






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 	UpperCamelCase	(							snake_case__  : Namespace	):




     '''simple docstring'''






     return TrainCommand(snake_case__	)




class       snake_case__ (				lowercase_):

       '''simple docstring'''



       @staticmethod
       def        __lowercase    (				a__     )			->   int:






            '''simple docstring'''





            __snake_case      :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__     )			->   Dict:






            '''simple docstring'''





            __snake_case      :int 							=    logging.get_logger("""transformers-cli/training"""     )

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

            os.makedirs(args.output      ,    exist_ok=a__     )
            __snake_case      :List[Any] 							=    args.output

            __snake_case      :Dict 							=    args.column_label
            __snake_case      :Optional[Any] 							=    args.column_text
            __snake_case      :Optional[int] 							=    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      :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      ,    )
            __snake_case      :Dict 							=    None
            if args.validation_data:
                 self.logger.info(F'''Loading validation dataset from {args.validation_data}'''     )
                 __snake_case      :Tuple 							=    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      :Any 							=    args.validation_split
            __snake_case      :Optional[int] 							=    args.train_batch_size
            __snake_case      :List[str] 							=    args.valid_batch_size
            __snake_case      :Optional[Any] 							=    args.learning_rate
            __snake_case      :Union[str, Any] 							=    args.adam_epsilon



       def        __lowercase    (				self     )			->   int:






            '''simple docstring'''





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



       def        __lowercase    (				self     )			->   Any:






            '''simple docstring'''





            raise NotImplementedError



       def        __lowercase    (				self     )			->   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 json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowerCamelCase__       		=  50_0000

lowerCamelCase__		,      lowerCamelCase__       		=  os.path.split(__file__)
lowerCamelCase__       		=  os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))






@get_duration
def 	UpperCamelCase	(							snake_case__  : datasets.Dataset						,**snake_case__  : Any	):




     '''simple docstring'''






     __snake_case      :List[str] 							=    dataset.map(**snake_case__	)






@get_duration
def 	UpperCamelCase	(							snake_case__  : datasets.Dataset						,**snake_case__  : Dict	):




     '''simple docstring'''






     __snake_case      :int 							=    dataset.filter(**snake_case__	)






def 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :str 							=    {"""num examples""": SPEED_TEST_N_EXAMPLES}
     with tempfile.TemporaryDirectory() as tmp_dir:
          __snake_case      :Dict 							=    datasets.Features({"""text""": datasets.Value("""string"""	), """numbers""": datasets.Value("""float32"""	)}	)
          __snake_case      :Optional[int] 							=    generate_example_dataset(
              os.path.join(snake_case__						,"""dataset.arrow"""	)						,snake_case__						,num_examples=snake_case__	)

          __snake_case      :str 							=    transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""						,use_fast=snake_case__	)

          def tokenize(snake_case__  : Optional[int]	):
               return tokenizer(examples["""text"""]	)

          __snake_case      :Optional[Any] 							=    map(snake_case__	)

          __snake_case      :Optional[Any] 							=    map(snake_case__						,batched=snake_case__	)

          __snake_case      :Optional[Any] 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""numpy"""	):
               __snake_case      :int 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""pandas"""	):
               __snake_case      :List[Any] 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""torch"""						,columns="""numbers"""	):
               __snake_case      :str 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""tensorflow"""						,columns="""numbers"""	):
               __snake_case      :Optional[int] 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          __snake_case      :Dict 							=    map(snake_case__						,function=snake_case__						,batched=snake_case__	)

          __snake_case      :Tuple 							=    filter(snake_case__	)

          # Activate later when tokenizer support batched inputs
          # with dataset.formatted_as(type='numpy'):
          #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

     with open(snake_case__						,"""wb"""	) as f:
          f.write(json.dumps(snake_case__	).encode("""utf-8"""	)	)


if __name__ == "__main__":  # useful to run the profiler
     benchmark_map_filter()


