




'''simple docstring'''





import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
			raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')

# TF training parameters
UpperCamelCase_						:      Dict       =						False
UpperCamelCase_						:      Optional[int]       =						False





def      __a     (       _UpperCamelCase:   Namespace   )						->			Optional[Any]:

					"""simple docstring"""




					return TrainCommand(_UpperCamelCase   )





class        _a    (						_UpperCamelCase							):






					@staticmethod
					def     _lowercase     (			_SCREAMING_SNAKE_CASE   )							->      Dict:
										_snake_case							=				parser.add_parser("train" ,help="CLI tool to train a model on a task."   )

										train_parser.add_argument(
										    "--train_data" ,type=_SCREAMING_SNAKE_CASE ,required=_SCREAMING_SNAKE_CASE ,help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences." ,)
										train_parser.add_argument(
										    "--column_label" ,type=_SCREAMING_SNAKE_CASE ,default=0 ,help="Column of the dataset csv file with example labels."   )
										train_parser.add_argument(
										    "--column_text" ,type=_SCREAMING_SNAKE_CASE ,default=1 ,help="Column of the dataset csv file with example texts."   )
										train_parser.add_argument(
										    "--column_id" ,type=_SCREAMING_SNAKE_CASE ,default=2 ,help="Column of the dataset csv file with example ids."   )
										train_parser.add_argument(
										    "--skip_first_row" ,action="store_true" ,help="Skip the first row of the csv file (headers)."   )

										train_parser.add_argument("--validation_data" ,type=_SCREAMING_SNAKE_CASE ,default="" ,help="path to validation dataset."   )
										train_parser.add_argument(
										    "--validation_split" ,type=_SCREAMING_SNAKE_CASE ,default=0.1 ,help="if validation dataset is not provided, fraction of train dataset to use as validation dataset." ,)

										train_parser.add_argument("--output" ,type=_SCREAMING_SNAKE_CASE ,default="./" ,help="path to saved the trained model."   )

										train_parser.add_argument(
										    "--task" ,type=_SCREAMING_SNAKE_CASE ,default="text_classification" ,help="Task to train the model on."   )
										train_parser.add_argument(
										    "--model" ,type=_SCREAMING_SNAKE_CASE ,default="bert-base-uncased" ,help="Model\'s name or path to stored model."   )
										train_parser.add_argument("--train_batch_size" ,type=_SCREAMING_SNAKE_CASE ,default=32 ,help="Batch size for training."   )
										train_parser.add_argument("--valid_batch_size" ,type=_SCREAMING_SNAKE_CASE ,default=64 ,help="Batch size for validation."   )
										train_parser.add_argument("--learning_rate" ,type=_SCREAMING_SNAKE_CASE ,default=3e-5 ,help="Learning rate."   )
										train_parser.add_argument("--adam_epsilon" ,type=_SCREAMING_SNAKE_CASE ,default=1e-08 ,help="Epsilon for Adam optimizer."   )
										train_parser.set_defaults(func=_SCREAMING_SNAKE_CASE   )






					def __init__(			self ,_SCREAMING_SNAKE_CASE   )							->      Optional[int]:
										_snake_case							=				logging.get_logger("transformers-cli/training"   )

										_snake_case							=				"tf" if is_tf_available() else "torch"

										os.makedirs(args.output ,exist_ok=_SCREAMING_SNAKE_CASE   )
										_snake_case							=				args.output

										_snake_case							=				args.column_label
										_snake_case							=				args.column_text
										_snake_case							=				args.column_id

										self.logger.info(f"""Loading {args.task} pipeline for {args.model}"""   )
										if args.task == "text_classification":
															_snake_case							=				TextClassificationPipeline.from_pretrained(args.model   )
										elif args.task == "token_classification":
															raise NotImplementedError
										elif args.task == "question_answering":
															raise NotImplementedError

										self.logger.info(f"""Loading dataset from {args.train_data}"""   )
										_snake_case							=				Processor.create_from_csv(
										    args.train_data ,column_label=args.column_label ,column_text=args.column_text ,column_id=args.column_id ,skip_first_row=args.skip_first_row ,)
										_snake_case							=				None
										if args.validation_data:
															self.logger.info(f"""Loading validation dataset from {args.validation_data}"""   )
															_snake_case							=				Processor.create_from_csv(
															    args.validation_data ,column_label=args.column_label ,column_text=args.column_text ,column_id=args.column_id ,skip_first_row=args.skip_first_row ,)

										_snake_case							=				args.validation_split
										_snake_case							=				args.train_batch_size
										_snake_case							=				args.valid_batch_size
										_snake_case							=				args.learning_rate
										_snake_case							=				args.adam_epsilon






					def     _lowercase     (			self   )							->      List[str]:
										if self.framework == "tf":
															return self.run_tf()
										return self.run_torch()






					def     _lowercase     (			self   )							->      Union[str, Any]:
										raise NotImplementedError






					def     _lowercase     (			self   )							->      Union[str, Any]:
										self.pipeline.fit(
										    self.train_dataset ,validation_data=self.valid_dataset ,validation_split=self.validation_split ,learning_rate=self.learning_rate ,adam_epsilon=self.adam_epsilon ,train_batch_size=self.train_batch_size ,valid_batch_size=self.valid_batch_size ,)

										# Save trained pipeline
										self.pipeline.save_pretrained(self.output   )








'''simple docstring'''





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





def      __a     (       _UpperCamelCase:   int   )						->			str:

     """simple docstring"""




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

     _snake_case							=				precision
     _snake_case							=				ceil(precision / 14   )
     _snake_case							=				426_880 * Decimal(10_005   ).sqrt()
     _snake_case							=				1
     _snake_case							=				13_591_409
     _snake_case							=				Decimal(_UpperCamelCase   )
     for k in range(1 ,						_UpperCamelCase   ):
          _snake_case							=				factorial(6 * k   ) // (factorial(3 * k   ) * factorial(_UpperCamelCase   ) ** 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__":
   UpperCamelCase_						:      Any       =						50
   print(F'The first {n} digits of pi is: {pi(n)}')


