






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__	:    Optional[Any]			 = False
A__	:    List[Any]			 = False

def 			_snake_case    (					lowerCamelCase__     :   Namespace  )    ->     Union[str, Any]:
							return TrainCommand(lowerCamelCase__  )







class        lowercase__      (							snake_case__       ):


			@staticmethod
			def      UpperCAmelCase__						(				snake_case__					:  ArgumentParser ):
										lowerCamelCase_	:							Union[str, Any]									=parser.add_parser("train"       ,	help="CLI tool to train a model on a task." )

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

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


			def __init__(				self					:  Optional[int]       ,	snake_case__					:  Namespace ):
										lowerCamelCase_	:							Tuple									=logging.get_logger("transformers-cli/training" )

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

										os.makedirs(args.output       ,	exist_ok=snake_case__ )
										lowerCamelCase_	:							int									=args.output

										lowerCamelCase_	:							Tuple									=args.column_label
										lowerCamelCase_	:							List[str]									=args.column_text
										lowerCamelCase_	:							Dict									=args.column_id

										self.logger.info(F"""Loading {args.task} pipeline for {args.model}""" )
										if args.task == "text_classification":
																	lowerCamelCase_	:							Tuple									=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_	:							List[str]									=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_	:							List[Any]									=None
										if args.validation_data:
																	self.logger.info(F"""Loading validation dataset from {args.validation_data}""" )
																	lowerCamelCase_	:							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       ,	)

										lowerCamelCase_	:							Dict									=args.validation_split
										lowerCamelCase_	:							str									=args.train_batch_size
										lowerCamelCase_	:							int									=args.valid_batch_size
										lowerCamelCase_	:							Dict									=args.learning_rate
										lowerCamelCase_	:							int									=args.adam_epsilon


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


			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										raise NotImplementedError


			def      UpperCAmelCase__						(				self					:  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 )











"""simple docstring"""



import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows

def 			_snake_case    (					lowerCamelCase__     :   Any  )    ->     Union[str, Any]:  # picklable for multiprocessing
							return i + 1

@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def 			_snake_case    (					)    ->     List[Any]:
							with parallel_backend("spark"  ):
														assert ParallelBackendConfig.backend_name == "spark"

							lowerCamelCase_	:							Optional[Any]									=[1, 2, 3]
							with pytest.raises(lowerCamelCase__  ):
														with parallel_backend("unsupported backend"  ):
																					map_nested(lowerCamelCase__    ,						lowerCamelCase__    ,						num_proc=2  )

							with pytest.raises(lowerCamelCase__  ):
														with parallel_backend("unsupported backend"  ):
																					map_nested(lowerCamelCase__    ,						lowerCamelCase__    ,						num_proc=-1  )

@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("num_proc"    ,						[2, -1]  )
def 			_snake_case    (					lowerCamelCase__     :   Tuple  )    ->     Optional[Any]:
							lowerCamelCase_	:							str									=[1, 2]
							lowerCamelCase_	:							List[str]									={"a": 1, "b": 2}
							lowerCamelCase_	:							List[str]									={"a": [1, 2], "b": [3, 4]}
							lowerCamelCase_	:							Optional[int]									={"a": {"1": 1}, "b": 2}
							lowerCamelCase_	:							int									={"a": 1, "b": 2, "c": 3, "d": 4}
							lowerCamelCase_	:							Optional[int]									=[2, 3]
							lowerCamelCase_	:							List[Any]									={"a": 2, "b": 3}
							lowerCamelCase_	:							int									={"a": [2, 3], "b": [4, 5]}
							lowerCamelCase_	:							str									={"a": {"1": 2}, "b": 3}
							lowerCamelCase_	:							Dict									={"a": 2, "b": 3, "c": 4, "d": 5}

							with parallel_backend("spark"  ):
														assert map_nested(lowerCamelCase__    ,						lowerCamelCase__    ,						num_proc=lowerCamelCase__  ) == expected_map_nested_sa
														assert map_nested(lowerCamelCase__    ,						lowerCamelCase__    ,						num_proc=lowerCamelCase__  ) == expected_map_nested_sa
														assert map_nested(lowerCamelCase__    ,						lowerCamelCase__    ,						num_proc=lowerCamelCase__  ) == expected_map_nested_sa
														assert map_nested(lowerCamelCase__    ,						lowerCamelCase__    ,						num_proc=lowerCamelCase__  ) == expected_map_nested_sa
														assert map_nested(lowerCamelCase__    ,						lowerCamelCase__    ,						num_proc=lowerCamelCase__  ) == expected_map_nested_sa



