


"""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    __A       ( _SCREAMING_SNAKE_CASE,					unittest.TestCase    ):






 """simple docstring"""




 __lowerCAmelCase				      =						SpeechaTextaTokenizer
 __lowerCAmelCase				      =						False


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
     super().setUp()

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

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

     a					=tempfile.mkdtemp()
     a					=os.path.join(self.tmpdirname				,					VOCAB_FILES_NAMES['''vocab_file''']		)
     a					=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(__A		) + '''\n'''		)

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


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[Any]:
     a					=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(__A		)				,					14		)


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Dict:
     self.assertEqual(self.get_tokenizer().vocab_size				,					14		)


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Any:
     a					=SpeechaTextaTokenizer.from_pretrained(self.tmpdirname		)

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

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


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Tuple:
     a					=SpeechaTextaTokenizer.from_pretrained(self.tmpdirname		)

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

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

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

     self.assertIsNotNone(__A		)





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

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

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



"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_     :     int    		=						logging.get_logger(__name__)

lowerCamelCase_     :     Optional[Any]    		=						{
    """dandelin/vilt-b32-mlm""": """https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"""
}






class    __A       ( _SCREAMING_SNAKE_CASE    ):






 """simple docstring"""




 __lowerCAmelCase				      =						"vilt"


 def __init__(	self				,					__A=3_0522				,					__A=2				,					__A=2				,					__A=40				,					__A=768				,					__A=12				,					__A=12				,					__A=3072				,					__A="gelu"				,					__A=0.0				,					__A=0.0				,					__A=0.02				,					__A=1E-1_2				,					__A=384				,					__A=32				,					__A=3				,					__A=True				,					__A=-1				,					__A=False				,					__A=-1				,					**__A				,					)      ->  Dict:
     super().__init__(tie_word_embeddings=__A				,					**__A		)

     a					=vocab_size
     a					=type_vocab_size
     a					=modality_type_vocab_size
     a					=max_position_embeddings

     a					=hidden_size
     a					=num_hidden_layers
     a					=num_attention_heads
     a					=intermediate_size
     a					=hidden_act
     a					=hidden_dropout_prob
     a					=attention_probs_dropout_prob
     a					=initializer_range
     a					=layer_norm_eps

     a					=image_size
     a					=patch_size
     a					=num_channels
     a					=qkv_bias
     a					=max_image_length
     a					=num_images