




"""simple docstring"""




import random
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():
			import PIL

			from transformers import FlavaImageProcessor
			from transformers.image_utils import PILImageResampling
			from transformers.models.flava.image_processing_flava import (
			    FLAVA_CODEBOOK_MEAN,
			    FLAVA_CODEBOOK_STD,
			    FLAVA_IMAGE_MEAN,
			    FLAVA_IMAGE_STD,
			)
else:
			a_            =   a_            =   a_            =   a_            =   None

class        UpperCAmelCase_  (	unittest.TestCase						):
		def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_=7							,		UpperCamelCase_=3							,		UpperCamelCase_=30							,		UpperCamelCase_=4_00							,		UpperCamelCase_=True							,		UpperCamelCase_=None							,		UpperCamelCase_=True							,		UpperCamelCase_=None							,		UpperCamelCase_=None							,		UpperCamelCase_=True							,		UpperCamelCase_=1 / 2_55							,		UpperCamelCase_=True							,		UpperCamelCase_=FLAVA_IMAGE_MEAN							,		UpperCamelCase_=FLAVA_IMAGE_STD							,		UpperCamelCase_=14							,		UpperCamelCase_=75							,		UpperCamelCase_=None							,		UpperCamelCase_=16							,		UpperCamelCase_=0.3							,		UpperCamelCase_=None							,		UpperCamelCase_=True							,		UpperCamelCase_=None							,		UpperCamelCase_=None							,		UpperCamelCase_=True							,		UpperCamelCase_=None							,		UpperCamelCase_=True							,		UpperCamelCase_=True							,		UpperCamelCase_=FLAVA_CODEBOOK_MEAN							,		UpperCamelCase_=FLAVA_CODEBOOK_STD							,		)		->   str:
					__lowercase	:       Dict				=   size if size is not None else {'''height''': 2_24, '''width''': 2_24}
					__lowercase	:       List[str]				=   crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
					__lowercase	:       int				=   codebook_size if codebook_size is not None else {'''height''': 1_12, '''width''': 1_12}
					__lowercase	:       List[Any]				=   codebook_crop_size if codebook_crop_size is not None else {'''height''': 1_12, '''width''': 1_12}

					__lowercase	:       Dict				=   parent
					__lowercase	:       Tuple				=   batch_size
					__lowercase	:       Union[str, Any]				=   num_channels
					__lowercase	:       List[Any]				=   do_resize
					__lowercase	:       Union[str, Any]				=   do_rescale
					__lowercase	:       List[str]				=   rescale_factor
					__lowercase	:       str				=   min_resolution
					__lowercase	:       int				=   max_resolution
					__lowercase	:       Dict				=   size
					__lowercase	:       Tuple				=   resample if resample is not None else PILImageResampling.BICUBIC
					__lowercase	:       int				=   do_normalize
					__lowercase	:       Optional[Any]				=   image_mean
					__lowercase	:       Optional[int]				=   image_std
					__lowercase	:       Dict				=   do_center_crop
					__lowercase	:       Optional[Any]				=   crop_size

					__lowercase	:       Union[str, Any]				=   input_size_patches
					__lowercase	:       Union[str, Any]				=   total_mask_patches
					__lowercase	:       Any				=   mask_group_max_patches
					__lowercase	:       List[Any]				=   mask_group_min_patches
					__lowercase	:       Dict				=   mask_group_min_aspect_ratio
					__lowercase	:       Tuple				=   mask_group_max_aspect_ratio

					__lowercase	:       Tuple				=   codebook_do_resize
					__lowercase	:       Optional[Any]				=   codebook_size
					__lowercase	:       List[Any]				=   codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
					__lowercase	:       List[Any]				=   codebook_do_center_crop
					__lowercase	:       Any				=   codebook_crop_size
					__lowercase	:       Dict				=   codebook_do_map_pixels
					__lowercase	:       Any				=   codebook_do_normalize
					__lowercase	:       str				=   codebook_image_mean
					__lowercase	:       List[str]				=   codebook_image_std
		def 					_lowerCamelCase    (		self   )		->   Dict:
					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "resample": self.resample,
					    "do_rescale": self.do_rescale,
					    "rescale_factor": self.rescale_factor,
					    "do_center_crop": self.do_center_crop,
					    "crop_size": self.crop_size,
					    "input_size_patches": self.input_size_patches,
					    "total_mask_patches": self.total_mask_patches,
					    "mask_group_max_patches": self.mask_group_max_patches,
					    "mask_group_min_patches": self.mask_group_min_patches,
					    "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
					    "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
					    "codebook_do_resize": self.codebook_do_resize,
					    "codebook_size": self.codebook_size,
					    "codebook_resample": self.codebook_resample,
					    "codebook_do_center_crop": self.codebook_do_center_crop,
					    "codebook_crop_size": self.codebook_crop_size,
					    "codebook_do_map_pixels": self.codebook_do_map_pixels,
					    "codebook_do_normalize": self.codebook_do_normalize,
					    "codebook_image_mean": self.codebook_image_mean,
					    "codebook_image_std": self.codebook_image_std,
					}
		def 					_lowerCamelCase    (		self   )		->   int:
					return (self.size["height"], self.size["width"])
		def 					_lowerCamelCase    (		self   )		->   Optional[int]:
					return (
					    (self.input_size_patches, self.input_size_patches)
					    if not isinstance(self.input_size_patches							,		UpperCamelCase_   )
					    else self.input_size_patches
					)
		def 					_lowerCamelCase    (		self   )		->   Any:
					return (self.codebook_size["height"], self.codebook_size["width"])


@require_torch
@require_vision
class        UpperCAmelCase_  (	snake_case ,			unittest.TestCase						):
		UpperCamelCase									=FlavaImageProcessor if is_vision_available() else None
		UpperCamelCase									=None
		def 					_lowerCamelCase    (		self   )		->   Optional[Any]:
					__lowercase	:       Optional[Any]				=   FlavaImageProcessingTester(self   )
		@property
		def 					_lowerCamelCase    (		self   )		->   int:
					return self.image_processor_tester.prepare_image_processor_dict()
		def 					_lowerCamelCase    (		self   )		->   Any:
					__lowercase	:       str				=   self.image_processing_class(**self.image_processor_dict   )
					self.assertTrue(hasattr(UpperCamelCase_							,		'''image_mean'''   )   )
					self.assertTrue(hasattr(UpperCamelCase_							,		'''image_std'''   )   )
					self.assertTrue(hasattr(UpperCamelCase_							,		'''do_normalize'''   )   )
					self.assertTrue(hasattr(UpperCamelCase_							,		'''do_resize'''   )   )
					self.assertTrue(hasattr(UpperCamelCase_							,		'''resample'''   )   )
					self.assertTrue(hasattr(UpperCamelCase_							,		'''crop_size'''   )   )
					self.assertTrue(hasattr(UpperCamelCase_							,		'''do_center_crop'''   )   )
					self.assertTrue(hasattr(UpperCamelCase_							,		'''do_rescale'''   )   )
					self.assertTrue(hasattr(UpperCamelCase_							,		'''rescale_factor'''   )   )
					self.assertTrue(hasattr(UpperCamelCase_							,		'''masking_generator'''   )   )
					self.assertTrue(hasattr(UpperCamelCase_							,		'''codebook_do_resize'''   )   )
					self.assertTrue(hasattr(UpperCamelCase_							,		'''codebook_size'''   )   )
					self.assertTrue(hasattr(UpperCamelCase_							,		'''codebook_resample'''   )   )
					self.assertTrue(hasattr(UpperCamelCase_							,		'''codebook_do_center_crop'''   )   )
					self.assertTrue(hasattr(UpperCamelCase_							,		'''codebook_crop_size'''   )   )
					self.assertTrue(hasattr(UpperCamelCase_							,		'''codebook_do_map_pixels'''   )   )
					self.assertTrue(hasattr(UpperCamelCase_							,		'''codebook_do_normalize'''   )   )
					self.assertTrue(hasattr(UpperCamelCase_							,		'''codebook_image_mean'''   )   )
					self.assertTrue(hasattr(UpperCamelCase_							,		'''codebook_image_std'''   )   )
		def 					_lowerCamelCase    (		self   )		->   Tuple:
					__lowercase	:       Tuple				=   self.image_processing_class.from_dict(self.image_processor_dict   )
					self.assertEqual(image_processor.size							,		{'''height''': 2_24, '''width''': 2_24}   )
					self.assertEqual(image_processor.crop_size							,		{'''height''': 2_24, '''width''': 2_24}   )
					self.assertEqual(image_processor.codebook_size							,		{'''height''': 1_12, '''width''': 1_12}   )
					self.assertEqual(image_processor.codebook_crop_size							,		{'''height''': 1_12, '''width''': 1_12}   )

					__lowercase	:       List[str]				=   self.image_processing_class.from_dict(
					    self.image_processor_dict							,		size=42							,		crop_size=84							,		codebook_size=33							,		codebook_crop_size=66   )
					self.assertEqual(image_processor.size							,		{'''height''': 42, '''width''': 42}   )
					self.assertEqual(image_processor.crop_size							,		{'''height''': 84, '''width''': 84}   )
					self.assertEqual(image_processor.codebook_size							,		{'''height''': 33, '''width''': 33}   )
					self.assertEqual(image_processor.codebook_crop_size							,		{'''height''': 66, '''width''': 66}   )
		def 					_lowerCamelCase    (		self   )		->   List[Any]:
					pass
		def 					_lowerCamelCase    (		self   )		->   Dict:
					# Initialize image_processing
					__lowercase	:       Optional[Any]				=   self.image_processing_class(**self.image_processor_dict   )
					# create random PIL images
					__lowercase	:       Tuple				=   prepare_image_inputs(self.image_processor_tester							,		equal_resolution=UpperCamelCase_   )
					for image in image_inputs:
								self.assertIsInstance(UpperCamelCase_							,		PIL.Image.Image   )

					# Test not batched input
					__lowercase	:       Tuple				=   image_processing(image_inputs[0]							,		return_tensors='''pt'''   )

					# Test no bool masked pos
					self.assertFalse('''bool_masked_pos''' in encoded_images   )

					__lowercase	,__lowercase	:       Optional[int]				=   self.image_processor_tester.get_expected_image_size()

					self.assertEqual(
					    encoded_images.pixel_values.shape							,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,		)

					# Test batched
					__lowercase	:       List[Any]				=   image_processing(UpperCamelCase_							,		return_tensors='''pt'''   )
					__lowercase	,__lowercase	:       str				=   self.image_processor_tester.get_expected_image_size()

					# Test no bool masked pos
					self.assertFalse('''bool_masked_pos''' in encoded_images   )

					self.assertEqual(
					    encoded_images.pixel_values.shape							,		(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )							,		)
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_   )		->   Union[str, Any]:
					# Initialize image_processing
					__lowercase	:       int				=   self.image_processing_class(**self.image_processor_dict   )
					# create random tensors
					__lowercase	:       Tuple				=   prepare_image_inputs(self.image_processor_tester							,		equal_resolution=UpperCamelCase_							,		**UpperCamelCase_   )
					for image in image_inputs:
								self.assertIsInstance(UpperCamelCase_							,		UpperCamelCase_   )

					# Test not batched input
					__lowercase	:       str				=   image_processing(image_inputs[0]							,		return_tensors='''pt'''   )

					__lowercase	,__lowercase	:       Dict				=   self.image_processor_tester.get_expected_image_size()
					self.assertEqual(
					    encoded_images.pixel_values.shape							,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,		)

					__lowercase	:       Union[str, Any]				=   image_processing(UpperCamelCase_							,		return_image_mask=UpperCamelCase_							,		return_tensors='''pt'''   )

					__lowercase	,__lowercase	:       int				=   self.image_processor_tester.get_expected_image_size()
					self.assertEqual(
					    encoded_images.pixel_values.shape							,		(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )							,		)

					__lowercase	,__lowercase	:       Tuple				=   self.image_processor_tester.get_expected_mask_size()
					self.assertEqual(
					    encoded_images.bool_masked_pos.shape							,		(
					        self.image_processor_tester.batch_size,
					        expected_height,
					        expected_width,
					    )							,		)

					# Test batched
					__lowercase	:       Optional[Any]				=   image_processing(UpperCamelCase_							,		return_tensors='''pt'''   ).pixel_values

					__lowercase	,__lowercase	:       List[str]				=   self.image_processor_tester.get_expected_image_size()
					self.assertEqual(
					    encoded_images.shape							,		(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )							,		)

					# Test masking
					__lowercase	:       Dict				=   image_processing(UpperCamelCase_							,		return_image_mask=UpperCamelCase_							,		return_tensors='''pt'''   )

					__lowercase	,__lowercase	:       List[Any]				=   self.image_processor_tester.get_expected_image_size()
					self.assertEqual(
					    encoded_images.pixel_values.shape							,		(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )							,		)

					__lowercase	,__lowercase	:       Any				=   self.image_processor_tester.get_expected_mask_size()
					self.assertEqual(
					    encoded_images.bool_masked_pos.shape							,		(
					        self.image_processor_tester.batch_size,
					        expected_height,
					        expected_width,
					    )							,		)
		def 					_lowerCamelCase    (		self   )		->   Optional[Any]:
					self._test_call_framework(np.ndarray							,		prepare_kwargs={'''numpify''': True}   )
		def 					_lowerCamelCase    (		self   )		->   Union[str, Any]:
					self._test_call_framework(torch.Tensor							,		prepare_kwargs={'''torchify''': True}   )
		def 					_lowerCamelCase    (		self   )		->   Optional[int]:
					# Initialize image_processing
					random.seed(12_34   )
					__lowercase	:       Optional[Any]				=   self.image_processing_class(**self.image_processor_dict   )
					__lowercase	:       Tuple				=   prepare_image_inputs(self.image_processor_tester							,		equal_resolution=UpperCamelCase_							,		torchify=UpperCamelCase_   )

					# Test not batched input
					__lowercase	:       Tuple				=   image_processing(image_inputs[0]							,		return_image_mask=UpperCamelCase_							,		return_tensors='''pt'''   )
					self.assertEqual(encoded_images.bool_masked_pos.sum().item()							,		75   )
		def 					_lowerCamelCase    (		self   )		->   Dict:
					# Initialize image_processing
					__lowercase	:       Dict				=   self.image_processing_class(**self.image_processor_dict   )
					# create random PIL images
					__lowercase	:       List[Any]				=   prepare_image_inputs(self.image_processor_tester							,		equal_resolution=UpperCamelCase_   )
					for image in image_inputs:
								self.assertIsInstance(UpperCamelCase_							,		PIL.Image.Image   )

					# Test not batched input
					__lowercase	:       str				=   image_processing(image_inputs[0]							,		return_codebook_pixels=UpperCamelCase_							,		return_tensors='''pt'''   )
					__lowercase	,__lowercase	:       Any				=   self.image_processor_tester.get_expected_codebook_image_size()
					self.assertEqual(
					    encoded_images.codebook_pixel_values.shape							,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,		)

					# Test batched
					__lowercase	:       Optional[Any]				=   image_processing(UpperCamelCase_							,		return_codebook_pixels=UpperCamelCase_							,		return_tensors='''pt'''   )
					__lowercase	,__lowercase	:       Tuple				=   self.image_processor_tester.get_expected_codebook_image_size()
					self.assertEqual(
					    encoded_images.codebook_pixel_values.shape							,		(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )							,		)









"""simple docstring"""




from collections.abc import Callable

a_            =   Callable[[float], float]  # type alias for a real -> real function







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					,							):
			__lowercase	:       str				=   float(__UpperCamelCase					)
			while True:
						try:
									__lowercase	:       Dict				=   prev_guess - function(__UpperCamelCase					) / derivative(__UpperCamelCase					)
						except ZeroDivisionError:
									raise ZeroDivisionError('''Could not find root'''					) from None
						if abs(prev_guess - next_guess					) < 10**-5:
									return next_guess
						__lowercase	:       List[Any]				=   next_guess







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return (x**3) - (2 * x) - 5







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return 3 * (x**2) - 2


if __name__ == "__main__":
			print(newton(f, fa, 3))



