




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
UpperCAmelCase_		:				Tuple									=     False
UpperCAmelCase_		:				Any									=     False




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				return TrainCommand(lowerCamelCase   )




class 		__A      (			UpperCamelCase__			):

						@staticmethod
						def     A__						(		__snake_case   :ArgumentParser ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=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   :List[str]    , __snake_case   :Namespace ):
										'''simple docstring'''

										__magic_name__	:	Dict								=logging.get_logger("""transformers-cli/training""" )

										__magic_name__	:	Any								="""tf""" if is_tf_available() else """torch"""

										os.makedirs(args.output    , exist_ok=__snake_case )
										__magic_name__	:	Any								=args.output

										__magic_name__	:	Optional[Any]								=args.column_label
										__magic_name__	:	List[Any]								=args.column_text
										__magic_name__	:	Tuple								=args.column_id

										self.logger.info(f"Loading {args.task} pipeline for {args.model}" )
										if args.task == "text_classification":
														__magic_name__	:	Optional[int]								=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}" )
										__magic_name__	:	Dict								=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    , )
										__magic_name__	:	int								=None
										if args.validation_data:
														self.logger.info(f"Loading validation dataset from {args.validation_data}" )
														__magic_name__	:	int								=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    , )

										__magic_name__	:	List[str]								=args.validation_split
										__magic_name__	:	Union[str, Any]								=args.train_batch_size
										__magic_name__	:	str								=args.valid_batch_size
										__magic_name__	:	Optional[Any]								=args.learning_rate
										__magic_name__	:	List[Any]								=args.adam_epsilon

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

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

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										raise NotImplementedError


						def     A__						(		self   :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 )










import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version




def 		lowerCAmelCase_   (	*lowerCamelCase	,     lowerCamelCase = None	,     lowerCamelCase=True	,     lowerCamelCase=2   ):
				from .. import __version__

				__magic_name__	:	Optional[int]								=take_from
				__magic_name__	:	Tuple								=()
				if not isinstance(args[0]	,     lowerCamelCase   ):
								__magic_name__	:	List[Any]								=(args,)

				for attribute, version_name, message in args:
								if version.parse(version.parse(lowerCamelCase   ).base_version   ) >= version.parse(lowerCamelCase   ):
												raise ValueError(
												    F"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
												    F" version {__version__} is >= {version_name}"   )

								__magic_name__	:	List[str]								=None
								if isinstance(lowerCamelCase	,     lowerCamelCase   ) and attribute in deprecated_kwargs:
												values += (deprecated_kwargs.pop(lowerCamelCase   ),)
												__magic_name__	:	List[str]								=F"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
								elif hasattr(lowerCamelCase	,     lowerCamelCase   ):
												values += (getattr(lowerCamelCase	,     lowerCamelCase   ),)
												__magic_name__	:	List[str]								=F"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
								elif deprecated_kwargs is None:
												__magic_name__	:	List[str]								=F"`{attribute}` is deprecated and will be removed in version {version_name}."

								if warning is not None:
												__magic_name__	:	List[Any]								=warning + """ """ if standard_warn else """"""
												warnings.warn(warning + message	,     lowerCamelCase	,     stacklevel=lowerCamelCase   )

				if isinstance(lowerCamelCase	,     lowerCamelCase   ) and len(lowerCamelCase   ) > 0:
								__magic_name__	:	List[Any]								=inspect.getouterframes(inspect.currentframe()   )[1]
								__magic_name__	:	Optional[int]								=call_frame.filename
								__magic_name__	:	Tuple								=call_frame.lineno
								__magic_name__	:	str								=call_frame.function
								__magic_name__				, __magic_name__	:	Optional[int]								=next(iter(deprecated_kwargs.items()   )   )
								raise TypeError(F"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"   )

				if len(lowerCamelCase   ) == 0:
								return
				elif len(lowerCamelCase   ) == 1:
								return values[0]
				return values




