


'''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 SegformerImageProcessor
class 	a__				(				unittest.TestCase      ):





   '''simple docstring'''

   def __init__(     self       : Optional[Any] ,						lowerCAmelCase_       : Any ,						lowerCAmelCase_       : Tuple=7 ,						lowerCAmelCase_       : int=3 ,						lowerCAmelCase_       : Optional[int]=30 ,						lowerCAmelCase_       : str=400 ,						lowerCAmelCase_       : Tuple=True ,						lowerCAmelCase_       : List[Any]=None ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : Dict=[0.5, 0.5, 0.5] ,						lowerCAmelCase_       : Tuple=[0.5, 0.5, 0.5] ,						lowerCAmelCase_       : List[str]=False ,						)			->		str:
      __A=  size if size is not None else {'height': 30, 'width': 30}
      __A=  parent
      __A=  batch_size
      __A=  num_channels
      __A=  min_resolution
      __A=  max_resolution
      __A=  do_resize
      __A=  size
      __A=  do_normalize
      __A=  image_mean
      __A=  image_std
      __A=  do_reduce_labels

   def 				lowerCAmelCase    (     self       : List[str]  )			->		Optional[int]:
      return {
          "do_resize": self.do_resize,
          "size": self.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__(							):






   """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 	UpperCAmelCase__(							):






   """simple docstring"""




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

   __A=  Image.open(dataset[0]['file']   )
   __A=  Image.open(dataset[1]['file']   )
   __A=  Image.open(dataset[2]['file']   )
   __A=  Image.open(dataset[3]['file']   )

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






@require_torch
@require_vision
class 	a__				(				a_			,     unittest.TestCase      ):





   '''simple docstring'''

   A	:							Any							= SegformerImageProcessor if is_vision_available() else None
   def 				lowerCAmelCase    (     self       : str  )			->		List[Any]:
      __A=  SegformerImageProcessingTester(self  )
   @property
   def 				lowerCAmelCase    (     self       : Any  )			->		List[Any]:
      return self.image_processor_tester.prepare_image_processor_dict()
   def 				lowerCAmelCase    (     self       : List[str]  )			->		Tuple:
      __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_normalize'  )  )
      self.assertTrue(hasattr(lowerCAmelCase_ ,						'image_mean'  )  )
      self.assertTrue(hasattr(lowerCAmelCase_ ,						'image_std'  )  )
      self.assertTrue(hasattr(lowerCAmelCase_ ,						'do_reduce_labels'  )  )
   def 				lowerCAmelCase    (     self       : List[Any]  )			->		List[Any]:
      __A=  self.image_processing_class.from_dict(self.image_processor_dict  )
      self.assertEqual(image_processor.size ,						{'height': 30, 'width': 30}  )
      self.assertEqual(image_processor.do_reduce_labels ,						lowerCAmelCase_  )

      __A=  self.image_processing_class.from_dict(self.image_processor_dict ,						size=42 ,						reduce_labels=lowerCAmelCase_  )
      self.assertEqual(image_processor.size ,						{'height': 42, 'width': 42}  )
      self.assertEqual(image_processor.do_reduce_labels ,						lowerCAmelCase_  )
   def 				lowerCAmelCase    (     self       : List[Any]  )			->		Optional[int]:
      pass
   def 				lowerCAmelCase    (     self       : List[Any]  )			->		List[str]:
      # Initialize image_processing
      __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.size['height'],
              self.image_processor_tester.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.size['height'],
              self.image_processor_tester.size['width'],
          ) ,						)
   def 				lowerCAmelCase    (     self       : List[Any]  )			->		int:
      # Initialize image_processing
      __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.size['height'],
              self.image_processor_tester.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.size['height'],
              self.image_processor_tester.size['width'],
          ) ,						)
   def 				lowerCAmelCase    (     self       : Dict  )			->		Optional[int]:
      # Initialize image_processing
      __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.size['height'],
              self.image_processor_tester.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.size['height'],
              self.image_processor_tester.size['width'],
          ) ,						)
   def 				lowerCAmelCase    (     self       : str  )			->		str:
      # Initialize image_processing
      __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.size['height'],
              self.image_processor_tester.size['width'],
          ) ,						)
      self.assertEqual(
          encoding['labels'].shape ,						(
              1,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.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
      __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.size['height'],
              self.image_processor_tester.size['width'],
          ) ,						)
      self.assertEqual(
          encoding['labels'].shape ,						(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.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)
      __A,  __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.size['height'],
              self.image_processor_tester.size['width'],
          ) ,						)
      self.assertEqual(
          encoding['labels'].shape ,						(
              1,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.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)
      __A,  __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.size['height'],
              self.image_processor_tester.size['width'],
          ) ,						)
      self.assertEqual(
          encoding['labels'].shape ,						(
              2,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.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       : Dict  )			->		Optional[Any]:
      # Initialize image_processing
      __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,  __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() <= 150  )

      __A=  True
      __A=  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_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__      =							logging.get_logger(__name__)

UpperCAmelCase__      =							{
    '''dandelin/vilt-b32-mlm''': '''https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json'''
}
class 	a__				(				a_      ):





			'''simple docstring'''

			A	:							Dict							= '''vilt'''
			def __init__(     self       : str ,						lowerCAmelCase_       : Tuple=30_522 ,						lowerCAmelCase_       : List[Any]=2 ,						lowerCAmelCase_       : Optional[Any]=2 ,						lowerCAmelCase_       : Union[str, Any]=40 ,						lowerCAmelCase_       : str=768 ,						lowerCAmelCase_       : Tuple=12 ,						lowerCAmelCase_       : List[Any]=12 ,						lowerCAmelCase_       : int=3_072 ,						lowerCAmelCase_       : List[Any]="gelu" ,						lowerCAmelCase_       : int=0.0 ,						lowerCAmelCase_       : str=0.0 ,						lowerCAmelCase_       : Optional[Any]=0.02 ,						lowerCAmelCase_       : Union[str, Any]=1E-12 ,						lowerCAmelCase_       : Dict=384 ,						lowerCAmelCase_       : Optional[int]=32 ,						lowerCAmelCase_       : Optional[Any]=3 ,						lowerCAmelCase_       : str=True ,						lowerCAmelCase_       : List[Any]=-1 ,						lowerCAmelCase_       : Union[str, Any]=False ,						lowerCAmelCase_       : str=-1 ,						**lowerCAmelCase_       : Any ,						)			->		Tuple:
						super().__init__(tie_word_embeddings=lowerCAmelCase_ ,						**lowerCAmelCase_  )

						__A=  vocab_size
						__A=  type_vocab_size
						__A=  modality_type_vocab_size
						__A=  max_position_embeddings

						__A=  hidden_size
						__A=  num_hidden_layers
						__A=  num_attention_heads
						__A=  intermediate_size
						__A=  hidden_act
						__A=  hidden_dropout_prob
						__A=  attention_probs_dropout_prob
						__A=  initializer_range
						__A=  layer_norm_eps

						__A=  image_size
						__A=  patch_size
						__A=  num_channels
						__A=  qkv_bias
						__A=  max_image_length
						__A=  num_images



