



from typing import TYPE_CHECKING

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


_snake_case				=					{
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_snake_case				=					['LayoutLMv2TokenizerFast']

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_snake_case				=					['LayoutLMv2FeatureExtractor']
			_snake_case				=					['LayoutLMv2ImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_snake_case				=					[
			    'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'LayoutLMv2ForQuestionAnswering',
			    'LayoutLMv2ForSequenceClassification',
			    'LayoutLMv2ForTokenClassification',
			    'LayoutLMv2Layer',
			    'LayoutLMv2Model',
			    'LayoutLMv2PreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
			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_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_layoutlmva import (
						    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LayoutLMvaForQuestionAnswering,
						    LayoutLMvaForSequenceClassification,
						    LayoutLMvaForTokenClassification,
						    LayoutLMvaLayer,
						    LayoutLMvaModel,
						    LayoutLMvaPreTrainedModel,
						)
else:
			import sys

			_snake_case				=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






from itertools import count





def a(lowercase__ = 50						):





	'''simple docstring'''
	snake_case_   		=			[1] * min_block_length

	for n in count(lowercase__						):
		fill_count_functions.append(1						)

		for block_length in range(lowercase__      ,			n + 1						):
			for block_start in range(n - block_length						):
				fill_count_functions[n] += fill_count_functions[
				    n - block_start - block_length - 1
				]

			fill_count_functions[n] += 1

		if fill_count_functions[n] > 1000000:
			break

	return n


if __name__ == "__main__":
		print(f"""{solution() = }""")
