



import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class SCREAMING_SNAKE_CASE__	(						A__					,		unittest.TestCase       ):




    '''simple docstring'''






    __lowerCamelCase     :					Optional[int]   = BarthezTokenizer
    __lowerCamelCase     :					Union[str, Any]   = BarthezTokenizerFast
    __lowerCamelCase     :					List[Any]   = True
    __lowerCamelCase     :					str   = True






    def      _lowerCAmelCase   (			self						):
        super().setUp()

        A					:			Tuple		=  BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""						)
        tokenizer.save_pretrained(self.tmpdirname						)
        tokenizer.save_pretrained(self.tmpdirname,						legacy_format=_UpperCamelCase						)
        A					:			List[str]		=  tokenizer






    def      _lowerCAmelCase   (			self						):
        A					:			str		=  """<pad>"""
        A					:			Optional[Any]		=  1

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






    def      _lowerCAmelCase   (			self						):
        A					:			str		=  list(self.get_tokenizer().get_vocab().keys()						)

        self.assertEqual(vocab_keys[0],						"""<s>"""						)
        self.assertEqual(vocab_keys[1],						"""<pad>"""						)
        self.assertEqual(vocab_keys[-1],						"""<mask>"""						)
        self.assertEqual(len(_UpperCamelCase						),						10_1122						)






    def      _lowerCAmelCase   (			self						):
        self.assertEqual(self.get_tokenizer().vocab_size,						10_1122						)






    @require_torch
    def      _lowerCAmelCase   (			self						):
        A					:			Union[str, Any]		=  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
        A					:			List[str]		=  [0, 57, 3018, 7_0307, 91, 2]

        A					:			Tuple		=  self.tokenizer(
            _UpperCamelCase,						max_length=len(_UpperCamelCase						),						padding=_UpperCamelCase,						truncation=_UpperCamelCase,						return_tensors="""pt"""						)
        self.assertIsInstance(_UpperCamelCase,						_UpperCamelCase						)

        self.assertEqual((2, 6),						batch.input_ids.shape						)
        self.assertEqual((2, 6),						batch.attention_mask.shape						)
        A					:			List[Any]		=  batch.input_ids.tolist()[0]
        self.assertListEqual(_UpperCamelCase,						_UpperCamelCase						)






    def      _lowerCAmelCase   (			self						):
        if not self.test_rust_tokenizer:
            return

        A					:			Optional[Any]		=  self.get_tokenizer()
        A					:			Union[str, Any]		=  self.get_rust_tokenizer()

        A					:			Optional[int]		=  """I was born in 92000, and this is falsé."""

        A					:			Optional[Any]		=  tokenizer.tokenize(_UpperCamelCase						)
        A					:			Any		=  rust_tokenizer.tokenize(_UpperCamelCase						)
        self.assertListEqual(_UpperCamelCase,						_UpperCamelCase						)

        A					:			List[Any]		=  tokenizer.encode(_UpperCamelCase,						add_special_tokens=_UpperCamelCase						)
        A					:			int		=  rust_tokenizer.encode(_UpperCamelCase,						add_special_tokens=_UpperCamelCase						)
        self.assertListEqual(_UpperCamelCase,						_UpperCamelCase						)

        A					:			int		=  self.get_rust_tokenizer()
        A					:			Optional[int]		=  tokenizer.encode(_UpperCamelCase						)
        A					:			Optional[int]		=  rust_tokenizer.encode(_UpperCamelCase						)
        self.assertListEqual(_UpperCamelCase,						_UpperCamelCase						)






    @slow
    def      _lowerCAmelCase   (			self						):
        # fmt: off
        A					:			List[Any]		=  {"""input_ids""": [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 2, 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, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], """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, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
        # fmt: on

        # moussaKam/mbarthez is a french model. So we also use french texts.
        A					:			Union[str, Any]		=  [
            """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=_UpperCamelCase,						model_name="""moussaKam/mbarthez""",						revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6""",						sequences=_UpperCamelCase,						)









UpperCAmelCase_           =	{
    "A": ".-", "B": "-...", "C": "-.-.", "D": "-..", "E": ".", "F": "..-.", "G": "--.",
    "H": "....", "I": "..", "J": ".---", "K": "-.-", "L": ".-..", "M": "--", "N": "-.",
    "O": "---", "P": ".--.", "Q": "--.-", "R": ".-.", "S": "...", "T": "-", "U": "..-",
    "V": "...-", "W": ".--", "X": "-..-", "Y": "-.--", "Z": "--..", "1": ".----",
    "2": "..---", "3": "...--", "4": "....-", "5": ".....", "6": "-....", "7": "--...",
    "8": "---..", "9": "----.", "0": "-----", "&": ".-...", "@": ".--.-.",
    ":": "---...", ",": "--..--", ".": ".-.-.-", "'": ".----.", "\"": ".-..-.",
    "?": "..--..", "/": "-..-.", "=": "-...-", "+": ".-.-.", "-": "-....-",
    "(": "-.--.", ")": "-.--.-", "!": "-.-.--", " ": "/"
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
UpperCAmelCase_           =	{value: key for key, value in MORSE_CODE_DICT.items()}
def      A__       (    SCREAMING_SNAKE_CASE_   :      str						)  ->  str:






   """simple docstring"""







   return " ".join(MORSE_CODE_DICT[char] for char in message.upper()						)
def      A__       (    SCREAMING_SNAKE_CASE_   :      str						)  ->  str:






   """simple docstring"""







   return "".join(REVERSE_DICT[char] for char in message.split()						)
def      A__       (    )  ->  None:






   """simple docstring"""







   _UpperCAmelCase           =   '''Morse code here!'''
   print(SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   encrypt(SCREAMING_SNAKE_CASE_						)
   print(SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   decrypt(SCREAMING_SNAKE_CASE_						)
   print(SCREAMING_SNAKE_CASE_						)


if __name__ == "__main__":
   main()