




'''simple docstring'''






import unittest

import numpy as np
from datasets import load_dataset

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():
      from PIL import Image

      from transformers import BeitImageProcessor






class        lowercase_      (						unittest.TestCase							):





     def __init__(     self     :					Dict     ,							__lowerCamelCase     :					List[Any]     ,							__lowerCamelCase     :					Tuple=7     ,							__lowerCamelCase     :					Tuple=3     ,							__lowerCamelCase     :					Union[str, Any]=18     ,							__lowerCamelCase     :					Tuple=30     ,							__lowerCamelCase     :					List[str]=400     ,							__lowerCamelCase     :					Any=True     ,							__lowerCamelCase     :					str=None     ,							__lowerCamelCase     :					int=True     ,							__lowerCamelCase     :					Any=None     ,							__lowerCamelCase     :					str=True     ,							__lowerCamelCase     :					Union[str, Any]=[0.5, 0.5, 0.5]     ,							__lowerCamelCase     :					List[Any]=[0.5, 0.5, 0.5]     ,							__lowerCamelCase     :					Optional[Any]=False     ,							):
           snake_case__       :   Any	      =    size if size is not None else {'height': 20, 'width': 20}
           snake_case__       :   Any	      =    crop_size if crop_size is not None else {'height': 18, 'width': 18}
           snake_case__       :   Union[str, Any]	      =    parent
           snake_case__       :   Union[str, Any]	      =    batch_size
           snake_case__       :   int	      =    num_channels
           snake_case__       :   Tuple	      =    image_size
           snake_case__       :   Optional[int]	      =    min_resolution
           snake_case__       :   Optional[int]	      =    max_resolution
           snake_case__       :   Any	      =    do_resize
           snake_case__       :   List[Any]	      =    size
           snake_case__       :   Any	      =    do_center_crop
           snake_case__       :   str	      =    crop_size
           snake_case__       :   Optional[int]	      =    do_normalize
           snake_case__       :   Dict	      =    image_mean
           snake_case__       :   List[str]	      =    image_std
           snake_case__       :   Tuple	      =    do_reduce_labels





     def 			_lowerCAmelCase      (     self     :					int			):
           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_reduce_labels": self.do_reduce_labels,
           }






def 		UpperCamelCase__      (		)    ->   List[Any]:
      snake_case__       :   List[Any]	      =    load_dataset('hf-internal-testing/fixtures_ade20k'				,      split='test'		)

      snake_case__       :   Tuple	      =    Image.open(dataset[0]['file']		)
      snake_case__       :   Dict	      =    Image.open(dataset[1]['file']		)

      return image, map






def 		UpperCamelCase__      (		)    ->   Any:
      snake_case__       :   Optional[Any]	      =    load_dataset('hf-internal-testing/fixtures_ade20k'				,      split='test'		)

      snake_case__       :   Optional[int]	      =    Image.open(ds[0]['file']		)
      snake_case__       :   int	      =    Image.open(ds[1]['file']		)
      snake_case__       :   Dict	      =    Image.open(ds[2]['file']		)
      snake_case__       :   int	      =    Image.open(ds[3]['file']		)

      return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class        lowercase_      (						lowerCAmelCase_						,       unittest.TestCase							):
     A_											=			BeitImageProcessor if is_vision_available() else None





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           snake_case__       :   Any	      =    BeitImageProcessingTester(self			)





     @property
     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           return self.image_processor_tester.prepare_image_processor_dict()





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           snake_case__       :   Optional[Any]	      =    self.image_processing_class(**self.image_processor_dict			)
           self.assertTrue(hasattr(__lowerCamelCase     ,							'do_resize'			)			)
           self.assertTrue(hasattr(__lowerCamelCase     ,							'size'			)			)
           self.assertTrue(hasattr(__lowerCamelCase     ,							'do_center_crop'			)			)
           self.assertTrue(hasattr(__lowerCamelCase     ,							'center_crop'			)			)
           self.assertTrue(hasattr(__lowerCamelCase     ,							'do_normalize'			)			)
           self.assertTrue(hasattr(__lowerCamelCase     ,							'image_mean'			)			)
           self.assertTrue(hasattr(__lowerCamelCase     ,							'image_std'			)			)





     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           snake_case__       :   str	      =    self.image_processing_class.from_dict(self.image_processor_dict			)
           self.assertEqual(image_processor.size     ,							{'height': 20, 'width': 20}			)
           self.assertEqual(image_processor.crop_size     ,							{'height': 18, 'width': 18}			)
           self.assertEqual(image_processor.do_reduce_labels     ,							__lowerCamelCase			)

           snake_case__       :   Dict	      =    self.image_processing_class.from_dict(
               self.image_processor_dict     ,							size=42     ,							crop_size=84     ,							reduce_labels=__lowerCamelCase			)
           self.assertEqual(image_processor.size     ,							{'height': 42, 'width': 42}			)
           self.assertEqual(image_processor.crop_size     ,							{'height': 84, 'width': 84}			)
           self.assertEqual(image_processor.do_reduce_labels     ,							__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Dict			):
           pass





     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           # Initialize image_processing
           snake_case__       :   Any	      =    self.image_processing_class(**self.image_processor_dict			)
           # create random PIL images
           snake_case__       :   Union[str, Any]	      =    prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=__lowerCamelCase			)
           for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase     ,							Image.Image			)

           # Test not batched input
           snake_case__       :   List[Any]	      =    image_processing(image_inputs[0]     ,							return_tensors='pt'			).pixel_values
           self.assertEqual(
               encoded_images.shape     ,							(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )     ,							)

           # Test batched
           snake_case__       :   Union[str, Any]	      =    image_processing(__lowerCamelCase     ,							return_tensors='pt'			).pixel_values
           self.assertEqual(
               encoded_images.shape     ,							(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )     ,							)





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           # Initialize image_processing
           snake_case__       :   str	      =    self.image_processing_class(**self.image_processor_dict			)
           # create random numpy tensors
           snake_case__       :   Dict	      =    prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=__lowerCamelCase     ,							numpify=__lowerCamelCase			)
           for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase     ,							np.ndarray			)

           # Test not batched input
           snake_case__       :   Union[str, Any]	      =    image_processing(image_inputs[0]     ,							return_tensors='pt'			).pixel_values
           self.assertEqual(
               encoded_images.shape     ,							(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )     ,							)

           # Test batched
           snake_case__       :   Union[str, Any]	      =    image_processing(__lowerCamelCase     ,							return_tensors='pt'			).pixel_values
           self.assertEqual(
               encoded_images.shape     ,							(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )     ,							)





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           # Initialize image_processing
           snake_case__       :   Any	      =    self.image_processing_class(**self.image_processor_dict			)
           # create random PyTorch tensors
           snake_case__       :   Optional[int]	      =    prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=__lowerCamelCase     ,							torchify=__lowerCamelCase			)
           for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase     ,							torch.Tensor			)

           # Test not batched input
           snake_case__       :   List[str]	      =    image_processing(image_inputs[0]     ,							return_tensors='pt'			).pixel_values
           self.assertEqual(
               encoded_images.shape     ,							(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )     ,							)

           # Test batched
           snake_case__       :   List[Any]	      =    image_processing(__lowerCamelCase     ,							return_tensors='pt'			).pixel_values
           self.assertEqual(
               encoded_images.shape     ,							(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )     ,							)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           # Initialize image_processing
           snake_case__       :   Optional[Any]	      =    self.image_processing_class(**self.image_processor_dict			)
           # create random PyTorch tensors
           snake_case__       :   List[Any]	      =    prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=__lowerCamelCase     ,							torchify=__lowerCamelCase			)
           snake_case__       :   int	      =    []
           for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase     ,							torch.Tensor			)
                 maps.append(torch.zeros(image.shape[-2:]			).long()			)

           # Test not batched input
           snake_case__       :   Tuple	      =    image_processing(image_inputs[0]     ,							maps[0]     ,							return_tensors='pt'			)
           self.assertEqual(
               encoding['pixel_values'].shape     ,							(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )     ,							)
           self.assertEqual(
               encoding['labels'].shape     ,							(
                   1,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )     ,							)
           self.assertEqual(encoding['labels'].dtype     ,							torch.long			)
           self.assertTrue(encoding['labels'].min().item() >= 0			)
           self.assertTrue(encoding['labels'].max().item() <= 255			)

           # Test batched
           snake_case__       :   Optional[int]	      =    image_processing(__lowerCamelCase     ,							__lowerCamelCase     ,							return_tensors='pt'			)
           self.assertEqual(
               encoding['pixel_values'].shape     ,							(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )     ,							)
           self.assertEqual(
               encoding['labels'].shape     ,							(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )     ,							)
           self.assertEqual(encoding['labels'].dtype     ,							torch.long			)
           self.assertTrue(encoding['labels'].min().item() >= 0			)
           self.assertTrue(encoding['labels'].max().item() <= 255			)

           # Test not batched input (PIL images)
           snake_case__      ,							snake_case__       :   List[str]	      =    prepare_semantic_single_inputs()

           snake_case__       :   int	      =    image_processing(__lowerCamelCase     ,							__lowerCamelCase     ,							return_tensors='pt'			)
           self.assertEqual(
               encoding['pixel_values'].shape     ,							(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )     ,							)
           self.assertEqual(
               encoding['labels'].shape     ,							(
                   1,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )     ,							)
           self.assertEqual(encoding['labels'].dtype     ,							torch.long			)
           self.assertTrue(encoding['labels'].min().item() >= 0			)
           self.assertTrue(encoding['labels'].max().item() <= 255			)

           # Test batched input (PIL images)
           snake_case__      ,							snake_case__       :   List[str]	      =    prepare_semantic_batch_inputs()

           snake_case__       :   List[Any]	      =    image_processing(__lowerCamelCase     ,							__lowerCamelCase     ,							return_tensors='pt'			)
           self.assertEqual(
               encoding['pixel_values'].shape     ,							(
                   2,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )     ,							)
           self.assertEqual(
               encoding['labels'].shape     ,							(
                   2,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )     ,							)
           self.assertEqual(encoding['labels'].dtype     ,							torch.long			)
           self.assertTrue(encoding['labels'].min().item() >= 0			)
           self.assertTrue(encoding['labels'].max().item() <= 255			)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           # Initialize image_processing
           snake_case__       :   Dict	      =    self.image_processing_class(**self.image_processor_dict			)

           # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
           snake_case__      ,							snake_case__       :   Optional[Any]	      =    prepare_semantic_single_inputs()
           snake_case__       :   int	      =    image_processing(__lowerCamelCase     ,							__lowerCamelCase     ,							return_tensors='pt'			)
           self.assertTrue(encoding['labels'].min().item() >= 0			)
           self.assertTrue(encoding['labels'].max().item() <= 150			)

           snake_case__       :   Union[str, Any]	      =    True
           snake_case__       :   Any	      =    image_processing(__lowerCamelCase     ,							__lowerCamelCase     ,							return_tensors='pt'			)
           self.assertTrue(encoding['labels'].min().item() >= 0			)
           self.assertTrue(encoding['labels'].max().item() <= 255			)






'''simple docstring'''






from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *
