

'''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		):



 '''simple docstring'''







 def __init__(self				,     A				,     A=7				,     A=3				,     A=18				,     A=30				,     A=400				,     A=True				,     A=None				,     A=True				,     A=None				,     A=True				,     A=[0.5, 0.5, 0.5]				,     A=[0.5, 0.5, 0.5]				,     A=False				,     )					->  int:


      """simple docstring"""



      _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       a__						(self )					->  Any:


      """simple docstring"""



      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 lowerCAmelCase						():



     """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 lowerCAmelCase						():



     """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    (						A     ,     unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase					: int    = BeitImageProcessor if is_vision_available() else None



 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						BeitImageProcessingTester(self )



 @property
 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      return self.image_processor_tester.prepare_image_processor_dict()



 def       a__						(self )					->  Any:


      """simple docstring"""



      _a											=						self.image_processing_class(**self.image_processor_dict )
      self.assertTrue(hasattr(A				,     '''do_resize''' ) )
      self.assertTrue(hasattr(A				,     '''size''' ) )
      self.assertTrue(hasattr(A				,     '''do_center_crop''' ) )
      self.assertTrue(hasattr(A				,     '''center_crop''' ) )
      self.assertTrue(hasattr(A				,     '''do_normalize''' ) )
      self.assertTrue(hasattr(A				,     '''image_mean''' ) )
      self.assertTrue(hasattr(A				,     '''image_std''' ) )



 def       a__						(self )					->  int:


      """simple docstring"""



      _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				,     A )

      _a											=						self.image_processing_class.from_dict(
          self.image_processor_dict				,     size=42				,     crop_size=84				,     reduce_labels=A )
      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				,     A )



 def       a__						(self )					->  Dict:


      """simple docstring"""



      pass



 def       a__						(self )					->  int:


      """simple docstring"""



      _a											=						self.image_processing_class(**self.image_processor_dict )
      # create random PIL images
      _a											=						prepare_image_inputs(self.image_processor_tester				,     equal_resolution=A )
      for image in image_inputs:
           self.assertIsInstance(A				,     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(A				,     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       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      _a											=						self.image_processing_class(**self.image_processor_dict )
      # create random numpy tensors
      _a											=						prepare_image_inputs(self.image_processor_tester				,     equal_resolution=A				,     numpify=A )
      for image in image_inputs:
           self.assertIsInstance(A				,     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(A				,     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       a__						(self )					->  Any:


      """simple docstring"""



      _a											=						self.image_processing_class(**self.image_processor_dict )
      # create random PyTorch tensors
      _a											=						prepare_image_inputs(self.image_processor_tester				,     equal_resolution=A				,     torchify=A )
      for image in image_inputs:
           self.assertIsInstance(A				,     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(A				,     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       a__						(self )					->  Any:


      """simple docstring"""



      _a											=						self.image_processing_class(**self.image_processor_dict )
      # create random PyTorch tensors
      _a											=						prepare_image_inputs(self.image_processor_tester				,     equal_resolution=A				,     torchify=A )
      _a											=						[]
      for image in image_inputs:
           self.assertIsInstance(A				,     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() <= 255 )

      # Test batched
      _a											=						image_processing(A				,     A				,     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)
      _a						,					_a											=						prepare_semantic_single_inputs()

      _a											=						image_processing(A				,     A				,     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)
      _a						,					_a											=						prepare_semantic_batch_inputs()

      _a											=						image_processing(A				,     A				,     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       a__						(self )					->  Any:


      """simple docstring"""



      _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(A				,     A				,     return_tensors='''pt''' )
      self.assertTrue(encoding['''labels'''].min().item() >= 0 )
      self.assertTrue(encoding['''labels'''].max().item() <= 150 )

      _a											=						True
      _a											=						image_processing(A				,     A				,     return_tensors='''pt''' )
      self.assertTrue(encoding['''labels'''].min().item() >= 0 )
      self.assertTrue(encoding['''labels'''].max().item() <= 255 )








'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
  import torch

  from transformers.generation import DisjunctiveConstraint






@require_torch
class __A    (						unittest.TestCase		):



 '''simple docstring'''







 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      _a											=						[[1, 2, 4], [1, 2, 3, 4]]
      _a											=						DisjunctiveConstraint(A )
      self.assertTrue(isinstance(dc.token_ids				,     A ) )

      with self.assertRaises(A ):
           DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )

      with self.assertRaises(A ):
           DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )



 def       a__						(self )					->  Any:


      """simple docstring"""



      _a											=						[[1, 2], [1, 2, 3, 4]]

      with self.assertRaises(A ):
           DisjunctiveConstraint(A )  # fails here



 def       a__						(self )					->  Dict:


      """simple docstring"""



      _a											=						[[1, 2, 3], [1, 2, 4]]

      _a											=						DisjunctiveConstraint(A )

      _a						,					_a						,					_a											=						dc.update(1 )
      _a											=						stepped is True and completed is False and reset is False
      self.assertTrue(A )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1] )

      _a						,					_a						,					_a											=						dc.update(2 )
      _a											=						stepped is True and completed is False and reset is False
      self.assertTrue(A )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1, 2] )

      _a						,					_a						,					_a											=						dc.update(3 )
      _a											=						stepped is True and completed is True and reset is False
      self.assertTrue(A )
      self.assertTrue(dc.completed )  # Completed!
      self.assertTrue(dc.current_seq == [1, 2, 3] )



 def       a__						(self )					->  List[Any]:


      """simple docstring"""



      _a											=						[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

      _a											=						DisjunctiveConstraint(A )

      _a						,					_a						,					_a											=						dc.update(1 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1] )

      _a						,					_a						,					_a											=						dc.update(2 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1, 2] )

      _a						,					_a						,					_a											=						dc.update(4 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1, 2, 4] )

      _a						,					_a						,					_a											=						dc.update(5 )
      self.assertTrue(dc.completed )  # Completed!
      self.assertTrue(dc.current_seq == [1, 2, 4, 5] )

      dc.reset()

      _a						,					_a						,					_a											=						dc.update(1 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.remaining() == 3 )
      self.assertTrue(dc.current_seq == [1] )

      _a						,					_a						,					_a											=						dc.update(2 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.remaining() == 2 )
      self.assertTrue(dc.current_seq == [1, 2] )

      _a						,					_a						,					_a											=						dc.update(5 )
      self.assertTrue(dc.completed )  # Completed!
      self.assertTrue(dc.remaining() == 0 )
      self.assertTrue(dc.current_seq == [1, 2, 5] )



