






"""simple docstring"""





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:
  _SCREAMING_SNAKE_CASE :    Any    =						None






class 					a						( unittest.TestCase		):






    def __init__(	self					:							Optional[Any]	,						__SCREAMING_SNAKE_CASE					:							Optional[int]	,						__SCREAMING_SNAKE_CASE					:							List[str]=7	,						__SCREAMING_SNAKE_CASE					:							Union[str, Any]=3	,						__SCREAMING_SNAKE_CASE					:							Tuple=30	,						__SCREAMING_SNAKE_CASE					:							List[str]=400	,						__SCREAMING_SNAKE_CASE					:							str=True	,						__SCREAMING_SNAKE_CASE					:							int=None	,						__SCREAMING_SNAKE_CASE					:							Optional[int]=True	,						__SCREAMING_SNAKE_CASE					:							Union[str, Any]=None	,						__SCREAMING_SNAKE_CASE					:							List[str]=None	,						__SCREAMING_SNAKE_CASE					:							Tuple=True	,						__SCREAMING_SNAKE_CASE					:							Tuple=1 / 255	,						__SCREAMING_SNAKE_CASE					:							Union[str, Any]=True	,						__SCREAMING_SNAKE_CASE					:							str=FLAVA_IMAGE_MEAN	,						__SCREAMING_SNAKE_CASE					:							Any=FLAVA_IMAGE_STD	,						__SCREAMING_SNAKE_CASE					:							str=14	,						__SCREAMING_SNAKE_CASE					:							Dict=75	,						__SCREAMING_SNAKE_CASE					:							Tuple=None	,						__SCREAMING_SNAKE_CASE					:							List[str]=16	,						__SCREAMING_SNAKE_CASE					:							List[str]=0.3	,						__SCREAMING_SNAKE_CASE					:							Union[str, Any]=None	,						__SCREAMING_SNAKE_CASE					:							Any=True	,						__SCREAMING_SNAKE_CASE					:							int=None	,						__SCREAMING_SNAKE_CASE					:							str=None	,						__SCREAMING_SNAKE_CASE					:							int=True	,						__SCREAMING_SNAKE_CASE					:							int=None	,						__SCREAMING_SNAKE_CASE					:							int=True	,						__SCREAMING_SNAKE_CASE					:							Dict=True	,						__SCREAMING_SNAKE_CASE					:							Tuple=FLAVA_CODEBOOK_MEAN	,						__SCREAMING_SNAKE_CASE					:							Dict=FLAVA_CODEBOOK_STD	,						)					->		Any:
      lowerCamelCase_   					=						size if size is not None else {'height': 224, 'width': 224}
      lowerCamelCase_   					=						crop_size if crop_size is not None else {'height': 224, 'width': 224}
      lowerCamelCase_   					=						codebook_size if codebook_size is not None else {'height': 112, 'width': 112}
      lowerCamelCase_   					=						codebook_crop_size if codebook_crop_size is not None else {'height': 112, 'width': 112}

      lowerCamelCase_   					=						parent
      lowerCamelCase_   					=						batch_size
      lowerCamelCase_   					=						num_channels
      lowerCamelCase_   					=						do_resize
      lowerCamelCase_   					=						do_rescale
      lowerCamelCase_   					=						rescale_factor
      lowerCamelCase_   					=						min_resolution
      lowerCamelCase_   					=						max_resolution
      lowerCamelCase_   					=						size
      lowerCamelCase_   					=						resample if resample is not None else PILImageResampling.BICUBIC
      lowerCamelCase_   					=						do_normalize
      lowerCamelCase_   					=						image_mean
      lowerCamelCase_   					=						image_std
      lowerCamelCase_   					=						do_center_crop
      lowerCamelCase_   					=						crop_size

      lowerCamelCase_   					=						input_size_patches
      lowerCamelCase_   					=						total_mask_patches
      lowerCamelCase_   					=						mask_group_max_patches
      lowerCamelCase_   					=						mask_group_min_patches
      lowerCamelCase_   					=						mask_group_min_aspect_ratio
      lowerCamelCase_   					=						mask_group_max_aspect_ratio

      lowerCamelCase_   					=						codebook_do_resize
      lowerCamelCase_   					=						codebook_size
      lowerCamelCase_   					=						codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
      lowerCamelCase_   					=						codebook_do_center_crop
      lowerCamelCase_   					=						codebook_crop_size
      lowerCamelCase_   					=						codebook_do_map_pixels
      lowerCamelCase_   					=						codebook_do_normalize
      lowerCamelCase_   					=						codebook_image_mean
      lowerCamelCase_   					=						codebook_image_std






    def        UpperCamelCase	(	self					:							Optional[int] )					->		Tuple:
      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					:							Optional[int] )					->		Union[str, Any]:
      return (self.size["height"], self.size["width"])






    def        UpperCamelCase	(	self					:							Tuple )					->		Optional[Any]:
      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 )					->		Optional[int]:
      return (self.codebook_size["height"], self.codebook_size["width"])






@require_torch
@require_vision
class 					a						( __snake_case  ,				unittest.TestCase		):
    SCREAMING_SNAKE_CASE						:					str						=			FlavaImageProcessor if is_vision_available() else None
    SCREAMING_SNAKE_CASE						:					int						=			None






    def        UpperCamelCase	(	self					:							Dict )					->		Union[str, Any]:
      lowerCamelCase_   					=						FlavaImageProcessingTester(self )






    @property
    def        UpperCamelCase	(	self					:							Dict )					->		Any:
      return self.image_processor_tester.prepare_image_processor_dict()






    def        UpperCamelCase	(	self					:							Optional[int] )					->		List[str]:
      lowerCamelCase_   					=						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					:							Dict )					->		List[Any]:
      lowerCamelCase_   					=						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} )

      lowerCamelCase_   					=						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[Any] )					->		int:
      pass






    def        UpperCamelCase	(	self					:							str )					->		Optional[int]:
      # Initialize image_processing
      lowerCamelCase_   					=						self.image_processing_class(**self.image_processor_dict )
      # create random PIL images
      lowerCamelCase_   					=						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
      lowerCamelCase_   					=						image_processing(image_inputs[0]	,						return_tensors='pt' )

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

      lowerCamelCase_					,     lowerCamelCase_   					=						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
      lowerCamelCase_   					=						image_processing(__SCREAMING_SNAKE_CASE	,						return_tensors='pt' )
      lowerCamelCase_					,     lowerCamelCase_   					=						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					:							Any	,						__SCREAMING_SNAKE_CASE					:							Dict	,						__SCREAMING_SNAKE_CASE					:							Optional[int] )					->		Optional[Any]:
      # Initialize image_processing
      lowerCamelCase_   					=						self.image_processing_class(**self.image_processor_dict )
      # create random tensors
      lowerCamelCase_   					=						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
      lowerCamelCase_   					=						image_processing(image_inputs[0]	,						return_tensors='pt' )

      lowerCamelCase_					,     lowerCamelCase_   					=						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)	,						)

      lowerCamelCase_   					=						image_processing(__SCREAMING_SNAKE_CASE	,						return_image_mask=__SCREAMING_SNAKE_CASE	,						return_tensors='pt' )

      lowerCamelCase_					,     lowerCamelCase_   					=						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,
          )	,						)

      lowerCamelCase_					,     lowerCamelCase_   					=						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
      lowerCamelCase_   					=						image_processing(__SCREAMING_SNAKE_CASE	,						return_tensors='pt' ).pixel_values

      lowerCamelCase_					,     lowerCamelCase_   					=						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
      lowerCamelCase_   					=						image_processing(__SCREAMING_SNAKE_CASE	,						return_image_mask=__SCREAMING_SNAKE_CASE	,						return_tensors='pt' )

      lowerCamelCase_					,     lowerCamelCase_   					=						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,
          )	,						)

      lowerCamelCase_					,     lowerCamelCase_   					=						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					:							List[Any] )					->		str:
      self._test_call_framework(np.ndarray	,						prepare_kwargs={'numpify': True} )






    def        UpperCamelCase	(	self					:							Tuple )					->		Tuple:
      self._test_call_framework(torch.Tensor	,						prepare_kwargs={'torchify': True} )






    def        UpperCamelCase	(	self					:							int )					->		str:
      # Initialize image_processing
      random.seed(1234 )
      lowerCamelCase_   					=						self.image_processing_class(**self.image_processor_dict )
      lowerCamelCase_   					=						prepare_image_inputs(self.image_processor_tester	,						equal_resolution=__SCREAMING_SNAKE_CASE	,						torchify=__SCREAMING_SNAKE_CASE )

      # Test not batched input
      lowerCamelCase_   					=						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					:							int )					->		Union[str, Any]:
      # Initialize image_processing
      lowerCamelCase_   					=						self.image_processing_class(**self.image_processor_dict )
      # create random PIL images
      lowerCamelCase_   					=						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
      lowerCamelCase_   					=						image_processing(image_inputs[0]	,						return_codebook_pixels=__SCREAMING_SNAKE_CASE	,						return_tensors='pt' )
      lowerCamelCase_					,     lowerCamelCase_   					=						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
      lowerCamelCase_   					=						image_processing(__SCREAMING_SNAKE_CASE	,						return_codebook_pixels=__SCREAMING_SNAKE_CASE	,						return_tensors='pt' )
      lowerCamelCase_					,     lowerCamelCase_   					=						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,
          )	,						)












"""simple docstring"""





import numpy as np







def 					lowerCamelCase__	(							_lowerCamelCase	:		Dict		,    _lowerCamelCase	:		Any		,    _lowerCamelCase	:		Tuple			)				->    str:
  lowerCamelCase_   					=						n_draws * class_counts / class_counts.sum()
  # floored means we don't overshoot n_samples, but probably undershoot
  lowerCamelCase_   					=						np.floor(_lowerCamelCase			)
  # we add samples according to how much "left over" probability
  # they had, until we arrive at n_samples
  lowerCamelCase_   					=						int(n_draws - floored.sum()			)
  if need_to_add > 0:
    lowerCamelCase_   					=						continuous - floored
    lowerCamelCase_   					=						np.sort(np.unique(_lowerCamelCase			)			)[::-1]
    # add according to remainder, but break ties
    # randomly to avoid biases
    for value in values:
      ((lowerCamelCase_)					,     )   					=						np.where(remainder == value			)
      # if we need_to_add less than what's in inds
      # we draw randomly from them.
      # if we need to add more, we add them all and
      # go to the next value
      lowerCamelCase_   					=						min(len(_lowerCamelCase			)		,    _lowerCamelCase			)
      lowerCamelCase_   					=						rng.choice(_lowerCamelCase		,    size=_lowerCamelCase		,    replace=_lowerCamelCase			)
      floored[inds] += 1
      need_to_add -= add_now
      if need_to_add == 0:
        break
  return floored.astype(np.intaa			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		Any		,    _lowerCamelCase	:		int		,    _lowerCamelCase	:		Optional[Any]		,    _lowerCamelCase	:		List[str]		,    _lowerCamelCase	:		Optional[int]=10			)				->    Any:
  lowerCamelCase_					,     lowerCamelCase_   					=						np.unique(_lowerCamelCase		,    return_inverse=_lowerCamelCase			)
  lowerCamelCase_   					=						classes.shape[0]
  lowerCamelCase_   					=						np.bincount(_lowerCamelCase			)
  if np.min(_lowerCamelCase			) < 2:
    raise ValueError('Minimum class count error'			)
  if n_train < n_classes:
    raise ValueError(
        'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)			)
  if n_test < n_classes:
    raise ValueError(
        'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)			)
  lowerCamelCase_   					=						np.split(np.argsort(_lowerCamelCase		,    kind='mergesort'			)		,    np.cumsum(_lowerCamelCase			)[:-1]			)
  for _ in range(_lowerCamelCase			):
    lowerCamelCase_   					=						approximate_mode(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)
    lowerCamelCase_   					=						class_counts - n_i
    lowerCamelCase_   					=						approximate_mode(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)

    lowerCamelCase_   					=						[]
    lowerCamelCase_   					=						[]

    for i in range(_lowerCamelCase			):
      lowerCamelCase_   					=						rng.permutation(class_counts[i]			)
      lowerCamelCase_   					=						class_indices[i].take(_lowerCamelCase		,    mode='clip'			)
      train.extend(perm_indices_class_i[: n_i[i]]			)
      test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]			)
    lowerCamelCase_   					=						rng.permutation(_lowerCamelCase			)
    lowerCamelCase_   					=						rng.permutation(_lowerCamelCase			)

    yield train, test




