





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
_UpperCAmelCase				:       List[str]	   =				False
_UpperCAmelCase				:       Dict	   =				False


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     int:
    return TrainCommand(_UpperCAmelCase )


class    lowerCAmelCase							(					__UpperCamelCase			):
 @staticmethod
 def      A_	(      UpperCAmelCase  :      ArgumentParser	)			->	Union[str, Any]:
     lowerCamelCase__    :      Optional[int]					 =		parser.add_parser('train'			,     help='CLI tool to train a model on a task.'	)

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

     train_parser.add_argument(
         '--task'			,     type=UpperCAmelCase			,     default='text_classification'			,     help='Task to train the model on.'	)
     train_parser.add_argument(
         '--model'			,     type=UpperCAmelCase			,     default='bert-base-uncased'			,     help='Model\'s name or path to stored model.'	)
     train_parser.add_argument('--train_batch_size'			,     type=UpperCAmelCase			,     default=32			,     help='Batch size for training.'	)
     train_parser.add_argument('--valid_batch_size'			,     type=UpperCAmelCase			,     default=64			,     help='Batch size for validation.'	)
     train_parser.add_argument('--learning_rate'			,     type=UpperCAmelCase			,     default=3e-5			,     help='Learning rate.'	)
     train_parser.add_argument('--adam_epsilon'			,     type=UpperCAmelCase			,     default=1e-08			,     help='Epsilon for Adam optimizer.'	)
     train_parser.set_defaults(func=UpperCAmelCase	)
 def __init__(      self  :      Optional[int]			,     UpperCAmelCase  :      Namespace	)			->	Dict:
     lowerCamelCase__    :      List[Any]					 =		logging.get_logger('transformers-cli/training'	)

     lowerCamelCase__    :      Optional[int]					 =		'tf' if is_tf_available() else 'torch'

     os.makedirs(args.output			,     exist_ok=UpperCAmelCase	)
     lowerCamelCase__    :      str					 =		args.output

     lowerCamelCase__    :      List[str]					 =		args.column_label
     lowerCamelCase__    :      int					 =		args.column_text
     lowerCamelCase__    :      Any					 =		args.column_id

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

     lowerCamelCase__    :      Optional[Any]					 =		args.validation_split
     lowerCamelCase__    :      Any					 =		args.train_batch_size
     lowerCamelCase__    :      Any					 =		args.valid_batch_size
     lowerCamelCase__    :      Dict					 =		args.learning_rate
     lowerCamelCase__    :      Union[str, Any]					 =		args.adam_epsilon
 def      A_	(      self  :      Optional[Any]	)			->	Optional[int]:
     if self.framework == "tf":
         return self.run_tf()
     return self.run_torch()
 def      A_	(      self  :      int	)			->	Optional[Any]:
     raise NotImplementedError







 def      A_	(      self  :      str	)			->	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	)






"""simple docstring"""
import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__					       =	get_tests_dir('fixtures/test_sentencepiece.model')
lowercase__					       =	get_tests_dir('fixtures/test_sentencepiece_bpe.model')

lowercase__					       =	'pt' if is_torch_available() else 'tf'




@require_sentencepiece
@require_tokenizers
class    __snake_case       (     __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		CamembertTokenizer
      a__        =		CamembertTokenizerFast
      a__        =		True
      a__        =		True



      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        super().setUp()

        # We have a SentencePiece fixture for testing
        a__:     Tuple										=	CamembertTokenizer(lowercase)
        tokenizer.save_pretrained(self.tmpdirname)



      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        a__:     Optional[Any]										=	'<pad>'
        a__:     List[Any]										=	1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase)    ,			lowercase)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase)    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     str										=	list(self.get_tokenizer().get_vocab().keys())

        self.assertEqual(vocab_keys[0]    ,			'<s>NOTUSED')
        self.assertEqual(vocab_keys[1]    ,			'<pad>')
        self.assertEqual(vocab_keys[-1]    ,			'<mask>')
        self.assertEqual(len(lowercase)    ,			10_04)



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        self.assertEqual(self.get_tokenizer().vocab_size    ,			10_05)



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        a__:     Optional[Any]										=	CamembertTokenizer(lowercase)
        tokenizer.save_pretrained(self.tmpdirname)
        a__:     List[Any]										=	CamembertTokenizerFast.from_pretrained(self.tmpdirname)

        a__:     Dict										=	'I was born in 92000, and this is falsé.'

        a__:     Optional[int]										=	tokenizer.encode(lowercase)
        a__:     Any										=	rust_tokenizer.encode(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        a__:     Optional[Any]										=	tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        a__:     str										=	rust_tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        # <unk> tokens are not the same for `rust` than for `slow`.
        # Because spm gives back raw token instead of `unk` in EncodeAsPieces
        # tokens = tokenizer.tokenize(sequence)
        a__:     Tuple										=	tokenizer.convert_ids_to_tokens(lowercase)
        a__:     Tuple										=	rust_tokenizer.tokenize(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        if not self.test_rust_tokenizer:
          return

        a__:     Dict										=	self.get_tokenizer()
        a__:     str										=	self.get_rust_tokenizer()

        a__:     int										=	'I was born in 92000, and this is falsé.'

        a__:     Optional[Any]										=	tokenizer.tokenize(lowercase)
        a__:     List[Any]										=	rust_tokenizer.tokenize(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        a__:     str										=	tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        a__:     str										=	rust_tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        a__:     Tuple										=	self.get_rust_tokenizer()
        a__:     Union[str, Any]										=	tokenizer.encode(lowercase)
        a__:     List[Any]										=	rust_tokenizer.encode(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)



      @slow
      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        a__:     Union[str, Any]										=	{'input_ids': [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
        # fmt: on

        # camembert is a french model. So we also use french texts.
        a__:     int										=	[
            'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
            'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
            'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
            'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
            'telles que la traduction et la synthèse de texte.',
        ]

        self.tokenizer_integration_test_util(
            expected_encoding=lowercase    ,			model_name='camembert-base'    ,			revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'    ,			sequences=lowercase    ,			)


