CASE		:							Any=3_0    ,_SCREAMING_SNAKE_CASE		:							Union[str, Any]=4_0_0    ,_SCREAMING_SNAKE_CASE		:							Any=True    ,_SCREAMING_SNAKE_CASE		:							List[str]=None    ,_SCREAMING_SNAKE_CASE		:							int=True    ,_SCREAMING_SNAKE_CASE		:							List[str]=[0.5, 0.5, 0.5]    ,_SCREAMING_SNAKE_CASE		:							Optional[Any]=[0.5, 0.5, 0.5]    ,)     -> Tuple:


						'''simple docstring'''
						A									=   size if size is not None else {'height': 1_8, 'width': 1_8}
						A									=   parent
						A									=   batch_size
						A									=   num_channels
						A									=   image_size
						A									=   min_resolution
						A									=   max_resolution
						A									=   do_resize
						A									=   size
						A									=   do_normalize
						A									=   image_mean
						A									=   image_std







		def    A(	self		:							str		)     -> Optional[int]:


						'''simple docstring'''
						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "size": self.size,
						}


@require_torch
@require_vision
class   UpperCamelCase			(					snake_case__      ,							unittest.TestCase			):







		"""simple docstring"""



		snake_case			 = EfficientNetImageProcessor if is_vision_available() else None
		def    A(	self		:							int		)     -> Any:


						'''simple docstring'''
						A									=   EfficientNetImageProcessorTester(self		)
		@property
		def    A(	self		:							List[str]		)     -> Any:


						'''simple docstring'''
						return self.image_processor_tester.prepare_image_processor_dict()
		def    A(	self		:							Union[str, Any]		)     -> Optional[int]:


						'''simple docstring'''
						A									=   self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'image_mean'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'image_std'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_normalize'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_resize'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'size'		)		)
		def    A(	self		:							Optional[int]		)     -> Any:


						'''simple docstring'''
						A									=   self.image_processing_class.from_dict(self.image_processor_dict		)
						self.assertEqual(image_processor.size    ,{'height': 1_8, 'width': 1_8}		)

						A									=   self.image_processing_class.from_dict(self.image_processor_dict    ,size=4_2		)
						self.assertEqual(image_processor.size    ,{'height': 4_2, 'width': 4_2}		)
		def    A(	self		:							Tuple		)     -> Any:


						'''simple docstring'''
						# Initialize image_processing
						A									=   self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						A									=   prepare_image_inputs(self.image_processor_tester    ,equal_resolution=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,Image.Image		)

						# Test not batched input
						A									=   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['height'],
						        self.image_processor_tester.size['width'],
						    )    ,)

						# Test batched
						A									=   image_processing(_SCREAMING_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['height'],
						        self.image_processor_tester.size['width'],
						    )    ,)
		def    A(	self		:							str		)     -> Optional[Any]:


						'''simple docstring'''
						# Initialize image_processing
						A									=   self.image_processing_class(**self.image_processor_dict		)
						# create random numpy tensors
						A									=   prepare_image_inputs(self.image_processor_tester    ,equal_resolution=_SCREAMING_SNAKE_CASE    ,numpify=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,np.ndarray		)

						# Test not batched input
						A									=   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['height'],
						        self.image_processor_tester.size['width'],
						    )    ,)

						# Test batched
						A									=   image_processing(_SCREAMING_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['height'],
						        self.image_processor_tester.size['width'],
						    )    ,)







		def    A(	self		:							Dict		)     -> List[str]:


						'''simple docstring'''
						# Initialize image_processing
						A									=   self.image_processing_class(**self.image_processor_dict		)
						# create random PyTorch tensors
						A									=   prepare_image_inputs(self.image_processor_tester    ,equal_resolution=_SCREAMING_SNAKE_CASE    ,torchify=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,torch.Tensor		)

						# Test not batched input
						A									=   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['height'],
						        self.image_processor_tester.size['width'],
						    )    ,)

						# Test batched
						A									=   image_processing(_SCREAMING_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['height'],
						        self.image_processor_tester.size['width'],
						    )    ,)













def   snake_case		(				UpperCAmelCase						:							Optional[int],    UpperCAmelCase						:							str ):
				# "Simpson Rule"
				# int(f) = delta_x/2 * (b-a)/3*(f1 + 4f2 + 2f_3 + ... + fn)
				A									=   (boundary[1] - boundary[0]) / steps
				A									=   boundary[0]
				A									=   boundary[1]
				A									=   make_points(UpperCAmelCase,    UpperCAmelCase,    UpperCAmelCase )
				A									=   0.0
				y += (h / 3.0) * f(UpperCAmelCase )
				A									=   2
				for i in x_i:
								y += (h / 3) * (4 - 2 * (cnt % 2)) * f(UpperCAmelCase )
								cnt += 1
				y += (h / 3.0) * f(UpperCAmelCase )
				return y







def   snake_case		(				UpperCAmelCase						:							int,    UpperCAmelCase						:							int,    UpperCAmelCase						:							List[Any] ):
				A									=   a + h
				while x < (b - h):
								yield x
								A									=   x + h







def   snake_case		(				UpperCAmelCase						:							str ):  # enter your function here
				A									=   (x - 0) * (x - 0)
				return y







def   snake_case		(				):
				A									=   0.0  # Lower bound of integration
				A									=   1.0  # Upper bound of integration
				A									=   10.0  # define number of steps or resolution
				A									=   [a, b]  # define boundary of integration
				A									=   method_a(UpperCAmelCase,    UpperCAmelCase )
				print(f'y = {y}' )


if __name__ == "__main__":
					main()





