


'''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        a   (   unittest.TestCase				):
 def __init__(       self					, __magic_name__					, __magic_name__=7					, __magic_name__=3					, __magic_name__=18					, __magic_name__=30					, __magic_name__=4_00					, __magic_name__=True					, __magic_name__=None					, __magic_name__=True					, __magic_name__=None					, __magic_name__=True					, __magic_name__=[0.5, 0.5, 0.5]					, __magic_name__=[0.5, 0.5, 0.5]					, __magic_name__=False					, )    ->							List[str]:
      _a						=							size if size is not None else {'''height''': 20, '''width''': 20}
      _a						=							crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
      _a						=							parent
      _a						=							batch_size
      _a						=							num_channels
      _a						=							image_size
      _a						=							min_resolution
      _a						=							max_resolution
      _a						=							do_resize
      _a						=							size
      _a						=							do_center_crop
      _a						=							crop_size
      _a						=							do_normalize
      _a						=							image_mean
      _a						=							image_std
      _a						=							do_reduce_labels




 def 							__UpperCAmelCase						(       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 		_A ()      ->     Optional[int]:




     '''simple docstring'''



     _a						=							load_dataset('hf-internal-testing/fixtures_ade20k' ,  split='test'						)

     _a						=							Image.open(dataset[0]['file']						)
     _a						=							Image.open(dataset[1]['file']						)

     return image, map






def 		_A ()      ->     Any:




     '''simple docstring'''



     _a						=							load_dataset('hf-internal-testing/fixtures_ade20k' ,  split='test'						)

     _a						=							Image.open(ds[0]['file']						)
     _a						=							Image.open(ds[1]['file']						)
     _a						=							Image.open(ds[2]['file']						)
     _a						=							Image.open(ds[3]['file']						)

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






@require_torch
@require_vision
class        a   (   SCREAMING_SNAKE_CASE__     , unittest.TestCase				):
 _lowerCAmelCase           =		BeitImageProcessor if is_vision_available() else None
 def 							__UpperCAmelCase						(       self  )    ->							Any:
      _a						=							BeitImageProcessingTester(self  )
 @property
 def 							__UpperCAmelCase						(       self  )    ->							Optional[int]:
      return self.image_processor_tester.prepare_image_processor_dict()
 def 							__UpperCAmelCase						(       self  )    ->							Dict:
      _a						=							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 							__UpperCAmelCase						(       self  )    ->							Tuple:
      _a						=							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  )

      _a						=							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 							__UpperCAmelCase						(       self  )    ->							str:
      pass
 def 							__UpperCAmelCase						(       self  )    ->							List[str]:
      _a						=							self.image_processing_class(**self.image_processor_dict  )
      # create random PIL images
      _a						=							prepare_image_inputs(self.image_processor_tester					, equal_resolution=__lowerCamelCase  )
      for image in image_inputs:
           self.assertIsInstance(__lowerCamelCase					, Image.Image  )

      # Test not batched input
      _a						=							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
      _a						=							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 							__UpperCAmelCase						(       self  )    ->							Dict:
      _a						=							self.image_processing_class(**self.image_processor_dict  )
      # create random numpy tensors
      _a						=							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
      _a						=							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
      _a						=							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 							__UpperCAmelCase						(       self  )    ->							List[str]:
      _a						=							self.image_processing_class(**self.image_processor_dict  )
      # create random PyTorch tensors
      _a						=							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
      _a						=							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
      _a						=							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 							__UpperCAmelCase						(       self  )    ->							Union[str, Any]:
      _a						=							self.image_processing_class(**self.image_processor_dict  )
      # create random PyTorch tensors
      _a						=							prepare_image_inputs(self.image_processor_tester					, equal_resolution=__lowerCamelCase					, torchify=__lowerCamelCase  )
      _a						=							[]
      for image in image_inputs:
           self.assertIsInstance(__lowerCamelCase					, torch.Tensor  )
           maps.append(torch.zeros(image.shape[-2:]  ).long()  )

      # Test not batched input
      _a						=							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() <= 2_55  )

      # Test batched
      _a						=							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() <= 2_55  )

      # Test not batched input (PIL images)
      _a						=							prepare_semantic_single_inputs()

      _a						=							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() <= 2_55  )

      # Test batched input (PIL images)
      _a						=							prepare_semantic_batch_inputs()

      _a						=							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() <= 2_55  )




 def 							__UpperCAmelCase						(       self  )    ->							Dict:
      _a						=							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
      _a						=							prepare_semantic_single_inputs()
      _a						=							image_processing(__lowerCamelCase					, __lowerCamelCase					, return_tensors='pt'  )
      self.assertTrue(encoding['labels'].min().item() >= 0  )
      self.assertTrue(encoding['labels'].max().item() <= 1_50  )

      _a						=							True
      _a						=							image_processing(__lowerCamelCase					, __lowerCamelCase					, return_tensors='pt'  )
      self.assertTrue(encoding['labels'].min().item() >= 0  )
      self.assertTrue(encoding['labels'].max().item() <= 2_55  )








"""simple docstring"""



import logging

from transformers.configuration_utils import PretrainedConfig


a_ =			logging.getLogger(__name__)






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """masked_bert"""

 def __init__(    self					,						__lowerCamelCase=3_0522					,						__lowerCamelCase=768					,						__lowerCamelCase=12					,						__lowerCamelCase=12					,						__lowerCamelCase=3072					,						__lowerCamelCase="gelu"					,						__lowerCamelCase=0.1					,						__lowerCamelCase=0.1					,						__lowerCamelCase=512					,						__lowerCamelCase=2					,						__lowerCamelCase=0.0_2					,						__lowerCamelCase=1e-1_2					,						__lowerCamelCase=0					,						__lowerCamelCase="topK"					,						__lowerCamelCase="constant"					,						__lowerCamelCase=0.0					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     super().__init__(pad_token_id=__lowerCamelCase					,						**__lowerCamelCase  )

     __A    :						Dict						 =     vocab_size
     __A    :						Union[str, Any]						 =     hidden_size
     __A    :						Tuple						 =     num_hidden_layers
     __A    :						Tuple						 =     num_attention_heads
     __A    :						Optional[Any]						 =     hidden_act
     __A    :						List[str]						 =     intermediate_size
     __A    :						Any						 =     hidden_dropout_prob
     __A    :						Optional[Any]						 =     attention_probs_dropout_prob
     __A    :						Any						 =     max_position_embeddings
     __A    :						str						 =     type_vocab_size
     __A    :						List[Any]						 =     initializer_range
     __A    :						str						 =     layer_norm_eps
     __A    :						Optional[int]						 =     pruning_method
     __A    :						str						 =     mask_init
     __A    :						Any						 =     mask_scale




