
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						):

						def __init__(      self							,				a							,				a = True							,				a = None							,				a = 32							,				a = True							,				a = 1 / 2_55							,				a = True							,				a = True							,				a = [0.4814_5466, 0.457_8275, 0.4082_1073]							,				a = [0.2686_2954, 0.2613_0258, 0.2757_7711]							,				a = True							,				a=7							,				a=30							,				a=4_00							,				a=3							,				)       ->   Optional[int]:


								'''simple docstring'''

								_UpperCamelCase         =						parent
								_UpperCamelCase         =						do_resize
								_UpperCamelCase         =						size if size is not None else {"""shortest_edge""": 2_88}
								_UpperCamelCase         =						size_divisor
								_UpperCamelCase         =						do_rescale
								_UpperCamelCase         =						rescale_factor
								_UpperCamelCase         =						do_normalize
								_UpperCamelCase         =						do_center_crop
								_UpperCamelCase         =						image_mean
								_UpperCamelCase         =						image_std
								_UpperCamelCase         =						do_pad
								_UpperCamelCase         =						batch_size
								_UpperCamelCase         =						num_channels
								_UpperCamelCase         =						min_resolution
								_UpperCamelCase         =						max_resolution

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


								'''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 			A_					(      self							,				a							,				a=False					)       ->   Optional[int]:


								'''simple docstring'''

								if not batched:
										_UpperCamelCase         =						self.size["""shortest_edge"""]
										_UpperCamelCase         =						image_inputs[0]
										if isinstance(lowerCamelCase_							,				Image.Image					):
												_UpperCamelCase     ,						_UpperCamelCase         =						image.size
										else:
												_UpperCamelCase     ,						_UpperCamelCase         =						image.shape[1], image.shape[2]
										_UpperCamelCase         =						size / min(lowerCamelCase_							,				lowerCamelCase_					)
										if h < w:
												_UpperCamelCase     ,						_UpperCamelCase         =						size, scale * w
										else:
												_UpperCamelCase     ,						_UpperCamelCase         =						scale * h, size

										_UpperCamelCase         =						int((13_33 / 8_00) * size					)
										if max(lowerCamelCase_							,				lowerCamelCase_					) > max_size:
												_UpperCamelCase         =						max_size / max(lowerCamelCase_							,				lowerCamelCase_					)
												_UpperCamelCase         =						newh * scale
												_UpperCamelCase         =						neww * scale

										_UpperCamelCase     ,						_UpperCamelCase         =						int(newh + 0.5					), int(neww + 0.5					)
										_UpperCamelCase     ,						_UpperCamelCase         =						(
										    newh // self.size_divisor * self.size_divisor,
										    neww // self.size_divisor * self.size_divisor,
										)

								else:
										_UpperCamelCase         =						[]
										for image in image_inputs:
												_UpperCamelCase     ,						_UpperCamelCase         =						self.get_expected_values([image]					)
												expected_values.append((expected_height, expected_width)					)
										_UpperCamelCase         =						max(lowerCamelCase_							,				key=lambda a					: item[0]					)[0]
										_UpperCamelCase         =						max(lowerCamelCase_							,				key=lambda a					: item[1]					)[1]

								return expected_height, expected_width







@require_torch
@require_vision
class 							lowerCAmelCase__   ( __lowercase		,						unittest.TestCase						):
						UpperCamelCase_				:			str						=		BridgeTowerImageProcessor if is_vision_available() else None

						def 			A_					(      self					)       ->   List[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						BridgeTowerImageProcessingTester(self					)

						@property
						def 			A_					(      self					)       ->   Any:


								'''simple docstring'''

								return self.image_processor_tester.prepare_image_processor_dict()

						def 			A_					(      self					)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase         =						self.image_processing_class(**self.image_processor_dict					)
								self.assertTrue(hasattr(lowerCamelCase_							,				"""image_mean"""					)					)
								self.assertTrue(hasattr(lowerCamelCase_							,				"""image_std"""					)					)
								self.assertTrue(hasattr(lowerCamelCase_							,				"""do_normalize"""					)					)
								self.assertTrue(hasattr(lowerCamelCase_							,				"""do_resize"""					)					)
								self.assertTrue(hasattr(lowerCamelCase_							,				"""size"""					)					)
								self.assertTrue(hasattr(lowerCamelCase_							,				"""size_divisor"""					)					)

						def 			A_					(      self					)       ->   Tuple:


								'''simple docstring'''

								pass

						def 			A_					(      self					)       ->   Dict:


								'''simple docstring'''

								_UpperCamelCase         =						self.image_processing_class(**self.image_processor_dict					)
								# create random PIL images
								_UpperCamelCase         =						prepare_image_inputs(self.image_processor_tester							,				equal_resolution=lowerCamelCase_					)
								for image in image_inputs:
										self.assertIsInstance(lowerCamelCase_							,				Image.Image					)

								# Test not batched input
								_UpperCamelCase         =						image_processing(image_inputs[0]							,				return_tensors="""pt"""					).pixel_values

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

								# Test batched
								_UpperCamelCase         =						image_processing(lowerCamelCase_							,				return_tensors="""pt"""					).pixel_values

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

						def 			A_					(      self					)       ->   Dict:


								'''simple docstring'''

								_UpperCamelCase         =						self.image_processing_class(**self.image_processor_dict					)
								# create random numpy tensors
								_UpperCamelCase         =						prepare_image_inputs(self.image_processor_tester							,				equal_resolution=lowerCamelCase_							,				numpify=lowerCamelCase_					)
								for image in image_inputs:
										self.assertIsInstance(lowerCamelCase_							,				np.ndarray					)

								# Test not batched input
								_UpperCamelCase         =						image_processing(image_inputs[0]							,				return_tensors="""pt"""					).pixel_values

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

								# Test batched
								_UpperCamelCase         =						image_processing(lowerCamelCase_							,				return_tensors="""pt"""					).pixel_values

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







						def 			A_					(      self					)       ->   Tuple:


								'''simple docstring'''

								_UpperCamelCase         =						self.image_processing_class(**self.image_processor_dict					)
								# create random PyTorch tensors
								_UpperCamelCase         =						prepare_image_inputs(self.image_processor_tester							,				equal_resolution=lowerCamelCase_							,				torchify=lowerCamelCase_					)
								for image in image_inputs:
										self.assertIsInstance(lowerCamelCase_							,				torch.Tensor					)

								# Test not batched input
								_UpperCamelCase         =						image_processing(image_inputs[0]							,				return_tensors="""pt"""					).pixel_values

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

								# Test batched
								_UpperCamelCase         =						image_processing(lowerCamelCase_							,				return_tensors="""pt"""					).pixel_values

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






"""simple docstring"""


from math import pow, sqrt



def   _A					(	*_a	:						float				):



      """simple docstring"""






      A				  =		len(_a				) > 0 and all(value > 0.0 for value in values				)
      return result



def   _A					(	_a	:						float     ,   _a	:						float				):



      """simple docstring"""






      return (
          round(sqrt(molar_mass_a / molar_mass_a				)     ,   6				)
          if validate(_a     ,   _a				)
          else ValueError("""Input Error: Molar mass values must greater than 0."""				)
      )



def   _A					(	_a	:						float     ,   _a	:						float     ,   _a	:						float				):



      """simple docstring"""






      return (
          round(effusion_rate * sqrt(molar_mass_a / molar_mass_a				)     ,   6				)
          if validate(_a     ,   _a     ,   _a				)
          else ValueError(
              """Input Error: Molar mass and effusion rate values must greater than 0."""				)
      )



def   _A					(	_a	:						float     ,   _a	:						float     ,   _a	:						float				):



      """simple docstring"""






      return (
          round(effusion_rate / sqrt(molar_mass_a / molar_mass_a				)     ,   6				)
          if validate(_a     ,   _a     ,   _a				)
          else ValueError(
              """Input Error: Molar mass and effusion rate values must greater than 0."""				)
      )



def   _A					(	_a	:						float     ,   _a	:						float     ,   _a	:						float				):



      """simple docstring"""






      return (
          round(molar_mass / pow(effusion_rate_a / effusion_rate_a     ,   2				)     ,   6				)
          if validate(_a     ,   _a     ,   _a				)
          else ValueError(
              """Input Error: Molar mass and effusion rate values must greater than 0."""				)
      )





def   _A					(	_a	:						float     ,   _a	:						float     ,   _a	:						float				):



      """simple docstring"""






      return (
          round(pow(effusion_rate_a / effusion_rate_a     ,   2				) / molar_mass     ,   6				)
          if validate(_a     ,   _a     ,   _a				)
          else ValueError(
              """Input Error: Molar mass and effusion rate values must greater than 0."""				)
      )




