


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):


       """simple docstring"""




       def __init__(   self							:			Dict					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			Dict = True					,      __lowerCamelCase							:			str = None					,      __lowerCamelCase							:			List[str] = 32					,      __lowerCamelCase							:			Any = True					,      __lowerCamelCase							:			int = 1 / 255					,      __lowerCamelCase							:			Optional[int] = True					,      __lowerCamelCase							:			Tuple = True					,      __lowerCamelCase							:			Dict = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]					,      __lowerCamelCase							:			List[str] = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]					,      __lowerCamelCase							:			Union[str, Any] = True					,      __lowerCamelCase							:			Tuple=7					,      __lowerCamelCase							:			Optional[Any]=30					,      __lowerCamelCase							:			str=400					,      __lowerCamelCase							:			List[Any]=3					,      ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  parent
             lowerCamelCase__		:				List[str]    =  do_resize
             lowerCamelCase__		:				Optional[Any]    =  size if size is not None else {"shortest_edge": 288}
             lowerCamelCase__		:				str    =  size_divisor
             lowerCamelCase__		:				List[str]    =  do_rescale
             lowerCamelCase__		:				str    =  rescale_factor
             lowerCamelCase__		:				List[Any]    =  do_normalize
             lowerCamelCase__		:				List[Any]    =  do_center_crop
             lowerCamelCase__		:				str    =  image_mean
             lowerCamelCase__		:				Union[str, Any]    =  image_std
             lowerCamelCase__		:				int    =  do_pad
             lowerCamelCase__		:				Optional[Any]    =  batch_size
             lowerCamelCase__		:				Optional[int]    =  num_channels
             lowerCamelCase__		:				Optional[Any]    =  min_resolution
             lowerCamelCase__		:				int    =  max_resolution





       def 						lowerCAmelCase     (   self							:			Optional[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 						lowerCAmelCase     (   self							:			Optional[int]					,      __lowerCamelCase							:			Optional[Any]					,      __lowerCamelCase							:			str=False       ):


             '''simple docstring'''



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

                   lowerCamelCase__		:				Optional[int]    =  int((1333 / 800) * size       )
                   if max(__lowerCamelCase					,      __lowerCamelCase       ) > max_size:
                         lowerCamelCase__		:				Dict    =  max_size / max(__lowerCamelCase					,      __lowerCamelCase       )
                         lowerCamelCase__		:				str    =  newh * scale
                         lowerCamelCase__		:				Any    =  neww * scale

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

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

             return expected_height, expected_width







@require_torch
@require_vision
class 			_lowercase (							lowercase_							,  unittest.TestCase):


       """simple docstring"""




       A__											=			BridgeTowerImageProcessor if is_vision_available() else None





       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  BridgeTowerImageProcessingTester(self       )





       @property
       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             return self.image_processor_tester.prepare_image_processor_dict()





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  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 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             pass





       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  self.image_processing_class(**self.image_processor_dict       )
             # create random PIL images
             lowerCamelCase__		:				Optional[int]    =  prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__lowerCamelCase       )
             for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase					,      Image.Image       )

             # Test not batched input
             lowerCamelCase__		:				Optional[int]    =  image_processing(image_inputs[0]					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				List[Any]    =  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
             lowerCamelCase__		:				int    =  image_processing(__lowerCamelCase					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				Any    =  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 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



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

             lowerCamelCase__    ,   lowerCamelCase__		:				Dict    =  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
             lowerCamelCase__		:				Union[str, Any]    =  image_processing(__lowerCamelCase					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				Union[str, Any]    =  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 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  self.image_processing_class(**self.image_processor_dict       )
             # create random PyTorch tensors
             lowerCamelCase__		:				Dict    =  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
             lowerCamelCase__		:				Union[str, Any]    =  image_processing(image_inputs[0]					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				List[Any]    =  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
             lowerCamelCase__		:				Optional[Any]    =  image_processing(__lowerCamelCase					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				List[Any]    =  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,
                 )					,      )





class  lowercase				:




				def __init__(					self      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    name
											snake_case_							=    value
											snake_case_							=    weight




				def __repr__(					self       ):
											return F'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''




				def     a							(					self       ):
											return self.value




				def     a							(					self       ):
											return self.name




				def     a							(					self       ):
											return self.weight




				def     a							(					self       ):
											return self.value / self.weight





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    []
							for i in range(len(UpperCamelCase__      )      ):
														menu.append(Things(name[i]   ,							value[i]   ,							weight[i]      )      )
							return menu





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    sorted(UpperCamelCase__   ,							key=UpperCamelCase__   ,							reverse=UpperCamelCase__      )
							snake_case_							=    []
							snake_case_				,    snake_case_							=    0.0, 0.0
							for i in range(len(UpperCamelCase__      )      ):
														if (total_cost + items_copy[i].get_weight()) <= max_cost:
																					result.append(items_copy[i]      )
																					total_cost += items_copy[i].get_weight()
																					total_value += items_copy[i].get_value()
							return (result, total_value)





def 					__lowerCamelCase	( ):







							'''simple docstring'''








if __name__ == "__main__":
					import doctest

					doctest.testmod()




