
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
lowercase__          =False
lowercase__          =False




def        __UpperCamelCase					(    lowerCAmelCase__							:    Namespace  ):
 return TrainCommand(lowerCAmelCase__  )



class 		UpperCamelCase__  (	__lowercase   ):
   @staticmethod
   def 				lowerCAmelCase      (snake_case_			: ArgumentParser       ):
    __a			:  Optional[int]		    =  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			: Optional[int] ,    snake_case_			: Namespace       ):
    __a			:  Union[str, Any]		    =  logging.get_logger('''transformers-cli/training'''       )

    __a			:  Dict		    =  '''tf''' if is_tf_available() else '''torch'''

    os.makedirs(args.output ,    exist_ok=snake_case_       )
    __a			:  List[str]		    =  args.output

    __a			:  str		    =  args.column_label
    __a			:  Union[str, Any]		    =  args.column_text
    __a			:  Optional[Any]		    =  args.column_id

    self.logger.info(f"Loading {args.task} pipeline for {args.model}"       )
    if args.task == "text_classification":
     __a			:  List[str]		    =  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			:  Union[str, 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 ,    )
    __a			:  Optional[int]		    =  None
    if args.validation_data:
     self.logger.info(f"Loading validation dataset from {args.validation_data}"       )
     __a			:  str		    =  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			:  List[str]		    =  args.validation_split
    __a			:  Optional[int]		    =  args.train_batch_size
    __a			:  Union[str, Any]		    =  args.valid_batch_size
    __a			:  List[str]		    =  args.learning_rate
    __a			:  Union[str, Any]		    =  args.adam_epsilon
   def 				lowerCAmelCase      (self			: int       ):
    if self.framework == "tf":
     return self.run_tf()
    return self.run_torch()
   def 				lowerCAmelCase      (self			: Tuple       ):
    raise NotImplementedError







   def 				lowerCAmelCase      (self			: Tuple       ):
    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 List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


lowercase__          =logging.get_logger(__name__)

lowercase__          ={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase__          ={
    'vocab_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'
    },
    'merges_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'
    },
}

lowercase__          ={'allegro/herbert-base-cased': 514}
lowercase__          ={}



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							str     	=				VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE				:							Tuple     	=				PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE				:							List[str]     	=				PRETRAINED_INIT_CONFIGURATION
   _SCREAMING_SNAKE_CASE				:							Dict     	=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				HerbertTokenizer
   def __init__(self			: str ,    snake_case_			: Optional[Any]=None ,    snake_case_			: Tuple=None ,    snake_case_			: Dict=None ,    snake_case_			: List[str]="<s>" ,    snake_case_			: Any="<unk>" ,    snake_case_			: Optional[int]="<pad>" ,    snake_case_			: Optional[Any]="<mask>" ,    snake_case_			: List[Any]="</s>" ,    **snake_case_			: Optional[int] ,    ):
    super().__init__(
        snake_case_ ,    snake_case_ ,    tokenizer_file=snake_case_ ,    cls_token=snake_case_ ,    unk_token=snake_case_ ,    pad_token=snake_case_ ,    mask_token=snake_case_ ,    sep_token=snake_case_ ,    **snake_case_ ,    )
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None       ):
    __a			:  int		    =  [self.cls_token_id]
    __a			:  Union[str, Any]		    =  [self.sep_token_id]
    if token_ids_a is None:
     return cls + token_ids_a + sep

    return cls + token_ids_a + sep + token_ids_a + sep
   def 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None ,    snake_case_			: bool = False       ):
    if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=snake_case_ ,    token_ids_a=snake_case_ ,    already_has_special_tokens=snake_case_       )

    if token_ids_a is None:
     return [1] + ([0] * len(snake_case_       )) + [1]
    return [1] + ([0] * len(snake_case_       )) + [1] + ([0] * len(snake_case_       )) + [1]
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None       ):
    __a			:  Optional[int]		    =  [self.sep_token_id]
    __a			:  int		    =  [self.cls_token_id]

    if token_ids_a is None:
     return len(cls + token_ids_a + sep       ) * [0]
    return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]







   def 				lowerCAmelCase      (self			: int ,    snake_case_			: str ,    snake_case_			: Optional[str] = None       ):
    __a			:  Any		    =  self._tokenizer.model.save(snake_case_ ,    name=snake_case_       )
    return tuple(snake_case_       )
