# 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,
)


UpperCAmelCase				 =   {
    """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:
  UpperCAmelCase				 =   [
      """TVLT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TvltModel""",
      """TvltForPreTraining""",
      """TvltForAudioVisualClassification""",
      """TvltPreTrainedModel""",
  ]

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				 =   ["""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

  UpperCAmelCase				 =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
UpperCAmelCase				 =   {
    0: """underwear""",
    1: """pants""",
    2: """belt""",
    3: """suit""",
    4: """shoe""",
    5: """socks""",
    6: """shirt""",
    7: """tie""",
    8: """watch""",
}

UpperCAmelCase				 =   [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

UpperCAmelCase				 =   [0 for x in range(len(graph))]
UpperCAmelCase				 =   []







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						List[Any]:
     a	:     Tuple						      =    1
     while stack:
          a	:     Optional[Any]						      =    stack.pop()
          print(lowerCAmelCase_    ,   clothes[current_clothing]							)
          order += 1







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Any:
     a	:     Union[str, Any]						      =    1
     for v in graph[u]:
          if not visited[v]:
               depth_first_search(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)

     stack.append(lowerCAmelCase_							)







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Optional[Any]:
     for v in range(len(lowerCAmelCase_							)							):
          if not visited[v]:
               depth_first_search(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)


if __name__ == "__main__":
  topological_sort(graph, visited)
  print(stack)
  print_stack(stack, clothes)