






from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest





@require_tokenizers
class __lowerCamelCase			(				snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=				SqueezeBertTokenizer
	UpperCamelCase__								=				SqueezeBertTokenizerFast
	UpperCamelCase__								=				True

	def 	UpperCamelCase   (							self	,   **UpperCAmelCase   ):
							"""simple docstring"""







							return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname	,   **UpperCAmelCase   )

	@slow
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      SqueezeBertTokenizer.from_pretrained('squeezebert/squeezebert-mnli-headless'   )

							_UpperCAmelCase     =      tokenizer.encode('sequence builders'	,   add_special_tokens=UpperCAmelCase   )
							_UpperCAmelCase     =      tokenizer.encode('multi-sequence build'	,   add_special_tokens=UpperCAmelCase   )

							_UpperCAmelCase     =      tokenizer.build_inputs_with_special_tokens(UpperCAmelCase   )
							_UpperCAmelCase     =      tokenizer.build_inputs_with_special_tokens(UpperCAmelCase	,   UpperCAmelCase   )

							assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
							assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
							    tokenizer.sep_token_id
							]








import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir





@dataclass
class __lowerCamelCase			:





	"""simple docstring"""






	UpperCamelCase__								=				42





class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      get_tests_dir('fixtures/test_sentencepiece.model'   )
							_UpperCAmelCase     =      get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model'   )

							_UpperCAmelCase     =      FakeOriginalTokenizer(vocab_file=UpperCAmelCase   )

							with warnings.catch_warnings(record=UpperCAmelCase   ) as w:
													_UpperCAmelCase     =      SpmConverter(UpperCAmelCase   )
							self.assertEqual(len(UpperCAmelCase   )	,   0   )

							_UpperCAmelCase     =      FakeOriginalTokenizer(vocab_file=UpperCAmelCase   )

							with warnings.catch_warnings(record=UpperCAmelCase   ) as w:
													_UpperCAmelCase     =      SpmConverter(UpperCAmelCase   )
							self.assertEqual(len(UpperCAmelCase   )	,   1   )

							self.assertIn(
							    'The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'
							    ' which is not implemented in the fast tokenizers.'	,   str(w[0].message   )	,   )
