code: 
  
  
  '''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:
  							lowerCAmelCase   :List[Any]								=    None
  class _lowerCamelCase (		unittest.TestCase							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(      self	:		str						,  _A	:		Tuple						,  _A	:		Union[str, Any]=7						,  _A	:		Optional[Any]=3						,  _A	:		List[Any]=30						,  _A	:		List[Any]=400						,  _A	:		List[str]=True						,  _A	:		int=None						,  _A	:		Any=True						,  _A	:		Union[str, Any]=None						,  _A	:		int=None						,  _A	:		str=True						,  _A	:		List[str]=1 / 255						,  _A	:		List[str]=True						,  _A	:		Dict=FLAVA_IMAGE_MEAN						,  _A	:		Optional[Any]=FLAVA_IMAGE_STD						,  _A	:		Dict=14						,  _A	:		List[Any]=75						,  _A	:		Optional[Any]=None						,  _A	:		Dict=16						,  _A	:		Optional[int]=0.3						,  _A	:		Optional[int]=None						,  _A	:		List[str]=True						,  _A	:		Any=None						,  _A	:		Optional[int]=None						,  _A	:		List[str]=True						,  _A	:		Optional[Any]=None						,  _A	:		List[Any]=True						,  _A	:		str=True						,  _A	:		Any=FLAVA_CODEBOOK_MEAN						,  _A	:		str=FLAVA_CODEBOOK_STD						,  )						->				Tuple:
  			__magic_name__ :					Tuple  			=  size if size is not None else {'''height''': 224, '''width''': 224}
  			__magic_name__ :					Any  			=  crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
  			__magic_name__ :					Dict  			=  codebook_size if codebook_size is not None else {'''height''': 112, '''width''': 112}
  			__magic_name__ :					Tuple  			=  codebook_crop_size if codebook_crop_size is not None else {'''height''': 112, '''width''': 112}
  
  			__magic_name__ :					Any  			=  parent
  			__magic_name__ :					Tuple  			=  batch_size
  			__magic_name__ :					int  			=  num_channels
  			__magic_name__ :					Union[str, Any]  			=  do_resize
  			__magic_name__ :					str  			=  do_rescale
  			__magic_name__ :					Any  			=  rescale_factor
  			__magic_name__ :					Tuple  			=  min_resolution
  			__magic_name__ :					str  			=  max_resolution
  			__magic_name__ :					List[Any]  			=  size
  			__magic_name__ :					Optional[Any]  			=  resample if resample is not None else PILImageResampling.BICUBIC
  			__magic_name__ :					Optional[int]  			=  do_normalize
  			__magic_name__ :					Optional[Any]  			=  image_mean
  			__magic_name__ :					int  			=  image_std
  			__magic_name__ :					Dict  			=  do_center_crop
  			__magic_name__ :					List[Any]  			=  crop_size
  
  			__magic_name__ :					Any  			=  input_size_patches
  			__magic_name__ :					Dict  			=  total_mask_patches
  			__magic_name__ :					Optional[int]  			=  mask_group_max_patches
  			__magic_name__ :					Optional[Any]  			=  mask_group_min_patches
  			__magic_name__ :					List[str]  			=  mask_group_min_aspect_ratio
  			__magic_name__ :					Tuple  			=  mask_group_max_aspect_ratio
  
  			__magic_name__ :					int  			=  codebook_do_resize
  			__magic_name__ :					List[str]  			=  codebook_size
  			__magic_name__ :					int  			=  codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
  			__magic_name__ :					Dict  			=  codebook_do_center_crop
  			__magic_name__ :					List[str]  			=  codebook_crop_size
  			__magic_name__ :					Dict  			=  codebook_do_map_pixels
  			__magic_name__ :					Any  			=  codebook_do_normalize
  			__magic_name__ :					Union[str, Any]  			=  codebook_image_mean
  			__magic_name__ :					List[Any]  			=  codebook_image_std
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Any    )						->				List[str]:
  			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      __lowerCAmelCase	(      self	:		str    )						->				Any:
  			return (self.size["height"], self.size["width"])
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[Any]    )						->				Dict:
  			return (
  			    (self.input_size_patches, self.input_size_patches)
  			    if not isinstance(self.input_size_patches						,  _A    )
  			    else self.input_size_patches
  			)
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[Any]    )						->				List[str]:
  			return (self.codebook_size["height"], self.codebook_size["width"])
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class _lowerCamelCase (		A_	,  unittest.TestCase							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    int   					=       FlavaImageProcessor if is_vision_available() else None
  		A_			:    List[Any]   					=       None
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		str    )						->				Union[str, Any]:
  			__magic_name__ :					Tuple  			=  FlavaImageProcessingTester(self    )
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		Dict    )						->				Optional[int]:
  			return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[str]    )						->				Union[str, Any]:
  			__magic_name__ :					Tuple  			=  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						,  'resample'    )    )
  			self.assertTrue(hasattr(_A						,  'crop_size'    )    )
  			self.assertTrue(hasattr(_A						,  'do_center_crop'    )    )
  			self.assertTrue(hasattr(_A						,  'do_rescale'    )    )
  			self.assertTrue(hasattr(_A						,  'rescale_factor'    )    )
  			self.assertTrue(hasattr(_A						,  'masking_generator'    )    )
  			self.assertTrue(hasattr(_A						,  'codebook_do_resize'    )    )
  			self.assertTrue(hasattr(_A						,  'codebook_size'    )    )
  			self.assertTrue(hasattr(_A						,  'codebook_resample'    )    )
  			self.assertTrue(hasattr(_A						,  'codebook_do_center_crop'    )    )
  			self.assertTrue(hasattr(_A						,  'codebook_crop_size'    )    )
  			self.assertTrue(hasattr(_A						,  'codebook_do_map_pixels'    )    )
  			self.assertTrue(hasattr(_A						,  'codebook_do_normalize'    )    )
  			self.assertTrue(hasattr(_A						,  'codebook_image_mean'    )    )
  			self.assertTrue(hasattr(_A						,  'codebook_image_std'    )    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Any    )						->				Tuple:
  			__magic_name__ :					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}    )
  
  			__magic_name__ :					List[str]  			=  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      __lowerCAmelCase	(      self	:		int    )						->				Any:
  			pass
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[str]    )						->				Tuple:
  			__magic_name__ :					str  			=  self.image_processing_class(**self.image_processor_dict    )
  			# create random PIL images
  			__magic_name__ :					Optional[int]  			=  prepare_image_inputs(self.image_processor_tester						,  equal_resolution=_A    )
  			for image in image_inputs:
  				self.assertIsInstance(_A						,  PIL.Image.Image    )
  
  			# Test not batched input
  			__magic_name__ :					int  			=  image_processing(image_inputs[0]						,  return_tensors='pt'    )
  
  			# Test no bool masked pos
  			self.assertFalse('bool_masked_pos' in encoded_images    )
  
  			__magic_name__ :					int  			=  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
  			__magic_name__ :					str  			=  image_processing(_A						,  return_tensors='pt'    )
  			__magic_name__ :					int  			=  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      __lowerCAmelCase	(      self	:		Any						,  _A	:		List[str]						,  _A	:		str    )						->				Any:
  			__magic_name__ :					List[Any]  			=  self.image_processing_class(**self.image_processor_dict    )
  			# create random tensors
  			__magic_name__ :					int  			=  prepare_image_inputs(self.image_processor_tester						,  equal_resolution=_A						,  **_A    )
  			for image in image_inputs:
  				self.assertIsInstance(_A						,  _A    )
  
  			# Test not batched input
  			__magic_name__ :					Dict  			=  image_processing(image_inputs[0]						,  return_tensors='pt'    )
  
  			__magic_name__ :					Optional[int]  			=  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)						,  )
  
  			__magic_name__ :					List[Any]  			=  image_processing(_A						,  return_image_mask=_A						,  return_tensors='pt'    )
  
  			__magic_name__ :					List[str]  			=  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,
  			    )						,  )
  
  			__magic_name__ :					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
  			__magic_name__ :					Union[str, Any]  			=  image_processing(_A						,  return_tensors='pt'    ).pixel_values
  
  			__magic_name__ :					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
  			__magic_name__ :					Any  			=  image_processing(_A						,  return_image_mask=_A						,  return_tensors='pt'    )
  
  			__magic_name__ :					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,
  			    )						,  )
  
  			__magic_name__ :					int  			=  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      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				Dict:
  			self._test_call_framework(np.ndarray						,  prepare_kwargs={'numpify': True}    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[Any]    )						->				Any:
  			self._test_call_framework(torch.Tensor						,  prepare_kwargs={'torchify': True}    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[str]    )						->				Union[str, Any]:
  			random.seed(1234    )
  			__magic_name__ :					Union[str, Any]  			=  self.image_processing_class(**self.image_processor_dict    )
  			__magic_name__ :					Union[str, Any]  			=  prepare_image_inputs(self.image_processor_tester						,  equal_resolution=_A						,  torchify=_A    )
  
  			# Test not batched input
  			__magic_name__ :					Union[str, Any]  			=  image_processing(image_inputs[0]						,  return_image_mask=_A						,  return_tensors='pt'    )
  			self.assertEqual(encoded_images.bool_masked_pos.sum().item()						,  75    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[Any]    )						->				List[Any]:
  			__magic_name__ :					Optional[Any]  			=  self.image_processing_class(**self.image_processor_dict    )
  			# create random PIL images
  			__magic_name__ :					Tuple  			=  prepare_image_inputs(self.image_processor_tester						,  equal_resolution=_A    )
  			for image in image_inputs:
  				self.assertIsInstance(_A						,  PIL.Image.Image    )
  
  			# Test not batched input
  			__magic_name__ :					Union[str, Any]  			=  image_processing(image_inputs[0]						,  return_codebook_pixels=_A						,  return_tensors='pt'    )
  			__magic_name__ :					List[Any]  			=  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
  			__magic_name__ :					List[str]  			=  image_processing(_A						,  return_codebook_pixels=_A						,  return_tensors='pt'    )
  			__magic_name__ :					Any  			=  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,
  			    )						,  )
style_context: 
  
  
  '''simple docstring'''
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowerCAmelCase   :List[Any]								=    logging.get_logger(__name__)
  
  lowerCAmelCase   :Dict								=    {
      '''uw-madison/yoso-4096''': '''https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json''',
      # See all YOSO models at https://huggingface.co/models?filter=yoso
  }
  class _lowerCamelCase (		lowercase__							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    Optional[Any]   					=       """yoso"""
  
  
  
  
  
  
  
  		def __init__(      self	:		Any						,  _A	:		List[Any]=50265						,  _A	:		Any=768						,  _A	:		List[str]=12						,  _A	:		Dict=12						,  _A	:		Union[str, Any]=3072						,  _A	:		List[Any]="gelu"						,  _A	:		Dict=0.1						,  _A	:		Union[str, Any]=0.1						,  _A	:		Union[str, Any]=4096						,  _A	:		Dict=1						,  _A	:		Tuple=0.02						,  _A	:		Any=1E-12						,  _A	:		Optional[int]="absolute"						,  _A	:		Tuple=True						,  _A	:		Any=9						,  _A	:		Tuple=64						,  _A	:		str=None						,  _A	:		Dict=True						,  _A	:		str=True						,  _A	:		int=1						,  _A	:		Optional[int]=0						,  _A	:		Tuple=2						,  **_A	:		Optional[Any]						,  )						->				Any:
  			super().__init__(pad_token_id=_A						,  bos_token_id=_A						,  eos_token_id=_A						,  **_A    )
  
  			__magic_name__ :					List[Any]  			=  vocab_size
  			__magic_name__ :					int  			=  max_position_embeddings
  			__magic_name__ :					int  			=  hidden_size
  			__magic_name__ :					Any  			=  num_hidden_layers
  			__magic_name__ :					List[str]  			=  num_attention_heads
  			__magic_name__ :					str  			=  intermediate_size
  			__magic_name__ :					Any  			=  hidden_act
  			__magic_name__ :					Union[str, Any]  			=  hidden_dropout_prob
  			__magic_name__ :					str  			=  attention_probs_dropout_prob
  			__magic_name__ :					int  			=  initializer_range
  			__magic_name__ :					List[Any]  			=  type_vocab_size
  			__magic_name__ :					Tuple  			=  layer_norm_eps
  			__magic_name__ :					Tuple  			=  position_embedding_type
  			__magic_name__ :					Optional[Any]  			=  use_expectation
  			__magic_name__ :					Union[str, Any]  			=  hash_code_len
  			__magic_name__ :					int  			=  num_hash
  			__magic_name__ :					List[Any]  			=  conv_window
  			__magic_name__ :					Optional[Any]  			=  use_fast_hash
  			__magic_name__ :					int  			=  lsh_backward