import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE_		(			lowercase__		):

   '''simple docstring'''





   __lowerCAmelCase											=							["""image_processor""", """tokenizer"""]
   __lowerCAmelCase											=							"""AutoImageProcessor"""
   __lowerCAmelCase											=							"""AutoTokenizer"""







   def __init__(    self	:	Tuple      ,      lowerCamelCase_	:	Dict=None      ,      lowerCamelCase_	:	Optional[int]=None      ,      **lowerCamelCase_	:	Optional[int]							):





    """simple docstring"""


    UpperCamelCase      =			None
    if "feature_extractor" in kwargs:
     warnings.warn(
         """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
         """ instead."""      ,      _A      ,      )
     UpperCamelCase      =			kwargs.pop("""feature_extractor"""							)

    UpperCamelCase      =			image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
     raise ValueError("""You have to specify an image_processor."""							)
    if tokenizer is None:
     raise ValueError("""You have to specify a tokenizer."""							)

    super().__init__(_A      ,      _A							)
    UpperCamelCase      =			self.image_processor







   def __call__(    self	:	str      ,      lowerCamelCase_	:	Dict=None      ,      lowerCamelCase_	:	Optional[int]=None      ,      lowerCamelCase_	:	Any=None      ,      **lowerCamelCase_	:	str							):





    """simple docstring"""


    if text is None and images is None:
     raise ValueError("""You have to specify either text or images. Both cannot be none."""							)

    if text is not None:
     UpperCamelCase      =			self.tokenizer(_A      ,      return_tensors=_A      ,      **_A							)

    if images is not None:
     UpperCamelCase      =			self.image_processor(_A      ,      return_tensors=_A      ,      **_A							)

    if text is not None and images is not None:
     UpperCamelCase      =			image_features.pixel_values
     return encoding
    elif text is not None:
     return encoding
    else:
     return BatchEncoding(data=dict(**_A							)      ,      tensor_type=_A							)







   def 	lowerCamelCase_  (    self	:	Optional[int]      ,      *lowerCamelCase_	:	List[str]      ,      **lowerCamelCase_	:	str							):





    """simple docstring"""


    return self.tokenizer.batch_decode(*_A      ,      **_A							)







   def 	lowerCamelCase_  (    self	:	Any      ,      *lowerCamelCase_	:	Tuple      ,      **lowerCamelCase_	:	Union[str, Any]							):





    """simple docstring"""


    return self.tokenizer.decode(*_A      ,      **_A							)







   @property
   def 	lowerCamelCase_  (    self	:	Tuple							):





    """simple docstring"""


    UpperCamelCase      =			self.tokenizer.model_input_names
    UpperCamelCase      =			self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)







   @property
   def 	lowerCamelCase_  (    self	:	List[str]							):





    """simple docstring"""


    warnings.warn(
        """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""      ,      _A      ,      )
    return self.image_processor_class







   @property
   def 	lowerCamelCase_  (    self	:	Union[str, Any]							):





    """simple docstring"""


    warnings.warn(
        """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""      ,      _A      ,      )
    return self.image_processor

import numpy as np





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)		->  List[str]:







 '''simple docstring'''
 UpperCamelCase      =			n_draws * class_counts / class_counts.sum()
 # floored means we don't overshoot n_samples, but probably undershoot
 UpperCamelCase      =			np.floor(UpperCamelCase_					)
 # we add samples according to how much "left over" probability
 # they had, until we arrive at n_samples
 UpperCamelCase      =			int(n_draws - floored.sum()					)
 if need_to_add > 0:
  UpperCamelCase      =			continuous - floored
  UpperCamelCase      =			np.sort(np.unique(UpperCamelCase_					)					)[::-1]
  # add according to remainder, but break ties
  # randomly to avoid biases
  for value in values:
   ((UpperCamelCase)     ,    )      =			np.where(remainder == value					)
   # if we need_to_add less than what's in inds
   # we draw randomly from them.
   # if we need to add more, we add them all and
   # go to the next value
   UpperCamelCase      =			min(len(UpperCamelCase_					)       ,				UpperCamelCase_					)
   UpperCamelCase      =			rng.choice(UpperCamelCase_       ,				size=UpperCamelCase_       ,				replace=UpperCamelCase_					)
   floored[inds] += 1
   need_to_add -= add_now
   if need_to_add == 0:
    break
 return floored.astype(np.intaa					)





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_=10					)		->  Dict:







 '''simple docstring'''
 UpperCamelCase     ,    UpperCamelCase      =			np.unique(UpperCamelCase_       ,				return_inverse=UpperCamelCase_					)
 UpperCamelCase      =			classes.shape[0]
 UpperCamelCase      =			np.bincount(UpperCamelCase_					)
 if np.min(UpperCamelCase_					) < 2:
  raise ValueError("""Minimum class count error"""					)
 if n_train < n_classes:
  raise ValueError(
      """The train_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_train, n_classes)					)
 if n_test < n_classes:
  raise ValueError(
      """The test_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_test, n_classes)					)
 UpperCamelCase      =			np.split(np.argsort(UpperCamelCase_       ,				kind="""mergesort"""					)       ,				np.cumsum(UpperCamelCase_					)[:-1]					)
 for _ in range(UpperCamelCase_					):
  UpperCamelCase      =			approximate_mode(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  UpperCamelCase      =			class_counts - n_i
  UpperCamelCase      =			approximate_mode(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)

  UpperCamelCase      =			[]
  UpperCamelCase      =			[]

  for i in range(UpperCamelCase_					):
   UpperCamelCase      =			rng.permutation(class_counts[i]					)
   UpperCamelCase      =			class_indices[i].take(UpperCamelCase_       ,				mode="""clip"""					)
   train.extend(perm_indices_class_i[: n_i[i]]					)
   test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]					)
  UpperCamelCase      =			rng.permutation(UpperCamelCase_					)
  UpperCamelCase      =			rng.permutation(UpperCamelCase_					)

  yield train, test
