

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
lowercase_			=  False
lowercase_			=  False
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Union[str, Any]				)							->				Optional[Any]:





    '''simple docstring'''






    return TrainCommand(_lowerCAmelCase				)


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    @staticmethod
    def 						snake_case__					(				lowercase_			:	ArgumentParser		)->   str:





        '''simple docstring'''







        A__   				=       parser.add_parser('train',help='CLI tool to train a model on a task.'		)

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

        train_parser.add_argument(
            '--task',type=_lowerCAmelCase,default='text_classification',help='Task to train the model on.'		)
        train_parser.add_argument(
            '--model',type=_lowerCAmelCase,default='bert-base-uncased',help='Model\'s name or path to stored model.'		)
        train_parser.add_argument('--train_batch_size',type=_lowerCAmelCase,default=3_2,help='Batch size for training.'		)
        train_parser.add_argument('--valid_batch_size',type=_lowerCAmelCase,default=6_4,help='Batch size for validation.'		)
        train_parser.add_argument('--learning_rate',type=_lowerCAmelCase,default=3E-5,help='Learning rate.'		)
        train_parser.add_argument('--adam_epsilon',type=_lowerCAmelCase,default=1E-08,help='Epsilon for Adam optimizer.'		)
        train_parser.set_defaults(func=_lowerCAmelCase		)


    def __init__(				self			:	Dict,lowercase_			:	Namespace		)->   int:





        '''simple docstring'''







        A__   				=       logging.get_logger('transformers-cli/training'		)

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

        os.makedirs(args.output,exist_ok=_lowerCAmelCase		)
        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 						snake_case__					(				self			:	Optional[int]		)->   Union[str, Any]:





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	Optional[int]		)->   Union[str, Any]:





        '''simple docstring'''







        raise NotImplementedError


    def 						snake_case__					(				self			:	Union[str, Any]		)->   Dict:





        '''simple docstring'''







        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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






    def update_area_of_max_square(SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int				) -> int:
        # BASE CASE
        if row >= rows or col >= cols:
            return 0

        A__   				=       update_area_of_max_square(SCREAMING_SNAKE_CASE__   ,      col + 1				)
        A__   				=       update_area_of_max_square(row + 1   ,      col + 1				)
        A__   				=       update_area_of_max_square(row + 1   ,      SCREAMING_SNAKE_CASE__				)

        if mat[row][col]:
            A__   				=       1 + min([right, diagonal, down]				)
            A__   				=       max(largest_square_area[0]   ,      SCREAMING_SNAKE_CASE__				)
            return sub_problem_sol
        else:
            return 0

    A__   				=       [0]
    update_area_of_max_square(0   ,      0				)
    return largest_square_area[0]
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






    def update_area_of_max_square_using_dp_array(
        SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				) -> int:
        if row >= rows or col >= cols:
            return 0
        if dp_array[row][col] != -1:
            return dp_array[row][col]

        A__   				=       update_area_of_max_square_using_dp_array(SCREAMING_SNAKE_CASE__   ,      col + 1   ,      SCREAMING_SNAKE_CASE__				)
        A__   				=       update_area_of_max_square_using_dp_array(row + 1   ,      col + 1   ,      SCREAMING_SNAKE_CASE__				)
        A__   				=       update_area_of_max_square_using_dp_array(row + 1   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)

        if mat[row][col]:
            A__   				=       1 + min([right, diagonal, down]				)
            A__   				=       max(largest_square_area[0]   ,      SCREAMING_SNAKE_CASE__				)
            A__   				=       sub_problem_sol
            return sub_problem_sol
        else:
            return 0

    A__   				=       [0]
    A__   				=       [[-1] * cols for _ in range(SCREAMING_SNAKE_CASE__				)]
    update_area_of_max_square_using_dp_array(0   ,      0   ,      SCREAMING_SNAKE_CASE__				)

    return largest_square_area[0]
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






    A__   				=       [[0] * (cols + 1) for _ in range(rows + 1				)]
    A__   				=       0
    for row in range(rows - 1   ,      -1   ,      -1				):
        for col in range(cols - 1   ,      -1   ,      -1				):
            A__   				=       dp_array[row][col + 1]
            A__   				=       dp_array[row + 1][col + 1]
            A__   				=       dp_array[row + 1][col]

            if mat[row][col] == 1:
                A__   				=       1 + min(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
                A__   				=       max(dp_array[row][col]   ,      SCREAMING_SNAKE_CASE__				)
            else:
                A__   				=       0

    return largest_square_area
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






    A__   				=       [0] * (cols + 1)
    A__   				=       [0] * (cols + 1)
    A__   				=       0
    for row in range(rows - 1   ,      -1   ,      -1				):
        for col in range(cols - 1   ,      -1   ,      -1				):
            A__   				=       current_row[col + 1]
            A__   				=       next_row[col + 1]
            A__   				=       next_row[col]

            if mat[row][col] == 1:
                A__   				=       1 + min(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
                A__   				=       max(current_row[col]   ,      SCREAMING_SNAKE_CASE__				)
            else:
                A__   				=       0
        A__   				=       current_row

    return largest_square_area


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))

