




'''simple docstring'''

import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a       :						Optional[Any]								=		logging.get_logger(__name__)

a       :						Dict								=		{'''vocab_file''': '''sentencepiece.model'''}

a       :						Tuple								=		{
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
}

a       :						str								=		{
    '''google/rembert''': 256,
}



class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):
			__SCREAMING_SNAKE_CASE    =			VOCAB_FILES_NAMES
			__SCREAMING_SNAKE_CASE    =			PRETRAINED_VOCAB_FILES_MAP
			__SCREAMING_SNAKE_CASE    =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





			def __init__(      self						:    Optional[Any]      ,		a_						:    int      ,		a_						:    Any=False      ,		a_						:    List[Any]=True      ,		a_						:    List[Any]=True      ,		a_						:    List[Any]="[CLS]"      ,		a_						:    List[Any]="[SEP]"      ,		a_						:    List[Any]="[UNK]"      ,		a_						:    str="[SEP]"      ,		a_						:    List[str]="[PAD]"      ,		a_						:    Optional[int]="[CLS]"      ,		a_						:    List[str]="[MASK]"      ,		**a_						:    str      ,		):



						"""simple docstring"""




						super().__init__(
						    do_lower_case=a_      ,		remove_space=a_      ,		keep_accents=a_      ,		bos_token=a_      ,		eos_token=a_      ,		unk_token=a_      ,		sep_token=a_      ,		pad_token=a_      ,		cls_token=a_      ,		mask_token=a_      ,		**a_      ,		)

						__snake_case			=		do_lower_case
						__snake_case			=		remove_space
						__snake_case			=		keep_accents
						__snake_case			=		vocab_file

						__snake_case			=		spm.SentencePieceProcessor()
						self.sp_model.Load(a_							)





			@property
			def 							A (      self						:    Optional[Any]							):



						"""simple docstring"""




						return len(self.sp_model							)





			def 							A (      self						:    Optional[Any]							):



						"""simple docstring"""




						__snake_case			=		{self.convert_ids_to_tokens(a_							): i for i in range(self.vocab_size							)}
						vocab.update(self.added_tokens_encoder							)
						return vocab





			def __getstate__(      self						:    Dict							):



						"""simple docstring"""




						__snake_case			=		self.__dict__.copy()
						__snake_case			=		None
						return state





			def __setstate__(      self						:    str      ,		a_						:    Optional[int]							):



						"""simple docstring"""




						__snake_case			=		d
						__snake_case			=		spm.SentencePieceProcessor()
						self.sp_model.Load(self.vocab_file							)





			def 							A (      self						:    Tuple      ,		a_						:    Optional[int]      ,		a_						:    int=False							):



						"""simple docstring"""




						__snake_case			=		self.sp_model.EncodeAsPieces(a_							)
						return pieces





			def 							A (      self						:    Any      ,		a_						:    Optional[Any]							):



						"""simple docstring"""




						return self.sp_model.PieceToId(a_							)





			def 							A (      self						:    Optional[Any]      ,		a_						:    List[str]							):



						"""simple docstring"""




						return self.sp_model.IdToPiece(a_							)





			def 							A (      self						:    Optional[Any]      ,		a_						:    int							):



						"""simple docstring"""




						__snake_case			=		self.sp_model.decode_pieces(a_							)
						return out_string





			def 							A (      self						:    Union[str, Any]      ,		a_						:    List[int]      ,		a_						:    Optional[List[int]] = None							):



						"""simple docstring"""




						__snake_case			=		[self.sep_token_id]
						__snake_case			=		[self.cls_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 							A (      self						:    List[str]      ,		a_						:    List[int]      ,		a_						:    Optional[List[int]] = None      ,		a_						:    bool = False							):



						"""simple docstring"""




						if already_has_special_tokens:
									if token_ids_a is not None:
												raise ValueError(
												    "You should not supply a second sequence if the provided sequence of "
												    "ids is already formatted with special tokens for the model."							)
									return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

						if token_ids_a is not None:
									return [1] + ([0] * len(a_							)) + [1] + ([0] * len(a_							)) + [1]
						return [1] + ([0] * len(a_							)) + [1]





			def 							A (      self						:    Tuple      ,		a_						:    List[int]      ,		a_						:    Optional[List[int]] = None							):



						"""simple docstring"""




						__snake_case			=		[self.sep_token_id]
						__snake_case			=		[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 							A (      self						:    List[Any]      ,		a_						:    str      ,		a_						:    Optional[str] = None							):



						"""simple docstring"""




						if not os.path.isdir(a_							):
									logger.error("Vocabulary path ({}) should be a directory".format(a_							)							)
									return
						__snake_case			=		os.path.join(
						    a_      ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

						if os.path.abspath(self.vocab_file							) != os.path.abspath(a_							):
									copyfile(self.vocab_file      ,		a_							)

						return (out_vocab_file,)







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
_snake_case      :							Any     	=    False
_snake_case      :							int     	=    False

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				return TrainCommand(__magic_name__						)





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						@staticmethod
						def        SCREAMING_SNAKE_CASE(			lowerCAmelCase__							:ArgumentParser     )  ->str:
										lowercase         =      parser.add_parser("train"		,    help="CLI tool to train a model on a task."     )

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

										train_parser.add_argument(
										    "--task"		,    type=lowerCAmelCase__		,    default="text_classification"		,    help="Task to train the model on."     )
										train_parser.add_argument(
										    "--model"		,    type=lowerCAmelCase__		,    default="bert-base-uncased"		,    help="Model's name or path to stored model."     )
										train_parser.add_argument("--train_batch_size"		,    type=lowerCAmelCase__		,    default=32		,    help="Batch size for training."     )
										train_parser.add_argument("--valid_batch_size"		,    type=lowerCAmelCase__		,    default=64		,    help="Batch size for validation."     )
										train_parser.add_argument("--learning_rate"		,    type=lowerCAmelCase__		,    default=3E-5		,    help="Learning rate."     )
										train_parser.add_argument("--adam_epsilon"		,    type=lowerCAmelCase__		,    default=1E-0_8		,    help="Epsilon for Adam optimizer."     )
										train_parser.set_defaults(func=lowerCAmelCase__     )





						def __init__(			self							:Optional[int]		,    lowerCAmelCase__							:Namespace     )  ->str:
										lowercase         =      logging.get_logger("transformers-cli/training"     )

										lowercase         =      "tf" if is_tf_available() else "torch"

										os.makedirs(args.output		,    exist_ok=lowerCAmelCase__     )
										lowercase         =      args.output

										lowercase         =      args.column_label
										lowercase         =      args.column_text
										lowercase         =      args.column_id

										self.logger.info(F'''Loading {args.task} pipeline for {args.model}'''     )
										if args.task == "text_classification":
														lowercase         =      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         =      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         =      None
										if args.validation_data:
														self.logger.info(F'''Loading validation dataset from {args.validation_data}'''     )
														lowercase         =      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         =      args.validation_split
										lowercase         =      args.train_batch_size
										lowercase         =      args.valid_batch_size
										lowercase         =      args.learning_rate
										lowercase         =      args.adam_epsilon





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->List[str]:
										if self.framework == "tf":
														return self.run_tf()
										return self.run_torch()





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->Dict:
										raise NotImplementedError





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->str:
										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     )
