
"""simple docstring"""




import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin




class 	UpperCamelCase      (			__magic_name__    , unittest.TestCase ):

				"""simple docstring"""



				UpperCAmelCase_							: Dict		    =							SpeechaTextaTokenizer
				UpperCAmelCase_							: List[Any]		    =							False


				def 				A   (							self     )       ->       List[str]:
									"""simple docstring"""







									super().setUp()

									SCREAMING_SNAKE_CASE           =      '<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'.split(' '     )
									SCREAMING_SNAKE_CASE           =      ['he re</w> 123', 'here a 1456']
									SCREAMING_SNAKE_CASE           =      dict(zip(lowercase__						,      range(len(lowercase__     )     )     )     )

									SCREAMING_SNAKE_CASE           =      {'pad_token': '<pad>', 'unk_token': '<unk>', 'bos_token': '<s>', 'eos_token': '</s>'}

									SCREAMING_SNAKE_CASE           =      tempfile.mkdtemp()
									SCREAMING_SNAKE_CASE           =      os.path.join(self.tmpdirname						,      VOCAB_FILES_NAMES['vocab_file']     )
									SCREAMING_SNAKE_CASE           =      os.path.join(self.tmpdirname						,      VOCAB_FILES_NAMES['merges_file']     )

									with open(self.vocab_file						,      'w'						,      encoding='utf-8'     ) as fp:
														fp.write(json.dumps(lowercase__     ) + '\n'     )

									with open(self.merges_file						,      'w'     ) as fp:
														fp.write('\n'.join(lowercase__     )     )


				def 				A   (							self     )       ->       List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      list(self.get_tokenizer().get_vocab().keys()     )

									self.assertEqual(vocab_keys[0]						,      '<s>'     )
									self.assertEqual(vocab_keys[1]						,      '<pad>'     )
									self.assertEqual(vocab_keys[-1]						,      'vocab'     )
									self.assertEqual(len(lowercase__     )						,      14     )


				def 				A   (							self     )       ->       Any:
									"""simple docstring"""







									self.assertEqual(self.get_tokenizer().vocab_size						,      14     )


				def 				A   (							self     )       ->       Optional[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      SpeechaTextaTokenizer.from_pretrained(self.tmpdirname     )

									# make sure @@ is correctly concatenated
									SCREAMING_SNAKE_CASE           =      [4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
									SCREAMING_SNAKE_CASE           =      tokenizer.decode(lowercase__     )

									self.assertTrue(output_string == 'herecouple words ofthe'     )


				def 				A   (							self     )       ->       Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      SpeechaTextaTokenizer.from_pretrained(self.tmpdirname     )

									with tempfile.TemporaryDirectory() as tmp_dirname:
														tokenizer.save_pretrained(lowercase__     )
														os.remove(os.path.join(lowercase__						,      'merges.txt'     )     )

														# load tokenizer without merges file should not throw an error
														SCREAMING_SNAKE_CASE           =      SpeechaTextaTokenizer.from_pretrained(lowercase__     )

									with tempfile.TemporaryDirectory() as tmp_dirname:
														# save tokenizer and load again
														tokenizer.save_pretrained(lowercase__     )
														SCREAMING_SNAKE_CASE           =      SpeechaTextaTokenizer.from_pretrained(lowercase__     )

									self.assertIsNotNone(lowercase__     )




				def 				A   (							self     )       ->       Dict:
									"""simple docstring"""







									if not self.test_slow_tokenizer:
														return

									SCREAMING_SNAKE_CASE           =      inspect.signature(self.tokenizer_class.__init__     )
									SCREAMING_SNAKE_CASE           =      self.get_tokenizer()

									for parameter_name, parameter in signature.parameters.items():
														if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
																			self.assertIn(lowercase__						,      tokenizer.init_kwargs     )


"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case					  =						{'configuration_qdqbert': ['QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'QDQBertConfig']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case					  =						[
	    'QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'QDQBertForMaskedLM',
	    'QDQBertForMultipleChoice',
	    'QDQBertForNextSentencePrediction',
	    'QDQBertForQuestionAnswering',
	    'QDQBertForSequenceClassification',
	    'QDQBertForTokenClassification',
	    'QDQBertLayer',
	    'QDQBertLMHeadModel',
	    'QDQBertModel',
	    'QDQBertPreTrainedModel',
	    'load_tf_weights_in_qdqbert',
	]


if TYPE_CHECKING:
	from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_qdqbert import (
		    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    QDQBertForMaskedLM,
		    QDQBertForMultipleChoice,
		    QDQBertForNextSentencePrediction,
		    QDQBertForQuestionAnswering,
		    QDQBertForSequenceClassification,
		    QDQBertForTokenClassification,
		    QDQBertLayer,
		    QDQBertLMHeadModel,
		    QDQBertModel,
		    QDQBertPreTrainedModel,
		    load_tf_weights_in_qdqbert,
		)


else:
	import sys

	snake_case					  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
