



import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

__lowerCamelCase         =			logging.get_logger(__name__)


__lowerCamelCase         =			{name: getattr(transformers, name + '''Fast''') for name in SLOW_TO_FAST_CONVERTERS}







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case    )     ->     Union[str, Any]:






      '''simple docstring'''







      if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
            raise ValueError(F"""Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()    )}."""    )

      if tokenizer_name is None:
            UpperCAmelCase_  :	List[str]				      =		TOKENIZER_CLASSES
      else:
            UpperCAmelCase_  :	List[Any]				      =		{tokenizer_name: getattr(__snake_case					,  tokenizer_name + "Fast"    )}

      logger.info(F"""Loading tokenizer classes: {tokenizer_names}"""    )

      for tokenizer_name in tokenizer_names:
            UpperCAmelCase_  :	Optional[int]				      =		TOKENIZER_CLASSES[tokenizer_name]

            UpperCAmelCase_  :	int				      =		True
            if checkpoint_name is None:
                  UpperCAmelCase_  :	Union[str, Any]				      =		list(tokenizer_class.max_model_input_sizes.keys()    )
            else:
                  UpperCAmelCase_  :	Optional[Any]				      =		[checkpoint_name]

            logger.info(F"""For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}"""    )

            for checkpoint in checkpoint_names:
                  logger.info(F"""Loading {tokenizer_class.__class__.__name__} {checkpoint}"""    )

                  # Load tokenizer
                  UpperCAmelCase_  :	List[Any]				      =		tokenizer_class.from_pretrained(__snake_case					,  force_download=__snake_case    )

                  # Save fast tokenizer
                  logger.info(F"""Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}"""    )

                  # For organization names we create sub-directories
                  if "/" in checkpoint:
                        UpperCAmelCase_  :	Union[str, Any]				      =		checkpoint.split("/"    )
                        UpperCAmelCase_  :	Tuple				      =		os.path.join(__snake_case					,  __snake_case    )
                  elif add_prefix:
                        UpperCAmelCase_  :	Any				      =		checkpoint
                        UpperCAmelCase_  :	Dict				      =		dump_path
                  else:
                        UpperCAmelCase_  :	Dict				      =		None
                        UpperCAmelCase_  :	int				      =		dump_path

                  logger.info(F"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""    )

                  if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()    )[0]:
                        UpperCAmelCase_  :	int				      =		list(tokenizer.pretrained_vocab_files_map.values()    )[0][checkpoint]
                        UpperCAmelCase_  :	Tuple				      =		file_path.split(__snake_case    )[-1][0]
                        if next_char == "/":
                              UpperCAmelCase_  :	int				      =		os.path.join(__snake_case					,  __snake_case    )
                              UpperCAmelCase_  :	List[Any]				      =		None

                        logger.info(F"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""    )

                  UpperCAmelCase_  :	Any				      =		tokenizer.save_pretrained(
                      __snake_case					,  legacy_format=__snake_case					,  filename_prefix=__snake_case    )
                  logger.info(F"""=> File names {file_names}"""    )

                  for file_name in file_names:
                        if not file_name.endswith("tokenizer.json"    ):
                              os.remove(__snake_case    )
                              logger.info(F"""=> removing {file_name}"""    )


if __name__ == "__main__":
   __lowerCamelCase         =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--dump_path''', default=None, type=str, required=True, help='''Path to output generated fast tokenizer files.'''
   )
   parser.add_argument(
       '''--tokenizer_name''',
       default=None,
       type=str,
       help=(
           F'''Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will '''
           '''download and convert all the checkpoints from AWS.'''
       ),
   )
   parser.add_argument(
       '''--checkpoint_name''',
       default=None,
       type=str,
       help='''Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.''',
   )
   parser.add_argument(
       '''--force_download''',
       action='''store_true''',
       help='''Re-download checkpoints.''',
   )
   __lowerCamelCase         =			parser.parse_args()

   convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)





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    _snake_case (       __snake_case    )     ->     Any:






      '''simple docstring'''







      return TrainCommand(__snake_case    )





class   snake_case_ (lowercase__  ):




       """simple docstring"""






       @staticmethod
       def  A_   (     lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		parser.add_parser("train"     ,help="CLI tool to train a model on a task.")

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

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



       def __init__(     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		logging.get_logger("transformers-cli/training")

             UpperCAmelCase_  :	List[Any]				      =		"tf" if is_tf_available() else "torch"

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

             UpperCAmelCase_  :	Optional[int]				      =		args.column_label
             UpperCAmelCase_  :	Dict				      =		args.column_text
             UpperCAmelCase_  :	Optional[Any]				      =		args.column_id

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

             UpperCAmelCase_  :	List[str]				      =		args.validation_split
             UpperCAmelCase_  :	Tuple				      =		args.train_batch_size
             UpperCAmelCase_  :	Optional[int]				      =		args.valid_batch_size
             UpperCAmelCase_  :	Dict				      =		args.learning_rate
             UpperCAmelCase_  :	List[str]				      =		args.adam_epsilon



       def  A_   (     self):

             """simple docstring"""





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



       def  A_   (     self):

             """simple docstring"""





             raise NotImplementedError



       def  A_   (     self):

             """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)
