



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


SCREAMING_SNAKE_CASE     				=						get_tests_dir("fixtures/test_sentencepiece.model")
SCREAMING_SNAKE_CASE     				=						get_tests_dir("fixtures/test_sentencepiece_bpe.model")

SCREAMING_SNAKE_CASE     				=						"pt" if is_torch_available() else "tf"



@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase      (						lowercase__,     unittest.TestCase  ):







					'''simple docstring'''







					lowerCAmelCase_				:    int									=  CamembertTokenizer
					lowerCAmelCase_				:    int									=  CamembertTokenizerFast
					lowerCAmelCase_				:    Tuple									=  True
					lowerCAmelCase_				:    Union[str, Any]									=  True



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

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



					def    A__		(			self     ):
							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     ):
							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     ):
							self.assertEqual(self.get_tokenizer().vocab_size    ,	1005     )



					def    A__		(			self     ):
							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     ):
							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     ):
							# fmt: off
							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, 2_7575, 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, 2_2804, 1_8818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 1_0326, 24, 2267, 20, 416, 5072, 1_5612, 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    ,	)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE     				=						{
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "GPTBigCodeForSequenceClassification",
			    "GPTBigCodeForTokenClassification",
			    "GPTBigCodeForCausalLM",
			    "GPTBigCodeModel",
			    "GPTBigCodePreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_bigcode import (
						    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTBigCodeForCausalLM,
						    GPTBigCodeForSequenceClassification,
						    GPTBigCodeForTokenClassification,
						    GPTBigCodeModel,
						    GPTBigCodePreTrainedModel,
						)


else:
			import sys

			SCREAMING_SNAKE_CASE     				=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

