






'''simple docstring'''





from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


a_      = {
    'configuration_utils': ['GenerationConfig'],
    'streamers': ['TextIteratorStreamer', 'TextStreamer'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a_      = [
							    'Constraint',
							    'ConstraintListState',
							    'DisjunctiveConstraint',
							    'PhrasalConstraint',
							]
							a_      = [
							    'BeamHypotheses',
							    'BeamScorer',
							    'BeamSearchScorer',
							    'ConstrainedBeamSearchScorer',
							]
							a_      = [
							    'EpsilonLogitsWarper',
							    'EtaLogitsWarper',
							    'ForcedBOSTokenLogitsProcessor',
							    'ForcedEOSTokenLogitsProcessor',
							    'HammingDiversityLogitsProcessor',
							    'InfNanRemoveLogitsProcessor',
							    'LogitsProcessor',
							    'LogitsProcessorList',
							    'LogitsWarper',
							    'MinLengthLogitsProcessor',
							    'MinNewTokensLengthLogitsProcessor',
							    'NoBadWordsLogitsProcessor',
							    'NoRepeatNGramLogitsProcessor',
							    'PrefixConstrainedLogitsProcessor',
							    'RepetitionPenaltyLogitsProcessor',
							    'SequenceBiasLogitsProcessor',
							    'EncoderRepetitionPenaltyLogitsProcessor',
							    'TemperatureLogitsWarper',
							    'TopKLogitsWarper',
							    'TopPLogitsWarper',
							    'TypicalLogitsWarper',
							    'EncoderNoRepeatNGramLogitsProcessor',
							    'ExponentialDecayLengthPenalty',
							    'LogitNormalization',
							]
							a_      = [
							    'MaxNewTokensCriteria',
							    'MaxLengthCriteria',
							    'MaxTimeCriteria',
							    'StoppingCriteria',
							    'StoppingCriteriaList',
							    'validate_stopping_criteria',
							]
							a_      = [
							    'GenerationMixin',
							    'top_k_top_p_filtering',
							    'GreedySearchEncoderDecoderOutput',
							    'GreedySearchDecoderOnlyOutput',
							    'SampleEncoderDecoderOutput',
							    'SampleDecoderOnlyOutput',
							    'BeamSearchEncoderDecoderOutput',
							    'BeamSearchDecoderOnlyOutput',
							    'BeamSampleEncoderDecoderOutput',
							    'BeamSampleDecoderOnlyOutput',
							    'ContrastiveSearchEncoderDecoderOutput',
							    'ContrastiveSearchDecoderOnlyOutput',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a_      = [
							    'TFForcedBOSTokenLogitsProcessor',
							    'TFForcedEOSTokenLogitsProcessor',
							    'TFLogitsProcessor',
							    'TFLogitsProcessorList',
							    'TFLogitsWarper',
							    'TFMinLengthLogitsProcessor',
							    'TFNoBadWordsLogitsProcessor',
							    'TFNoRepeatNGramLogitsProcessor',
							    'TFRepetitionPenaltyLogitsProcessor',
							    'TFTemperatureLogitsWarper',
							    'TFTopKLogitsWarper',
							    'TFTopPLogitsWarper',
							    'TFForceTokensLogitsProcessor',
							    'TFSuppressTokensAtBeginLogitsProcessor',
							    'TFSuppressTokensLogitsProcessor',
							]
							a_      = [
							    'TFGenerationMixin',
							    'tf_top_k_top_p_filtering',
							    'TFGreedySearchDecoderOnlyOutput',
							    'TFGreedySearchEncoderDecoderOutput',
							    'TFSampleEncoderDecoderOutput',
							    'TFSampleDecoderOnlyOutput',
							    'TFBeamSearchEncoderDecoderOutput',
							    'TFBeamSearchDecoderOnlyOutput',
							    'TFBeamSampleEncoderDecoderOutput',
							    'TFBeamSampleDecoderOnlyOutput',
							    'TFContrastiveSearchEncoderDecoderOutput',
							    'TFContrastiveSearchDecoderOnlyOutput',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a_      = [
							    'FlaxForcedBOSTokenLogitsProcessor',
							    'FlaxForcedEOSTokenLogitsProcessor',
							    'FlaxLogitsProcessor',
							    'FlaxLogitsProcessorList',
							    'FlaxLogitsWarper',
							    'FlaxMinLengthLogitsProcessor',
							    'FlaxTemperatureLogitsWarper',
							    'FlaxTopKLogitsWarper',
							    'FlaxTopPLogitsWarper',
							]
							a_      = [
							    'FlaxGenerationMixin',
							    'FlaxGreedySearchOutput',
							    'FlaxSampleOutput',
							    'FlaxBeamSearchOutput',
							]

if TYPE_CHECKING:
							from .configuration_utils import GenerationConfig
							from .streamers import TextIteratorStreamer, TextStreamer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
														from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
														from .logits_process import (
														    EncoderNoRepeatNGramLogitsProcessor,
														    EncoderRepetitionPenaltyLogitsProcessor,
														    EpsilonLogitsWarper,
														    EtaLogitsWarper,
														    ExponentialDecayLengthPenalty,
														    ForcedBOSTokenLogitsProcessor,
														    ForcedEOSTokenLogitsProcessor,
														    HammingDiversityLogitsProcessor,
														    InfNanRemoveLogitsProcessor,
														    LogitNormalization,
														    LogitsProcessor,
														    LogitsProcessorList,
														    LogitsWarper,
														    MinLengthLogitsProcessor,
														    MinNewTokensLengthLogitsProcessor,
														    NoBadWordsLogitsProcessor,
														    NoRepeatNGramLogitsProcessor,
														    PrefixConstrainedLogitsProcessor,
														    RepetitionPenaltyLogitsProcessor,
														    SequenceBiasLogitsProcessor,
														    TemperatureLogitsWarper,
														    TopKLogitsWarper,
														    TopPLogitsWarper,
														    TypicalLogitsWarper,
														)
														from .stopping_criteria import (
														    MaxLengthCriteria,
														    MaxNewTokensCriteria,
														    MaxTimeCriteria,
														    StoppingCriteria,
														    StoppingCriteriaList,
														    validate_stopping_criteria,
														)
														from .utils import (
														    BeamSampleDecoderOnlyOutput,
														    BeamSampleEncoderDecoderOutput,
														    BeamSearchDecoderOnlyOutput,
														    BeamSearchEncoderDecoderOutput,
														    ContrastiveSearchDecoderOnlyOutput,
														    ContrastiveSearchEncoderDecoderOutput,
														    GenerationMixin,
														    GreedySearchDecoderOnlyOutput,
														    GreedySearchEncoderDecoderOutput,
														    SampleDecoderOnlyOutput,
														    SampleEncoderDecoderOutput,
														    top_k_top_p_filtering,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tf_logits_process import (
														    TFForcedBOSTokenLogitsProcessor,
														    TFForcedEOSTokenLogitsProcessor,
														    TFForceTokensLogitsProcessor,
														    TFLogitsProcessor,
														    TFLogitsProcessorList,
														    TFLogitsWarper,
														    TFMinLengthLogitsProcessor,
														    TFNoBadWordsLogitsProcessor,
														    TFNoRepeatNGramLogitsProcessor,
														    TFRepetitionPenaltyLogitsProcessor,
														    TFSuppressTokensAtBeginLogitsProcessor,
														    TFSuppressTokensLogitsProcessor,
														    TFTemperatureLogitsWarper,
														    TFTopKLogitsWarper,
														    TFTopPLogitsWarper,
														)
														from .tf_utils import (
														    TFBeamSampleDecoderOnlyOutput,
														    TFBeamSampleEncoderDecoderOutput,
														    TFBeamSearchDecoderOnlyOutput,
														    TFBeamSearchEncoderDecoderOutput,
														    TFContrastiveSearchDecoderOnlyOutput,
														    TFContrastiveSearchEncoderDecoderOutput,
														    TFGenerationMixin,
														    TFGreedySearchDecoderOnlyOutput,
														    TFGreedySearchEncoderDecoderOutput,
														    TFSampleDecoderOnlyOutput,
														    TFSampleEncoderDecoderOutput,
														    tf_top_k_top_p_filtering,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .flax_logits_process import (
														    FlaxForcedBOSTokenLogitsProcessor,
														    FlaxForcedEOSTokenLogitsProcessor,
														    FlaxLogitsProcessor,
														    FlaxLogitsProcessorList,
														    FlaxLogitsWarper,
														    FlaxMinLengthLogitsProcessor,
														    FlaxTemperatureLogitsWarper,
														    FlaxTopKLogitsWarper,
														    FlaxTopPLogitsWarper,
														)
														from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
							import sys

							a_      = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)











'''simple docstring'''





from __future__ import annotations

a_      = [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]





class 		SCREAMING_SNAKE_CASE__   :



		def __init__(    self:    Optional[int]				,   a:    int)  ->None:






								'''simple docstring'''


								a_				     =   -1
								a_				     =   -1
								a_				     =   [-1] * length
								a_				     =   None
								a_				     =   None




		def __repr__(    self:    Union[str, Any])  ->str:






								'''simple docstring'''


								return f"""Node(min_value={self.minn} max_value={self.maxx})"""





def 			__UpperCAmelCase				(lowercase__				)		->				Node | None:

						'''simple docstring'''


						a_				     =   Node(len(lowercase__				)				)
						a_     ,       a_				     =   min(lowercase__				), max(lowercase__				)
						# Leaf node case where the node contains only one unique value
						if root.minn == root.maxx:
												return root
						a_				     =   (root.minn + root.maxx) // 2

						a_				     =   []
						a_				     =   []

						for index, num in enumerate(lowercase__				):
												if num <= pivot:
																		left_arr.append(lowercase__				)
												else:
																		right_arr.append(lowercase__				)
												a_				     =   len(lowercase__				)
						a_				     =   build_tree(lowercase__				)
						a_				     =   build_tree(lowercase__				)
						return root



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				int:

						'''simple docstring'''


						if index < 0 or node is None:
												return 0
						# Leaf node cases
						if node.minn == node.maxx:
												return index + 1 if node.minn == num else 0
						a_				     =   (node.minn + node.maxx) // 2
						if num <= pivot:
												# go the left subtree and map index to the left subtree
												return rank_till_index(node.left	,lowercase__	,node.map_left[index] - 1				)
						else:
												# go to the right subtree and map index to the right subtree
												return rank_till_index(node.right	,lowercase__	,index - node.map_left[index]				)



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__	,lowercase__				)		->				int:

						'''simple docstring'''


						if start > end:
												return 0
						a_				     =   rank_till_index(lowercase__	,lowercase__	,lowercase__				)
						a_				     =   rank_till_index(lowercase__	,lowercase__	,start - 1				)
						return rank_till_end - rank_before_start



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__	,lowercase__				)		->				int:

						'''simple docstring'''


						if index > (end - start) or start > end or node is None:
												return -1
						# Leaf node case
						if node.minn == node.maxx:
												return node.minn
						# Number of elements in the left subtree in interval [start, end]
						a_				     =   node.map_left[end] - (
						    node.map_left[start - 1] if start else 0
						)
						if num_elements_in_left_tree > index:
												return quantile(
												    node.left	,lowercase__	,(node.map_left[start - 1] if start else 0)	,node.map_left[end] - 1	,)
						else:
												return quantile(
												    node.right	,index - num_elements_in_left_tree	,start - (node.map_left[start - 1] if start else 0)	,end - node.map_left[end]	,)



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__	,lowercase__	,lowercase__				)		->				int:

						'''simple docstring'''


						if (
						    start > end
						    or node is None
						    or start_num > end_num
						    or node.minn > end_num
						    or node.maxx < start_num
						):
												return 0
						if start_num <= node.minn and node.maxx <= end_num:
												return end - start + 1
						a_				     =   range_counting(
						    node.left	,(node.map_left[start - 1] if start else 0)	,node.map_left[end] - 1	,lowercase__	,lowercase__	,)
						a_				     =   range_counting(
						    node.right	,start - (node.map_left[start - 1] if start else 0)	,end - node.map_left[end]	,lowercase__	,lowercase__	,)
						return left + right


if __name__ == "__main__":
							import doctest

							doctest.testmod()



