

"""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:
							a__			:						str							=			None


class       _snake_case  (    unittest.TestCase							):







						def __init__(self  ,				UpperCAmelCase_  ,				UpperCAmelCase_=7  ,				UpperCAmelCase_=3  ,				UpperCAmelCase_=3_0  ,				UpperCAmelCase_=4_0_0  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=None  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=None  ,				UpperCAmelCase_=None  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=1 / 2_5_5  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=FLAVA_IMAGE_MEAN  ,				UpperCAmelCase_=FLAVA_IMAGE_STD  ,				UpperCAmelCase_=1_4  ,				UpperCAmelCase_=7_5  ,				UpperCAmelCase_=None  ,				UpperCAmelCase_=1_6  ,				UpperCAmelCase_=0.3  ,				UpperCAmelCase_=None  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=None  ,				UpperCAmelCase_=None  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=None  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=FLAVA_CODEBOOK_MEAN  ,				UpperCAmelCase_=FLAVA_CODEBOOK_STD  ,				)      ->    int:
											__lowercase        =      size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
											__lowercase        =      crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
											__lowercase        =      codebook_size if codebook_size is not None else {'''height''': 1_1_2, '''width''': 1_1_2}
											__lowercase        =      codebook_crop_size if codebook_crop_size is not None else {'''height''': 1_1_2, '''width''': 1_1_2}

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

											__lowercase        =      input_size_patches
											__lowercase        =      total_mask_patches
											__lowercase        =      mask_group_max_patches
											__lowercase        =      mask_group_min_patches
											__lowercase        =      mask_group_min_aspect_ratio
											__lowercase        =      mask_group_max_aspect_ratio

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







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







						def 							_UpperCamelCase     (self				)      ->    Dict:
											return (
											    (self.input_size_patches, self.input_size_patches)
											    if not isinstance(self.input_size_patches  ,				UpperCAmelCase_				)
											    else self.input_size_patches
											)







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



@require_torch
@require_vision
class       _snake_case  (    UpperCamelCase    ,      unittest.TestCase							):
						__snake_case			      =  FlavaImageProcessor if is_vision_available() else None
						__snake_case			      =  None







						def 							_UpperCamelCase     (self				)      ->    Any:
											__lowercase        =      FlavaImageProcessingTester(self				)







						@property
						def 							_UpperCamelCase     (self				)      ->    Union[str, Any]:
											return self.image_processor_tester.prepare_image_processor_dict()







						def 							_UpperCamelCase     (self				)      ->    List[Any]:
											__lowercase        =      self.image_processing_class(**self.image_processor_dict				)
											self.assertTrue(hasattr(UpperCAmelCase_  ,				'''image_mean'''				)				)
											self.assertTrue(hasattr(UpperCAmelCase_  ,				'''image_std'''				)				)
											self.assertTrue(hasattr(UpperCAmelCase_  ,				'''do_normalize'''				)				)
											self.assertTrue(hasattr(UpperCAmelCase_  ,				'''do_resize'''				)				)
											self.assertTrue(hasattr(UpperCAmelCase_  ,				'''resample'''				)				)
											self.assertTrue(hasattr(UpperCAmelCase_  ,				'''crop_size'''				)				)
											self.assertTrue(hasattr(UpperCAmelCase_  ,				'''do_center_crop'''				)				)
											self.assertTrue(hasattr(UpperCAmelCase_  ,				'''do_rescale'''				)				)
											self.assertTrue(hasattr(UpperCAmelCase_  ,				'''rescale_factor'''				)				)
											self.assertTrue(hasattr(UpperCAmelCase_  ,				'''masking_generator'''				)				)
											self.assertTrue(hasattr(UpperCAmelCase_  ,				'''codebook_do_resize'''				)				)
											self.assertTrue(hasattr(UpperCAmelCase_  ,				'''codebook_size'''				)				)
											self.assertTrue(hasattr(UpperCAmelCase_  ,				'''codebook_resample'''				)				)
											self.assertTrue(hasattr(UpperCAmelCase_  ,				'''codebook_do_center_crop'''				)				)
											self.assertTrue(hasattr(UpperCAmelCase_  ,				'''codebook_crop_size'''				)				)
											self.assertTrue(hasattr(UpperCAmelCase_  ,				'''codebook_do_map_pixels'''				)				)
											self.assertTrue(hasattr(UpperCAmelCase_  ,				'''codebook_do_normalize'''				)				)
											self.assertTrue(hasattr(UpperCAmelCase_  ,				'''codebook_image_mean'''				)				)
											self.assertTrue(hasattr(UpperCAmelCase_  ,				'''codebook_image_std'''				)				)







						def 							_UpperCamelCase     (self				)      ->    List[Any]:
											__lowercase        =      self.image_processing_class.from_dict(self.image_processor_dict				)
											self.assertEqual(image_processor.size  ,				{'''height''': 2_2_4, '''width''': 2_2_4}				)
											self.assertEqual(image_processor.crop_size  ,				{'''height''': 2_2_4, '''width''': 2_2_4}				)
											self.assertEqual(image_processor.codebook_size  ,				{'''height''': 1_1_2, '''width''': 1_1_2}				)
											self.assertEqual(image_processor.codebook_crop_size  ,				{'''height''': 1_1_2, '''width''': 1_1_2}				)

											__lowercase        =      self.image_processing_class.from_dict(
											    self.image_processor_dict  ,				size=4_2  ,				crop_size=8_4  ,				codebook_size=3_3  ,				codebook_crop_size=6_6				)
											self.assertEqual(image_processor.size  ,				{'''height''': 4_2, '''width''': 4_2}				)
											self.assertEqual(image_processor.crop_size  ,				{'''height''': 8_4, '''width''': 8_4}				)
											self.assertEqual(image_processor.codebook_size  ,				{'''height''': 3_3, '''width''': 3_3}				)
											self.assertEqual(image_processor.codebook_crop_size  ,				{'''height''': 6_6, '''width''': 6_6}				)







						def 							_UpperCamelCase     (self				)      ->    Dict:
											pass







						def 							_UpperCamelCase     (self				)      ->    Any:
											# Initialize image_processing
											__lowercase        =      self.image_processing_class(**self.image_processor_dict				)
											# create random PIL images
											__lowercase        =      prepare_image_inputs(self.image_processor_tester  ,				equal_resolution=UpperCAmelCase_				)
											for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase_  ,				PIL.Image.Image				)

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

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

											__lowercase    ,			__lowercase        =      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
											__lowercase        =      image_processing(UpperCAmelCase_  ,				return_tensors='''pt'''				)
											__lowercase    ,			__lowercase        =      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  ,				UpperCAmelCase_  ,				UpperCAmelCase_				)      ->    Any:
											# Initialize image_processing
											__lowercase        =      self.image_processing_class(**self.image_processor_dict				)
											# create random tensors
											__lowercase        =      prepare_image_inputs(self.image_processor_tester  ,				equal_resolution=UpperCAmelCase_  ,				**UpperCAmelCase_				)
											for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase_  ,				UpperCAmelCase_				)

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

											__lowercase    ,			__lowercase        =      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)  ,				)

											__lowercase        =      image_processing(UpperCAmelCase_  ,				return_image_mask=UpperCAmelCase_  ,				return_tensors='''pt'''				)

											__lowercase    ,			__lowercase        =      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,
											    )  ,				)

											__lowercase    ,			__lowercase        =      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
											__lowercase        =      image_processing(UpperCAmelCase_  ,				return_tensors='''pt'''				).pixel_values

											__lowercase    ,			__lowercase        =      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
											__lowercase        =      image_processing(UpperCAmelCase_  ,				return_image_mask=UpperCAmelCase_  ,				return_tensors='''pt'''				)

											__lowercase    ,			__lowercase        =      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,
											    )  ,				)

											__lowercase    ,			__lowercase        =      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]:
											self._test_call_framework(np.ndarray  ,				prepare_kwargs={'''numpify''': True}				)







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







						def 							_UpperCamelCase     (self				)      ->    List[str]:
											# Initialize image_processing
											random.seed(1_2_3_4				)
											__lowercase        =      self.image_processing_class(**self.image_processor_dict				)
											__lowercase        =      prepare_image_inputs(self.image_processor_tester  ,				equal_resolution=UpperCAmelCase_  ,				torchify=UpperCAmelCase_				)

											# Test not batched input
											__lowercase        =      image_processing(image_inputs[0]  ,				return_image_mask=UpperCAmelCase_  ,				return_tensors='''pt'''				)
											self.assertEqual(encoded_images.bool_masked_pos.sum().item()  ,				7_5				)







						def 							_UpperCamelCase     (self				)      ->    Tuple:
											# Initialize image_processing
											__lowercase        =      self.image_processing_class(**self.image_processor_dict				)
											# create random PIL images
											__lowercase        =      prepare_image_inputs(self.image_processor_tester  ,				equal_resolution=UpperCAmelCase_				)
											for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase_  ,				PIL.Image.Image				)

											# Test not batched input
											__lowercase        =      image_processing(image_inputs[0]  ,				return_codebook_pixels=UpperCAmelCase_  ,				return_tensors='''pt'''				)
											__lowercase    ,			__lowercase        =      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
											__lowercase        =      image_processing(UpperCAmelCase_  ,				return_codebook_pixels=UpperCAmelCase_  ,				return_tensors='''pt'''				)
											__lowercase    ,			__lowercase        =      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 enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


a__			:						Optional[int]							=			set()
a__			:						str							=			get_logger(__name__)





def 					__UpperCamelCase (     a__:			Optional[str] = None      )					->      Dict:




					"""simple docstring"""



					def decorator(a__:			Callable      ):
										global _emitted_deprecation_warnings

										if inspect.isclass(a__      ):
															__lowercase        =      deprecated_class_or_function.__init__
															__lowercase        =      deprecated_class_or_function.__name__
										else:
															__lowercase        =      deprecated_class_or_function
															__lowercase        =      deprecated_function.__name__
															# Support deprecating __init__ class method: class name instead
															__lowercase        =      name if name != '''__init__''' else deprecated_function.__qualname__.split('''.'''      )[-2]

										__lowercase        =      (
										    f'''{name} is deprecated and will be removed in the next major version of datasets.''' + f''' {help_message}'''
										    if help_message
										    else ''''''
										)

										@wraps(a__      )
										def wrapper(*a__:			int			,						**a__:			int      ):
															__lowercase        =      hash(a__      )
															if func_hash not in _emitted_deprecation_warnings:
																				warnings.warn(a__			,						category=a__			,						stacklevel=2      )
																				_emitted_deprecation_warnings.add(a__      )
															return deprecated_function(*a__			,						**a__      )

										__lowercase        =      '''deprecated'''

										if inspect.isclass(a__      ):
															__lowercase        =      wrapper
															return deprecated_class_or_function
										else:
															return wrapper

					return decorator


class       _snake_case  (    enum.EnumMeta							):







						def __getattribute__(cls  ,				UpperCAmelCase_				)      ->    int:
											__lowercase        =      super().__getattribute__(UpperCAmelCase_				)
											if isinstance(UpperCAmelCase_  ,				enum.Enum				) and obj._on_access:
																obj._on_access()
											return obj







						def __getitem__(cls  ,				UpperCAmelCase_				)      ->    Union[str, Any]:
											__lowercase        =      super().__getitem__(UpperCAmelCase_				)
											if member._on_access:
																member._on_access()
											return member







						def __call__(cls  ,				UpperCAmelCase_  ,				UpperCAmelCase_=None  ,				*, UpperCAmelCase_=None  ,				UpperCAmelCase_=None  ,				UpperCAmelCase_=None  ,				UpperCAmelCase_=1				)      ->    List[Any]:
											__lowercase        =      super().__call__(UpperCAmelCase_  ,				UpperCAmelCase_  ,				module=UpperCAmelCase_  ,				qualname=UpperCAmelCase_  ,				type=UpperCAmelCase_  ,				start=UpperCAmelCase_				)
											if isinstance(UpperCAmelCase_  ,				enum.Enum				) and obj._on_access:
																obj._on_access()
											return obj



class       _snake_case  (    enum.Enum    ,      metaclass=UpperCamelCase							):







						def __new__(cls  ,				UpperCAmelCase_				)      ->    Optional[int]:
											__lowercase        =      object.__new__(cls				)
											__lowercase        =      value
											__lowercase        =      member.deprecate
											return member







						@property
						def 							_UpperCamelCase     (self				)      ->    Dict:
											return ""







						def 							_UpperCamelCase     (self				)      ->    Optional[Any]:
											__lowercase        =      f''' {self.help_message}''' if self.help_message else ''''''
											warnings.warn(
											    f'''\'{self.__objclass__.__name__}\' is deprecated and will be removed in the next major version of datasets.'''
											    + help_message  ,				UpperCAmelCase_  ,				stacklevel=3  ,				)


