



'''simple docstring'''





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase_	 =						logging.get_logger(__name__)






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: Optional[Any]    = ['pixel_values']



 def __init__(self				,     A = True				,     A = None				,     A = PILImageResampling.BICUBIC				,     A = True				,     A = True				,     A = 1 / 255				,     A = None				,     A = True				,     A = None				,     A = None				,     **A				,     )					->  None:


      """simple docstring"""



      super().__init__(**A )
      _a											=						size if size is not None else {'''height''': 224, '''width''': 224}
      _a											=						get_size_dict(A )
      _a											=						crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
      _a											=						get_size_dict(A				,     default_to_square=A				,     param_name='''crop_size''' )

      _a											=						do_resize
      _a											=						do_rescale
      _a											=						do_normalize
      _a											=						do_center_crop
      _a											=						crop_size
      _a											=						size
      _a											=						resample
      _a											=						rescale_factor
      _a											=						image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
      _a											=						image_std if image_std is not None else IMAGENET_DEFAULT_STD



 def       a__						(self				,     A				,     A				,     A = PILImageResampling.BILINEAR				,     A = None				,     **A				,     )					->  np.ndarray:


      """simple docstring"""



      _a											=						get_size_dict(A )

      if "shortest_edge" in size:
           _a											=						get_resize_output_image_size(A				,     size=size['''shortest_edge''']				,     default_to_square=A )
           # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
      elif "height" in size and "width" in size:
           _a											=						(size['''height'''], size['''width'''])
      else:
           raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''' )
      return resize(A				,     size=A				,     resample=A				,     data_format=A				,     **A )



 def       a__						(self				,     A				,     A				,     A = None				,     **A				,     )					->  np.ndarray:


      """simple docstring"""



      _a											=						get_size_dict(A )
      if "height" not in size or "width" not in size:
           raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
      return center_crop(A				,     size=(size['''height'''], size['''width'''])				,     data_format=A				,     **A )



 def       a__						(self				,     A				,     A				,     A = None				,     **A )					->  np.ndarray:


      """simple docstring"""



      return rescale(A				,     scale=A				,     data_format=A				,     **A )



 def       a__						(self				,     A				,     A				,     A				,     A = None				,     **A				,     )					->  np.ndarray:


      """simple docstring"""



      return normalize(A				,     mean=A				,     std=A				,     data_format=A				,     **A )



 def       a__						(self				,     A				,     A = None				,     A = None				,     A = None				,     A = None				,     A = None				,     A = None				,     A = None				,     A = None				,     A = None				,     A = None				,     A = None				,     A = ChannelDimension.FIRST				,     **A				,     )					->  BatchFeature:


      """simple docstring"""



      _a											=						do_resize if do_resize is not None else self.do_resize
      _a											=						do_rescale if do_rescale is not None else self.do_rescale
      _a											=						do_normalize if do_normalize is not None else self.do_normalize
      _a											=						do_center_crop if do_center_crop is not None else self.do_center_crop
      _a											=						crop_size if crop_size is not None else self.crop_size
      _a											=						get_size_dict(A				,     param_name='''crop_size'''				,     default_to_square=A )
      _a											=						resample if resample is not None else self.resample
      _a											=						rescale_factor if rescale_factor is not None else self.rescale_factor
      _a											=						image_mean if image_mean is not None else self.image_mean
      _a											=						image_std if image_std is not None else self.image_std

      _a											=						size if size is not None else self.size
      _a											=						get_size_dict(A )

      if not is_batched(A ):
           _a											=						[images]

      if not valid_images(A ):
           raise ValueError(
               '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
               '''torch.Tensor, tf.Tensor or jax.ndarray.''' )

      if do_resize and size is None:
           raise ValueError('''Size must be specified if do_resize is True.''' )

      if do_center_crop and crop_size is None:
           raise ValueError('''Crop size must be specified if do_center_crop is True.''' )

      if do_rescale and rescale_factor is None:
           raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )

      # All transformations expect numpy arrays.
      _a											=						[to_numpy_array(A ) for image in images]

      if do_resize:
           _a											=						[self.resize(image=A				,     size=A				,     resample=A ) for image in images]

      if do_center_crop:
           _a											=						[self.center_crop(image=A				,     size=A ) for image in images]

      if do_rescale:
           _a											=						[self.rescale(image=A				,     scale=A ) for image in images]

      if do_normalize:
           _a											=						[self.normalize(image=A				,     mean=A				,     std=A ) for image in images]

      _a											=						[to_channel_dimension_format(A				,     A ) for image in images]

      _a											=						{'''pixel_values''': images}
      return BatchFeature(data=A				,     tensor_type=A )








'''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] )



