



"""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      A__  (    unittest.TestCase				):




	'''simple docstring'''





	def __init__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						bool = True			,     _SCREAMING_SNAKE_CASE:						Dict[str, int] = None			,     _SCREAMING_SNAKE_CASE:						int = 32			,     _SCREAMING_SNAKE_CASE:						bool = True			,     _SCREAMING_SNAKE_CASE:						Union[int, float] = 1 / 255			,     _SCREAMING_SNAKE_CASE:						bool = True			,     _SCREAMING_SNAKE_CASE:						bool = True			,     _SCREAMING_SNAKE_CASE:						Optional[Union[float, List[float]]] = [0.4814_5466, 0.457_8275, 0.4082_1073]			,     _SCREAMING_SNAKE_CASE:						Optional[Union[float, List[float]]] = [0.2686_2954, 0.2613_0258, 0.2757_7711]			,     _SCREAMING_SNAKE_CASE:						bool = True			,     _SCREAMING_SNAKE_CASE:						int=7			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=30			,     _SCREAMING_SNAKE_CASE:						List[Any]=400			,     _SCREAMING_SNAKE_CASE:						Dict=3			,     )							->			List[str]:

				"""simple docstring"""

				__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 				_SCREAMING_SNAKE_CASE  (	self:						Any)							->			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,
				    "size_divisor": self.size_divisor,
				}







	def 				_SCREAMING_SNAKE_CASE  (	self:						Any			,     _SCREAMING_SNAKE_CASE:						Dict			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=False)							->			Dict:

				"""simple docstring"""

				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		,      __lowerCAmelCase :    Any				=			image.size
							else:
										__lowerCAmelCase		,      __lowerCAmelCase :    int				=			image.shape[1], image.shape[2]
							__lowerCAmelCase :    Any				=			size / min(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
							if h < w:
										__lowerCAmelCase		,      __lowerCAmelCase :    List[str]				=			size, scale * w
							else:
										__lowerCAmelCase		,      __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		,      __lowerCAmelCase :    Any				=			int(newh + 0.5), int(neww + 0.5)
							__lowerCAmelCase		,      __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		,      __lowerCAmelCase :    Any				=			self.get_expected_values([image])
										expected_values.append((expected_height, expected_width))
							__lowerCAmelCase :    List[Any]				=			max(_SCREAMING_SNAKE_CASE			,     key=lambda _SCREAMING_SNAKE_CASE: item[0])[0]
							__lowerCAmelCase :    Dict				=			max(_SCREAMING_SNAKE_CASE			,     key=lambda _SCREAMING_SNAKE_CASE: item[1])[1]

				return expected_height, expected_width






@require_torch
@require_vision
class      A__  (    __SCREAMING_SNAKE_CASE    ,						unittest.TestCase				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      BridgeTowerImageProcessor if is_vision_available() else None

	def 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any])							->			Tuple:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			BridgeTowerImageProcessingTester(self)

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			List[Any]:

				"""simple docstring"""

				return self.image_processor_tester.prepare_image_processor_dict()

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			List[str]:

				"""simple docstring"""

				__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 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int])							->			List[str]:

				"""simple docstring"""

				pass

	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			str:

				"""simple docstring"""

				__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		,      __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		,      __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 				_SCREAMING_SNAKE_CASE  (	self:						List[str])							->			Optional[int]:

				"""simple docstring"""

				__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		,      __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		,      __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 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			Dict:

				"""simple docstring"""

				__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		,      __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		,      __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...')