





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							:			int         =			False
__A							:			int         =			False





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> str:




    """simple docstring"""




    return TrainCommand(_SCREAMING_SNAKE_CASE				)







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     @staticmethod
     def        lowerCAmelCase__		(    snake_case_							):
         _A     =							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				,     snake_case_							):
         _A     =							logging.get_logger('transformers-cli/training'							)

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

         os.makedirs(args.output				,     exist_ok=snake_case_							)
         _A     =							args.output

         _A     =							args.column_label
         _A     =							args.column_text
         _A     =							args.column_id

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

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




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




     def        lowerCAmelCase__		(    self							):
         raise NotImplementedError




     def        lowerCAmelCase__		(    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_tokenizers_available,
    is_torch_available,
)


__A							:			Dict         =			{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			str         =			["BlenderbotTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			str         =			[
     "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "BlenderbotForCausalLM",
     "BlenderbotForConditionalGeneration",
     "BlenderbotModel",
     "BlenderbotPreTrainedModel",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			Union[str, Any]         =			[
     "TFBlenderbotForConditionalGeneration",
     "TFBlenderbotModel",
     "TFBlenderbotPreTrainedModel",
 ]


try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			Optional[Any]         =			[
     "FlaxBlenderbotForConditionalGeneration",
     "FlaxBlenderbotModel",
     "FlaxBlenderbotPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_blenderbot import (
     BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BlenderbotConfig,
     BlenderbotOnnxConfig,
 )
 from .tokenization_blenderbot import BlenderbotTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_blenderbot import (
      BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
      BlenderbotForCausalLM,
      BlenderbotForConditionalGeneration,
      BlenderbotModel,
      BlenderbotPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_blenderbot import (
      TFBlenderbotForConditionalGeneration,
      TFBlenderbotModel,
      TFBlenderbotPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_blenderbot import (
      FlaxBlenderbotForConditionalGeneration,
      FlaxBlenderbotModel,
      FlaxBlenderbotPreTrainedModel,
  )

else:
 import sys

 __A							:			str         =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





