




'''simple docstring'''


def 		snake_case_   (UpperCamelCase	:  str				):





							'''simple docstring'''


							_a     					=    0
							_a     					=    len(UpperCamelCase				)

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

							return num_inversions
def 		snake_case_   (UpperCamelCase	:  Union[str, Any]				):





							'''simple docstring'''


							if len(UpperCamelCase				) <= 1:
														return arr, 0
							_a     					=    len(UpperCamelCase				) // 2
							_a     					=    arr[0:mid]
							_a     					=    arr[mid:]

							_a					,							_a     					=    count_inversions_recursive(UpperCamelCase				)
							_a					,							_a     					=    count_inversions_recursive(UpperCamelCase				)
							_a					,							_a     					=    _count_cross_inversions(UpperCamelCase			,						UpperCamelCase				)

							_a     					=    inversion_p + inversions_q + cross_inversions
							return c, num_inversions
def 		snake_case_   (UpperCamelCase	:  Optional[int]			,						UpperCamelCase	:  Any				):





							'''simple docstring'''


							_a     					=    []
							_a     					=    _a     					=    _a     					=    0
							while i < len(UpperCamelCase				) and j < len(UpperCamelCase				):
														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(UpperCamelCase				) - i
																					r.append(q[j]				)
																					j += 1
														else:
																					r.append(p[i]				)
																					i += 1

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

							return r, num_inversion
def 		snake_case_   ():





							'''simple docstring'''


							_a     					=    [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)

							_a     					=    count_inversions_bf(UpperCamelCase				)
							_a					,							_a     					=    count_inversions_recursive(UpperCamelCase				)

							assert num_inversions_bf == num_inversions_recursive == 8

							print('''number of inversions = '''			,						UpperCamelCase				)

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

							arr_a.sort()
							_a     					=    count_inversions_bf(UpperCamelCase				)
							_a					,							_a     					=    count_inversions_recursive(UpperCamelCase				)

							assert num_inversions_bf == num_inversions_recursive == 0
							print('''number of inversions = '''			,						UpperCamelCase				)

							# an empty list should also have zero inversions
							_a     					=    []
							_a     					=    count_inversions_bf(UpperCamelCase				)
							_a					,							_a     					=    count_inversions_recursive(UpperCamelCase				)

							assert num_inversions_bf == num_inversions_recursive == 0
							print('''number of inversions = '''			,						UpperCamelCase				)


if __name__ == "__main__":
				main()







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case				:				List[str]       							=		{'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLNetConfig']}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				List[Any]       							=		['XLNetTokenizer']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				int       							=		['XLNetTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				Optional[int]       							=		[
				    'XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'XLNetForMultipleChoice',
				    'XLNetForQuestionAnswering',
				    'XLNetForQuestionAnsweringSimple',
				    'XLNetForSequenceClassification',
				    'XLNetForTokenClassification',
				    'XLNetLMHeadModel',
				    'XLNetModel',
				    'XLNetPreTrainedModel',
				    'load_tf_weights_in_xlnet',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				str       							=		[
				    'TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFXLNetForMultipleChoice',
				    'TFXLNetForQuestionAnsweringSimple',
				    'TFXLNetForSequenceClassification',
				    'TFXLNetForTokenClassification',
				    'TFXLNetLMHeadModel',
				    'TFXLNetMainLayer',
				    'TFXLNetModel',
				    'TFXLNetPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xlnet import XLNetTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xlnet_fast import XLNetTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xlnet import (
								    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    XLNetForMultipleChoice,
								    XLNetForQuestionAnswering,
								    XLNetForQuestionAnsweringSimple,
								    XLNetForSequenceClassification,
								    XLNetForTokenClassification,
								    XLNetLMHeadModel,
								    XLNetModel,
								    XLNetPreTrainedModel,
								    load_tf_weights_in_xlnet,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_xlnet import (
								    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFXLNetForMultipleChoice,
								    TFXLNetForQuestionAnsweringSimple,
								    TFXLNetForSequenceClassification,
								    TFXLNetForTokenClassification,
								    TFXLNetLMHeadModel,
								    TFXLNetMainLayer,
								    TFXLNetModel,
								    TFXLNetPreTrainedModel,
								)

else:
				import sys

				_snake_case				:				Union[str, Any]       							=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

