

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(_UpperCamelCase      )


class 				SCREAMING_SNAKE_CASE__ (_UpperCamelCase					):





    @staticmethod
    def 				snake_case_    (				a):
       lowercase__   :       str				=	parser.add_parser('train'						,       help='CLI tool to train a model on a task.')

       train_parser.add_argument(
           '--train_data'						,       type=_SCREAMING_SNAKE_CASE						,       required=_SCREAMING_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=_SCREAMING_SNAKE_CASE						,       default=0						,       help='Column of the dataset csv file with example labels.')
       train_parser.add_argument(
           '--column_text'						,       type=_SCREAMING_SNAKE_CASE						,       default=1						,       help='Column of the dataset csv file with example texts.')
       train_parser.add_argument(
           '--column_id'						,       type=_SCREAMING_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=_SCREAMING_SNAKE_CASE						,       default=''						,       help='path to validation dataset.')
       train_parser.add_argument(
           '--validation_split'						,       type=_SCREAMING_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=_SCREAMING_SNAKE_CASE						,       default='./'						,       help='path to saved the trained model.')

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





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

       lowercase__   :       Dict				=	'tf' if is_tf_available() else 'torch'

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

       lowercase__   :       Optional[int]				=	args.column_label
       lowercase__   :       List[Any]				=	args.column_text
       lowercase__   :       Optional[Any]				=	args.column_id

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

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





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





    def 				snake_case_    (				self):
       raise NotImplementedError





    def 				snake_case_    (				self):
       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)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


snake_case_        =			{'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_        =			['''EncoderDecoderModel''']

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_        =			['''TFEncoderDecoderModel''']

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_        =			['''FlaxEncoderDecoderModel''']

if TYPE_CHECKING:
       from .configuration_encoder_decoder import EncoderDecoderConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_encoder_decoder import EncoderDecoderModel

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
       import sys

       snake_case_        =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

