



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_   :str        =      False
a_   :Tuple        =      False
def        lowercase_							(A : Namespace      ):
       return TrainCommand(A      )





class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     @staticmethod
     def    lowercase_   (       _snake_case  :  ArgumentParser					)   ->Tuple:
            snake_case__							:	str   	=	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=3_2, help='Batch size for training.'					)
            train_parser.add_argument('--valid_batch_size', type=_snake_case, default=6_4, 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  :  str, _snake_case  :  Namespace					)   ->Union[str, Any]:
            snake_case__							:	Tuple   	=	logging.get_logger('transformers-cli/training'					)

            snake_case__							:	Union[str, Any]   	=	'tf' if is_tf_available() else 'torch'

            os.makedirs(args.output, exist_ok=_snake_case					)
            snake_case__							:	Union[str, Any]   	=	args.output

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

            self.logger.info(F'''Loading {args.task} pipeline for {args.model}'''					)
            if args.task == "text_classification":
                   snake_case__							:	Dict   	=	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}'''					)
            snake_case__							:	int   	=	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, )
            snake_case__							:	List[Any]   	=	None
            if args.validation_data:
                   self.logger.info(F'''Loading validation dataset from {args.validation_data}'''					)
                   snake_case__							:	Union[str, Any]   	=	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, )

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






     def    lowercase_   (       self  :  str					)   ->Dict:
            if self.framework == "tf":
                   return self.run_tf()
            return self.run_torch()






     def    lowercase_   (       self  :  List[str]					)   ->List[str]:
            raise NotImplementedError






     def    lowercase_   (       self  :  Any					)   ->int:
            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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a_   :Optional[Any]        =      {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :str        =      ["ReformerTokenizer"]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :int        =      ["ReformerTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :List[str]        =      [
     "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ReformerAttention",
     "ReformerForMaskedLM",
     "ReformerForQuestionAnswering",
     "ReformerForSequenceClassification",
     "ReformerLayer",
     "ReformerModel",
     "ReformerModelWithLMHead",
     "ReformerPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_reformer import ReformerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_reformer_fast import ReformerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_reformer import (
      REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      ReformerAttention,
      ReformerForMaskedLM,
      ReformerForQuestionAnswering,
      ReformerForSequenceClassification,
      ReformerLayer,
      ReformerModel,
      ReformerModelWithLMHead,
      ReformerPreTrainedModel,
  )

else:
 import sys

 a_   :Optional[Any]        =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






