





'''simple docstring'''







def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     int				)	->		str:
						if not isinstance(SCREAMING_SNAKE_CASE__,     SCREAMING_SNAKE_CASE__				):
												UpperCAmelCase_     : Optional[Any]         =					(
												    '''operation can not be conducted on a object of type '''
												    F"""{type(SCREAMING_SNAKE_CASE__				).__name__}"""
												)
												raise TypeError(SCREAMING_SNAKE_CASE__				)
						UpperCAmelCase_     : Dict         =					''''''
						for _ in range(0,     32				):
												bit_string += str(number % 2				)
												UpperCAmelCase_     : Optional[int]         =					number >> 1
						return bit_string





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     int				)	->		str:
						if number < 0:
												raise ValueError('''the value of input must be positive'''				)
						elif isinstance(SCREAMING_SNAKE_CASE__,     SCREAMING_SNAKE_CASE__				):
												raise TypeError('''Input value must be a \'int\' type'''				)
						elif isinstance(SCREAMING_SNAKE_CASE__,     SCREAMING_SNAKE_CASE__				):
												raise TypeError('''\'<\' not supported between instances of \'str\' and \'int\''''				)
						UpperCAmelCase_     : Optional[int]         =					0
						# iterator over [1 to 32],since we are dealing with 32 bit integer
						for _ in range(1,     33				):
												# left shift the bits by unity
												UpperCAmelCase_     : Optional[Any]         =					result << 1
												# get the end bit
												UpperCAmelCase_     : Any         =					number % 2
												# right shift the bits by unity
												UpperCAmelCase_     : Union[str, Any]         =					number >> 1
												# add that bit to our ans
												UpperCAmelCase_     : int         =					result | end_bit
						return get_reverse_bit_string(SCREAMING_SNAKE_CASE__				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()












'''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,
)


snake_case_       :    List[Any]          =					{
    "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:
					snake_case_       :    Optional[int]          =					[
					    "TVLT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TvltModel",
					    "TvltForPreTraining",
					    "TvltForAudioVisualClassification",
					    "TvltPreTrainedModel",
					]

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_       :    List[Any]          =					["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

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





