


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    UpperCAmelCase_	(							UpperCAmelCase__,     unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase      :     Tuple							=					BarthezTokenizer
       __UpperCamelCase      :     List[Any]							=					BarthezTokenizerFast
       __UpperCamelCase      :     str							=					True
       __UpperCamelCase      :     List[str]							=					True






       def  _lowercase   (     self						):




              """simple docstring"""




              super().setUp()

              UpperCamelCase    :	int   	=		BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''						)
              tokenizer.save_pretrained(self.tmpdirname						)
              tokenizer.save_pretrained(self.tmpdirname ,					legacy_format=__lowerCAmelCase						)
              UpperCamelCase    :	List[Any]   	=		tokenizer






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		'''<pad>'''
              UpperCamelCase    :	int   	=		1

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






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	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(__lowerCAmelCase						) ,					101_122						)






       def  _lowercase   (     self						):




              """simple docstring"""




              self.assertEqual(self.get_tokenizer().vocab_size ,					101_122						)






       @require_torch
       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[Any]   	=		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
              UpperCamelCase    :	List[str]   	=		[0, 57, 3_018, 70_307, 91, 2]

              UpperCamelCase    :	str   	=		self.tokenizer(
                  __lowerCAmelCase ,					max_length=len(__lowerCAmelCase						) ,					padding=__lowerCAmelCase ,					truncation=__lowerCAmelCase ,					return_tensors='''pt'''						)
              self.assertIsInstance(__lowerCAmelCase ,					__lowerCAmelCase						)

              self.assertEqual((2, 6) ,					batch.input_ids.shape						)
              self.assertEqual((2, 6) ,					batch.attention_mask.shape						)
              UpperCamelCase    :	str   	=		batch.input_ids.tolist()[0]
              self.assertListEqual(__lowerCAmelCase ,					__lowerCAmelCase						)






       def  _lowercase   (     self						):




              """simple docstring"""




              if not self.test_rust_tokenizer:
                     return

              UpperCamelCase    :	Optional[Any]   	=		self.get_tokenizer()
              UpperCamelCase    :	Dict   	=		self.get_rust_tokenizer()

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

              UpperCamelCase    :	str   	=		tokenizer.tokenize(__lowerCAmelCase						)
              UpperCamelCase    :	Optional[Any]   	=		rust_tokenizer.tokenize(__lowerCAmelCase						)
              self.assertListEqual(__lowerCAmelCase ,					__lowerCAmelCase						)

              UpperCamelCase    :	Any   	=		tokenizer.encode(__lowerCAmelCase ,					add_special_tokens=__lowerCAmelCase						)
              UpperCamelCase    :	str   	=		rust_tokenizer.encode(__lowerCAmelCase ,					add_special_tokens=__lowerCAmelCase						)
              self.assertListEqual(__lowerCAmelCase ,					__lowerCAmelCase						)

              UpperCamelCase    :	str   	=		self.get_rust_tokenizer()
              UpperCamelCase    :	Optional[Any]   	=		tokenizer.encode(__lowerCAmelCase						)
              UpperCamelCase    :	Any   	=		rust_tokenizer.encode(__lowerCAmelCase						)
              self.assertListEqual(__lowerCAmelCase ,					__lowerCAmelCase						)






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		{'''input_ids''': [[0, 490, 14_328, 4_507, 354, 47, 43_669, 95, 25, 78_117, 20_215, 19_779, 190, 22, 400, 4, 35_343, 80_310, 603, 86, 24_937, 105, 33_438, 94_762, 196, 39_642, 7, 15, 15_933, 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, 10_534, 87, 25, 66, 3_358, 196, 55_289, 8, 82_961, 81, 2_204, 75_203, 7, 15, 763, 12_956, 216, 178, 14_328, 9_595, 1_377, 69_693, 7, 448, 71_021, 196, 18_106, 1_437, 13_974, 108, 9_083, 4, 49_315, 7, 39, 86, 1_326, 2_793, 46_333, 4, 448, 196, 74_588, 7, 49_315, 7, 39, 21, 822, 38_470, 74, 21, 66_723, 62_480, 8, 22_050, 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.
              UpperCamelCase    :	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=__lowerCAmelCase ,					model_name='''moussaKam/mbarthez''' ,					revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6''' ,					sequences=__lowerCAmelCase ,					)






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
  from .camera import create_pan_cameras
  from .pipeline_shap_e import ShapEPipeline
  from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
  from .renderer import (
      BoundingBoxVolume,
      ImportanceRaySampler,
      MLPNeRFModelOutput,
      MLPNeRSTFModel,
      ShapEParamsProjModel,
      ShapERenderer,
      StratifiedRaySampler,
      VoidNeRFModel,
  )

