



'''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>"""]			)










'''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)





