

'''simple docstring'''
def  a__       (    _SCREAMING_SNAKE_CASE   :       List[Any]				)     ->					List[str]:


	"""simple docstring"""





	UpperCAmelCase_     :      Tuple						=  0
	UpperCAmelCase_     :      str						=  len(_SCREAMING_SNAKE_CASE				)

	for i in range(n - 1				):
		for j in range(i + 1	,				_SCREAMING_SNAKE_CASE				):
			if arr[i] > arr[j]:
				num_inversions += 1

	return num_inversions


def  a__       (    _SCREAMING_SNAKE_CASE   :       List[Any]				)     ->					Optional[int]:


	"""simple docstring"""





	if len(_SCREAMING_SNAKE_CASE				) <= 1:
		return arr, 0
	UpperCAmelCase_     :      List[str]						=  len(_SCREAMING_SNAKE_CASE				) // 2
	UpperCAmelCase_     :      Union[str, Any]						=  arr[0:mid]
	UpperCAmelCase_     :      Dict						=  arr[mid:]

	UpperCAmelCase_					,					UpperCAmelCase_     :      Tuple						=  count_inversions_recursive(_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_					,					UpperCAmelCase_     :      str						=  count_inversions_recursive(_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_					,					UpperCAmelCase_     :      Tuple						=  _count_cross_inversions(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)

	UpperCAmelCase_     :      Any						=  inversion_p + inversions_q + cross_inversions
	return c, num_inversions


def  a__       (    _SCREAMING_SNAKE_CASE   :       List[str]	,				_SCREAMING_SNAKE_CASE   :       Tuple				)     ->					Tuple:


	"""simple docstring"""





	UpperCAmelCase_     :      List[str]						=  []
	UpperCAmelCase_     :      str						=  0
	while i < len(_SCREAMING_SNAKE_CASE				) and j < len(_SCREAMING_SNAKE_CASE				):
		if p[i] > q[j]:
			# if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
			# These are all inversions. The claim emerges from the
			# property that P is sorted.
			num_inversion += len(_SCREAMING_SNAKE_CASE				) - i
			r.append(q[j]				)
			j += 1
		else:
			r.append(p[i]				)
			i += 1

	if i < len(_SCREAMING_SNAKE_CASE				):
		r.extend(p[i:]				)
	else:
		r.extend(q[j:]				)

	return r, num_inversion


def  a__       (    )     ->					List[Any]:


	"""simple docstring"""





	UpperCAmelCase_     :      Union[str, Any]						=  [10, 2, 1, 5, 5, 2, 11]

	# this arr has 8 inversions:
	# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

	UpperCAmelCase_     :      Optional[int]						=  count_inversions_bf(_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_					,					UpperCAmelCase_     :      Optional[int]						=  count_inversions_recursive(_SCREAMING_SNAKE_CASE				)

	assert num_inversions_bf == num_inversions_recursive == 8

	print("number of inversions = "	,				_SCREAMING_SNAKE_CASE				)

	# testing an array with zero inversion (a sorted arr_1)

	arr_a.sort()
	UpperCAmelCase_     :      Dict						=  count_inversions_bf(_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_					,					UpperCAmelCase_     :      Union[str, Any]						=  count_inversions_recursive(_SCREAMING_SNAKE_CASE				)

	assert num_inversions_bf == num_inversions_recursive == 0
	print("number of inversions = "	,				_SCREAMING_SNAKE_CASE				)

	# an empty list should also have zero inversions
	UpperCAmelCase_     :      Optional[int]						=  []
	UpperCAmelCase_     :      Optional[Any]						=  count_inversions_bf(_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_					,					UpperCAmelCase_     :      List[str]						=  count_inversions_recursive(_SCREAMING_SNAKE_CASE				)

	assert num_inversions_bf == num_inversions_recursive == 0
	print("number of inversions = "	,				_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
					main()







'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     ["""image_processor""", """tokenizer"""]
				_a    	=     """CLIPImageProcessor"""
				_a    	=     ("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")



				def __init__(    self,     _lowercase=None,     _lowercase=None,     **_lowercase       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				None
											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																		    ' instead.',     _lowercase,     )
																		SCREAMING_SNAKE_CASE_				=				kwargs.pop('feature_extractor'       )

											SCREAMING_SNAKE_CASE_				=				image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																		raise ValueError('You need to specify an `image_processor`.'       )
											if tokenizer is None:
																		raise ValueError('You need to specify a `tokenizer`.'       )

											super().__init__(_lowercase,     _lowercase       )



				def __call__(    self,     _lowercase=None,     _lowercase=None,     _lowercase=None,     **_lowercase       )     ->							str:

											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:
																		SCREAMING_SNAKE_CASE_				=				self.tokenizer(_lowercase,     return_tensors=_lowercase,     **_lowercase       )

											if images is not None:
																		SCREAMING_SNAKE_CASE_				=				self.image_processor(_lowercase,     return_tensors=_lowercase,     **_lowercase       )

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



				def   a__					(    self,     *_lowercase,     **_lowercase       )     ->							Optional[int]:
											return self.tokenizer.batch_decode(*_lowercase,     **_lowercase       )



				def   a__					(    self,     *_lowercase,     **_lowercase       )     ->							Optional[Any]:
											return self.tokenizer.decode(*_lowercase,     **_lowercase       )





				@property
				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				self.tokenizer.model_input_names
											SCREAMING_SNAKE_CASE_				=				self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )





