
"""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
A__ :       Dict				= False
A__ :       Optional[int]				= False




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  return TrainCommand(A__    )







class       __magic_name__							(							a__			):
       @staticmethod
       def 				lowercase_				(  A_							)  ->						str:





         """simple docstring"""




         _lowercase:      Dict   			=  parser.add_parser('''train'''			,					help='''CLI tool to train a model on a task.'''							)

         train_parser.add_argument(
             '''--train_data'''			,					type=__a			,					required=__a			,					help='''path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'''			,					)
         train_parser.add_argument(
             '''--column_label'''			,					type=__a			,					default=0			,					help='''Column of the dataset csv file with example labels.'''							)
         train_parser.add_argument(
             '''--column_text'''			,					type=__a			,					default=1			,					help='''Column of the dataset csv file with example texts.'''							)
         train_parser.add_argument(
             '''--column_id'''			,					type=__a			,					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=__a			,					default=''''''			,					help='''path to validation dataset.'''							)
         train_parser.add_argument(
             '''--validation_split'''			,					type=__a			,					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=__a			,					default='''./'''			,					help='''path to saved the trained model.'''							)

         train_parser.add_argument(
             '''--task'''			,					type=__a			,					default='''text_classification'''			,					help='''Task to train the model on.'''							)
         train_parser.add_argument(
             '''--model'''			,					type=__a			,					default='''bert-base-uncased'''			,					help='''Model\'s name or path to stored model.'''							)
         train_parser.add_argument('''--train_batch_size'''			,					type=__a			,					default=32			,					help='''Batch size for training.'''							)
         train_parser.add_argument('''--valid_batch_size'''			,					type=__a			,					default=64			,					help='''Batch size for validation.'''							)
         train_parser.add_argument('''--learning_rate'''			,					type=__a			,					default=3E-5			,					help='''Learning rate.'''							)
         train_parser.add_argument('''--adam_epsilon'''			,					type=__a			,					default=1E-08			,					help='''Epsilon for Adam optimizer.'''							)
         train_parser.set_defaults(func=__a							)
       def __init__(  self			,					A_							)  ->						Optional[Any]:





         """simple docstring"""




         _lowercase:      Dict   			=  logging.get_logger('''transformers-cli/training'''							)

         _lowercase:      Optional[Any]   			=  """tf""" if is_tf_available() else """torch"""

         os.makedirs(args.output			,					exist_ok=__a							)
         _lowercase:      Optional[Any]   			=  args.output

         _lowercase:      Tuple   			=  args.column_label
         _lowercase:      Tuple   			=  args.column_text
         _lowercase:      Tuple   			=  args.column_id

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

         _lowercase:      Dict   			=  args.validation_split
         _lowercase:      Optional[Any]   			=  args.train_batch_size
         _lowercase:      Union[str, Any]   			=  args.valid_batch_size
         _lowercase:      Union[str, Any]   			=  args.learning_rate
         _lowercase:      List[Any]   			=  args.adam_epsilon
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




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





         """simple docstring"""




         raise NotImplementedError



       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """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 os

import pytest

from transformers.dynamic_module_utils import get_imports


snake_case__				:	List[Any]					       =					'\nimport os\n'

snake_case__				:	List[str]					       =					'\ndef foo():\n    import os\n    return False\n'

snake_case__				:	List[Any]					       =					'\ndef foo():\n    def bar():\n        if True:\n            import os\n        return False\n    return bar()\n'

snake_case__				:	List[str]					       =					'\nimport os\n\ntry:\n    import bar\nexcept ImportError:\n    raise ValueError()\n'

snake_case__				:	Optional[Any]					       =					'\nimport os\n\ndef foo():\n    try:\n        import bar\n    except ImportError:\n        raise ValueError()\n'

snake_case__				:	Tuple					       =					'\nimport os\n\ntry:\n    import bar\nexcept (ImportError, AttributeError):\n    raise ValueError()\n'

snake_case__				:	str					       =					'\nimport os\n\ntry:\n    import bar\nexcept ImportError as e:\n    raise ValueError()\n'

snake_case__				:	Optional[Any]					       =					'\nimport os\n\ntry:\n    import bar\nexcept:\n    raise ValueError()\n'

snake_case__				:	Any					       =					'\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    raise ValueError()\n'

snake_case__				:	Union[str, Any]					       =					'\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    x = 1\n    raise ValueError()\n'

snake_case__				:	Any					       =					[
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]


@pytest.mark.parametrize("""case"""						,						A__					)
def    __lowerCamelCase   (      A__						: Dict						,						A__						: int					)     ->    List[Any]:
   lowerCamelCase_						:   List[str]		= os.path.join(A__						,						"""test_file.py"""					)
   with open(A__						,						"""w"""					) as _tmp_file:
      _tmp_file.write(A__					)

   lowerCamelCase_						:   str		= get_imports(A__					)
   assert parsed_imports == ["os"]





