



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE	:    str     				=   logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE	:    str     				=   {
    '''facebook/s2t-small-librispeech-asr''': (
        '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}


class 				lowerCamelCase_( lowerCAmelCase_     ):





					'''simple docstring'''



					lowercase__ :							Any              =			'speech_to_text'
					lowercase__ :							int              =			['past_key_values']
					lowercase__ :							int              =			{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

					def __init__(     self					,							lowerCamelCase__=1_0_0_0_0					,							lowerCamelCase__=1_2					,							lowerCamelCase__=2_0_4_8					,							lowerCamelCase__=4					,							lowerCamelCase__=6					,							lowerCamelCase__=2_0_4_8					,							lowerCamelCase__=4					,							lowerCamelCase__=0.0					,							lowerCamelCase__=0.0					,							lowerCamelCase__=True					,							lowerCamelCase__=True					,							lowerCamelCase__="relu"					,							lowerCamelCase__=2_5_6					,							lowerCamelCase__=0.1					,							lowerCamelCase__=0.0					,							lowerCamelCase__=0.0					,							lowerCamelCase__=0.0_2					,							lowerCamelCase__=2					,							lowerCamelCase__=True					,							lowerCamelCase__=1					,							lowerCamelCase__=0					,							lowerCamelCase__=2					,							lowerCamelCase__=6_0_0_0					,							lowerCamelCase__=1_0_2_4					,							lowerCamelCase__=2					,							lowerCamelCase__=(5, 5)					,							lowerCamelCase__=1_0_2_4					,							lowerCamelCase__=8_0					,							lowerCamelCase__=1					,							**lowerCamelCase__					,							):
									_lowerCamelCase   	=					vocab_size
									_lowerCamelCase   	=					d_model
									_lowerCamelCase   	=					encoder_ffn_dim
									_lowerCamelCase   	=					encoder_layers
									_lowerCamelCase   	=					encoder_attention_heads
									_lowerCamelCase   	=					decoder_ffn_dim
									_lowerCamelCase   	=					decoder_layers
									_lowerCamelCase   	=					decoder_attention_heads
									_lowerCamelCase   	=					dropout
									_lowerCamelCase   	=					attention_dropout
									_lowerCamelCase   	=					activation_dropout
									_lowerCamelCase   	=					activation_function
									_lowerCamelCase   	=					init_std
									_lowerCamelCase   	=					encoder_layerdrop
									_lowerCamelCase   	=					decoder_layerdrop
									_lowerCamelCase   	=					use_cache
									_lowerCamelCase   	=					encoder_layers
									_lowerCamelCase   	=					scale_embedding  # scale factor will be sqrt(d_model) if True
									_lowerCamelCase   	=					max_source_positions
									_lowerCamelCase   	=					max_target_positions
									_lowerCamelCase   	=					num_conv_layers
									_lowerCamelCase   	=					list(__lowerCAmelCase )
									_lowerCamelCase   	=					conv_channels
									_lowerCamelCase   	=					input_feat_per_channel
									_lowerCamelCase   	=					input_channels

									if len(self.conv_kernel_sizes ) != self.num_conv_layers:
													raise ValueError(
													    '''Configuration for convolutional module is incorrect. '''
													    '''It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '''
													    F"""but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes )}`, """
													    F"""`config.num_conv_layers = {self.num_conv_layers}`.""" )

									super().__init__(
									    pad_token_id=__lowerCAmelCase					,							bos_token_id=__lowerCAmelCase					,							eos_token_id=__lowerCAmelCase					,							is_encoder_decoder=__lowerCAmelCase					,							decoder_start_token_id=__lowerCAmelCase					,							**__lowerCAmelCase					,							)

"""simple docstring"""


import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 							a					(		lowerCAmelCase_						,				unittest.TestCase ):
    _snake_case  :	Dict  				=				MgpstrTokenizer
    _snake_case  :	Any  				=				False
    _snake_case  :	str  				=				{}
    _snake_case  :	Dict  				=				False


    def   lowerCAmelCase_	(       self						:	int       ):
      super().setUp()

      # fmt: off
      _UpperCAmelCase    			=   ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
      # fmt: on
      _UpperCAmelCase    			=   dict(zip(__lowerCAmelCase ,					range(len(__lowerCAmelCase       )       )       )       )

      _UpperCAmelCase    			=   os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES["""vocab_file"""]       )
      with open(self.vocab_file ,					"""w""" ,					encoding="""utf-8"""       ) as fp:
        fp.write(json.dumps(__lowerCAmelCase       ) + """\n"""       )


    def   lowerCAmelCase_	(       self						:	Tuple ,					**__lowerCAmelCase						:	Dict       ):
      return MgpstrTokenizer.from_pretrained(self.tmpdirname ,					**__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[str] ,					__lowerCAmelCase						:	int       ):
      _UpperCAmelCase    			=   """tester"""
      _UpperCAmelCase    			=   """tester"""
      return input_text, output_text


    @unittest.skip("""MGP-STR always lower cases letters."""       )
    def   lowerCAmelCase_	(       self						:	Tuple       ):
      pass


    def   lowerCAmelCase_	(       self						:	Any       ):
      _UpperCAmelCase    			=   self.get_tokenizers(do_lower_case=__lowerCAmelCase       )
      for tokenizer in tokenizers:
        with self.subTest(f'''{tokenizer.__class__.__name__}'''       ):
          _UpperCAmelCase    			=   """[SPECIAL_TOKEN]"""

          tokenizer.add_special_tokens({"""cls_token""": special_token}       )
          _UpperCAmelCase    			=   tokenizer.encode([special_token] ,					add_special_tokens=__lowerCAmelCase       )
          self.assertEqual(len(__lowerCAmelCase       ) ,					1       )

          _UpperCAmelCase    			=   tokenizer.decode(__lowerCAmelCase ,					skip_special_tokens=__lowerCAmelCase       )
          self.assertTrue(special_token not in decoded       )


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   self.get_tokenizers()
      for tokenizer in tokenizers:
        with self.subTest(f'''{tokenizer.__class__.__name__}'''       ):
          _UpperCAmelCase			,					_UpperCAmelCase    			=   self.get_input_output_texts(__lowerCAmelCase       )

          _UpperCAmelCase    			=   tokenizer.tokenize(__lowerCAmelCase       )
          _UpperCAmelCase    			=   tokenizer.convert_tokens_to_ids(__lowerCAmelCase       )
          _UpperCAmelCase    			=   tokenizer.encode(__lowerCAmelCase ,					add_special_tokens=__lowerCAmelCase       )
          self.assertListEqual(__lowerCAmelCase ,					__lowerCAmelCase       )

          _UpperCAmelCase    			=   tokenizer.convert_ids_to_tokens(__lowerCAmelCase       )
          self.assertNotEqual(len(__lowerCAmelCase       ) ,					0       )
          _UpperCAmelCase    			=   tokenizer.decode(__lowerCAmelCase       )
          self.assertIsInstance(__lowerCAmelCase ,					__lowerCAmelCase       )

          self.assertEqual(text_a.replace(""" """ ,					""""""       ) ,					__lowerCAmelCase       )


    @unittest.skip("""MGP-STR tokenizer only handles one sequence."""       )
    def   lowerCAmelCase_	(       self						:	List[str]       ):
      pass


    @unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer"""       )
    def   lowerCAmelCase_	(       self						:	Optional[int]       ):
      pass
