






"""simple docstring"""



import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor







class   lowercase__     (				unittest.TestCase  ):


			def __init__( self	:    Optional[Any]  ,			snake_case__	:    str  ,			snake_case__	:    bool = True  ,			snake_case__	:    Dict[str, int] = None  ,			snake_case__	:    int = 32  ,			snake_case__	:    bool = True  ,			snake_case__	:    Union[int, float] = 1 / 255  ,			snake_case__	:    bool = True  ,			snake_case__	:    bool = True  ,			snake_case__	:    Optional[Union[float, List[float]]] = [0.48_145_466, 0.4_578_275, 0.40_821_073]  ,			snake_case__	:    Optional[Union[float, List[float]]] = [0.26_862_954, 0.26_130_258, 0.27_577_711]  ,			snake_case__	:    bool = True  ,			snake_case__	:    int=7  ,			snake_case__	:    Optional[Any]=30  ,			snake_case__	:    List[Any]=400  ,			snake_case__	:    Dict=3  ,			):
										lowerCamelCase_			:				Union[str, Any]										=parent
										lowerCamelCase_			:				List[str]										=do_resize
										lowerCamelCase_			:				Optional[int]										=size if size is not None else {"shortest_edge": 288}
										lowerCamelCase_			:				Union[str, Any]										=size_divisor
										lowerCamelCase_			:				List[Any]										=do_rescale
										lowerCamelCase_			:				Union[str, Any]										=rescale_factor
										lowerCamelCase_			:				Dict										=do_normalize
										lowerCamelCase_			:				Optional[int]										=do_center_crop
										lowerCamelCase_			:				Optional[Any]										=image_mean
										lowerCamelCase_			:				Dict										=image_std
										lowerCamelCase_			:				Optional[int]										=do_pad
										lowerCamelCase_			:				Union[str, Any]										=batch_size
										lowerCamelCase_			:				List[str]										=num_channels
										lowerCamelCase_			:				Optional[Any]										=min_resolution
										lowerCamelCase_			:				Any										=max_resolution


			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,
										    "size_divisor": self.size_divisor,
										}


			def       UpperCAmelCase__		( self	:    Any  ,			snake_case__	:    Dict  ,			snake_case__	:    Union[str, Any]=False   ):

										if not batched:
																	lowerCamelCase_			:				Union[str, Any]										=self.size["shortest_edge"]
																	lowerCamelCase_			:				Optional[int]										=image_inputs[0]
																	if isinstance(_SCREAMING_SNAKE_CASE  ,			Image.Image   ):
																								lowerCamelCase_			:				Any										=image.size
																	else:
																								lowerCamelCase_			:				int										=image.shape[1], image.shape[2]
																	lowerCamelCase_			:				Any										=size / min(_SCREAMING_SNAKE_CASE  ,			_SCREAMING_SNAKE_CASE   )
																	if h < w:
																								lowerCamelCase_			:				List[str]										=size, scale * w
																	else:
																								lowerCamelCase_			:				Tuple										=scale * h, size

																	lowerCamelCase_			:				Any										=int((1333 / 800) * size   )
																	if max(_SCREAMING_SNAKE_CASE  ,			_SCREAMING_SNAKE_CASE   ) > max_size:
																								lowerCamelCase_			:				Tuple										=max_size / max(_SCREAMING_SNAKE_CASE  ,			_SCREAMING_SNAKE_CASE   )
																								lowerCamelCase_			:				List[str]										=newh * scale
																								lowerCamelCase_			:				Dict										=neww * scale

																	lowerCamelCase_			:				Any										=int(newh + 0.5   ), int(neww + 0.5   )
																	lowerCamelCase_			:				str										=(
																	    newh // self.size_divisor * self.size_divisor,
																	    neww // self.size_divisor * self.size_divisor,
																	)

										else:
																	lowerCamelCase_			:				Any										=[]
																	for image in image_inputs:
																								lowerCamelCase_			:				Any										=self.get_expected_values([image]   )
																								expected_values.append((expected_height, expected_width)   )
																	lowerCamelCase_			:				List[Any]										=max(_SCREAMING_SNAKE_CASE  ,			key=lambda snake_case__   : item[0]   )[0]
																	lowerCamelCase_			:				Dict										=max(_SCREAMING_SNAKE_CASE  ,			key=lambda snake_case__   : item[1]   )[1]

										return expected_height, expected_width







@require_torch
@require_vision
class   lowercase__     (				__SCREAMING_SNAKE_CASE,  unittest.TestCase  ):
			_UpperCAmelCase						:List[Any]       =					BridgeTowerImageProcessor if is_vision_available() else None


			def       UpperCAmelCase__		( self	:    Union[str, Any]   ):
										lowerCamelCase_			:				Any										=BridgeTowerImageProcessingTester(self   )


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


			def       UpperCAmelCase__		( self	:    List[Any]   ):
										lowerCamelCase_			:				Optional[Any]										=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"   )   )
										self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,			"size_divisor"   )   )


			def       UpperCAmelCase__		( self	:    Optional[int]   ):
										pass


			def       UpperCAmelCase__		( self	:    Tuple   ):
										lowerCamelCase_			:				int										=self.image_processing_class(**self.image_processor_dict   )
										# create random PIL images
										lowerCamelCase_			:				str										=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
										lowerCamelCase_			:				Any										=image_processing(image_inputs[0]  ,			return_tensors="pt"   ).pixel_values

										lowerCamelCase_			:				Any										=self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE   )
										self.assertEqual(
										    encoded_images.shape  ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,			)

										# Test batched
										lowerCamelCase_			:				Optional[int]										=image_processing(_SCREAMING_SNAKE_CASE  ,			return_tensors="pt"   ).pixel_values

										lowerCamelCase_			:				Union[str, Any]										=self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE  ,			batched=_SCREAMING_SNAKE_CASE   )
										self.assertEqual(
										    encoded_images.shape  ,			(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )  ,			)


			def       UpperCAmelCase__		( self	:    List[str]   ):
										lowerCamelCase_			:				Dict										=self.image_processing_class(**self.image_processor_dict   )
										# create random numpy tensors
										lowerCamelCase_			:				Dict										=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
										lowerCamelCase_			:				str										=image_processing(image_inputs[0]  ,			return_tensors="pt"   ).pixel_values

										lowerCamelCase_			:				str										=self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE   )
										self.assertEqual(
										    encoded_images.shape  ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,			)

										# Test batched
										lowerCamelCase_			:				Dict										=image_processing(_SCREAMING_SNAKE_CASE  ,			return_tensors="pt"   ).pixel_values

										lowerCamelCase_			:				Tuple										=self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE  ,			batched=_SCREAMING_SNAKE_CASE   )
										self.assertEqual(
										    encoded_images.shape  ,			(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )  ,			)


			def       UpperCAmelCase__		( self	:    int   ):
										lowerCamelCase_			:				List[Any]										=self.image_processing_class(**self.image_processor_dict   )
										# create random PyTorch tensors
										lowerCamelCase_			:				Optional[int]										=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
										lowerCamelCase_			:				Optional[int]										=image_processing(image_inputs[0]  ,			return_tensors="pt"   ).pixel_values

										lowerCamelCase_			:				Dict										=self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE   )
										self.assertEqual(
										    encoded_images.shape  ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,			)

										# Test batched
										lowerCamelCase_			:				Any										=image_processing(_SCREAMING_SNAKE_CASE  ,			return_tensors="pt"   ).pixel_values

										lowerCamelCase_			:				int										=self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE  ,			batched=_SCREAMING_SNAKE_CASE   )
										self.assertEqual(
										    encoded_images.shape  ,			(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )  ,			)








"""simple docstring"""






# Function to print upper half of diamond (pyramid)


def      _lowercase							(    __snake_case      )		->  Dict:
			for i in range(0   ,__snake_case      ):
						for _ in range(0   ,n - i - 1      ):  # printing spaces
									print(" "   ,end=""      )
						for _ in range(0   ,i + 1      ):  # printing stars
									print("* "   ,end=""      )
						print()


def      _lowercase							(    __snake_case      )		->  Tuple:
			for i in range(__snake_case   ,0   ,-1      ):
						for _ in range(__snake_case   ,0   ,-1      ):  # printing stars
									print("* "   ,end=""      )
						print()
						for _ in range(n - i + 1   ,0   ,-1      ):  # printing spaces
									print(" "   ,end=""      )


def      _lowercase							(    __snake_case      )		->  Any:
			if n <= 0:
						print("       ...       ....        nothing printing :("      )
						return
			floyd(__snake_case      )  # upper half
			reverse_floyd(__snake_case      )  # lower half


if __name__ == "__main__":
	print(R'| /\ | |- |  |-  |--| |\  /| |-')
	print(R'|/  \| |- |_ |_  |__| | \/ | |_')
	__snake_case				:  Tuple =       1
	while K:
		__snake_case				:  List[str] =       int(input('enter the number and , and see the magic : '))
		print()
		pretty_print(user_number)
		__snake_case				:  Optional[Any] =       int(input('press 0 to exit... and 1 to continue...'))

	print('Good Bye...')