code: 
  
  
  
  '''simple docstring'''
  
  
  
  import json
  import os
  import unittest
  
  from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
  from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
  from transformers.testing_utils import require_tokenizers, require_torch
  from transformers.utils import cached_property
  
  from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
  @require_tokenizers
  class        UpperCAmelCase__  (    UpperCAmelCase_							,			unittest.TestCase):
   __SCREAMING_SNAKE_CASE					      =				BartTokenizer
   __SCREAMING_SNAKE_CASE					      =				BartTokenizerFast
   __SCREAMING_SNAKE_CASE					      =				True
   __SCREAMING_SNAKE_CASE					      =				filter_roberta_detectors
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Dict:
    super().setUp()
    __UpperCamelCase				      =  [
        """l""",
        """o""",
        """w""",
        """e""",
        """r""",
        """s""",
        """t""",
        """i""",
        """d""",
        """n""",
        """\u0120""",
        """\u0120l""",
        """\u0120n""",
        """\u0120lo""",
        """\u0120low""",
        """er""",
        """\u0120lowest""",
        """\u0120newer""",
        """\u0120wider""",
        """<unk>""",
    ]
    __UpperCamelCase				      =  dict(zip(lowercase		,	range(len(lowercase			)			)			)			)
    __UpperCamelCase				      =  ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
    __UpperCamelCase				      =  {"""unk_token""": """<unk>"""}
  
    __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(lowercase			) + """\n"""			)
    with open(self.merges_file		,	"""w"""		,	encoding="""utf-8"""			) as fp:
     fp.write("""\n""".join(lowercase			)			)
  
  
  
   def    __lowerCamelCase				(  self		,	**lowercase			)  ->							Union[str, Any]:
    kwargs.update(self.special_tokens_map			)
    return self.tokenizer_class.from_pretrained(self.tmpdirname		,	**lowercase			)
  
  
  
   def    __lowerCamelCase				(  self		,	**lowercase			)  ->							List[Any]:
    kwargs.update(self.special_tokens_map			)
    return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,	**lowercase			)
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase			)  ->							List[Any]:
    return "lower newer", "lower newer"
  
  
  
   @cached_property
   def    __lowerCamelCase				(  self			)  ->							List[str]:
    return BartTokenizer.from_pretrained("""facebook/bart-large"""			)
  
  
  
   @cached_property
   def    __lowerCamelCase				(  self			)  ->							Any:
    return BartTokenizerFast.from_pretrained("""facebook/bart-large"""			)
  
  
  
   @require_torch
   def    __lowerCamelCase				(  self			)  ->							List[str]:
    __UpperCamelCase				      =  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
    __UpperCamelCase				      =  [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]
  
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     __UpperCamelCase				      =  tokenizer(lowercase		,	max_length=len(lowercase			)		,	padding=lowercase		,	return_tensors="""pt"""			)
     self.assertIsInstance(lowercase		,	lowercase			)
  
     self.assertEqual((2, 9)		,	batch.input_ids.shape			)
     self.assertEqual((2, 9)		,	batch.attention_mask.shape			)
     __UpperCamelCase				      =  batch.input_ids.tolist()[0]
     self.assertListEqual(lowercase		,	lowercase			)
     # Test that special tokens are reset
  
  
  
   @require_torch
   def    __lowerCamelCase				(  self			)  ->							Dict:
    __UpperCamelCase				      =  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     __UpperCamelCase				      =  tokenizer(lowercase		,	padding=lowercase		,	return_tensors="""pt"""			)
     # check if input_ids are returned and no labels
     self.assertIn("""input_ids"""		,	lowercase			)
     self.assertIn("""attention_mask"""		,	lowercase			)
     self.assertNotIn("""labels"""		,	lowercase			)
     self.assertNotIn("""decoder_attention_mask"""		,	lowercase			)
  
  
  
   @require_torch
   def    __lowerCamelCase				(  self			)  ->							str:
    __UpperCamelCase				      =  [
        """Summary of the text.""",
        """Another summary.""",
    ]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     __UpperCamelCase				      =  tokenizer(text_target=lowercase		,	max_length=3_2		,	padding="""max_length"""		,	return_tensors="""pt"""			)
     self.assertEqual(3_2		,	targets["""input_ids"""].shape[1]			)
  
  
  
   @require_torch
   def    __lowerCamelCase				(  self			)  ->							Optional[int]:
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     __UpperCamelCase				      =  tokenizer(
         ["""I am a small frog""" * 1_0_2_4, """I am a small frog"""]		,	padding=lowercase		,	truncation=lowercase		,	return_tensors="""pt"""			)
     self.assertIsInstance(lowercase		,	lowercase			)
     self.assertEqual(batch.input_ids.shape		,	(2, 1_0_2_4)			)
  
  
  
   @require_torch
   def    __lowerCamelCase				(  self			)  ->							Optional[int]:
    __UpperCamelCase				      =  ["""A long paragraph for summarization."""]
    __UpperCamelCase				      =  [
        """Summary of the text.""",
    ]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     __UpperCamelCase				      =  tokenizer(lowercase		,	return_tensors="""pt"""			)
     __UpperCamelCase				      =  tokenizer(text_target=lowercase		,	return_tensors="""pt"""			)
     __UpperCamelCase				      =  inputs["""input_ids"""]
     __UpperCamelCase				      =  targets["""input_ids"""]
     self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()			)
     self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()			)
     self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()			)
     self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							str:
    pass
  
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Union[str, Any]:
    for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"			):
      __UpperCamelCase				      =  self.rust_tokenizer_class.from_pretrained(lowercase		,	**lowercase			)
      __UpperCamelCase				      =  self.tokenizer_class.from_pretrained(lowercase		,	**lowercase			)
      __UpperCamelCase				      =  """A, <mask> AllenNLP sentence."""
      __UpperCamelCase				      =  tokenizer_r.encode_plus(lowercase		,	add_special_tokens=lowercase		,	return_token_type_ids=lowercase			)
      __UpperCamelCase				      =  tokenizer_p.encode_plus(lowercase		,	add_special_tokens=lowercase		,	return_token_type_ids=lowercase			)
  
      # token_type_ids should put 0 everywhere
      self.assertEqual(sum(tokens_r["""token_type_ids"""]			)		,	sum(tokens_p["""token_type_ids"""]			)			)
  
      # attention_mask should put 1 everywhere, so sum over length should be 1
      self.assertEqual(
          sum(tokens_r["""attention_mask"""]			) / len(tokens_r["""attention_mask"""]			)		,	sum(tokens_p["""attention_mask"""]			) / len(tokens_p["""attention_mask"""]			)		,	)
  
      __UpperCamelCase				      =  tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]			)
      __UpperCamelCase				      =  tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]			)
  
      # Rust correctly handles the space before the mask while python doesnt
      self.assertSequenceEqual(tokens_p["""input_ids"""]		,	[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]			)
      self.assertSequenceEqual(tokens_r["""input_ids"""]		,	[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]			)
  
      self.assertSequenceEqual(
          lowercase		,	["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]			)
      self.assertSequenceEqual(
          lowercase		,	["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]			)
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  TaConfig.from_json_file(__A       )
   print(f"Building PyTorch model from configuration: {config}"       )
   __UpperCamelCase				      =  TaForConditionalGeneration(__A       )
  
   # Load weights from tf checkpoint
   load_tf_weights_in_ta(__A							,__A							,__A       )
  
   # Save pytorch-model
   print(f"Save PyTorch model to {pytorch_dump_path}"       )
   model.save_pretrained(__A       )
  
  
  if __name__ == "__main__":
   a__						:			int										=			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.'
   )
   a__						:			Optional[Any]										=			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  
  
  
  