





'''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
_SCREAMING_SNAKE_CASE									=				False
_SCREAMING_SNAKE_CASE									=				False


def 						__a(SCREAMING_SNAKE_CASE_	:       Namespace     ):



    '''simple docstring'''





    return TrainCommand(SCREAMING_SNAKE_CASE_     )





class  lowerCAmelCase_		(		__magic_name__		):




 @staticmethod
 def 				_snake_case (						_lowerCAmelCase		)     ->				Any:
     _lowerCAmelCase						      =   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=32      ,  help="Batch size for training."		)
     train_parser.add_argument("--valid_batch_size"      ,  type=_lowerCAmelCase      ,  default=64      ,  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      ,  _lowerCAmelCase		)     ->				Tuple:
     _lowerCAmelCase						      =   logging.get_logger("transformers-cli/training"		)

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

     os.makedirs(args.output      ,  exist_ok=_lowerCAmelCase		)
     _lowerCAmelCase						      =   args.output

     _lowerCAmelCase						      =   args.column_label
     _lowerCAmelCase						      =   args.column_text
     _lowerCAmelCase						      =   args.column_id

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

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




 def 				_snake_case (						self		)     ->				Optional[Any]:
     if self.framework == "tf":
         return self.run_tf()
     return self.run_torch()




 def 				_snake_case (						self		)     ->				Optional[int]:
     raise NotImplementedError





 def 				_snake_case (						self		)     ->				List[str]:
     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		)






import doctest
from collections import deque

import numpy as np
class 						lowerCamelCase_							:



					'''simple docstring'''







					def __init__(					self    ) ->      None:







								'''simple docstring'''

								__lowercase			       = [2, 1, 2, -1]
								__lowercase			       = [1, 2, 3, 4]






					def A   (					self    ) ->      list[float]:







								'''simple docstring'''

								__lowercase			       = len(self.first_signal    )
								__lowercase			       = len(self.second_signal    )

								__lowercase			       = max(snake_case_			,							snake_case_    )

								# create a zero matrix of max_length x max_length
								__lowercase			       = [[0] * max_length for i in range(snake_case_    )]

								# fills the smaller signal with zeros to make both signals of same length
								if length_first_signal < length_second_signal:
											self.first_signal += [0] * (max_length - length_first_signal)
								elif length_first_signal > length_second_signal:
											self.second_signal += [0] * (max_length - length_second_signal)
								for i in range(snake_case_    ):
											__lowercase			       = deque(self.second_signal    )
											rotated_signal.rotate(snake_case_    )
											for j, item in enumerate(snake_case_    ):
														matrix[i][j] += item

        # multiply the matrix with the first signal
								__lowercase			       = np.matmul(np.transpose(snake_case_    )			,							np.transpose(self.first_signal    )    )

								# rounding-off to two decimal places
								return [round(snake_case_			,							2    ) for i in final_signal]


if __name__ == "__main__":
	doctest.testmod()


