

'''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__											=	False
lowerCAmelCase__											=	False


def 		_A     (    A__      ):






 """simple docstring"""



 return TrainCommand(A__      )






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  @staticmethod
  def 					SCREAMING_SNAKE_CASE      (      lowercase__   :     ArgumentParser			):
   __lowercase							  = 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=3_2					,help='''Batch size for training.'''			)
   train_parser.add_argument('''--valid_batch_size'''					,type=lowercase__					,default=6_4					,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-0_8					,help='''Epsilon for Adam optimizer.'''			)
   train_parser.set_defaults(func=lowercase__			)






  def __init__(      self   :     int					,lowercase__   :     Namespace			):
   __lowercase							  = logging.get_logger('''transformers-cli/training'''			)

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

   os.makedirs(args.output					,exist_ok=lowercase__			)
   __lowercase							  = args.output

   __lowercase							  = args.column_label
   __lowercase							  = args.column_text
   __lowercase							  = args.column_id

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

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






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






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   raise NotImplementedError






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   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 arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowerCamelCase_     :						Any						=					HfArgumentParser(InitializationArguments)
lowerCamelCase_     :						Union[str, Any]						=					parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowerCamelCase_     :						List[Any]						=					AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowerCamelCase_     :						Tuple						=					{
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
lowerCamelCase_     :						int						=					AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowerCamelCase_     :						Any						=					AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)