


"""simple docstring"""
from math import gcd






def 						lowercase				(	_snake_case		: int = 12_000	)	->int:
	"""simple docstring"""





	__snake_case      :		List[str]         =							0
	for d in range(max_d + 1	):
		for n in range(d // 3 + 1	,  (d + 1) // 2	):
			if gcd(_snake_case	,  _snake_case	) == 1:
				fractions_number += 1
	return fractions_number


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









"""simple docstring"""
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							{
    """configuration_bart""": ["""BART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BartConfig""", """BartOnnxConfig"""],
    """tokenization_bart""": ["""BartTokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       Dict							   =							["""BartTokenizerFast"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       List[Any]							   =							[
			    """BART_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """BartForCausalLM""",
			    """BartForConditionalGeneration""",
			    """BartForQuestionAnswering""",
			    """BartForSequenceClassification""",
			    """BartModel""",
			    """BartPretrainedModel""",
			    """PretrainedBartModel""",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       Any							   =							[
			    """TFBartForConditionalGeneration""",
			    """TFBartForSequenceClassification""",
			    """TFBartModel""",
			    """TFBartPretrainedModel""",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       str							   =							[
			    """FlaxBartDecoderPreTrainedModel""",
			    """FlaxBartForCausalLM""",
			    """FlaxBartForConditionalGeneration""",
			    """FlaxBartForQuestionAnswering""",
			    """FlaxBartForSequenceClassification""",
			    """FlaxBartModel""",
			    """FlaxBartPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
			from .tokenization_bart import BartTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bart_fast import BartTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bart import (
						    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BartForCausalLM,
						    BartForConditionalGeneration,
						    BartForQuestionAnswering,
						    BartForSequenceClassification,
						    BartModel,
						    BartPretrainedModel,
						    PretrainedBartModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_bart import (
						    TFBartForConditionalGeneration,
						    TFBartForSequenceClassification,
						    TFBartModel,
						    TFBartPretrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_bart import (
						    FlaxBartDecoderPreTrainedModel,
						    FlaxBartForCausalLM,
						    FlaxBartForConditionalGeneration,
						    FlaxBartForQuestionAnswering,
						    FlaxBartForSequenceClassification,
						    FlaxBartModel,
						    FlaxBartPreTrainedModel,
						)

else:
			import sys

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





