
"""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
_snake_case          =						False
_snake_case          =						False






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Namespace						):






							"""simple docstring"""



							return TrainCommand(SCREAMING_SNAKE_CASE						)

class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				@staticmethod
				def 				__lowerCamelCase	(					UpperCAmelCase_	:			ArgumentParser  )		->  int:







											"""simple docstring"""




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

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

											train_parser.add_argument(
											    '--task'				,     type=UpperCAmelCase_				,     default='text_classification'				,     help='Task to train the model on.'  )
											train_parser.add_argument(
											    '--model'				,     type=UpperCAmelCase_				,     default='bert-base-uncased'				,     help='Model\'s name or path to stored model.'  )
											train_parser.add_argument('--train_batch_size'				,     type=UpperCAmelCase_				,     default=32				,     help='Batch size for training.'  )
											train_parser.add_argument('--valid_batch_size'				,     type=UpperCAmelCase_				,     default=64				,     help='Batch size for validation.'  )
											train_parser.add_argument('--learning_rate'				,     type=UpperCAmelCase_				,     default=3E-5				,     help='Learning rate.'  )
											train_parser.add_argument('--adam_epsilon'				,     type=UpperCAmelCase_				,     default=1E-0_8				,     help='Epsilon for Adam optimizer.'  )
											train_parser.set_defaults(func=UpperCAmelCase_  )


				def __init__(					self	:			Tuple				,     UpperCAmelCase_	:			Namespace  )		->  Dict:







											"""simple docstring"""




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

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

											os.makedirs(args.output				,     exist_ok=UpperCAmelCase_  )
											_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 				__lowerCamelCase	(					self	:			List[Any]  )		->  str:







											"""simple docstring"""




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


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







											"""simple docstring"""




											raise NotImplementedError







				def 				__lowerCamelCase	(					self	:			List[str]  )		->  Optional[Any]:







											"""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  )








"""simple docstring"""



def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							_lowerCAmelCase  	=			generate_pascal_triangle(SCREAMING_SNAKE_CASE						)
							for row_idx in range(SCREAMING_SNAKE_CASE						):
														# Print left spaces
														for _ in range(num_rows - row_idx - 1						):
																					print(end=' '						)
														# Print row values
														for col_idx in range(row_idx + 1						):
																					if col_idx != row_idx:
																												print(triangle[row_idx][col_idx]     ,  end=' '						)
																					else:
																												print(triangle[row_idx][col_idx]     ,  end=''						)
														print()






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							if not isinstance(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						):
														raise TypeError('The input value of \'num_rows\' should be \'int\''						)

							if num_rows == 0:
														return []
							elif num_rows < 0:
														raise ValueError(
														    'The input value of \'num_rows\' should be greater than or equal to 0'						)

							_lowerCAmelCase  	=			[]
							for current_row_idx in range(SCREAMING_SNAKE_CASE						):
														_lowerCAmelCase  	=			populate_current_row(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
														triangle.append(SCREAMING_SNAKE_CASE						)
							return triangle






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					list[list[int]]     ,  SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[-1] * (current_row_idx + 1)
							# first and last elements of current row are equal to 1
							_lowerCAmelCase	,		_lowerCAmelCase  	=			1, 1
							for current_col_idx in range(1     ,  SCREAMING_SNAKE_CASE						):
														calculate_current_element(
														    SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
							return current_row






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					list[list[int]]     ,  SCREAMING_SNAKE_CASE:					list[int]     ,  SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					int     ,  ):






							"""simple docstring"""



							_lowerCAmelCase  	=			triangle[current_row_idx - 1][current_col_idx - 1]
							_lowerCAmelCase  	=			triangle[current_row_idx - 1][current_col_idx]
							_lowerCAmelCase  	=			above_to_left_elt + above_to_right_elt






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							if not isinstance(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						):
														raise TypeError('The input value of \'num_rows\' should be \'int\''						)

							if num_rows == 0:
														return []
							elif num_rows < 0:
														raise ValueError(
														    'The input value of \'num_rows\' should be greater than or equal to 0'						)

							_lowerCAmelCase  	=			[[1]]

							for row_index in range(1     ,  SCREAMING_SNAKE_CASE						):
														_lowerCAmelCase  	=			[0] + result[-1] + [0]
														_lowerCAmelCase  	=			row_index + 1
														# Calculate the number of distinct elements in a row
														_lowerCAmelCase  	=			sum(divmod(SCREAMING_SNAKE_CASE     ,  2						)						)
														_lowerCAmelCase  	=			[
														    temp_row[i - 1] + temp_row[i] for i in range(1     ,  distinct_elements + 1						)
														]
														_lowerCAmelCase  	=			row_first_half[: (row_index + 1) // 2]
														row_second_half.reverse()
														_lowerCAmelCase  	=			row_first_half + row_second_half
														result.append(SCREAMING_SNAKE_CASE						)

							return result






def 	__snake_case  (    ):






							"""simple docstring"""



							from collections.abc import Callable
							from timeit import timeit

							def benchmark_a_function(SCREAMING_SNAKE_CASE:					Callable     ,  SCREAMING_SNAKE_CASE:					int						) -> None:
														_lowerCAmelCase  	=			f"""{func.__name__}({value})"""
														_lowerCAmelCase  	=			timeit(f"""__main__.{call}"""     ,  setup='import __main__'						)
														# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
														print(f"""{call:38} -- {timing:.4f} seconds"""						)

							for value in range(15						):  # (1, 7, 14):
														for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
																					benchmark_a_function(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
														print()


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()






