

"""simple docstring"""




from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available







@dataclass
class 			lowercase    (	_lowerCamelCase      ):
				__SCREAMING_SNAKE_CASE :			List[Any]	      =      42
				__SCREAMING_SNAKE_CASE :			Optional[Any]	      =      42


if is_transformers_available() and is_torch_available():
					from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline







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    (	lowercase_     ,  unittest.TestCase      ):
				__SCREAMING_SNAKE_CASE :			List[str]	      =      BarthezTokenizer
				__SCREAMING_SNAKE_CASE :			str	      =      BarthezTokenizerFast
				__SCREAMING_SNAKE_CASE :			Optional[Any]	      =      True
				__SCREAMING_SNAKE_CASE :			str	      =      True




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

											snake_case_          =   BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'	)
											tokenizer.save_pretrained(self.tmpdirname	)
											tokenizer.save_pretrained(self.tmpdirname				,       legacy_format=snake_case	)
											snake_case_          =   tokenizer




				def    a							( self	):
											snake_case_          =   '<pad>'
											snake_case_          =   1

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




				def    a							( self	):
											snake_case_          =   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(snake_case	)				,       10_1122	)




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




				@require_torch
				def    a							( self	):
											snake_case_          =   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											snake_case_          =   [0, 57, 3018, 7_0307, 91, 2]

											snake_case_          =   self.tokenizer(
											    snake_case				,       max_length=len(snake_case	)				,       padding=snake_case				,       truncation=snake_case				,       return_tensors='pt'	)
											self.assertIsInstance(snake_case				,       snake_case	)

											self.assertEqual((2, 6)				,       batch.input_ids.shape	)
											self.assertEqual((2, 6)				,       batch.attention_mask.shape	)
											snake_case_          =   batch.input_ids.tolist()[0]
											self.assertListEqual(snake_case				,       snake_case	)




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

											snake_case_          =   self.get_tokenizer()
											snake_case_          =   self.get_rust_tokenizer()

											snake_case_          =   'I was born in 92000, and this is falsé.'

											snake_case_          =   tokenizer.tokenize(snake_case	)
											snake_case_          =   rust_tokenizer.tokenize(snake_case	)
											self.assertListEqual(snake_case				,       snake_case	)

											snake_case_          =   tokenizer.encode(snake_case				,       add_special_tokens=snake_case	)
											snake_case_          =   rust_tokenizer.encode(snake_case				,       add_special_tokens=snake_case	)
											self.assertListEqual(snake_case				,       snake_case	)

											snake_case_          =   self.get_rust_tokenizer()
											snake_case_          =   tokenizer.encode(snake_case	)
											snake_case_          =   rust_tokenizer.encode(snake_case	)
											self.assertListEqual(snake_case				,       snake_case	)




				@slow
				def    a							( self	):
											# fmt: off
											snake_case_          =   {'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.
											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=snake_case				,       model_name='moussaKam/mbarthez'				,       revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'				,       sequences=snake_case				,       )




