'''simple docstring'''

def      UpperCamelCase_						(					snake_case_       :       int      )					->     list:






 '''simple docstring'''

 if bit_count < 0:
  raise ValueError("""The given input must be positive"""      )

 # get the generated string sequence
 __lowerCAmelCase											=						gray_code_sequence_string(_lowerCAmelCase      )
 #
 # convert them to integers
 for i in range(len(_lowerCAmelCase      )      ):
  __lowerCAmelCase											=						int(sequence[i]						,		2      )

 return sequence







def      UpperCamelCase_						(					snake_case_       :       int      )					->     list:






 '''simple docstring'''

 if bit_count == 0:
  return ["0"]

 if bit_count == 1:
  return ["0", "1"]

 __lowerCAmelCase											=						1 << bit_count  # defines the length of the sequence
 # 1<< n is equivalent to 2^n

 # recursive answer will generate answer for n-1 bits
 __lowerCAmelCase											=						gray_code_sequence_string(bit_count - 1      )

 __lowerCAmelCase											=						[]

 # append 0 to first half of the smaller sequence generated
 for i in range(seq_len // 2      ):
  __lowerCAmelCase											=						'''0''' + smaller_sequence[i]
  sequence.append(_lowerCAmelCase      )

 # append 1 to second half ... start from the end of the list
 for i in reversed(range(seq_len // 2      )      ):
  __lowerCAmelCase											=						'''1''' + smaller_sequence[i]
  sequence.append(_lowerCAmelCase      )

 return sequence


if __name__ == "__main__":
 import doctest

 doctest.testmod()











'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__:   str								=			{
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:   int								=			["LxmertTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:   Union[str, Any]								=			[
						    "LxmertEncoder",
						    "LxmertForPreTraining",
						    "LxmertForQuestionAnswering",
						    "LxmertModel",
						    "LxmertPreTrainedModel",
						    "LxmertVisualFeatureEncoder",
						    "LxmertXLayer",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:   int								=			[
						    "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFLxmertForPreTraining",
						    "TFLxmertMainLayer",
						    "TFLxmertModel",
						    "TFLxmertPreTrainedModel",
						    "TFLxmertVisualFeatureEncoder",
						]


if TYPE_CHECKING:
						from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
						from .tokenization_lxmert import LxmertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_lxmert_fast import LxmertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_lxmert import (
												    LxmertEncoder,
												    LxmertForPreTraining,
												    LxmertForQuestionAnswering,
												    LxmertModel,
												    LxmertPreTrainedModel,
												    LxmertVisualFeatureEncoder,
												    LxmertXLayer,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_lxmert import (
												    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFLxmertForPreTraining,
												    TFLxmertMainLayer,
												    TFLxmertModel,
												    TFLxmertPreTrainedModel,
												    TFLxmertVisualFeatureEncoder,
												)

else:
						import sys

						UpperCamelCase__:   Tuple								=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





