

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase							:  Optional[Any]   	=						{
    """configuration_vivit""": ["""VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VivitConfig"""],
}
try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Optional[int]   	=						["""VivitImageProcessor"""]


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Optional[Any]   	=						[
						    """VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """VivitModel""",
						    """VivitPreTrainedModel""",
						    """VivitForVideoClassification""",
						]


if TYPE_CHECKING:
						from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .image_processing_vivit import VivitImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vivit import (
												    VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    VivitForVideoClassification,
												    VivitModel,
												    VivitPreTrainedModel,
												)


else:
						import sys

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














"""simple docstring"""




from collections.abc import Iterable
from typing import Generic, TypeVar

_snake_case          =   TypeVar('_T')






class        UpperCamelCase			(	Generic[_T]      ):


 def __init__(     self		:						Optional[int]							,						UpperCAmelCase__		:						Iterable[_T] | None = None							)					->       None:
   _a  :       list[_T]             =	list(iterable or []							)
   _a  :       list[_T]             =	[]


 def __len__(     self		:						str							)					->       int:
   return len(self._stacka							) + len(self._stacka							)


 def __repr__(     self		:						List[str]							)					->       str:
   return f"""Queue({tuple(self._stacka[::-1] + self._stacka							)})"""


 def _lowercase	(     self		:						Union[str, Any]							,						UpperCAmelCase__		:						_T							)					->       None:
   self._stacka.append(UpperCAmelCase__							)







 def _lowercase	(     self		:						Optional[Any]							)					->       _T:
   _a  :       Any             =	self._stacka.pop
   _a  :       Union[str, Any]             =	self._stacka.append

   if not self._stacka:
     while self._stacka:
       stacka_append(stacka_pop()							)

   if not self._stacka:
     raise IndexError("""Queue is empty"""							)
   return self._stacka.pop()


if __name__ == "__main__":
 from doctest import testmod

 testmod()

