
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      _UpperCAmelCase							(		unittest.TestCase							):




      def __init__(							self   ,       lowercase_   ,       lowercase_ = True   ,       lowercase_ = None   ,       lowercase_ = 3_2   ,       lowercase_ = True   ,       lowercase_ = 1 / 2_5_5   ,       lowercase_ = True   ,       lowercase_ = True   ,       lowercase_ = [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]   ,       lowercase_ = [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]   ,       lowercase_ = True   ,       lowercase_=7   ,       lowercase_=3_0   ,       lowercase_=4_0_0   ,       lowercase_=3   ,       )						->					Dict:
          UpperCAmelCase									=					parent
          UpperCAmelCase									=					do_resize
          UpperCAmelCase									=					size if size is not None else {'shortest_edge': 2_8_8}
          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]:
          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   ,       lowercase_   ,       lowercase_=False							)						->					str:
          if not batched:
              UpperCAmelCase									=					self.size['shortest_edge']
              UpperCAmelCase									=					image_inputs[0]
              if isinstance(lowercase_   ,       Image.Image							):
                  UpperCAmelCase			,							UpperCAmelCase									=					image.size
              else:
                  UpperCAmelCase			,							UpperCAmelCase									=					image.shape[1], image.shape[2]
              UpperCAmelCase									=					size / min(lowercase_   ,       lowercase_							)
              if h < w:
                  UpperCAmelCase			,							UpperCAmelCase									=					size, scale * w
              else:
                  UpperCAmelCase			,							UpperCAmelCase									=					scale * h, size

              UpperCAmelCase									=					int((1_3_3_3 / 8_0_0) * size							)
              if max(lowercase_   ,       lowercase_							) > max_size:
                  UpperCAmelCase									=					max_size / max(lowercase_   ,       lowercase_							)
                  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(lowercase_   ,       key=lambda lowercase_							: item[0]							)[0]
              UpperCAmelCase									=					max(lowercase_   ,       key=lambda lowercase_							: item[1]							)[1]

          return expected_height, expected_width

@require_torch
@require_vision
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	BridgeTowerImageProcessor if is_vision_available() else None




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					BridgeTowerImageProcessingTester(self							)




      @property
      def     a_      (							self							)						->					Tuple:
          return self.image_processor_tester.prepare_image_processor_dict()




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					self.image_processing_class(**self.image_processor_dict							)
          self.assertTrue(hasattr(lowercase_   ,       'image_mean'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'image_std'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'do_normalize'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'do_resize'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'size'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'size_divisor'							)							)




      def     a_      (							self							)						->					Optional[int]:
          pass




      def     a_      (							self							)						->					Tuple:
          # Initialize image processor
          UpperCAmelCase									=					self.image_processing_class(**self.image_processor_dict							)
          # create random PIL images
          UpperCAmelCase									=					prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=lowercase_							)
          for image in image_inputs:
              self.assertIsInstance(lowercase_   ,       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(lowercase_							)
          self.assertEqual(
              encoded_images.shape   ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,       )

          # Test batched
          UpperCAmelCase									=					image_processing(lowercase_   ,       return_tensors='pt'							).pixel_values

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




      def     a_      (							self							)						->					Any:
          # Initialize image processor
          UpperCAmelCase									=					self.image_processing_class(**self.image_processor_dict							)
          # create random numpy tensors
          UpperCAmelCase									=					prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=lowercase_   ,       numpify=lowercase_							)
          for image in image_inputs:
              self.assertIsInstance(lowercase_   ,       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(lowercase_							)
          self.assertEqual(
              encoded_images.shape   ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,       )

          # Test batched
          UpperCAmelCase									=					image_processing(lowercase_   ,       return_tensors='pt'							).pixel_values

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




      def     a_      (							self							)						->					List[Any]:
          # Initialize image processor
          UpperCAmelCase									=					self.image_processing_class(**self.image_processor_dict							)
          # create random PyTorch tensors
          UpperCAmelCase									=					prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=lowercase_   ,       torchify=lowercase_							)
          for image in image_inputs:
              self.assertIsInstance(lowercase_   ,       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(lowercase_							)
          self.assertEqual(
              encoded_images.shape   ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,       )

          # Test batched
          UpperCAmelCase									=					image_processing(lowercase_   ,       return_tensors='pt'							).pixel_values

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





"""simple docstring"""

from __future__ import annotations

import numpy as np




def     lowercase__						(					lowerCAmelCase				:					list[float] ) ->      Dict:






    """simple docstring"""




    return np.maximum(0 ,				lowerCAmelCase )


if __name__ == "__main__":
   print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]



