





# 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


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


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "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

     __A									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








class    _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def __init__(self				:   Optional[Any]       , UpperCAmelCase_				:   int)       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =n
   lowerCamelCase__:      Tuple			  =[None] * self.n
   lowerCamelCase__:      str			  =0  # index of the first element
   lowerCamelCase__:      Tuple			  =0
   lowerCamelCase__:      Optional[Any]			  =0

 def __len__(self				:   str)       ->int:



   '''simple docstring'''



   return self.size

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->bool:



   '''simple docstring'''



   return self.size == 0

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->str:



   '''simple docstring'''



   return False if self.is_empty() else self.array[self.front]

 def 							SCREAMING_SNAKE_CASE_					(self				:   int       , UpperCAmelCase_				:   Optional[int])       ->str:



   '''simple docstring'''



   if self.size >= self.n:
     raise Exception("QUEUE IS FULL")

   lowerCamelCase__:      List[Any]			  =data
   lowerCamelCase__:      Dict			  =(self.rear + 1) % self.n
   self.size += 1
   return self







 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->Tuple:



   '''simple docstring'''



   if self.size == 0:
     raise Exception("UNDERFLOW")

   lowerCamelCase__:      Optional[Any]			  =self.array[self.front]
   lowerCamelCase__:      Optional[int]			  =None
   lowerCamelCase__:      Dict			  =(self.front + 1) % self.n
   self.size -= 1
   return temp

