



from __future__ import annotations

from enum import Enum



class        lowerCAmelCase_	(						_SCREAMING_SNAKE_CASE							):
       UpperCAmelCase__       :      int       =						24
       UpperCAmelCase__       :      Optional[int]       =						21
       UpperCAmelCase__       :      int       =						18
       UpperCAmelCase__       :      Optional[int]       =						15
       UpperCAmelCase__       :      Optional[Any]       =						12
       UpperCAmelCase__       :      Optional[int]       =						9
       UpperCAmelCase__       :      Optional[Any]       =						6
       UpperCAmelCase__       :      Tuple       =						3
       UpperCAmelCase__       :      Union[str, Any]       =						2
       UpperCAmelCase__       :      str       =						1
       UpperCAmelCase__       :      Dict       =						-1
       UpperCAmelCase__       :      Tuple       =						-2
       UpperCAmelCase__       :      Tuple       =						-3
       UpperCAmelCase__       :      int       =						-6
       UpperCAmelCase__       :      List[str]       =						-9
       UpperCAmelCase__       :      int       =						-12
       UpperCAmelCase__       :      Dict       =						-15
       UpperCAmelCase__       :      Any       =						-18
       UpperCAmelCase__       :      List[Any]       =						-21
       UpperCAmelCase__       :      str       =						-24






class        lowerCAmelCase_	(						_SCREAMING_SNAKE_CASE							):
       UpperCAmelCase__       :      List[Any]       =						8
       UpperCAmelCase__       :      int       =						7
       UpperCAmelCase__       :      Tuple       =						6
       UpperCAmelCase__       :      Optional[int]       =						5
       UpperCAmelCase__       :      Union[str, Any]       =						4
       UpperCAmelCase__       :      Tuple       =						3
       UpperCAmelCase__       :      List[str]       =						2
       UpperCAmelCase__       :      int       =						1






def 	UpperCamelCase			(     snake_case__ :					List[str] ,  snake_case__ :					Dict ,  snake_case__ :					Optional[Any] ,  )			->  Optional[int]:
   if isinstance(snake_case__ ,  snake_case__			):
      UpperCamelCase					:						str					=			SIUnit[known_prefix.lower()]
   if isinstance(snake_case__ ,  snake_case__			):
      UpperCamelCase					:						Optional[Any]					=			SIUnit[unknown_prefix.lower()]
   UpperCamelCase					:						Optional[Any]					=			known_amount * (
       10 ** (known_prefix.value - unknown_prefix.value)
   )
   return unknown_amount


def 	UpperCamelCase			(     snake_case__ :					Any ,  snake_case__ :					str ,  snake_case__ :					Optional[int] ,  )			->  List[Any]:
   if isinstance(snake_case__ ,  snake_case__			):
      UpperCamelCase					:						List[str]					=			BinaryUnit[known_prefix.lower()]
   if isinstance(snake_case__ ,  snake_case__			):
      UpperCamelCase					:						Any					=			BinaryUnit[unknown_prefix.lower()]
   UpperCamelCase					:						str					=			known_amount * (
       2 ** ((known_prefix.value - unknown_prefix.value) * 10)
   )
   return unknown_amount


if __name__ == "__main__":
    import doctest

    doctest.testmod()








import unittest

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 ViltImageProcessor



class        lowerCAmelCase_	(						unittest.TestCase							):
       def __init__(    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_=7,    SCREAMING_SNAKE_CASE_=3,    SCREAMING_SNAKE_CASE_=18,    SCREAMING_SNAKE_CASE_=30,    SCREAMING_SNAKE_CASE_=400,    SCREAMING_SNAKE_CASE_=True,    SCREAMING_SNAKE_CASE_=None,    SCREAMING_SNAKE_CASE_=2,    SCREAMING_SNAKE_CASE_=True,    SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],    SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],    )  ->	List[Any]:
          UpperCamelCase					:						Optional[int]					=			size if size is not None else {'shortest_edge': 30}
          UpperCamelCase					:						Dict					=			parent
          UpperCamelCase					:						str					=			batch_size
          UpperCamelCase					:						str					=			num_channels
          UpperCamelCase					:						Optional[int]					=			image_size
          UpperCamelCase					:						List[Any]					=			min_resolution
          UpperCamelCase					:						Tuple					=			max_resolution
          UpperCamelCase					:						Tuple					=			do_resize
          UpperCamelCase					:						Any					=			size
          UpperCamelCase					:						int					=			size_divisor
          UpperCamelCase					:						str					=			do_normalize
          UpperCamelCase					:						Optional[int]					=			image_mean
          UpperCamelCase					:						List[str]					=			image_std
       def       snake_case_ (    self )  ->	List[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       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_=False )  ->	Union[str, Any]:
          if not batched:
             UpperCamelCase					:						Optional[Any]					=			self.size['shortest_edge']
             UpperCamelCase					:						int					=			image_inputs[0]
             if isinstance(SCREAMING_SNAKE_CASE_,    Image.Image ):
                UpperCamelCase			,  UpperCamelCase					:						List[Any]					=			image.size
             else:
                UpperCamelCase			,  UpperCamelCase					:						List[Any]					=			image.shape[1], image.shape[2]
             UpperCamelCase					:						Any					=			size / min(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )
             if h < w:
                UpperCamelCase			,  UpperCamelCase					:						Optional[int]					=			size, scale * w
             else:
                UpperCamelCase			,  UpperCamelCase					:						List[str]					=			scale * h, size

             UpperCamelCase					:						int					=			int((1333 / 800) * size )
             if max(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ ) > max_size:
                UpperCamelCase					:						Any					=			max_size / max(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )
                UpperCamelCase					:						List[Any]					=			newh * scale
                UpperCamelCase					:						Optional[int]					=			neww * scale

             UpperCamelCase			,  UpperCamelCase					:						List[Any]					=			int(newh + 0.5 ), int(neww + 0.5 )
             UpperCamelCase			,  UpperCamelCase					:						int					=			(
                 newh // self.size_divisor * self.size_divisor,
                 neww // self.size_divisor * self.size_divisor,
             )

          else:
             UpperCamelCase					:						Dict					=			[]
             for image in image_inputs:
                UpperCamelCase			,  UpperCamelCase					:						Union[str, Any]					=			self.get_expected_values([image] )
                expected_values.append((expected_height, expected_width) )
             UpperCamelCase					:						List[str]					=			max(SCREAMING_SNAKE_CASE_,    key=lambda SCREAMING_SNAKE_CASE_ : item[0] )[0]
             UpperCamelCase					:						List[str]					=			max(SCREAMING_SNAKE_CASE_,    key=lambda SCREAMING_SNAKE_CASE_ : item[1] )[1]

          return expected_height, expected_width






@require_torch
@require_vision
class        lowerCAmelCase_	(						a__ ,					unittest.TestCase							):
       UpperCAmelCase__       :      List[Any]       =						ViltImageProcessor if is_vision_available() else None
       def       snake_case_ (    self )  ->	Union[str, Any]:
          UpperCamelCase					:						Dict					=			ViltImageProcessingTester(self )
       @property
       def       snake_case_ (    self )  ->	Dict:
          return self.image_processor_tester.prepare_image_processor_dict()
       def       snake_case_ (    self )  ->	Union[str, Any]:
          UpperCamelCase					:						List[str]					=			self.image_processing_class(**self.image_processor_dict )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'image_mean' ) )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'image_std' ) )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'do_normalize' ) )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'do_resize' ) )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'size' ) )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'size_divisor' ) )
       def       snake_case_ (    self )  ->	List[Any]:
          UpperCamelCase					:						Union[str, Any]					=			self.image_processing_class.from_dict(self.image_processor_dict )
          self.assertEqual(image_processor.size,    {'shortest_edge': 30} )

          UpperCamelCase					:						Union[str, Any]					=			self.image_processing_class.from_dict(self.image_processor_dict,    size=42 )
          self.assertEqual(image_processor.size,    {'shortest_edge': 42} )
       def       snake_case_ (    self )  ->	Dict:
          pass
       def       snake_case_ (    self )  ->	str:
          # Initialize image_processing
          UpperCamelCase					:						int					=			self.image_processing_class(**self.image_processor_dict )
          # create random PIL images
          UpperCamelCase					:						Any					=			prepare_image_inputs(self.image_processor_tester,    equal_resolution=SCREAMING_SNAKE_CASE_ )
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,    Image.Image )

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

          UpperCamelCase			,  UpperCamelCase					:						Dict					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
          self.assertEqual(
              encoded_images.shape,    (1, self.image_processor_tester.num_channels, expected_height, expected_width),    )

          # Test batched
          UpperCamelCase					:						str					=			image_processing(SCREAMING_SNAKE_CASE_,    return_tensors='pt' ).pixel_values

          UpperCamelCase			,  UpperCamelCase					:						Tuple					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,    batched=SCREAMING_SNAKE_CASE_ )
          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 )  ->	List[str]:
          # Initialize image_processing
          UpperCamelCase					:						Optional[int]					=			self.image_processing_class(**self.image_processor_dict )
          # create random numpy tensors
          UpperCamelCase					:						Optional[Any]					=			prepare_image_inputs(self.image_processor_tester,    equal_resolution=SCREAMING_SNAKE_CASE_,    numpify=SCREAMING_SNAKE_CASE_ )
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,    np.ndarray )

          # Test not batched input
          UpperCamelCase					:						Optional[Any]					=			image_processing(image_inputs[0],    return_tensors='pt' ).pixel_values

          UpperCamelCase			,  UpperCamelCase					:						str					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
          self.assertEqual(
              encoded_images.shape,    (1, self.image_processor_tester.num_channels, expected_height, expected_width),    )

          # Test batched
          UpperCamelCase					:						Dict					=			image_processing(SCREAMING_SNAKE_CASE_,    return_tensors='pt' ).pixel_values

          UpperCamelCase			,  UpperCamelCase					:						Optional[Any]					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,    batched=SCREAMING_SNAKE_CASE_ )
          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[Any]:
          # Initialize image_processing
          UpperCamelCase					:						Optional[int]					=			self.image_processing_class(**self.image_processor_dict )
          # create random PyTorch tensors
          UpperCamelCase					:						Dict					=			prepare_image_inputs(self.image_processor_tester,    equal_resolution=SCREAMING_SNAKE_CASE_,    torchify=SCREAMING_SNAKE_CASE_ )
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,    torch.Tensor )

          # Test not batched input
          UpperCamelCase					:						List[str]					=			image_processing(image_inputs[0],    return_tensors='pt' ).pixel_values

          UpperCamelCase			,  UpperCamelCase					:						Union[str, Any]					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
          self.assertEqual(
              encoded_images.shape,    (1, self.image_processor_tester.num_channels, expected_height, expected_width),    )

          # Test batched
          UpperCamelCase					:						List[Any]					=			image_processing(SCREAMING_SNAKE_CASE_,    return_tensors='pt' ).pixel_values

          UpperCamelCase			,  UpperCamelCase					:						Dict					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,    batched=SCREAMING_SNAKE_CASE_ )
          self.assertEqual(
              encoded_images.shape,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              ),    )



