






import random
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():
 import PIL

 from transformers import FlavaImageProcessor
 from transformers.image_utils import PILImageResampling
 from transformers.models.flava.image_processing_flava import (
     FLAVA_CODEBOOK_MEAN,
     FLAVA_CODEBOOK_STD,
     FLAVA_IMAGE_MEAN,
     FLAVA_IMAGE_STD,
 )
else:
 __snake_case							 =  __snake_case							 =  __snake_case							 =  __snake_case							 =  None







class       UpperCAmelCase_						( unittest.TestCase			):



      """simple docstring"""
      def __init__(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_=7   ,    SCREAMING_SNAKE_CASE_=3   ,    SCREAMING_SNAKE_CASE_=30   ,    SCREAMING_SNAKE_CASE_=400   ,    SCREAMING_SNAKE_CASE_=True   ,    SCREAMING_SNAKE_CASE_=None   ,    SCREAMING_SNAKE_CASE_=True   ,    SCREAMING_SNAKE_CASE_=None   ,    SCREAMING_SNAKE_CASE_=None   ,    SCREAMING_SNAKE_CASE_=True   ,    SCREAMING_SNAKE_CASE_=1 / 255   ,    SCREAMING_SNAKE_CASE_=True   ,    SCREAMING_SNAKE_CASE_=FLAVA_IMAGE_MEAN   ,    SCREAMING_SNAKE_CASE_=FLAVA_IMAGE_STD   ,    SCREAMING_SNAKE_CASE_=14   ,    SCREAMING_SNAKE_CASE_=75   ,    SCREAMING_SNAKE_CASE_=None   ,    SCREAMING_SNAKE_CASE_=16   ,    SCREAMING_SNAKE_CASE_=0.3   ,    SCREAMING_SNAKE_CASE_=None   ,    SCREAMING_SNAKE_CASE_=True   ,    SCREAMING_SNAKE_CASE_=None   ,    SCREAMING_SNAKE_CASE_=None   ,    SCREAMING_SNAKE_CASE_=True   ,    SCREAMING_SNAKE_CASE_=None   ,    SCREAMING_SNAKE_CASE_=True   ,    SCREAMING_SNAKE_CASE_=True   ,    SCREAMING_SNAKE_CASE_=FLAVA_CODEBOOK_MEAN   ,    SCREAMING_SNAKE_CASE_=FLAVA_CODEBOOK_STD   ,    )    ->				Any:
          UpperCamelCase	:List[Any]     							=       size if size is not None else {'''height''': 224, '''width''': 224}
          UpperCamelCase	:Any     							=       crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
          UpperCamelCase	:int     							=       codebook_size if codebook_size is not None else {'''height''': 112, '''width''': 112}
          UpperCamelCase	:Any     							=       codebook_crop_size if codebook_crop_size is not None else {'''height''': 112, '''width''': 112}

          UpperCamelCase	:Union[str, Any]     							=       parent
          UpperCamelCase	:Optional[Any]     							=       batch_size
          UpperCamelCase	:List[Any]     							=       num_channels
          UpperCamelCase	:Optional[Any]     							=       do_resize
          UpperCamelCase	:Tuple     							=       do_rescale
          UpperCamelCase	:Optional[int]     							=       rescale_factor
          UpperCamelCase	:str     							=       min_resolution
          UpperCamelCase	:Any     							=       max_resolution
          UpperCamelCase	:List[str]     							=       size
          UpperCamelCase	:str     							=       resample if resample is not None else PILImageResampling.BICUBIC
          UpperCamelCase	:Tuple     							=       do_normalize
          UpperCamelCase	:Union[str, Any]     							=       image_mean
          UpperCamelCase	:Optional[int]     							=       image_std
          UpperCamelCase	:Any     							=       do_center_crop
          UpperCamelCase	:str     							=       crop_size

          UpperCamelCase	:Dict     							=       input_size_patches
          UpperCamelCase	:Tuple     							=       total_mask_patches
          UpperCamelCase	:Optional[Any]     							=       mask_group_max_patches
          UpperCamelCase	:Union[str, Any]     							=       mask_group_min_patches
          UpperCamelCase	:str     							=       mask_group_min_aspect_ratio
          UpperCamelCase	:List[str]     							=       mask_group_max_aspect_ratio

          UpperCamelCase	:List[str]     							=       codebook_do_resize
          UpperCamelCase	:List[str]     							=       codebook_size
          UpperCamelCase	:Tuple     							=       codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
          UpperCamelCase	:int     							=       codebook_do_center_crop
          UpperCamelCase	:Any     							=       codebook_crop_size
          UpperCamelCase	:Dict     							=       codebook_do_map_pixels
          UpperCamelCase	:int     							=       codebook_do_normalize
          UpperCamelCase	:Tuple     							=       codebook_image_mean
          UpperCamelCase	:int     							=       codebook_image_std
      def    UpperCAmelCase						(    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,
              "resample": self.resample,
              "do_rescale": self.do_rescale,
              "rescale_factor": self.rescale_factor,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
              "input_size_patches": self.input_size_patches,
              "total_mask_patches": self.total_mask_patches,
              "mask_group_max_patches": self.mask_group_max_patches,
              "mask_group_min_patches": self.mask_group_min_patches,
              "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
              "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
              "codebook_do_resize": self.codebook_do_resize,
              "codebook_size": self.codebook_size,
              "codebook_resample": self.codebook_resample,
              "codebook_do_center_crop": self.codebook_do_center_crop,
              "codebook_crop_size": self.codebook_crop_size,
              "codebook_do_map_pixels": self.codebook_do_map_pixels,
              "codebook_do_normalize": self.codebook_do_normalize,
              "codebook_image_mean": self.codebook_image_mean,
              "codebook_image_std": self.codebook_image_std,
          }
      def    UpperCAmelCase						(    self			)    ->				List[Any]:
          return (self.size["height"], self.size["width"])
      def    UpperCAmelCase						(    self			)    ->				int:
          return (
              (self.input_size_patches, self.input_size_patches)
              if not isinstance(self.input_size_patches   ,    SCREAMING_SNAKE_CASE_			)
              else self.input_size_patches
          )




      def    UpperCAmelCase						(    self			)    ->				str:
          return (self.codebook_size["height"], self.codebook_size["width"])







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



      """simple docstring"""
      UpperCamelCase_    : List[Any]									=FlavaImageProcessor if is_vision_available() else None
      UpperCamelCase_    : int									=None
      def    UpperCAmelCase						(    self			)    ->				Optional[int]:
          UpperCamelCase	:str     							=       FlavaImageProcessingTester(self			)
      @property
      def    UpperCAmelCase						(    self			)    ->				Any:
          return self.image_processor_tester.prepare_image_processor_dict()
      def    UpperCAmelCase						(    self			)    ->				List[Any]:
          UpperCamelCase	: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_   ,    '''resample'''			)			)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,    '''crop_size'''			)			)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,    '''do_center_crop'''			)			)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,    '''do_rescale'''			)			)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,    '''rescale_factor'''			)			)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,    '''masking_generator'''			)			)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,    '''codebook_do_resize'''			)			)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,    '''codebook_size'''			)			)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,    '''codebook_resample'''			)			)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,    '''codebook_do_center_crop'''			)			)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,    '''codebook_crop_size'''			)			)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,    '''codebook_do_map_pixels'''			)			)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,    '''codebook_do_normalize'''			)			)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,    '''codebook_image_mean'''			)			)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,    '''codebook_image_std'''			)			)
      def    UpperCAmelCase						(    self			)    ->				Any:
          UpperCamelCase	:int     							=       self.image_processing_class.from_dict(self.image_processor_dict			)
          self.assertEqual(image_processor.size   ,    {'''height''': 224, '''width''': 224}			)
          self.assertEqual(image_processor.crop_size   ,    {'''height''': 224, '''width''': 224}			)
          self.assertEqual(image_processor.codebook_size   ,    {'''height''': 112, '''width''': 112}			)
          self.assertEqual(image_processor.codebook_crop_size   ,    {'''height''': 112, '''width''': 112}			)

          UpperCamelCase	:Optional[Any]     							=       self.image_processing_class.from_dict(
              self.image_processor_dict   ,    size=42   ,    crop_size=84   ,    codebook_size=33   ,    codebook_crop_size=66			)
          self.assertEqual(image_processor.size   ,    {'''height''': 42, '''width''': 42}			)
          self.assertEqual(image_processor.crop_size   ,    {'''height''': 84, '''width''': 84}			)
          self.assertEqual(image_processor.codebook_size   ,    {'''height''': 33, '''width''': 33}			)
          self.assertEqual(image_processor.codebook_crop_size   ,    {'''height''': 66, '''width''': 66}			)
      def    UpperCAmelCase						(    self			)    ->				List[str]:
          pass
      def    UpperCAmelCase						(    self			)    ->				List[str]:
          # Initialize image_processing
          UpperCamelCase	:Optional[int]     							=       self.image_processing_class(**self.image_processor_dict			)
          # create random PIL images
          UpperCamelCase	:List[Any]     							=       prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=SCREAMING_SNAKE_CASE_			)
          for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,    PIL.Image.Image			)

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

          # Test no bool masked pos
          self.assertFalse('''bool_masked_pos''' in encoded_images			)

          UpperCamelCase							,       UpperCamelCase	:List[Any]     							=       self.image_processor_tester.get_expected_image_size()

          self.assertEqual(
              encoded_images.pixel_values.shape   ,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,    )

          # Test batched
          UpperCamelCase	:int     							=       image_processing(SCREAMING_SNAKE_CASE_   ,    return_tensors='''pt'''			)
          UpperCamelCase							,       UpperCamelCase	:str     							=       self.image_processor_tester.get_expected_image_size()

          # Test no bool masked pos
          self.assertFalse('''bool_masked_pos''' in encoded_images			)

          self.assertEqual(
              encoded_images.pixel_values.shape   ,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )   ,    )
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)    ->				Dict:
          # Initialize image_processing
          UpperCamelCase	:Any     							=       self.image_processing_class(**self.image_processor_dict			)
          # create random tensors
          UpperCamelCase	:Tuple     							=       prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=SCREAMING_SNAKE_CASE_   ,    **SCREAMING_SNAKE_CASE_			)
          for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

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

          UpperCamelCase							,       UpperCamelCase	:Any     							=       self.image_processor_tester.get_expected_image_size()
          self.assertEqual(
              encoded_images.pixel_values.shape   ,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,    )

          UpperCamelCase	:List[Any]     							=       image_processing(SCREAMING_SNAKE_CASE_   ,    return_image_mask=SCREAMING_SNAKE_CASE_   ,    return_tensors='''pt'''			)

          UpperCamelCase							,       UpperCamelCase	:int     							=       self.image_processor_tester.get_expected_image_size()
          self.assertEqual(
              encoded_images.pixel_values.shape   ,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )   ,    )

          UpperCamelCase							,       UpperCamelCase	:Tuple     							=       self.image_processor_tester.get_expected_mask_size()
          self.assertEqual(
              encoded_images.bool_masked_pos.shape   ,    (
                  self.image_processor_tester.batch_size,
                  expected_height,
                  expected_width,
              )   ,    )

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

          UpperCamelCase							,       UpperCamelCase	:Optional[int]     							=       self.image_processor_tester.get_expected_image_size()
          self.assertEqual(
              encoded_images.shape   ,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )   ,    )

          # Test masking
          UpperCamelCase	:int     							=       image_processing(SCREAMING_SNAKE_CASE_   ,    return_image_mask=SCREAMING_SNAKE_CASE_   ,    return_tensors='''pt'''			)

          UpperCamelCase							,       UpperCamelCase	:Optional[Any]     							=       self.image_processor_tester.get_expected_image_size()
          self.assertEqual(
              encoded_images.pixel_values.shape   ,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )   ,    )

          UpperCamelCase							,       UpperCamelCase	:Any     							=       self.image_processor_tester.get_expected_mask_size()
          self.assertEqual(
              encoded_images.bool_masked_pos.shape   ,    (
                  self.image_processor_tester.batch_size,
                  expected_height,
                  expected_width,
              )   ,    )
      def    UpperCAmelCase						(    self			)    ->				Tuple:
          self._test_call_framework(np.ndarray   ,    prepare_kwargs={'''numpify''': True}			)
      def    UpperCAmelCase						(    self			)    ->				List[Any]:
          self._test_call_framework(torch.Tensor   ,    prepare_kwargs={'''torchify''': True}			)
      def    UpperCAmelCase						(    self			)    ->				List[Any]:
          # Initialize image_processing
          random.seed(1234			)
          UpperCamelCase	:int     							=       self.image_processing_class(**self.image_processor_dict			)
          UpperCamelCase	:int     							=       prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=SCREAMING_SNAKE_CASE_   ,    torchify=SCREAMING_SNAKE_CASE_			)

          # Test not batched input
          UpperCamelCase	:Dict     							=       image_processing(image_inputs[0]   ,    return_image_mask=SCREAMING_SNAKE_CASE_   ,    return_tensors='''pt'''			)
          self.assertEqual(encoded_images.bool_masked_pos.sum().item()   ,    75			)




      def    UpperCAmelCase						(    self			)    ->				str:
          # Initialize image_processing
          UpperCamelCase	:Dict     							=       self.image_processing_class(**self.image_processor_dict			)
          # create random PIL images
          UpperCamelCase	:List[Any]     							=       prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=SCREAMING_SNAKE_CASE_			)
          for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,    PIL.Image.Image			)

          # Test not batched input
          UpperCamelCase	:Dict     							=       image_processing(image_inputs[0]   ,    return_codebook_pixels=SCREAMING_SNAKE_CASE_   ,    return_tensors='''pt'''			)
          UpperCamelCase							,       UpperCamelCase	:str     							=       self.image_processor_tester.get_expected_codebook_image_size()
          self.assertEqual(
              encoded_images.codebook_pixel_values.shape   ,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,    )

          # Test batched
          UpperCamelCase	:List[str]     							=       image_processing(SCREAMING_SNAKE_CASE_   ,    return_codebook_pixels=SCREAMING_SNAKE_CASE_   ,    return_tensors='''pt'''			)
          UpperCamelCase							,       UpperCamelCase	:int     							=       self.image_processor_tester.get_expected_codebook_image_size()
          self.assertEqual(
              encoded_images.codebook_pixel_values.shape   ,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_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()





