
"""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 			lowerCAmelCase_		(unittest.TestCase						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = True     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = 32     , SCREAMING_SNAKE_CASE__ = True     , SCREAMING_SNAKE_CASE__ = 1 / 2_55     , SCREAMING_SNAKE_CASE__ = True     , SCREAMING_SNAKE_CASE__ = True     , SCREAMING_SNAKE_CASE__ = [0.48145466, 0.4578275, 0.40821073]     , SCREAMING_SNAKE_CASE__ = [0.26862954, 0.26130258, 0.27577711]     , SCREAMING_SNAKE_CASE__ = True     , SCREAMING_SNAKE_CASE__=7     , SCREAMING_SNAKE_CASE__=30     , SCREAMING_SNAKE_CASE__=4_00     , SCREAMING_SNAKE_CASE__=3     , )      ->	Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: str     =     parent
											SCREAMING_SNAKE_CASE__		: Dict     =     do_resize
											SCREAMING_SNAKE_CASE__		: List[Any]     =     size if size is not None else {"""shortest_edge""": 2_88}
											SCREAMING_SNAKE_CASE__		: List[str]     =     size_divisor
											SCREAMING_SNAKE_CASE__		: str     =     do_rescale
											SCREAMING_SNAKE_CASE__		: Any     =     rescale_factor
											SCREAMING_SNAKE_CASE__		: Tuple     =     do_normalize
											SCREAMING_SNAKE_CASE__		: Any     =     do_center_crop
											SCREAMING_SNAKE_CASE__		: List[Any]     =     image_mean
											SCREAMING_SNAKE_CASE__		: List[str]     =     image_std
											SCREAMING_SNAKE_CASE__		: int     =     do_pad
											SCREAMING_SNAKE_CASE__		: Dict     =     batch_size
											SCREAMING_SNAKE_CASE__		: int     =     num_channels
											SCREAMING_SNAKE_CASE__		: Any     =     min_resolution
											SCREAMING_SNAKE_CASE__		: Any     =     max_resolution



				def 			__magic_name__					(self					)      ->	Tuple:



											"""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 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=False					)      ->	int:



											"""simple docstring"""




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

																		SCREAMING_SNAKE_CASE__		: List[str]     =     int((13_33 / 8_00) * size					)
																		if max(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					) > max_size:
																									SCREAMING_SNAKE_CASE__		: str     =     max_size / max(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)
																									SCREAMING_SNAKE_CASE__		: List[Any]     =     newh * scale
																									SCREAMING_SNAKE_CASE__		: List[str]     =     neww * scale

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

											else:
																		SCREAMING_SNAKE_CASE__		: int     =     []
																		for image in image_inputs:
																									SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Dict     =     self.get_expected_values([image]					)
																									expected_values.append((expected_height, expected_width)					)
																		SCREAMING_SNAKE_CASE__		: str     =     max(SCREAMING_SNAKE_CASE__     , key=lambda SCREAMING_SNAKE_CASE__					: item[0]					)[0]
																		SCREAMING_SNAKE_CASE__		: List[str]     =     max(SCREAMING_SNAKE_CASE__     , key=lambda SCREAMING_SNAKE_CASE__					: item[1]					)[1]

											return expected_height, expected_width






@require_torch
@require_vision
class 			lowerCAmelCase_		(a__				,      unittest.TestCase						):


				"""simple docstring"""



				__UpperCamelCase   :		Optional[int]       =     BridgeTowerImageProcessor if is_vision_available() else None



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: int     =     BridgeTowerImageProcessingTester(self					)



				@property
				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											return self.image_processor_tester.prepare_image_processor_dict()



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Tuple     =     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 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




											pass



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.image_processing_class(**self.image_processor_dict					)
											# create random PIL images
											SCREAMING_SNAKE_CASE__		: 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
											SCREAMING_SNAKE_CASE__		: Any     =     image_processing(image_inputs[0]     , return_tensors="""pt"""					).pixel_values

											SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Union[str, 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
											SCREAMING_SNAKE_CASE__		: int     =     image_processing(SCREAMING_SNAKE_CASE__     , return_tensors="""pt"""					).pixel_values

											SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: 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 			__magic_name__					(self					)      ->	List[str]:



											"""simple docstring"""




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

											SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Optional[int]     =     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
											SCREAMING_SNAKE_CASE__		: Dict     =     image_processing(SCREAMING_SNAKE_CASE__     , return_tensors="""pt"""					).pixel_values

											SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: 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 			__magic_name__					(self					)      ->	Dict:



											"""simple docstring"""




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

											SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Union[str, 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
											SCREAMING_SNAKE_CASE__		: Any     =     image_processing(SCREAMING_SNAKE_CASE__     , return_tensors="""pt"""					).pixel_values

											SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: List[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,
											    )     , )






"""simple docstring"""




from manim import *



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				def 			__magic_name__					(self					)      ->	List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Dict     =     Rectangle(height=0.5     , width=0.5					)
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     Rectangle(height=0.46     , width=0.46					).set_stroke(width=0					)

											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     [mem.copy() for i in range(6					)]
											SCREAMING_SNAKE_CASE__		: str     =     [mem.copy() for i in range(6					)]
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     VGroup(*SCREAMING_SNAKE_CASE__					).arrange(SCREAMING_SNAKE_CASE__     , buff=0					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     VGroup(*SCREAMING_SNAKE_CASE__					).arrange(SCREAMING_SNAKE_CASE__     , buff=0					)
											SCREAMING_SNAKE_CASE__		: Tuple     =     VGroup(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					).arrange(SCREAMING_SNAKE_CASE__     , buff=0					)
											SCREAMING_SNAKE_CASE__		: int     =     Text("""CPU"""     , font_size=24					)
											SCREAMING_SNAKE_CASE__		: Any     =     Group(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					).arrange(SCREAMING_SNAKE_CASE__     , buff=0.5     , aligned_edge=SCREAMING_SNAKE_CASE__					)
											cpu.move_to([-2.5, -0.5, 0]					)
											self.add(SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     [mem.copy() for i in range(1					)]
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     VGroup(*SCREAMING_SNAKE_CASE__					).arrange(SCREAMING_SNAKE_CASE__     , buff=0					)
											SCREAMING_SNAKE_CASE__		: str     =     Text("""GPU"""     , font_size=24					)
											SCREAMING_SNAKE_CASE__		: str     =     Group(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					).arrange(SCREAMING_SNAKE_CASE__     , buff=0.5     , aligned_edge=SCREAMING_SNAKE_CASE__					)
											gpu.align_to(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)
											gpu.set_x(gpu.get_x() - 1					)

											self.add(SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: List[Any]     =     [mem.copy() for i in range(6					)]
											SCREAMING_SNAKE_CASE__		: List[Any]     =     VGroup(*SCREAMING_SNAKE_CASE__					).arrange(SCREAMING_SNAKE_CASE__     , buff=0					)

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     Text("""Model"""     , font_size=24					)
											SCREAMING_SNAKE_CASE__		: Any     =     Group(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					).arrange(SCREAMING_SNAKE_CASE__     , buff=0.5     , aligned_edge=SCREAMING_SNAKE_CASE__					)
											model.move_to([3, -1.0, 0]					)

											self.play(
											    Create(SCREAMING_SNAKE_CASE__     , run_time=1					)     , Create(SCREAMING_SNAKE_CASE__     , run_time=1					)     , Create(SCREAMING_SNAKE_CASE__     , run_time=1					)     , )

											SCREAMING_SNAKE_CASE__		: Tuple     =     MarkupText(
											    F'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.'''     , font_size=24     , )

											SCREAMING_SNAKE_CASE__		: Dict     =     Square(side_length=2.2					)
											key.move_to([-5, 2, 0]					)

											SCREAMING_SNAKE_CASE__		: Tuple     =     MarkupText(
											    F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''     , font_size=18     , )

											key_text.move_to([-5, 2.4, 0]					)

											step_a.move_to([2, 2, 0]					)
											self.play(Write(SCREAMING_SNAKE_CASE__     , run_time=2.5					)     , Write(SCREAMING_SNAKE_CASE__					)     , Write(SCREAMING_SNAKE_CASE__					)					)

											self.add(SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     []
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     []
											SCREAMING_SNAKE_CASE__		: str     =     []
											for i, rect in enumerate(SCREAMING_SNAKE_CASE__					):
																		SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     Rectangle(height=0.46     , width=0.46					).set_stroke(width=0.0					).set_fill(SCREAMING_SNAKE_CASE__     , opacity=0.7					)
																		cpu_target.move_to(SCREAMING_SNAKE_CASE__					)
																		cpu_target.generate_target()
																		SCREAMING_SNAKE_CASE__		: Tuple     =     0.46 / 4
																		SCREAMING_SNAKE_CASE__		: int     =     0.46 / 3

																		if i == 0:
																									cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT					)     , buff=0.02     , direction=SCREAMING_SNAKE_CASE__					)
																									cpu_target.target.set_x(cpu_target.target.get_x() + 0.1					)
																		elif i == 3:
																									cpu_target.target.next_to(cpu_targs[0].target     , direction=SCREAMING_SNAKE_CASE__     , buff=0.0					)
																		else:
																									cpu_target.target.next_to(cpu_targs[i - 1].target     , direction=SCREAMING_SNAKE_CASE__     , buff=0.0					)
																		cpu_targs.append(SCREAMING_SNAKE_CASE__					)

																		first_animations.append(rect.animate(run_time=0.5					).set_stroke(SCREAMING_SNAKE_CASE__					)					)
																		second_animations.append(MoveToTarget(SCREAMING_SNAKE_CASE__     , run_time=1.5					)					)

											self.play(*SCREAMING_SNAKE_CASE__					)
											self.play(*SCREAMING_SNAKE_CASE__					)

											self.wait()




