



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):
     """simple docstring"""



     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					,      ):






           '''simple docstring'''



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







     def     _a   (self     ):






           '''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     ):






           '''simple docstring'''



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

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

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

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

           return expected_height, expected_width





@require_torch
@require_vision
class  lowerCamelCase__	(    UpperCAmelCase_							,     unittest.TestCase):
     """simple docstring"""



     _A      =      BridgeTowerImageProcessor if is_vision_available() else None







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		BridgeTowerImageProcessingTester(self     )







     @property
     def     _a   (self     ):






           '''simple docstring'''



           return self.image_processor_tester.prepare_image_processor_dict()







     def     _a   (self     ):






           '''simple docstring'''



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







     def     _a   (self     ):






           '''simple docstring'''



           pass







     def     _a   (self     ):






           '''simple docstring'''



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

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

           lowerCamelCase      ,				lowerCamelCase             =		self.image_processor_tester.get_expected_values(__a     )
           self.assertEqual(
               encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

           # Test batched
           lowerCamelCase             =		image_processing(__a					,      return_tensors="pt"     ).pixel_values

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







     def     _a   (self     ):






           '''simple docstring'''



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

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

           lowerCamelCase      ,				lowerCamelCase             =		self.image_processor_tester.get_expected_values(__a     )
           self.assertEqual(
               encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

           # Test batched
           lowerCamelCase             =		image_processing(__a					,      return_tensors="pt"     ).pixel_values

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







     def     _a   (self     ):






           '''simple docstring'''



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

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

           lowerCamelCase      ,				lowerCamelCase             =		self.image_processor_tester.get_expected_values(__a     )
           self.assertEqual(
               encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

           # Test batched
           lowerCamelCase             =		image_processing(__a					,      return_tensors="pt"     ).pixel_values

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




from ..utils import DummyObject, requires_backends





class  lowerCamelCase__	(    metaclass=UpperCAmelCase_):
     """simple docstring"""



     _A      =      ['transformers', 'torch', 'note_seq']







     def __init__(self					,      *__a					,      **__a     ):






           '''simple docstring'''



           requires_backends(self					,      ["transformers", "torch", "note_seq"]     )







     @classmethod
     def     _a   (cls					,      *__a					,      **__a     ):






           '''simple docstring'''



           requires_backends(cls					,      ["transformers", "torch", "note_seq"]     )







     @classmethod
     def     _a   (cls					,      *__a					,      **__a     ):






           '''simple docstring'''



           requires_backends(cls					,      ["transformers", "torch", "note_seq"]     )