




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 						lowercase							(   UpperCamelCase__,unittest.TestCase		):
     _a							 =						BarthezTokenizer
     _a							 =						BarthezTokenizerFast
     _a							 =						True
     _a							 =						True

     def 					a__	(  self				)						->							Tuple:
           super().setUp()

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

     def 					a__	(  self				)						->							Any:
           _A							:     Tuple			=						"""<pad>"""
           _A							:     List[Any]			=						1

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

     def 					a__	(  self				)						->							str:
           _A							:     Tuple			=						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(_a				)		,     10_1122				)

     def 					a__	(  self				)						->							List[Any]:
           self.assertEqual(self.get_tokenizer().vocab_size		,     10_1122				)

     @require_torch
     def 					a__	(  self				)						->							Tuple:
           _A							:     List[str]			=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
           _A							:     Optional[int]			=						[0, 57, 3018, 7_0307, 91, 2]

           _A							:     List[str]			=						self.tokenizer(
               _a		,     max_length=len(_a				)		,     padding=_a		,     truncation=_a		,     return_tensors="""pt"""				)
           self.assertIsInstance(_a		,     _a				)

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

     def 					a__	(  self				)						->							List[str]:
           if not self.test_rust_tokenizer:
                 return

           _A							:     Union[str, Any]			=						self.get_tokenizer()
           _A							:     Tuple			=						self.get_rust_tokenizer()

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

           _A							:     Any			=						tokenizer.tokenize(_a				)
           _A							:     Union[str, Any]			=						rust_tokenizer.tokenize(_a				)
           self.assertListEqual(_a		,     _a				)

           _A							:     Any			=						tokenizer.encode(_a		,     add_special_tokens=_a				)
           _A							:     List[str]			=						rust_tokenizer.encode(_a		,     add_special_tokens=_a				)
           self.assertListEqual(_a		,     _a				)

           _A							:     Optional[Any]			=						self.get_rust_tokenizer()
           _A							:     Any			=						tokenizer.encode(_a				)
           _A							:     List[str]			=						rust_tokenizer.encode(_a				)
           self.assertListEqual(_a		,     _a				)



     @slow
     def 					a__	(  self				)						->							int:
           # fmt: off
           _A							:     int			=						{"""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							:     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=_a		,     model_name="""moussaKam/mbarthez"""		,     revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""		,     sequences=_a		,     )





class 			_lowercase :  # Public class to implement a graph


       """simple docstring"""




       def __init__(   self							:			Dict					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			list[list[bool]]       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  row
             lowerCamelCase__		:				Optional[Any]    =  col
             lowerCamelCase__		:				Union[str, Any]    =  graph





       def 						lowerCAmelCase     (   self							:			Optional[int]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			list[list[bool]]       ):


             '''simple docstring'''



             return (
                 0 <= i < self.ROW
                 and 0 <= j < self.COL
                 and not visited[i][j]
                 and self.graph[i][j]
             )





       def 						lowerCAmelCase     (   self							:			Dict					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			list[list[bool]]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
             lowerCamelCase__		:				Tuple    =  [-1, 0, 1, -1, 1, -1, 0, 1]
             lowerCamelCase__		:				List[Any]    =  True  # Make those cells visited
             for k in range(8       ):
                   if self.is_safe(i + row_nbr[k]					,      j + col_nbr[k]					,      __lowerCamelCase       ):
                         self.diffs(i + row_nbr[k]					,      j + col_nbr[k]					,      __lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			List[Any]       ):  # And finally, count all islands.


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  [[False for j in range(self.COL       )] for i in range(self.ROW       )]
             lowerCamelCase__		:				Optional[Any]    =  0
             for i in range(self.ROW       ):
                   for j in range(self.COL       ):
                         if visited[i][j] is False and self.graph[i][j] == 1:
                               self.diffs(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase       )
                               count += 1
             return count


