






from typing import TYPE_CHECKING

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


A__			:    Dict         =				{
    'configuration_longformer': [
        'LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LongformerConfig',
        'LongformerOnnxConfig',
    ],
    'tokenization_longformer': ['LongformerTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A__			:    List[str]         =				['LongformerTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A__			:    Optional[Any]         =				[
		    'LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'LongformerForMaskedLM',
		    'LongformerForMultipleChoice',
		    'LongformerForQuestionAnswering',
		    'LongformerForSequenceClassification',
		    'LongformerForTokenClassification',
		    'LongformerModel',
		    'LongformerPreTrainedModel',
		    'LongformerSelfAttention',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A__			:    int         =				[
		    'TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFLongformerForMaskedLM',
		    'TFLongformerForMultipleChoice',
		    'TFLongformerForQuestionAnswering',
		    'TFLongformerForSequenceClassification',
		    'TFLongformerForTokenClassification',
		    'TFLongformerModel',
		    'TFLongformerPreTrainedModel',
		    'TFLongformerSelfAttention',
		]


if TYPE_CHECKING:
		from .configuration_longformer import (
		    LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    LongformerConfig,
		    LongformerOnnxConfig,
		)
		from .tokenization_longformer import LongformerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_longformer_fast import LongformerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_longformer import (
				    LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LongformerForMaskedLM,
				    LongformerForMultipleChoice,
				    LongformerForQuestionAnswering,
				    LongformerForSequenceClassification,
				    LongformerForTokenClassification,
				    LongformerModel,
				    LongformerPreTrainedModel,
				    LongformerSelfAttention,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_longformer import (
				    TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFLongformerForMaskedLM,
				    TFLongformerForMultipleChoice,
				    TFLongformerForQuestionAnswering,
				    TFLongformerForSequenceClassification,
				    TFLongformerForTokenClassification,
				    TFLongformerModel,
				    TFLongformerPreTrainedModel,
				    TFLongformerSelfAttention,
				)

else:
		import sys

		A__			:    List[str]         =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)












from itertools import count






def 						a    (		lowerCamelCase_ = 50       ):






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

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

								for block_length in range(lowerCamelCase_	,     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] > 100_0000:
												break

				return n


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




