


'''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      :      Optional[Any]            =					get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

_lowercase      :      str            =					"""pt""" if is_torch_available() else """tf"""






@require_sentencepiece
@require_tokenizers
class 	UpperCamelCase__(       lowerCAmelCase						,    unittest.TestCase	):
				__magic_name__    :				Dict  		=	CamembertTokenizer
				__magic_name__    :				Optional[int]  		=	CamembertTokenizerFast
				__magic_name__    :				str  		=	True
				__magic_name__    :				Dict  		=	True




				def 					a__(		self      :			Tuple				)->					List[Any]:

										"""simple docstring"""




										super().setUp()

										# We have a SentencePiece fixture for testing
										UpperCAmelCase					    =     CamembertTokenizer(lowerCAmelCase				)
										tokenizer.save_pretrained(self.tmpdirname				)




				def 					a__(		self      :			Union[str, Any]				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     '''<pad>'''
										UpperCAmelCase					    =     1

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




				def 					a__(		self      :			Dict				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     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(lowerCAmelCase				)				,   1004				)




				def 					a__(		self      :			Dict				)->					Optional[Any]:

										"""simple docstring"""




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




				def 					a__(		self      :			Dict				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     CamembertTokenizer(lowerCAmelCase				)
										tokenizer.save_pretrained(self.tmpdirname				)
										UpperCAmelCase					    =     CamembertTokenizerFast.from_pretrained(self.tmpdirname				)

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

										UpperCAmelCase					    =     tokenizer.encode(lowerCAmelCase				)
										UpperCAmelCase					    =     rust_tokenizer.encode(lowerCAmelCase				)
										self.assertListEqual(lowerCAmelCase				,   lowerCAmelCase				)

										UpperCAmelCase					    =     tokenizer.encode(lowerCAmelCase				,   add_special_tokens=lowerCAmelCase				)
										UpperCAmelCase					    =     rust_tokenizer.encode(lowerCAmelCase				,   add_special_tokens=lowerCAmelCase				)
										self.assertListEqual(lowerCAmelCase				,   lowerCAmelCase				)

										# <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)
										UpperCAmelCase					    =     tokenizer.convert_ids_to_tokens(lowerCAmelCase				)
										UpperCAmelCase					    =     rust_tokenizer.tokenize(lowerCAmelCase				)
										self.assertListEqual(lowerCAmelCase				,   lowerCAmelCase				)




				def 					a__(		self      :			List[str]				)->					str:

										"""simple docstring"""




										if not self.test_rust_tokenizer:
																return

										UpperCAmelCase					    =     self.get_tokenizer()
										UpperCAmelCase					    =     self.get_rust_tokenizer()

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

										UpperCAmelCase					    =     tokenizer.tokenize(lowerCAmelCase				)
										UpperCAmelCase					    =     rust_tokenizer.tokenize(lowerCAmelCase				)
										self.assertListEqual(lowerCAmelCase				,   lowerCAmelCase				)

										UpperCAmelCase					    =     tokenizer.encode(lowerCAmelCase				,   add_special_tokens=lowerCAmelCase				)
										UpperCAmelCase					    =     rust_tokenizer.encode(lowerCAmelCase				,   add_special_tokens=lowerCAmelCase				)
										self.assertListEqual(lowerCAmelCase				,   lowerCAmelCase				)

										UpperCAmelCase					    =     self.get_rust_tokenizer()
										UpperCAmelCase					    =     tokenizer.encode(lowerCAmelCase				)
										UpperCAmelCase					    =     rust_tokenizer.encode(lowerCAmelCase				)
										self.assertListEqual(lowerCAmelCase				,   lowerCAmelCase				)





				@slow
				def 					a__(		self      :			Union[str, Any]				)->					List[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     {'''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.
										UpperCAmelCase					    =     [
										    '''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='''camembert-base'''				,   revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'''				,   sequences=lowerCAmelCase				,   )









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase      :      List[str]            =					{
    """configuration_time_series_transformer""": [
        """TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TimeSeriesTransformerConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      Dict            =					[
	    """TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TimeSeriesTransformerForPrediction""",
	    """TimeSeriesTransformerModel""",
	    """TimeSeriesTransformerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_time_series_transformer import (
	    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TimeSeriesTransformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimeSeriesTransformerForPrediction,
		    TimeSeriesTransformerModel,
		    TimeSeriesTransformerPreTrainedModel,
		)

else:
	import sys

	_lowercase      :      Tuple            =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





