





'''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 					a_     (				snake_case_      ,    unittest.TestCase       ):

    '''simple docstring'''
    UpperCamelCase     = CamembertTokenizer
    UpperCamelCase     = CamembertTokenizerFast
    UpperCamelCase     = True
    UpperCamelCase     = True


    def 			snake_case_(		self       )		->   List[str]:
        super().setUp()

        # We have a SentencePiece fixture for testing
        _SCREAMING_SNAKE_CASE									=			CamembertTokenizer(A       )
        tokenizer.save_pretrained(self.tmpdirname       )


    def 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			"""<pad>"""
        _SCREAMING_SNAKE_CASE									=			1

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


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			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(A       ) ,		1004       )


    def 			snake_case_(		self       )		->   str:
        self.assertEqual(self.get_tokenizer().vocab_size ,		1005       )


    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			CamembertTokenizer(A       )
        tokenizer.save_pretrained(self.tmpdirname       )
        _SCREAMING_SNAKE_CASE									=			CamembertTokenizerFast.from_pretrained(self.tmpdirname       )

        _SCREAMING_SNAKE_CASE									=			"""I was born in 92000, and this is falsé."""

        _SCREAMING_SNAKE_CASE									=			tokenizer.encode(A       )
        _SCREAMING_SNAKE_CASE									=			rust_tokenizer.encode(A       )
        self.assertListEqual(A ,		A       )

        _SCREAMING_SNAKE_CASE									=			tokenizer.encode(A ,		add_special_tokens=A       )
        _SCREAMING_SNAKE_CASE									=			rust_tokenizer.encode(A ,		add_special_tokens=A       )
        self.assertListEqual(A ,		A       )

        # <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)
        _SCREAMING_SNAKE_CASE									=			tokenizer.convert_ids_to_tokens(A       )
        _SCREAMING_SNAKE_CASE									=			rust_tokenizer.tokenize(A       )
        self.assertListEqual(A ,		A       )


    def 			snake_case_(		self       )		->   Any:
        if not self.test_rust_tokenizer:
            return

        _SCREAMING_SNAKE_CASE									=			self.get_tokenizer()
        _SCREAMING_SNAKE_CASE									=			self.get_rust_tokenizer()

        _SCREAMING_SNAKE_CASE									=			"""I was born in 92000, and this is falsé."""

        _SCREAMING_SNAKE_CASE									=			tokenizer.tokenize(A       )
        _SCREAMING_SNAKE_CASE									=			rust_tokenizer.tokenize(A       )
        self.assertListEqual(A ,		A       )

        _SCREAMING_SNAKE_CASE									=			tokenizer.encode(A ,		add_special_tokens=A       )
        _SCREAMING_SNAKE_CASE									=			rust_tokenizer.encode(A ,		add_special_tokens=A       )
        self.assertListEqual(A ,		A       )

        _SCREAMING_SNAKE_CASE									=			self.get_rust_tokenizer()
        _SCREAMING_SNAKE_CASE									=			tokenizer.encode(A       )
        _SCREAMING_SNAKE_CASE									=			rust_tokenizer.encode(A       )
        self.assertListEqual(A ,		A       )





    @slow
    def 			snake_case_(		self       )		->   Optional[int]:
        # fmt: off
        _SCREAMING_SNAKE_CASE									=			{"""input_ids""": [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 2_7575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 2_2804, 1_8818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 1_0326, 24, 2267, 20, 416, 5072, 1_5612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 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.
        _SCREAMING_SNAKE_CASE									=			[
            """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=A ,		model_name="""camembert-base""" ,		revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf""" ,		sequences=A ,		)







'''simple docstring'''

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser





def __snake_case					(      ):
 lowerCamelCase_      =  ArgumentParser("Accelerate CLI tool"					,				usage="accelerate <command> [<args>]"					,				allow_abbrev=UpperCAmelCase_		)
 lowerCamelCase_      =  parser.add_subparsers(help="accelerate command helpers"		)

 # Register commands
 get_config_parser(subparsers=UpperCAmelCase_		)
 env_command_parser(subparsers=UpperCAmelCase_		)
 launch_command_parser(subparsers=UpperCAmelCase_		)
 tpu_command_parser(subparsers=UpperCAmelCase_		)
 test_command_parser(subparsers=UpperCAmelCase_		)

 # Let's go
 lowerCamelCase_      =  parser.parse_args()

 if not hasattr(UpperCAmelCase_					,				"func"		):
  parser.print_help()
  exit(1		)

 # Run
 args.func(UpperCAmelCase_		)


if __name__ == "__main__":
     main()



