



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

import sentencepiece as spm

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


lowerCAmelCase__				  =  logging.get_logger(__name__)

lowerCAmelCase__				  =  {'vocab_file': 'spiece.model'}

lowerCAmelCase__				  =  {
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

lowerCAmelCase__				  =  {'bert_for_seq_generation': 5_1_2}

class 				snake_case__(_lowerCamelCase      ):

					"""simple docstring"""






					lowercase_								=		VOCAB_FILES_NAMES
					lowercase_								=		PRETRAINED_VOCAB_FILES_MAP
					lowercase_								=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase_								=		[]
					lowercase_								=		["""input_ids""", """attention_mask"""]







					def __init__(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     Dict="<s>"				,       SCREAMING_SNAKE_CASE :     Optional[Any]="</s>"				,       SCREAMING_SNAKE_CASE :     Union[str, Any]="<unk>"				,       SCREAMING_SNAKE_CASE :     List[str]="<pad>"				,       SCREAMING_SNAKE_CASE :     Optional[Any]="<::::>"				,       SCREAMING_SNAKE_CASE :     Optional[Dict[str, Any]] = None				,       **SCREAMING_SNAKE_CASE :     Optional[Any]				,       ):
									lowercase__					:   Any													=					{} if sp_model_kwargs is None else sp_model_kwargs

									# Add extra_ids to the special token list
									super().__init__(
									    bos_token=SCREAMING_SNAKE_CASE				,       eos_token=SCREAMING_SNAKE_CASE				,       unk_token=SCREAMING_SNAKE_CASE				,       pad_token=SCREAMING_SNAKE_CASE				,       sep_token=SCREAMING_SNAKE_CASE				,       sp_model_kwargs=self.sp_model_kwargs				,       **SCREAMING_SNAKE_CASE				,       )

									lowercase__					:   Any													=					vocab_file

									lowercase__					:   Dict													=					spm.SentencePieceProcessor(**self.sp_model_kwargs					)
									self.sp_model.Load(SCREAMING_SNAKE_CASE					)







					@property
					def 			snake_case					(							self :     Any					):
									return self.sp_model.get_piece_size()







					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   List[str]													=					{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE					): i for i in range(self.vocab_size					)}
									vocab.update(self.added_tokens_encoder					)
									return vocab







					def __getstate__(							self :     int					):
									lowercase__					:   Dict													=					self.__dict__.copy()
									lowercase__					:   List[str]													=					None
									return state







					def __setstate__(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Optional[Any]					):
									lowercase__					:   str													=					d

									# for backward compatibility
									if not hasattr(self				,       "sp_model_kwargs"					):
													lowercase__					:   Dict													=					{}

									lowercase__					:   int													=					spm.SentencePieceProcessor(**self.sp_model_kwargs					)
									self.sp_model.Load(self.vocab_file					)







					def 			snake_case					(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     str					):
									return self.sp_model.encode(SCREAMING_SNAKE_CASE				,       out_type=SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     str				,       SCREAMING_SNAKE_CASE :     List[str]					):
									return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     Tuple					):
									lowercase__					:   str													=					self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE					)
									return token







					def 			snake_case					(							self :     Dict				,       SCREAMING_SNAKE_CASE :     int					):
									lowercase__					:   Optional[int]													=					[]
									lowercase__					:   str													=					""
									for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																	out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE					) + token
																	lowercase__					:   Dict													=					[]
													else:
																	current_sub_tokens.append(SCREAMING_SNAKE_CASE					)
									out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE					)
									return out_string.strip()







					def 			snake_case					(							self :     List[str]				,       SCREAMING_SNAKE_CASE :     str				,       SCREAMING_SNAKE_CASE :     Optional[str] = None					):


									if not os.path.isdir(SCREAMING_SNAKE_CASE					):
													logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
													return
									lowercase__					:   int													=					os.path.join(
									    SCREAMING_SNAKE_CASE				,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]					)

									if os.path.abspath(self.vocab_file					) != os.path.abspath(SCREAMING_SNAKE_CASE					) and os.path.isfile(self.vocab_file					):
													copyfile(self.vocab_file				,       SCREAMING_SNAKE_CASE					)
									elif not os.path.isfile(self.vocab_file					):
													with open(SCREAMING_SNAKE_CASE				,       "wb"					) as fi:
																	lowercase__					:   Optional[int]													=					self.sp_model.serialized_model_proto()
																	fi.write(SCREAMING_SNAKE_CASE					)

									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
lowerCAmelCase__					  =  False
lowerCAmelCase__					  =  False


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				return TrainCommand(lowerCamelCase__						)

class 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""






					@staticmethod
					def 			snake_case					(							SCREAMING_SNAKE_CASE :     ArgumentParser					):
									lowercase__					:   Optional[int]													=					parser.add_parser("train"				,       help="CLI tool to train a model on a task."					)

									train_parser.add_argument(
									    "--train_data"				,       type=SCREAMING_SNAKE_CASE				,       required=SCREAMING_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=SCREAMING_SNAKE_CASE				,       default=0				,       help="Column of the dataset csv file with example labels."					)
									train_parser.add_argument(
									    "--column_text"				,       type=SCREAMING_SNAKE_CASE				,       default=1				,       help="Column of the dataset csv file with example texts."					)
									train_parser.add_argument(
									    "--column_id"				,       type=SCREAMING_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=SCREAMING_SNAKE_CASE				,       default=""				,       help="path to validation dataset."					)
									train_parser.add_argument(
									    "--validation_split"				,       type=SCREAMING_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=SCREAMING_SNAKE_CASE				,       default="./"				,       help="path to saved the trained model."					)

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







					def __init__(							self :     int				,       SCREAMING_SNAKE_CASE :     Namespace					):
									lowercase__					:   int													=					logging.get_logger("transformers-cli/training"					)

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

									os.makedirs(args.output				,       exist_ok=SCREAMING_SNAKE_CASE					)
									lowercase__					:   Optional[Any]													=					args.output

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

									self.logger.info(f"""Loading {args.task} pipeline for {args.model}"""					)
									if args.task == "text_classification":
													lowercase__					:   int													=					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__					:   List[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				,       )
									lowercase__					:   Union[str, Any]													=					None
									if args.validation_data:
													self.logger.info(f"""Loading validation dataset from {args.validation_data}"""					)
													lowercase__					:   Optional[int]													=					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__					:   List[str]													=					args.train_batch_size
									lowercase__					:   Any													=					args.valid_batch_size
									lowercase__					:   Optional[int]													=					args.learning_rate
									lowercase__					:   int													=					args.adam_epsilon







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







					def 			snake_case					(							self :     Union[str, Any]					):
									raise NotImplementedError







					def 			snake_case					(							self :     Union[str, Any]					):
									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					)




