


'''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
a__  :					Any        =       False
a__  :					int        =       False







def _UpperCamelCase       (     __A    )							->		Tuple:


      '''simple docstring'''



      return TrainCommand(__SCREAMING_SNAKE_CASE    )



class 					lowercase_     (    __lowerCamelCase   ):


    @staticmethod
    def    __a							(     a    ):
          UpperCamelCase__										=				parser.add_parser("train"  ,					help="CLI tool to train a model on a task."    )

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

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


    def __init__(     self  ,					a    ):
          UpperCamelCase__										=				logging.get_logger("transformers-cli/training"    )

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

          os.makedirs(args.output  ,					exist_ok=a    )
          UpperCamelCase__										=				args.output

          UpperCamelCase__										=				args.column_label
          UpperCamelCase__										=				args.column_text
          UpperCamelCase__										=				args.column_id

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

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


    def    __a							(     self    ):
          if self.framework == "tf":
                return self.run_tf()
          return self.run_torch()


    def    __a							(     self    ):
          raise NotImplementedError


    def    __a							(     self    ):
          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    )







def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	global f  # a global dp table for knapsack
	if f[i][j] < 0:
		if j < wt[i - 1]:
			lowercase       =    mf_knapsack(i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
		else:
			lowercase       =    max(
			    mf_knapsack(i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							) ,							mf_knapsack(i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							j - wt[i - 1]							) + val[i - 1] ,							)
		lowercase       =    val
	return f[i][j]






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    [[0] * (w + 1) for _ in range(n + 1							)]

	for i in range(1 ,							n + 1							):
		for w_ in range(1 ,							w + 1							):
			if wt[i - 1] <= w_:
				lowercase       =    max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] ,							dp[i - 1][w_]							)
			else:
				lowercase       =    dp[i - 1][w_]

	return dp[n][w_], dp






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	if not (isinstance(__SCREAMING_SNAKE_CASE ,							(list, tuple)							) and isinstance(__SCREAMING_SNAKE_CASE ,							(list, tuple)							)):
		raise ValueError(
		    'Both the weights and values vectors must be either lists or tuples'							)

	lowercase       =    len(__SCREAMING_SNAKE_CASE							)
	if num_items != len(__SCREAMING_SNAKE_CASE							):
		lowercase       =    (
		    'The number of weights must be the same as the number of values.\n'
		    F'''But got {num_items} weights and {len(__SCREAMING_SNAKE_CASE							)} values'''
		)
		raise ValueError(__SCREAMING_SNAKE_CASE							)
	for i in range(__SCREAMING_SNAKE_CASE							):
		if not isinstance(wt[i] ,							__SCREAMING_SNAKE_CASE							):
			lowercase       =    (
			    'All weights must be integers but got weight of '
			    F'''type {type(wt[i]							)} at index {i}'''
			)
			raise TypeError(__SCREAMING_SNAKE_CASE							)

	lowercase  ,					lowercase       =    knapsack(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
	lowercase       =    set()
	_construct_solution(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)

	return optimal_val, example_optional_set






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	# for the current item i at a maximum weight j to be part of an optimal subset,
	# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
	# where i - 1 means considering only the previous items at the given maximum weight
	if i > 0 and j > 0:
		if dp[i - 1][j] == dp[i][j]:
			_construct_solution(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
		else:
			optimal_set.add(__SCREAMING_SNAKE_CASE							)
			_construct_solution(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							i - 1 ,							j - wt[i - 1] ,							__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
	UpperCAmelCase	      = [3, 2, 4, 4]
	UpperCAmelCase	      = [4, 3, 2, 3]
	UpperCAmelCase	      = 4
	UpperCAmelCase	      = 6
	UpperCAmelCase	      = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
	UpperCAmelCase      , UpperCAmelCase	      = knapsack(w, wt, val, n)
	print(optimal_solution)
	print(mf_knapsack(n, wt, val, w))  # switched the n and w

	# testing the dynamic programming problem with example
	# the optimal subset for the above example are items 3 and 4
	UpperCAmelCase      , UpperCAmelCase	      = knapsack_with_example_solution(w, wt, val)
	assert optimal_solution == 8
	assert optimal_subset == {3, 4}
	print('''optimal_value = ''', optimal_solution)
	print('''An optimal subset corresponding to the optimal value''', optimal_subset)

