


from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any







class   lowerCAmelCase__ :




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				("_front", "_back", "_len")







     @dataclass
     class   lowerCAmelCase__ :




          '''simple docstring'''
          __SCREAMING_SNAKE_CASE						    =				None
          __SCREAMING_SNAKE_CASE						    =				None
          __SCREAMING_SNAKE_CASE						    =				None







     class   lowerCAmelCase__ :




          '''simple docstring'''
          __SCREAMING_SNAKE_CASE						    =				("_cur",)


          def __init__(		self				,		__lowerCamelCase)    ->       None:
                 _A   : Union[str, Any]       					=				cur


          def __iter__(		self)    ->       Deque._Iterator:
                 return self


          def 				_lowerCamelCase				(		self)    ->       Any:
                 if self._cur is None:
                        # finished iterating
                        raise StopIteration
                 _A   : List[Any]       					=				self._cur.val
                 _A   : List[Any]       					=				self._cur.next_node

                 return val




     def __init__(		self				,		__lowerCamelCase = None)    ->       None:
            _A   : Any       					=				None
            _A   : Any       					=				None
            _A   : int       					=				0

            if iterable is not None:
                   # append every value to the deque
                   for val in iterable:
                          self.append(__lowerCamelCase)


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       None:
            _A   : int       					=				self._Node(__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)
            if self.is_empty():
                   # front = back
                   _A   : int       					=				node
                   _A   : Optional[Any]       					=				1
            else:
                   # connect nodes
                   _A   : Optional[int]       					=				node
                   _A   : List[str]       					=				self._back
                   _A   : str       					=				node  # assign new back to the new node

                   self._len += 1

                   # make sure there were no errors
                   assert not self.is_empty(), "Error on appending value."


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       None:
            _A   : Tuple       					=				self._Node(__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)
            if self.is_empty():
                   # front = back
                   _A   : Optional[Any]       					=				node
                   _A   : Optional[Any]       					=				1
            else:
                   # connect nodes
                   _A   : Any       					=				self._front
                   _A   : Any       					=				node
                   _A   : Any       					=				node  # assign new front to the new node

                   self._len += 1

                   # make sure there were no errors
                   assert not self.is_empty(), "Error on appending value."


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       None:
            for val in iterable:
                   self.append(__lowerCamelCase)


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       None:
            for val in iterable:
                   self.appendleft(__lowerCamelCase)


     def 				_lowerCamelCase				(		self)    ->       Any:
            assert not self.is_empty(), "Deque is empty."

            _A   : Optional[Any]       					=				self._back
            _A   : Optional[int]       					=				self._back.prev_node  # set new back
            # drop the last node - python will deallocate memory automatically
            _A   : List[str]       					=				None

            self._len -= 1

            return topop.val


     def 				_lowerCamelCase				(		self)    ->       Any:
            assert not self.is_empty(), "Deque is empty."

            _A   : Dict       					=				self._front
            _A   : List[str]       					=				self._front.next_node  # set new front and drop the first node
            _A   : Dict       					=				None

            self._len -= 1

            return topop.val


     def 				_lowerCamelCase				(		self)    ->       bool:
            return self._front is None


     def __len__(		self)    ->       int:
            return self._len


     def __eq__(		self				,		__lowerCamelCase)    ->       bool:

            if not isinstance(__lowerCamelCase				,		__lowerCamelCase):
                   return NotImplemented

            _A   : List[Any]       					=				self._front
            _A   : str       					=				other._front

            # if the length of the dequeues are not the same, they are not equal
            if len(self) != len(__lowerCamelCase):
                   return False

            while me is not None and oth is not None:
                   # compare every value
                   if me.val != oth.val:
                          return False
                   _A   : int       					=				me.next_node
                   _A   : str       					=				oth.next_node

            return True


     def __iter__(		self)    ->       Deque._Iterator:
            return Deque._Iterator(self._front)


     def __repr__(		self)    ->       str:
            _A   : str       					=				[]
            _A   : Dict       					=				self._front
            while aux is not None:
                   # append the values in a list to display
                   values_list.append(aux.val)
                   _A   : int       					=				aux.next_node

            return F"[{', '.join(repr(__lowerCamelCase) for val in values_list)}]"


if __name__ == "__main__":
    import doctest

    doctest.testmod()




from typing import TYPE_CHECKING

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


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

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

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

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__   	=				[
        '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

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