'''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[Any]			=     False
lowerCAmelCase_	:		int			=     False
def 						__A							(     UpperCAmelCase					)     ->			str:




						'''simple docstring'''
						return TrainCommand(UpperCAmelCase					)






class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





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


										'''simple docstring'''




										_UpperCamelCase			:			List[Any]   			=						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[Any]       ,		lowercase__					:						Namespace					)							->Optional[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			List[str]   			=						logging.get_logger("transformers-cli/training"					)

										_UpperCamelCase			:			str   			=						"tf" if is_tf_available() else "torch"

										os.makedirs(args.output       ,		exist_ok=lowercase__					)
										_UpperCamelCase			:			Any   			=						args.output

										_UpperCamelCase			:			Dict   			=						args.column_label
										_UpperCamelCase			:			Optional[int]   			=						args.column_text
										_UpperCamelCase			:			Any   			=						args.column_id

										self.logger.info(f'''Loading {args.task} pipeline for {args.model}'''					)
										if args.task == "text_classification":
																_UpperCamelCase			:			List[Any]   			=						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			:			Optional[int]   			=						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			:			Union[str, Any]   			=						None
										if args.validation_data:
																self.logger.info(f'''Loading validation dataset from {args.validation_data}'''					)
																_UpperCamelCase			:			List[str]   			=						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			:			Any   			=						args.validation_split
										_UpperCamelCase			:			int   			=						args.train_batch_size
										_UpperCamelCase			:			List[Any]   			=						args.valid_batch_size
										_UpperCamelCase			:			int   			=						args.learning_rate
										_UpperCamelCase			:			str   			=						args.adam_epsilon





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


										'''simple docstring'''




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





				def  snake_case__				(						self					:						Optional[int]					)							->List[Any]:


										'''simple docstring'''




										raise NotImplementedError







				def  snake_case__				(						self					:						Tuple					)							->Optional[int]:


										'''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'''





from __future__ import annotations

from cmath import sqrt
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase					)     ->			tuple[complex, complex]:




						'''simple docstring'''

						if a == 0:
												raise ValueError("Coefficient 'a' must not be zero."					)
						_UpperCamelCase			:			List[str]   			=						b * b - 4 * a * c

						_UpperCamelCase			:			Dict   			=						(-b + sqrt(UpperCAmelCase					)) / (2 * a)
						_UpperCamelCase			:			Optional[int]   			=						(-b - sqrt(UpperCAmelCase					)) / (2 * a)

						return (
						    root_a.real if not root_a.imag else root_a,
						    root_a.real if not root_a.imag else root_a,
						)
def 						__A							(     )     ->			List[Any]:




						'''simple docstring'''
						_UpperCamelCase			,       _UpperCamelCase			:			Any   			=						quadratic_roots(a=5     ,b=6     ,c=1					)
						print(f'''The solutions are: {solutiona} and {solutiona}'''					)


if __name__ == "__main__":
			main()


