



from typing import TYPE_CHECKING

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


snake_case__					:     Optional[Any]    		=   {
    """configuration_layoutlmv3""": [
        """LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LayoutLMv3Config""",
        """LayoutLMv3OnnxConfig""",
    ],
    """processing_layoutlmv3""": ["""LayoutLMv3Processor"""],
    """tokenization_layoutlmv3""": ["""LayoutLMv3Tokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case__					:     Any    		=   ["""LayoutLMv3TokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case__					:     List[str]    		=   [
	    """LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """LayoutLMv3ForQuestionAnswering""",
	    """LayoutLMv3ForSequenceClassification""",
	    """LayoutLMv3ForTokenClassification""",
	    """LayoutLMv3Model""",
	    """LayoutLMv3PreTrainedModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case__					:     List[str]    		=   [
	    """TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFLayoutLMv3ForQuestionAnswering""",
	    """TFLayoutLMv3ForSequenceClassification""",
	    """TFLayoutLMv3ForTokenClassification""",
	    """TFLayoutLMv3Model""",
	    """TFLayoutLMv3PreTrainedModel""",
	]

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case__					:     List[Any]    		=   ["""LayoutLMv3FeatureExtractor"""]
	snake_case__					:     List[str]    		=   ["""LayoutLMv3ImageProcessor"""]


if TYPE_CHECKING:
	from .configuration_layoutlmva import (
	    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    LayoutLMvaConfig,
	    LayoutLMvaOnnxConfig,
	)
	from .processing_layoutlmva import LayoutLMvaProcessor
	from .tokenization_layoutlmva import LayoutLMvaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_layoutlmva import (
		    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
		    LayoutLMvaForQuestionAnswering,
		    LayoutLMvaForSequenceClassification,
		    LayoutLMvaForTokenClassification,
		    LayoutLMvaModel,
		    LayoutLMvaPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_layoutlmva import (
		    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFLayoutLMvaForQuestionAnswering,
		    TFLayoutLMvaForSequenceClassification,
		    TFLayoutLMvaForTokenClassification,
		    TFLayoutLMvaModel,
		    TFLayoutLMvaPreTrainedModel,
		)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
		from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
	import sys

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








def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):

	# bit count represents no. of bits in the gray code
	if bit_count < 0:
		raise ValueError("The given input must be positive"   )

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

	return sequence



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):

	# The approach is a recursive one
	# Base case achieved when either n = 0 or n=1
	if bit_count == 0:
		return ["0"]

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

	__lowercase		       =			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
	__lowercase		       =			gray_code_sequence_string(bit_count - 1   )

	__lowercase		       =			[]

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

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

	return sequence


if __name__ == "__main__":
	import doctest

	doctest.testmod()



