






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 ViTImageProcessor







class       UpperCAmelCase_						( unittest.TestCase			):



      """simple docstring"""
      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_=True   ,    SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]   ,    SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]   ,    )    ->				Dict:
          UpperCamelCase	:List[Any]     							=       size if size is not None else {'''height''': 18, '''width''': 18}
          UpperCamelCase	:List[Any]     							=       parent
          UpperCamelCase	:Union[str, Any]     							=       batch_size
          UpperCamelCase	:str     							=       num_channels
          UpperCamelCase	:Any     							=       image_size
          UpperCamelCase	:Dict     							=       min_resolution
          UpperCamelCase	:Any     							=       max_resolution
          UpperCamelCase	:str     							=       do_resize
          UpperCamelCase	:Optional[int]     							=       size
          UpperCamelCase	:int     							=       do_normalize
          UpperCamelCase	:Dict     							=       image_mean
          UpperCamelCase	:Dict     							=       image_std




      def    UpperCAmelCase						(    self			)    ->				int:
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
          }







@require_torch
@require_vision
class       UpperCAmelCase_						( lowercase,     unittest.TestCase			):



      """simple docstring"""
      UpperCamelCase_    : List[Any]									=ViTImageProcessor if is_vision_available() else None
      def    UpperCAmelCase						(    self			)    ->				Tuple:
          UpperCamelCase	:Dict     							=       ViTImageProcessingTester(self			)
      @property
      def    UpperCAmelCase						(    self			)    ->				Union[str, Any]:
          return self.image_processor_tester.prepare_image_processor_dict()
      def    UpperCAmelCase						(    self			)    ->				Tuple:
          UpperCamelCase	:Optional[int]     							=       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'''			)			)
      def    UpperCAmelCase						(    self			)    ->				Union[str, Any]:
          UpperCamelCase	:Any     							=       self.image_processing_class.from_dict(self.image_processor_dict			)
          self.assertEqual(image_processor.size   ,    {'''height''': 18, '''width''': 18}			)

          UpperCamelCase	:Dict     							=       self.image_processing_class.from_dict(self.image_processor_dict   ,    size=42			)
          self.assertEqual(image_processor.size   ,    {'''height''': 42, '''width''': 42}			)
      def    UpperCAmelCase						(    self			)    ->				int:
          pass
      def    UpperCAmelCase						(    self			)    ->				Any:
          # Initialize image_processing
          UpperCamelCase	:str     							=       self.image_processing_class(**self.image_processor_dict			)
          # create random PIL images
          UpperCamelCase	:Dict     							=       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	:List[str]     							=       image_processing(image_inputs[0]   ,    return_tensors='''pt'''			).pixel_values
          self.assertEqual(
              encoded_images.shape   ,    (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size['''height'''],
                  self.image_processor_tester.size['''width'''],
              )   ,    )

          # Test batched
          UpperCamelCase	:List[str]     							=       image_processing(SCREAMING_SNAKE_CASE_   ,    return_tensors='''pt'''			).pixel_values
          self.assertEqual(
              encoded_images.shape   ,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size['''height'''],
                  self.image_processor_tester.size['''width'''],
              )   ,    )
      def    UpperCAmelCase						(    self			)    ->				str:
          # Initialize image_processing
          UpperCamelCase	: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	:List[Any]     							=       image_processing(image_inputs[0]   ,    return_tensors='''pt'''			).pixel_values
          self.assertEqual(
              encoded_images.shape   ,    (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size['''height'''],
                  self.image_processor_tester.size['''width'''],
              )   ,    )

          # Test batched
          UpperCamelCase	:Any     							=       image_processing(SCREAMING_SNAKE_CASE_   ,    return_tensors='''pt'''			).pixel_values
          self.assertEqual(
              encoded_images.shape   ,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size['''height'''],
                  self.image_processor_tester.size['''width'''],
              )   ,    )




      def    UpperCAmelCase						(    self			)    ->				Tuple:
          # Initialize image_processing
          UpperCamelCase	:Union[str, Any]     							=       self.image_processing_class(**self.image_processor_dict			)
          # create random PyTorch tensors
          UpperCamelCase	:Optional[int]     							=       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	:Dict     							=       image_processing(image_inputs[0]   ,    return_tensors='''pt'''			).pixel_values
          self.assertEqual(
              encoded_images.shape   ,    (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size['''height'''],
                  self.image_processor_tester.size['''width'''],
              )   ,    )

          # Test batched
          UpperCamelCase	:int     							=       image_processing(SCREAMING_SNAKE_CASE_   ,    return_tensors='''pt'''			).pixel_values
          self.assertEqual(
              encoded_images.shape   ,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size['''height'''],
                  self.image_processor_tester.size['''width'''],
              )   ,    )













from __future__ import annotations

from enum import Enum







class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Optional[Any]									=24
      UpperCamelCase_    : Any									=21
      UpperCamelCase_    : Union[str, Any]									=18
      UpperCamelCase_    : Tuple									=15
      UpperCamelCase_    : List[Any]									=12
      UpperCamelCase_    : Optional[Any]									=9
      UpperCamelCase_    : List[str]									=6
      UpperCamelCase_    : List[str]									=3
      UpperCamelCase_    : Any									=2
      UpperCamelCase_    : List[str]									=1
      UpperCamelCase_    : Optional[Any]									=-1
      UpperCamelCase_    : str									=-2
      UpperCamelCase_    : Any									=-3
      UpperCamelCase_    : Optional[int]									=-6
      UpperCamelCase_    : int									=-9
      UpperCamelCase_    : Optional[int]									=-12
      UpperCamelCase_    : Optional[Any]									=-15
      UpperCamelCase_    : str									=-18
      UpperCamelCase_    : int									=-21
      UpperCamelCase_    : Union[str, Any]									=-24







class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=8
      UpperCamelCase_    : str									=7
      UpperCamelCase_    : List[str]									=6
      UpperCamelCase_    : List[str]									=5
      UpperCamelCase_    : int									=4
      UpperCamelCase_    : Optional[Any]									=3
      UpperCamelCase_    : Optional[Any]									=2
      UpperCamelCase_    : List[str]									=1







def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     str | SIUnit					,  SCREAMING_SNAKE_CASE__  :     str | SIUnit					,  ):
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:str     							=       SIUnit[known_prefix.lower()]
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:Any     							=       SIUnit[unknown_prefix.lower()]
    UpperCamelCase	:float     							=       known_amount * (
        10 ** (known_prefix.value - unknown_prefix.value)
    )
    return unknown_amount


def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     str | BinaryUnit					,  SCREAMING_SNAKE_CASE__  :     str | BinaryUnit					,  ):
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:Dict     							=       BinaryUnit[known_prefix.lower()]
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:List[str]     							=       BinaryUnit[unknown_prefix.lower()]
    UpperCamelCase	:float     							=       known_amount * (
        2 ** ((known_prefix.value - unknown_prefix.value) * 10)
    )
    return unknown_amount


if __name__ == "__main__":
 import doctest

 doctest.testmod()





