






'''simple docstring'''



from typing import TYPE_CHECKING

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


lowerCamelCase	 =       {
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase	 =       ["""RoFormerTokenizerFast"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase	 =       [
		    """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """RoFormerForCausalLM""",
		    """RoFormerForMaskedLM""",
		    """RoFormerForMultipleChoice""",
		    """RoFormerForQuestionAnswering""",
		    """RoFormerForSequenceClassification""",
		    """RoFormerForTokenClassification""",
		    """RoFormerLayer""",
		    """RoFormerModel""",
		    """RoFormerPreTrainedModel""",
		    """load_tf_weights_in_roformer""",
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase	 =       [
		    """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFRoFormerForCausalLM""",
		    """TFRoFormerForMaskedLM""",
		    """TFRoFormerForMultipleChoice""",
		    """TFRoFormerForQuestionAnswering""",
		    """TFRoFormerForSequenceClassification""",
		    """TFRoFormerForTokenClassification""",
		    """TFRoFormerLayer""",
		    """TFRoFormerModel""",
		    """TFRoFormerPreTrainedModel""",
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase	 =       [
		    """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """FlaxRoFormerForMaskedLM""",
		    """FlaxRoFormerForMultipleChoice""",
		    """FlaxRoFormerForQuestionAnswering""",
		    """FlaxRoFormerForSequenceClassification""",
		    """FlaxRoFormerForTokenClassification""",
		    """FlaxRoFormerModel""",
		    """FlaxRoFormerPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
		from .tokenization_roformer import RoFormerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_roformer_fast import RoFormerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roformer import (
				    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoFormerForCausalLM,
				    RoFormerForMaskedLM,
				    RoFormerForMultipleChoice,
				    RoFormerForQuestionAnswering,
				    RoFormerForSequenceClassification,
				    RoFormerForTokenClassification,
				    RoFormerLayer,
				    RoFormerModel,
				    RoFormerPreTrainedModel,
				    load_tf_weights_in_roformer,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roformer import (
				    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRoFormerForCausalLM,
				    TFRoFormerForMaskedLM,
				    TFRoFormerForMultipleChoice,
				    TFRoFormerForQuestionAnswering,
				    TFRoFormerForSequenceClassification,
				    TFRoFormerForTokenClassification,
				    TFRoFormerLayer,
				    TFRoFormerModel,
				    TFRoFormerPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roformer import (
				    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FlaxRoFormerForMaskedLM,
				    FlaxRoFormerForMultipleChoice,
				    FlaxRoFormerForQuestionAnswering,
				    FlaxRoFormerForSequenceClassification,
				    FlaxRoFormerForTokenClassification,
				    FlaxRoFormerModel,
				    FlaxRoFormerPreTrainedModel,
				)


else:
		import sys

		lowerCamelCase	 =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








'''simple docstring'''



from decimal import Decimal, getcontext
from math import ceil, factorial



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							if not isinstance(_lowerCAmelCase  ,   _lowerCAmelCase					):
														raise TypeError('Undefined for non-integers'					)
							elif precision < 1:
														raise ValueError('Undefined for non-natural numbers'					)

							__lowercase       	=precision
							__lowercase       	=ceil(precision / 14					)
							__lowercase       	=426_880 * Decimal(10_005					).sqrt()
							__lowercase       	=1
							__lowercase       	=13_591_409
							__lowercase       	=Decimal(_lowerCAmelCase					)
							for k in range(1  ,   _lowerCAmelCase					):
														__lowercase       	=factorial(6 * k					) // (factorial(3 * k					) * factorial(_lowerCAmelCase					) ** 3)
														linear_term += 545_140_134
														exponential_term *= -262_537_412_640_768_000
														partial_sum += Decimal(multinomial_term * linear_term					) / exponential_term
							return str(constant_term / partial_sum					)[:-1]


if __name__ == "__main__":
		lowerCamelCase	 =       50
		print(f"The first {n} digits of pi is: {pi(n)}")



