






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 GLPNImageProcessor
class      UpperCamelCase (				unittest.TestCase ):







		"""simple docstring"""



		def __init__(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       List[str]						,_SCREAMING_SNAKE_CASE		:       Optional[int]=7						,_SCREAMING_SNAKE_CASE		:       List[str]=3						,_SCREAMING_SNAKE_CASE		:       Dict=1_8						,_SCREAMING_SNAKE_CASE		:       List[str]=3_0						,_SCREAMING_SNAKE_CASE		:       List[Any]=4_0_0						,_SCREAMING_SNAKE_CASE		:       Optional[int]=True						,_SCREAMING_SNAKE_CASE		:       int=3_2						,_SCREAMING_SNAKE_CASE		:       Dict=True						,)    ->   List[str]:


						'''simple docstring'''
						A	 =      parent
						A	 =      batch_size
						A	 =      num_channels
						A	 =      image_size
						A	 =      min_resolution
						A	 =      max_resolution
						A	 =      do_resize
						A	 =      size_divisor
						A	 =      do_rescale







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


						'''simple docstring'''
						return {
						    "do_resize": self.do_resize,
						    "size_divisor": self.size_divisor,
						    "do_rescale": self.do_rescale,
						}


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







		"""simple docstring"""



		snake_case    =   GLPNImageProcessor if is_vision_available() else None
		def       A(  self		:       Optional[Any]		)    ->   Union[str, Any]:


						'''simple docstring'''
						A	 =      GLPNImageProcessingTester(self		)
		@property
		def       A(  self		:       Dict		)    ->   Dict:


						'''simple docstring'''
						return self.image_processor_tester.prepare_image_processor_dict()
		def       A(  self		:       Dict		)    ->   Any:


						'''simple docstring'''
						A	 =      self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE						,'do_resize'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE						,'size_divisor'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE						,'resample'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE						,'do_rescale'		)		)
		def       A(  self		:       Optional[int]		)    ->   str:


						'''simple docstring'''
						pass
		def       A(  self		:       Union[str, Any]		)    ->   Optional[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 (GLPNImageProcessor doesn't support batching)
						A	 =      image_processing(image_inputs[0]						,return_tensors='pt'		).pixel_values
						self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0		)
						self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0		)
		def       A(  self		:       str		)    ->   int:


						'''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 (GLPNImageProcessor doesn't support batching)
						A	 =      image_processing(image_inputs[0]						,return_tensors='pt'		).pixel_values
						self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0		)
						self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0		)







		def       A(  self		:       Optional[int]		)    ->   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 (GLPNImageProcessor doesn't support batching)
						A	 =      image_processing(image_inputs[0]						,return_tensors='pt'		).pixel_values
						self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0		)
						self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0		)













from manim import *
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		def       A(  self		:       Dict		)    ->   Tuple:


						'''simple docstring'''
						A	 =      Rectangle(height=0.5						,width=0.5		)
						A	 =      Rectangle(height=0.46						,width=0.46		).set_stroke(width=0		)

						A	 =      [mem.copy() for i in range(6		)]
						A	 =      [mem.copy() for i in range(6		)]
						A	 =      VGroup(*_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0		)
						A	 =      VGroup(*_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0		)
						A	 =      VGroup(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0		)
						A	 =      Text('CPU'						,font_size=2_4		)
						A	 =      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		)

						A	 =      [mem.copy() for i in range(1		)]
						A	 =      VGroup(*_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0		)
						A	 =      Text('GPU'						,font_size=2_4		)
						A	 =      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		)

						A	 =      [mem.copy() for i in range(6		)]
						A	 =      VGroup(*_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0		)

						A	 =      Text('Model'						,font_size=2_4		)
						A	 =      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		)						,)

						A	 =      MarkupText(
						    f'First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.'						,font_size=2_4						,)

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

						A	 =      MarkupText(
						    f'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'						,font_size=1_8						,)

						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		)

						A	 =      []
						A	 =      []
						A	 =      []
						for i, rect in enumerate(_SCREAMING_SNAKE_CASE		):
										A	 =      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()
										A	 =      0.46 / 4
										A	 =      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()





