




"""simple docstring"""






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 lowerCAmelCase__	(     lowercase,	unittest.TestCase						):






			'''simple docstring'''



			lowerCamelCase__      =			BarthezTokenizer
			lowerCamelCase__      =			BarthezTokenizerFast
			lowerCamelCase__      =			True
			lowerCamelCase__      =			True
			def 					A_    (  self  ):
							super().setUp()

							_lowerCamelCase     :			Union[str, Any]    =    BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'  )
							tokenizer.save_pretrained(self.tmpdirname  )
							tokenizer.save_pretrained(self.tmpdirname  ,      legacy_format=lowercase  )
							_lowerCamelCase     :			Union[str, Any]    =    tokenizer
			def 					A_    (  self  ):
							_lowerCamelCase     :			Dict    =    '<pad>'
							_lowerCamelCase     :			Tuple    =    1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase  )  ,      lowercase  )
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase  )  ,      lowercase  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			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(lowercase  )  ,      101122  )
			def 					A_    (  self  ):
							self.assertEqual(self.get_tokenizer().vocab_size  ,      101122  )
			@require_torch
			def 					A_    (  self  ):
							_lowerCamelCase     :			Any    =    ['A long paragraph for summarization.', 'Another paragraph for summarization.']
							_lowerCamelCase     :			List[str]    =    [0, 57, 3018, 70307, 91, 2]

							_lowerCamelCase     :			Tuple    =    self.tokenizer(
							    lowercase  ,      max_length=len(lowercase  )  ,      padding=lowercase  ,      truncation=lowercase  ,      return_tensors='pt'  )
							self.assertIsInstance(lowercase  ,      lowercase  )

							self.assertEqual((2, 6)  ,      batch.input_ids.shape  )
							self.assertEqual((2, 6)  ,      batch.attention_mask.shape  )
							_lowerCamelCase     :			int    =    batch.input_ids.tolist()[0]
							self.assertListEqual(lowercase  ,      lowercase  )
			def 					A_    (  self  ):
							if not self.test_rust_tokenizer:
											return

							_lowerCamelCase     :			str    =    self.get_tokenizer()
							_lowerCamelCase     :			Optional[int]    =    self.get_rust_tokenizer()

							_lowerCamelCase     :			Union[str, Any]    =    'I was born in 92000, and this is falsé.'

							_lowerCamelCase     :			List[Any]    =    tokenizer.tokenize(lowercase  )
							_lowerCamelCase     :			Dict    =    rust_tokenizer.tokenize(lowercase  )
							self.assertListEqual(lowercase  ,      lowercase  )

							_lowerCamelCase     :			Dict    =    tokenizer.encode(lowercase  ,      add_special_tokens=lowercase  )
							_lowerCamelCase     :			Dict    =    rust_tokenizer.encode(lowercase  ,      add_special_tokens=lowercase  )
							self.assertListEqual(lowercase  ,      lowercase  )

							_lowerCamelCase     :			Any    =    self.get_rust_tokenizer()
							_lowerCamelCase     :			List[str]    =    tokenizer.encode(lowercase  )
							_lowerCamelCase     :			List[Any]    =    rust_tokenizer.encode(lowercase  )
							self.assertListEqual(lowercase  ,      lowercase  )






			@slow
			def 					A_    (  self  ):
							# fmt: off
							_lowerCamelCase     :			List[str]    =    {'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 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, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 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.
							_lowerCamelCase     :			List[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=lowercase  ,      model_name='moussaKam/mbarthez'  ,      revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'  ,      sequences=lowercase  ,      )







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,
	)
