
'''simple docstring'''







class  SCREAMING_SNAKE_CASE__       :
	def __init__(				self			):



					'''simple docstring'''






					__a       :     dict[int, list[int]]								=	{}
					# indexmap keys are an index and it's values are the number at that index
					__a       :     dict[int, int]								=	{}
	def __lowerCamelCase    (				self  ,    __UpperCamelCase  ,    __UpperCamelCase			):



					'''simple docstring'''






					if isinstance(__UpperCamelCase  ,    (range, str)			):
									__a       :     List[Any]								=	list(__UpperCamelCase			)
					elif not isinstance(__UpperCamelCase  ,    __UpperCamelCase			):
									raise TypeError(
									    """binary_search_delete() only accepts either a list, range or str"""			)

					__a       :     Optional[Any]								=	0
					__a       :     Optional[int]								=	len(__UpperCamelCase			) - 1

					while low <= high:
									__a       :     Optional[int]								=	(low + high) // 2
									if array[mid] == item:
													array.pop(__UpperCamelCase			)
													return array
									elif array[mid] < item:
													__a       :     Optional[Any]								=	mid + 1
									else:
													__a       :     Any								=	mid - 1
					raise ValueError(
					    """Either the item is not in the array or the array was unsorted"""			)
	def __lowerCamelCase    (				self  ,    __UpperCamelCase  ,    __UpperCamelCase			):



					'''simple docstring'''






					if isinstance(__UpperCamelCase  ,    (range, str)			):
									__a       :     Dict								=	list(__UpperCamelCase			)
					elif not isinstance(__UpperCamelCase  ,    __UpperCamelCase			):
									raise TypeError(
									    """binary_search_insert() only accepts either a list, range or str"""			)

					__a       :     Tuple								=	0
					__a       :     Union[str, Any]								=	len(__UpperCamelCase			) - 1

					while low <= high:
									__a       :     Optional[Any]								=	(low + high) // 2
									if array[mid] == index:
													# If the item already exists in the array,
													# insert it after the existing item
													array.insert(mid + 1  ,    __UpperCamelCase			)
													return array
									elif array[mid] < index:
													__a       :     Tuple								=	mid + 1
									else:
													__a       :     str								=	mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
					array.insert(__UpperCamelCase  ,    __UpperCamelCase			)
					return array
	def __lowerCamelCase    (				self  ,    __UpperCamelCase  ,    __UpperCamelCase			):



					'''simple docstring'''






					if index in self.indexmap:
									__a       :     Optional[Any]								=	self.indexmap[index]
									if len(self.numbermap[n]			) == 1:
													del self.numbermap[n]
									else:
													__a       :     int								=	self.binary_search_delete(self.numbermap[n]  ,    __UpperCamelCase			)

        # Set new index
					__a       :     Optional[Any]								=	number

					# Number not seen before or empty so insert number value
					if number not in self.numbermap:
									__a       :     Union[str, Any]								=	[index]

					# Here we need to perform a binary search insertion in order to insert
					# The item in the correct place
					else:
									__a       :     Any								=	self.binary_search_insert(
									    self.numbermap[number]  ,    __UpperCamelCase			)

	def __lowerCamelCase    (				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					return self.numbermap.get(__UpperCamelCase  ,    [-1]			)[0]


if __name__ == "__main__":
					import doctest

					doctest.testmod()

'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__SCREAMING_SNAKE_CASE		:  Tuple        =      {
    'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE		:  Dict        =      ['SegformerFeatureExtractor']
					__SCREAMING_SNAKE_CASE		:  str        =      ['SegformerImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE		:  List[str]        =      [
					    'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'SegformerDecodeHead',
					    'SegformerForImageClassification',
					    'SegformerForSemanticSegmentation',
					    'SegformerLayer',
					    'SegformerModel',
					    'SegformerPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE		:  List[str]        =      [
					    'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFSegformerDecodeHead',
					    'TFSegformerForImageClassification',
					    'TFSegformerForSemanticSegmentation',
					    'TFSegformerModel',
					    'TFSegformerPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_segformer import SegformerFeatureExtractor
										from .image_processing_segformer import SegformerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_segformer import (
										    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SegformerDecodeHead,
										    SegformerForImageClassification,
										    SegformerForSemanticSegmentation,
										    SegformerLayer,
										    SegformerModel,
										    SegformerPreTrainedModel,
										)
					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_segformer import (
										    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFSegformerDecodeHead,
										    TFSegformerForImageClassification,
										    TFSegformerForSemanticSegmentation,
										    TFSegformerModel,
										    TFSegformerPreTrainedModel,
										)

else:
					import sys

					__SCREAMING_SNAKE_CASE		:  Optional[Any]        =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)