



"""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:
		_lowercase					:					Tuple 		=		None

class 			__SCREAMING_SNAKE_CASE      ( unittest.TestCase  ):


					'''simple docstring'''





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

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

								lowerCamelCase__						:						List[str]						      =input_size_patches
								lowerCamelCase__						:						int						      =total_mask_patches
								lowerCamelCase__						:						List[str]						      =mask_group_max_patches
								lowerCamelCase__						:						Any						      =mask_group_min_patches
								lowerCamelCase__						:						int						      =mask_group_min_aspect_ratio
								lowerCamelCase__						:						Any						      =mask_group_max_aspect_ratio

								lowerCamelCase__						:						Union[str, Any]						      =codebook_do_resize
								lowerCamelCase__						:						Any						      =codebook_size
								lowerCamelCase__						:						str						      =codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
								lowerCamelCase__						:						Optional[Any]						      =codebook_do_center_crop
								lowerCamelCase__						:						Any						      =codebook_crop_size
								lowerCamelCase__						:						int						      =codebook_do_map_pixels
								lowerCamelCase__						:						Optional[int]						      =codebook_do_normalize
								lowerCamelCase__						:						Union[str, Any]						      =codebook_image_mean
								lowerCamelCase__						:						int						      =codebook_image_std



					def 						snake_case			(			self				:    Optional[int]	)->   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,
								    "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 						snake_case			(			self				:    int	)->   Optional[int]:
								return (self.size["height"], self.size["width"])



					def 						snake_case			(			self				:    Optional[Any]	)->   Tuple:
								return (
								    (self.input_size_patches, self.input_size_patches)
								    if not isinstance(self.input_size_patches,    lowerCamelCase	)
								    else self.input_size_patches
								)




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





@require_torch
@require_vision
class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_						,  unittest.TestCase  ):


					'''simple docstring'''





					_a       =				FlavaImageProcessor if is_vision_available() else None
					_a       =				None



					def 						snake_case			(			self				:    List[Any]	)->   str:
								lowerCamelCase__						:						str						      =FlavaImageProcessingTester(self	)



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



					def 						snake_case			(			self				:    Tuple	)->   Optional[int]:
								lowerCamelCase__						:						Tuple						      =self.image_processing_class(**self.image_processor_dict	)
								self.assertTrue(hasattr(lowerCamelCase,    '''image_mean'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''image_std'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''do_normalize'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''do_resize'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''resample'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''crop_size'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''do_center_crop'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''do_rescale'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''rescale_factor'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''masking_generator'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''codebook_do_resize'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''codebook_size'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''codebook_resample'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''codebook_do_center_crop'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''codebook_crop_size'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''codebook_do_map_pixels'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''codebook_do_normalize'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''codebook_image_mean'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''codebook_image_std'''	)	)



					def 						snake_case			(			self				:    Any	)->   Union[str, Any]:
								lowerCamelCase__						:						str						      =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__						:						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 						snake_case			(			self				:    Optional[Any]	)->   Dict:
								pass



					def 						snake_case			(			self				:    int	)->   Optional[Any]:
								# Initialize image_processing
								lowerCamelCase__						:						Optional[int]						      =self.image_processing_class(**self.image_processor_dict	)
								# create random PIL images
								lowerCamelCase__						:						int						      =prepare_image_inputs(self.image_processor_tester,    equal_resolution=lowerCamelCase	)
								for image in image_inputs:
											self.assertIsInstance(lowerCamelCase,    PIL.Image.Image	)

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

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

								lowerCamelCase__      ,  lowerCamelCase__						:						Union[str, 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
								lowerCamelCase__						:						Dict						      =image_processing(lowerCamelCase,    return_tensors='''pt'''	)
								lowerCamelCase__      ,  lowerCamelCase__						:						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 						snake_case			(			self				:    Union[str, Any],    lowerCamelCase				:    str,    lowerCamelCase				:    Tuple	)->   Optional[int]:
								# Initialize image_processing
								lowerCamelCase__						:						Tuple						      =self.image_processing_class(**self.image_processor_dict	)
								# create random tensors
								lowerCamelCase__						:						Union[str, Any]						      =prepare_image_inputs(self.image_processor_tester,    equal_resolution=lowerCamelCase,    **lowerCamelCase	)
								for image in image_inputs:
											self.assertIsInstance(lowerCamelCase,    lowerCamelCase	)

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

								lowerCamelCase__      ,  lowerCamelCase__						:						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),    )

								lowerCamelCase__						:						List[str]						      =image_processing(lowerCamelCase,    return_image_mask=lowerCamelCase,    return_tensors='''pt'''	)

								lowerCamelCase__      ,  lowerCamelCase__						:						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,
								    ),    )

								lowerCamelCase__      ,  lowerCamelCase__						:						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
								lowerCamelCase__						:						int						      =image_processing(lowerCamelCase,    return_tensors='''pt'''	).pixel_values

								lowerCamelCase__      ,  lowerCamelCase__						:						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
								lowerCamelCase__						:						List[str]						      =image_processing(lowerCamelCase,    return_image_mask=lowerCamelCase,    return_tensors='''pt'''	)

								lowerCamelCase__      ,  lowerCamelCase__						:						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,
								    ),    )

								lowerCamelCase__      ,  lowerCamelCase__						:						str						      =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 						snake_case			(			self				:    Optional[int]	)->   Tuple:
								self._test_call_framework(np.ndarray,    prepare_kwargs={'''numpify''': True}	)



					def 						snake_case			(			self				:    Optional[Any]	)->   Dict:
								self._test_call_framework(torch.Tensor,    prepare_kwargs={'''torchify''': True}	)



					def 						snake_case			(			self				:    Dict	)->   int:
								# Initialize image_processing
								random.seed(1234	)
								lowerCamelCase__						:						Optional[int]						      =self.image_processing_class(**self.image_processor_dict	)
								lowerCamelCase__						:						Any						      =prepare_image_inputs(self.image_processor_tester,    equal_resolution=lowerCamelCase,    torchify=lowerCamelCase	)

								# Test not batched input
								lowerCamelCase__						:						List[str]						      =image_processing(image_inputs[0],    return_image_mask=lowerCamelCase,    return_tensors='''pt'''	)
								self.assertEqual(encoded_images.bool_masked_pos.sum().item(),    75	)




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

								# Test not batched input
								lowerCamelCase__						:						Optional[Any]						      =image_processing(image_inputs[0],    return_codebook_pixels=lowerCamelCase,    return_tensors='''pt'''	)
								lowerCamelCase__      ,  lowerCamelCase__						:						Dict						      =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__						:						List[Any]						      =image_processing(lowerCamelCase,    return_codebook_pixels=lowerCamelCase,    return_tensors='''pt'''	)
								lowerCamelCase__      ,  lowerCamelCase__						:						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,
								    ),    )






import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig





class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       =CustomConfig



       def __init__(							self ,     _A	)				->				int:
          super().__init__(_A	)
          SCREAMING_SNAKE_CASE_	=      torch.nn.Linear(config.hidden_size ,     config.hidden_size	)



       def     _UpperCamelCase	(							self ,     _A	)				->				Optional[int]:
          return self.linear(_A	)




       def     _UpperCamelCase	(							self ,     _A	)				->				int:
          pass







class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       =NoSuperInitConfig



       def __init__(							self ,     _A	)				->				Optional[int]:
          super().__init__(_A	)
          SCREAMING_SNAKE_CASE_	=      torch.nn.Linear(config.attribute ,     config.attribute	)



       def     _UpperCamelCase	(							self ,     _A	)				->				Any:
          return self.linear(_A	)




       def     _UpperCamelCase	(							self ,     _A	)				->				Tuple:
          pass


