

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 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def __init__(       self					:			Union[str, Any]     , __lowerCamelCase					:			Union[str, Any]     , __lowerCamelCase					:			bool = True     , __lowerCamelCase					:			Dict[str, int] = None     , __lowerCamelCase					:			int = 32     , __lowerCamelCase					:			bool = True     , __lowerCamelCase					:			Union[int, float] = 1 / 255     , __lowerCamelCase					:			bool = True     , __lowerCamelCase					:			bool = True     , __lowerCamelCase					:			Optional[Union[float, List[float]]] = [0.48_145_466, 0.4_578_275, 0.40_821_073]     , __lowerCamelCase					:			Optional[Union[float, List[float]]] = [0.26_862_954, 0.26_130_258, 0.27_577_711]     , __lowerCamelCase					:			bool = True     , __lowerCamelCase					:			Optional[int]=7     , __lowerCamelCase					:			str=30     , __lowerCamelCase					:			Any=400     , __lowerCamelCase					:			Tuple=3     , ):
       SCREAMING_SNAKE_CASE										=   parent
       SCREAMING_SNAKE_CASE										=   do_resize
       SCREAMING_SNAKE_CASE										=   size if size is not None else {"shortest_edge": 288}
       SCREAMING_SNAKE_CASE										=   size_divisor
       SCREAMING_SNAKE_CASE										=   do_rescale
       SCREAMING_SNAKE_CASE										=   rescale_factor
       SCREAMING_SNAKE_CASE										=   do_normalize
       SCREAMING_SNAKE_CASE										=   do_center_crop
       SCREAMING_SNAKE_CASE										=   image_mean
       SCREAMING_SNAKE_CASE										=   image_std
       SCREAMING_SNAKE_CASE										=   do_pad
       SCREAMING_SNAKE_CASE										=   batch_size
       SCREAMING_SNAKE_CASE										=   num_channels
       SCREAMING_SNAKE_CASE										=   min_resolution
       SCREAMING_SNAKE_CASE										=   max_resolution





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

           SCREAMING_SNAKE_CASE										=   int((1333 / 800) * size			)
           if max(__lowerCamelCase     , __lowerCamelCase			) > max_size:
               SCREAMING_SNAKE_CASE										=   max_size / max(__lowerCamelCase     , __lowerCamelCase			)
               SCREAMING_SNAKE_CASE										=   newh * scale
               SCREAMING_SNAKE_CASE										=   neww * scale

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

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

       return expected_height, expected_width





@require_torch
@require_vision
class 			_SCREAMING_SNAKE_CASE (   __snake_case					,       unittest.TestCase		):







   '''simple docstring'''




   lowerCamelCase__				=      BridgeTowerImageProcessor if is_vision_available() else None





   def      _snake_case		(       self					:			str			):
       SCREAMING_SNAKE_CASE										=   BridgeTowerImageProcessingTester(self			)





   @property
   def      _snake_case		(       self					:			Union[str, Any]			):
       return self.image_processor_tester.prepare_image_processor_dict()





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   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      _snake_case		(       self					:			Tuple			):
       pass





   def      _snake_case		(       self					:			int			):
       # Initialize image processor
       SCREAMING_SNAKE_CASE										=   self.image_processing_class(**self.image_processor_dict			)
       # create random PIL images
       SCREAMING_SNAKE_CASE										=   prepare_image_inputs(self.image_processor_tester     , equal_resolution=__lowerCamelCase			)
       for image in image_inputs:
           self.assertIsInstance(__lowerCamelCase     , Image.Image			)

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

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   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
       SCREAMING_SNAKE_CASE										=   image_processing(__lowerCamelCase     , return_tensors="pt"			).pixel_values

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   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      _snake_case		(       self					:			Any			):
       # Initialize image processor
       SCREAMING_SNAKE_CASE										=   self.image_processing_class(**self.image_processor_dict			)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE										=   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
       SCREAMING_SNAKE_CASE										=   image_processing(image_inputs[0]     , return_tensors="pt"			).pixel_values

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   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
       SCREAMING_SNAKE_CASE										=   image_processing(__lowerCamelCase     , return_tensors="pt"			).pixel_values

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   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      _snake_case		(       self					:			Optional[int]			):
       # Initialize image processor
       SCREAMING_SNAKE_CASE										=   self.image_processing_class(**self.image_processor_dict			)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE										=   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
       SCREAMING_SNAKE_CASE										=   image_processing(image_inputs[0]     , return_tensors="pt"			).pixel_values

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   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
       SCREAMING_SNAKE_CASE										=   image_processing(__lowerCamelCase     , return_tensors="pt"			).pixel_values

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   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,
           )     , )


import os





def 	__a     (      ):
    SCREAMING_SNAKE_CASE										=   os.path.join(os.path.dirname(A__       )			,  "num.txt"       )
    with open(A__       ) as file_hand:
        return str(sum(int(A__       ) for line in file_hand       )       )[:10]


if __name__ == "__main__":
    print(solution())