
'''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			:				str    		=			get_tests_dir('fixtures/test_sentencepiece.model')
lowercase			:				str    		=			get_tests_dir('fixtures/test_sentencepiece_bpe.model')

lowercase			:				str    		=			'pt' if is_torch_available() else 'tf'





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





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         super().setUp()

         # We have a SentencePiece fixture for testing
         A       :   List[str]					=						CamembertTokenizer(SCREAMING_SNAKE_CASE	)
         tokenizer.save_pretrained(self.tmpdirname	)





     def 				__lowerCAmelCase							(							self	) ->				List[Any]:






         """simple docstring"""






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

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





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         A       :   List[Any]					=						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(SCREAMING_SNAKE_CASE	)						,			1004	)





     def 				__lowerCAmelCase							(							self	) ->				List[Any]:






         """simple docstring"""






         self.assertEqual(self.get_tokenizer().vocab_size						,			1005	)





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         A       :   Tuple					=						CamembertTokenizer(SCREAMING_SNAKE_CASE	)
         tokenizer.save_pretrained(self.tmpdirname	)
         A       :   List[Any]					=						CamembertTokenizerFast.from_pretrained(self.tmpdirname	)

         A       :   List[Any]					=						'''I was born in 92000, and this is falsé.'''

         A       :   Union[str, Any]					=						tokenizer.encode(SCREAMING_SNAKE_CASE	)
         A       :   List[Any]					=						rust_tokenizer.encode(SCREAMING_SNAKE_CASE	)
         self.assertListEqual(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         A       :   Tuple					=						tokenizer.encode(SCREAMING_SNAKE_CASE						,			add_special_tokens=SCREAMING_SNAKE_CASE	)
         A       :   Any					=						rust_tokenizer.encode(SCREAMING_SNAKE_CASE						,			add_special_tokens=SCREAMING_SNAKE_CASE	)
         self.assertListEqual(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         # <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       :   Any					=						tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE	)
         A       :   Tuple					=						rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE	)
         self.assertListEqual(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         if not self.test_rust_tokenizer:
             return

         A       :   int					=						self.get_tokenizer()
         A       :   Optional[Any]					=						self.get_rust_tokenizer()

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

         A       :   List[Any]					=						tokenizer.tokenize(SCREAMING_SNAKE_CASE	)
         A       :   Union[str, Any]					=						rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE	)
         self.assertListEqual(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         A       :   Dict					=						tokenizer.encode(SCREAMING_SNAKE_CASE						,			add_special_tokens=SCREAMING_SNAKE_CASE	)
         A       :   Optional[Any]					=						rust_tokenizer.encode(SCREAMING_SNAKE_CASE						,			add_special_tokens=SCREAMING_SNAKE_CASE	)
         self.assertListEqual(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

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





     @slow
     def 				__lowerCAmelCase							(							self	) ->				Dict:






         """simple docstring"""






         A       :   Tuple					=						{'''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, 27575, 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, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 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.
         A       :   Tuple					=						[
             '''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=SCREAMING_SNAKE_CASE						,			model_name='''camembert-base'''						,			revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'''						,			sequences=SCREAMING_SNAKE_CASE						,			)






'''simple docstring'''




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

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)





@dataclass
class  A				(							__snake_case						):
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		42


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
      from .pipeline_if import IFPipeline
      from .pipeline_if_imgaimg import IFImgaImgPipeline
      from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
      from .pipeline_if_inpainting import IFInpaintingPipeline
      from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
      from .pipeline_if_superresolution import IFSuperResolutionPipeline
      from .safety_checker import IFSafetyChecker
      from .watermark import IFWatermarker




