



"""simple docstring"""





# 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

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


SCREAMING_SNAKE_CASE				:     str          =	{
    """configuration_tvlt""": ["""TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TvltConfig"""],
    """feature_extraction_tvlt""": ["""TvltFeatureExtractor"""],
    """processing_tvlt""": ["""TvltProcessor"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE				:     Any          =	[
      """TVLT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TvltModel""",
      """TvltForPreTraining""",
      """TvltForAudioVisualClassification""",
      """TvltPreTrainedModel""",
  ]

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE				:     Tuple          =	["""TvltImageProcessor"""]


if TYPE_CHECKING:
  from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
  from .processing_tvlt import TvltProcessor
  from .feature_extraction_tvlt import TvltFeatureExtractor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tvlt import (
        TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TvltForAudioVisualClassification,
        TvltForPreTraining,
        TvltModel,
        TvltPreTrainedModel,
    )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_tvlt import TvltImageProcessor


else:
  import sys

  SCREAMING_SNAKE_CASE				:     List[str]          =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




"""simple docstring"""





def 	__UpperCAmelCase					(       snake_case_  :  Optional[int]   ,	snake_case_  :  List[str]   )							->	Optional[int]:






   """simple docstring"""


   if n < 0:
      return 0
   # table[i] represents the number of ways to get to amount i
   _lowerCAmelCase					     =		[0] * (n + 1)

   # There is exactly 1 way to get to zero(You pick no coins).
   _lowerCAmelCase					     =		1

   # Pick all coins one by one and update table[] values
   # after the index greater than or equal to the value of the
   # picked coin
   for coin_val in s:
      for j in range(snake_case_   ,	n + 1   ):
         table[j] += table[j - coin_val]

   return table[n]


if __name__ == "__main__":
  import doctest

  doctest.testmod()