




'''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 						lowerCAmelCase_   ( lowerCamelCase_			,  unittest.TestCase   ):







  '''simple docstring'''







  lowerCAmelCase_				:					List[str]				     =						SpeechaTextaTokenizer
  lowerCAmelCase_				:					List[Any]				     =						False


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any] ):






   """simple docstring"""






   super().setUp()

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

   UpperCAmelCase__										=    {"""pad_token""": """<pad>""", """unk_token""": """<unk>""", """bos_token""": """<s>""", """eos_token""": """</s>"""}

   UpperCAmelCase__										=    tempfile.mkdtemp()
   UpperCAmelCase__										=    os.path.join(self.tmpdirname						,		VOCAB_FILES_NAMES["""vocab_file"""] )
   UpperCAmelCase__										=    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(_UpperCAmelCase ) + """\n""" )

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


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str] ):






   """simple docstring"""






   UpperCAmelCase__										=    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(_UpperCAmelCase )						,		14 )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any] ):






   """simple docstring"""






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


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   UpperCAmelCase__										=    SpeechaTextaTokenizer.from_pretrained(self.tmpdirname )

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

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


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple ):






   """simple docstring"""






   UpperCAmelCase__										=    SpeechaTextaTokenizer.from_pretrained(self.tmpdirname )

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

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

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

   self.assertIsNotNone(_UpperCAmelCase )



  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple ):






   """simple docstring"""






   if not self.test_slow_tokenizer:
    return

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

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






'''simple docstring'''




import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      List[str]       ,						SCREAMING_SNAKE_CASE__			:      str       ,						SCREAMING_SNAKE_CASE__			:      Tuple       ):
 '''simple docstring'''





 UpperCAmelCase__										=    TaConfig.from_json_file(SCREAMING_SNAKE_CASE__       )
 print(F'''Building PyTorch model from configuration: {config}'''       )
 UpperCAmelCase__										=    TaForConditionalGeneration(SCREAMING_SNAKE_CASE__       )

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       )

 # Save pytorch-model
 print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
 model.save_pretrained(SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
    UpperCAmelCase_       				=      argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
        ),
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    UpperCAmelCase_       				=      parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
