





'''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
__lowerCAmelCase							:     Optional[int]		     =False
__lowerCAmelCase							:     Tuple		     =False





def 						UpperCamelCase    (							_lowerCamelCase : Namespace	):
    return TrainCommand(_lowerCamelCase	)






class        UpperCAmelCase     (	UpperCamelCase__    ):
 @staticmethod
 def 					UpperCAmelCase_							(    lowercase_				:ArgumentParser							)->							str:
     A__    =      parser.add_parser("train"						,	help="CLI tool to train a model on a task."							)

     train_parser.add_argument(
         "--train_data"						,	type=lowercase_						,	required=lowercase_						,	help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."						,	)
     train_parser.add_argument(
         "--column_label"						,	type=lowercase_						,	default=0						,	help="Column of the dataset csv file with example labels."							)
     train_parser.add_argument(
         "--column_text"						,	type=lowercase_						,	default=1						,	help="Column of the dataset csv file with example texts."							)
     train_parser.add_argument(
         "--column_id"						,	type=lowercase_						,	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=lowercase_						,	default=""						,	help="path to validation dataset."							)
     train_parser.add_argument(
         "--validation_split"						,	type=lowercase_						,	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=lowercase_						,	default="./"						,	help="path to saved the trained model."							)

     train_parser.add_argument(
         "--task"						,	type=lowercase_						,	default="text_classification"						,	help="Task to train the model on."							)
     train_parser.add_argument(
         "--model"						,	type=lowercase_						,	default="bert-base-uncased"						,	help="Model's name or path to stored model."							)
     train_parser.add_argument("--train_batch_size"						,	type=lowercase_						,	default=32						,	help="Batch size for training."							)
     train_parser.add_argument("--valid_batch_size"						,	type=lowercase_						,	default=64						,	help="Batch size for validation."							)
     train_parser.add_argument("--learning_rate"						,	type=lowercase_						,	default=3E-5						,	help="Learning rate."							)
     train_parser.add_argument("--adam_epsilon"						,	type=lowercase_						,	default=1E-08						,	help="Epsilon for Adam optimizer."							)
     train_parser.set_defaults(func=lowercase_							)
 def __init__(    self				:List[str]						,	lowercase_				:Namespace							)->							Dict:
     A__    =      logging.get_logger("transformers-cli/training"							)

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

     os.makedirs(args.output						,	exist_ok=lowercase_							)
     A__    =      args.output

     A__    =      args.column_label
     A__    =      args.column_text
     A__    =      args.column_id

     self.logger.info(F"Loading {args.task} pipeline for {args.model}"							)
     if args.task == "text_classification":
         A__    =      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}"							)
     A__    =      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						,	)
     A__    =      None
     if args.validation_data:
         self.logger.info(F"Loading validation dataset from {args.validation_data}"							)
         A__    =      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						,	)

     A__    =      args.validation_split
     A__    =      args.train_batch_size
     A__    =      args.valid_batch_size
     A__    =      args.learning_rate
     A__    =      args.adam_epsilon
 def 					UpperCAmelCase_							(    self				:Dict							)->							Union[str, Any]:
     if self.framework == "tf":
         return self.run_tf()
     return self.run_torch()
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Any:
     raise NotImplementedError



 def 					UpperCAmelCase_							(    self				:int							)->							List[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 __future__ import annotations

from math import gcd





def 						UpperCamelCase    (							_lowerCamelCase : int						,	_lowerCamelCase : int = 2						,	_lowerCamelCase : int = 1						,	_lowerCamelCase : int = 3						,	):
    # A value less than 2 can cause an infinite loop in the algorithm.
    if num < 2:
        raise ValueError("The input value cannot be less than 2"	)

    # Because of the relationship between ``f(f(x))`` and ``f(x)``, this
    # algorithm struggles to find factors that are divisible by two.
    # As a workaround, we specifically check for two and even inputs.
    #   See: https://math.stackexchange.com/a/2856214/165820
    if num > 2 and num % 2 == 0:
        return 2

    # Pollard's Rho algorithm requires a function that returns pseudorandom
    # values between 0 <= X < ``num``.  It doesn't need to be random in the
    # sense that the output value is cryptographically secure or difficult
    # to calculate, it only needs to be random in the sense that all output
    # values should be equally likely to appear.
    # For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
    # However, the success of Pollard's algorithm isn't guaranteed and is
    # determined in part by the initial seed and the chosen random function.
    # To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
    # where ``C`` is a value that we can modify between each attempt.
    def rand_fn(_lowerCamelCase : int						,	_lowerCamelCase : int						,	_lowerCamelCase : int	) -> int:
        return (pow(_lowerCamelCase						,	2	) + step) % modulus

    for _ in range(_lowerCamelCase	):
        # These track the position within the cycle detection logic.
        A__    =      seed
        A__    =      seed

        while True:
            # At each iteration, the tortoise moves one step and the hare moves two.
            A__    =      rand_fn(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
            A__    =      rand_fn(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
            A__    =      rand_fn(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)

            # At some point both the tortoise and the hare will enter a cycle whose
            # length ``p`` is a divisor of ``num``.  Once in that cycle, at some point
            # the tortoise and hare will end up on the same value modulo ``p``.
            # We can detect when this happens because the position difference between
            # the tortoise and the hare will share a common divisor with ``num``.
            A__    =      gcd(hare - tortoise						,	_lowerCamelCase	)

            if divisor == 1:
                # No common divisor yet, just keep searching.
                continue
            else:
                # We found a common divisor!
                if divisor == num:
                    # Unfortunately, the divisor is ``num`` itself and is useless.
                    break
                else:
                    # The divisor is a nontrivial factor of ``num``!
                    return divisor

        # If we made it here, then this attempt failed.
        # We need to pick a new starting seed for the tortoise and hare
        # in addition to a new step value for the random function.
        # To keep this example implementation deterministic, the
        # new values will be generated based on currently available
        # values instead of using something like ``random.randint``.

        # We can use the hare's position as the new seed.
        # This is actually what Richard Brent's the "optimized" variant does.
        A__    =      hare

        # The new step value for the random function can just be incremented.
        # At first the results will be similar to what the old function would
        # have produced, but the value will quickly diverge after a bit.
        step += 1

    # We haven't found a divisor within the requested number of attempts.
    # We were unlucky or ``num`` itself is actually prime.
    return None


if __name__ == "__main__":
  import argparse

  __lowerCAmelCase							:     Optional[int]		     =argparse.ArgumentParser()
  parser.add_argument(
      "num",
      type=int,
      help="The value to find a divisor of",
  )
  parser.add_argument(
      "--attempts",
      type=int,
      default=3,
      help="The number of attempts before giving up",
  )
  __lowerCAmelCase							:     Optional[int]		     =parser.parse_args()

  __lowerCAmelCase							:     Dict		     =pollard_rho(args.num, attempts=args.attempts)
  if divisor is None:
    print(f"""{args.num} is probably prime""")
  else:
    __lowerCAmelCase							:     Optional[Any]		     =args.num // divisor
    print(f"""{args.num} = {divisor} * {quotient}""")



