




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    (      _a ,       unittest.TestCase ):
   __SCREAMING_SNAKE_CASE		:				Union[str, Any]      					=       SpeechaTextaTokenizer
   __SCREAMING_SNAKE_CASE		:				Dict      					=       False






   def 			_a    (						self :							Tuple  )->      Optional[Any]:
     super().setUp()

     _lowercase:			Any      =     '<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'.split(' '  )
     _lowercase:			Optional[int]      =     ['he re</w> 123', 'here a 1456']
     _lowercase:			str      =     dict(zip(snake_case_    ,       range(len(snake_case_  )  )  )  )

     _lowercase:			List[Any]      =     {'pad_token': '<pad>', 'unk_token': '<unk>', 'bos_token': '<s>', 'eos_token': '</s>'}

     _lowercase:			str      =     tempfile.mkdtemp()
     _lowercase:			str      =     os.path.join(self.tmpdirname    ,       VOCAB_FILES_NAMES['vocab_file']  )
     _lowercase:			List[Any]      =     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(snake_case_  ) + '\n'  )

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






   def 			_a    (						self :							List[Any]  )->      int:
     _lowercase:			str      =     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(snake_case_  )    ,       14  )






   def 			_a    (						self :							Optional[Any]  )->      Dict:
     self.assertEqual(self.get_tokenizer().vocab_size    ,       14  )






   def 			_a    (						self :							int  )->      str:
     _lowercase:			Any      =     SpeechaTextaTokenizer.from_pretrained(self.tmpdirname  )

     # make sure @@ is correctly concatenated
     _lowercase:			int      =     [4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
     _lowercase:			Optional[int]      =     tokenizer.decode(snake_case_  )

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






   def 			_a    (						self :							Optional[Any]  )->      List[Any]:
     _lowercase:			Optional[Any]      =     SpeechaTextaTokenizer.from_pretrained(self.tmpdirname  )

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

       # load tokenizer without merges file should not throw an error
       _lowercase:			Union[str, Any]      =     SpeechaTextaTokenizer.from_pretrained(snake_case_  )

     with tempfile.TemporaryDirectory() as tmp_dirname:
       # save tokenizer and load again
       tokenizer.save_pretrained(snake_case_  )
       _lowercase:			Optional[int]      =     SpeechaTextaTokenizer.from_pretrained(snake_case_  )

     self.assertIsNotNone(snake_case_  )






   def 			_a    (						self :							Dict  )->      Optional[int]:
     if not self.test_slow_tokenizer:
       return

     _lowercase:			str      =     inspect.signature(self.tokenizer_class.__init__  )
     _lowercase:			int      =     self.get_tokenizer()

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








"""simple docstring"""





import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig
class  __lowerCamelCase     (	_a	):
   a   :					List[Any]			    =CustomConfig

   def __init__(       self			,					snake_case_   )		->							str:
       super().__init__(snake_case_   )
       UpperCamelCase__								=     torch.nn.Linear(config.hidden_size			,					config.hidden_size   )

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Optional[int]:
       return self.linear(snake_case_   )







   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Union[str, Any]:
       pass







class  __lowerCamelCase     (	_a	):
   a   :					Optional[int]			    =NoSuperInitConfig

   def __init__(       self			,					snake_case_   )		->							Optional[int]:
       super().__init__(snake_case_   )
       UpperCamelCase__								=     torch.nn.Linear(config.attribute			,					config.attribute   )

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							int:
       return self.linear(snake_case_   )







   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Union[str, Any]:
       pass




