



"""simple docstring"""







import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase     =							get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")






@require_sentencepiece
@require_tokenizers
class       UpperCamelCase__			(   _lowerCAmelCase       ,		unittest.TestCase      ):

			"""simple docstring"""


			A__	:					Any							  =     PegasusTokenizer
			A__	:					Union[str, Any]							  =     PegasusTokenizerFast
			A__	:					int							  =     True
			A__	:					Optional[Any]							  =     True


			def   snake_case__	(      self		)      ->	List[Any]:
				super().setUp()

				# We have a SentencePiece fixture for testing
				A__											=					PegasusTokenizer(SCREAMING_SNAKE_CASE__		)
				tokenizer.save_pretrained(self.tmpdirname		)


			@cached_property
			def   snake_case__	(      self		)      ->	Optional[Any]:
				return PegasusTokenizer.from_pretrained("google/pegasus-large"		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	PegasusTokenizer:
				return PegasusTokenizer.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	Any:
				return ("This is a test", "This is a test")


			def   snake_case__	(      self		)      ->	Any:
				A__											=					"</s>"
				A__											=					1

				self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE__		)						,	SCREAMING_SNAKE_CASE__		)
				self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE__		)						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	Any:
				A__											=					list(self.get_tokenizer().get_vocab().keys()		)

				self.assertEqual(vocab_keys[0]						,	"<pad>"		)
				self.assertEqual(vocab_keys[1]						,	"</s>"		)
				self.assertEqual(vocab_keys[-1]						,	"v"		)
				self.assertEqual(len(SCREAMING_SNAKE_CASE__		)						,	1103		)


			def   snake_case__	(      self		)      ->	List[Any]:
				self.assertEqual(self.get_tokenizer().vocab_size						,	1103		)


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
				A__											=					self.tokenizer_class.from_pretrained(self.tmpdirname		)
				A__											=					(
				    "Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"
				    " </s> <pad> <pad> <pad>"
				)
				A__											=					rust_tokenizer([raw_input_str]						,	return_tensors=SCREAMING_SNAKE_CASE__						,	add_special_tokens=SCREAMING_SNAKE_CASE__		).input_ids[0]
				A__											=					py_tokenizer([raw_input_str]						,	return_tensors=SCREAMING_SNAKE_CASE__						,	add_special_tokens=SCREAMING_SNAKE_CASE__		).input_ids[0]
				self.assertListEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self._large_tokenizer
				# <mask_1> masks whole sentence while <mask_2> masks single word
				A__											=					"<mask_1> To ensure a <mask_2> flow of bank resolutions."
				A__											=					[2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
				A__											=					tokenizer([raw_input_str]						,	return_tensors=SCREAMING_SNAKE_CASE__		).input_ids[0]
				self.assertListEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	Dict:
				A__											=					self._large_tokenizer
				# The tracebacks for the following asserts are **better** without messages or self.assertEqual
				assert tokenizer.vocab_size == 96103
				assert tokenizer.pad_token_id == 0
				assert tokenizer.eos_token_id == 1
				assert tokenizer.offset == 103
				assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
				assert tokenizer.unk_token == "<unk>"
				assert tokenizer.model_max_length == 1024
				A__											=					"To ensure a smooth flow of bank resolutions."
				A__											=					[413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
				A__											=					tokenizer([raw_input_str]						,	return_tensors=SCREAMING_SNAKE_CASE__		).input_ids[0]
				self.assertListEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
				assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]		) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]


			@require_torch
			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					["This is going to be way too long." * 150, "short example"]
				A__											=					["not super long but more than 5 tokens", "tiny"]
				A__											=					self._large_tokenizer(SCREAMING_SNAKE_CASE__						,	padding=SCREAMING_SNAKE_CASE__						,	truncation=SCREAMING_SNAKE_CASE__						,	return_tensors="pt"		)
				A__											=					self._large_tokenizer(
				    text_target=SCREAMING_SNAKE_CASE__						,	max_length=5						,	padding=SCREAMING_SNAKE_CASE__						,	truncation=SCREAMING_SNAKE_CASE__						,	return_tensors="pt"		)

				assert batch.input_ids.shape == (2, 1024)
				assert batch.attention_mask.shape == (2, 1024)
				assert targets["input_ids"].shape == (2, 5)
				assert len(SCREAMING_SNAKE_CASE__		) == 2  # input_ids, attention_mask.







			@slow
			def   snake_case__	(      self		)      ->	Tuple:
				# fmt: off
				A__											=					{"input_ids": [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
				# fmt: on

				self.tokenizer_integration_test_util(
				    expected_encoding=SCREAMING_SNAKE_CASE__						,	model_name="google/bigbird-pegasus-large-arxiv"						,	revision="ba85d0851d708441f91440d509690f1ab6353415"						,	)






@require_sentencepiece
@require_tokenizers
class       UpperCamelCase__			(   _lowerCAmelCase       ,		unittest.TestCase      ):

			"""simple docstring"""


			A__	:					List[Any]							  =     PegasusTokenizer
			A__	:					List[str]							  =     PegasusTokenizerFast
			A__	:					Optional[Any]							  =     True
			A__	:					Union[str, Any]							  =     True


			def   snake_case__	(      self		)      ->	List[str]:
				super().setUp()

				# We have a SentencePiece fixture for testing
				A__											=					PegasusTokenizer(SCREAMING_SNAKE_CASE__						,	offset=0						,	mask_token_sent=SCREAMING_SNAKE_CASE__						,	mask_token="[MASK]"		)
				tokenizer.save_pretrained(self.tmpdirname		)


			@cached_property
			def   snake_case__	(      self		)      ->	int:
				return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv"		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	PegasusTokenizer:
				return PegasusTokenizer.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	List[str]:
				return ("This is a test", "This is a test")


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
				A__											=					self.tokenizer_class.from_pretrained(self.tmpdirname		)
				A__											=					(
				    "Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
				    " <pad> <pad> <pad>"
				)
				A__											=					rust_tokenizer([raw_input_str]						,	return_tensors=SCREAMING_SNAKE_CASE__						,	add_special_tokens=SCREAMING_SNAKE_CASE__		).input_ids[0]
				A__											=					py_tokenizer([raw_input_str]						,	return_tensors=SCREAMING_SNAKE_CASE__						,	add_special_tokens=SCREAMING_SNAKE_CASE__		).input_ids[0]
				self.assertListEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)


			@require_torch
			def   snake_case__	(      self		)      ->	Union[str, Any]:
				A__											=					["This is going to be way too long." * 1000, "short example"]
				A__											=					["not super long but more than 5 tokens", "tiny"]
				A__											=					self._large_tokenizer(SCREAMING_SNAKE_CASE__						,	padding=SCREAMING_SNAKE_CASE__						,	truncation=SCREAMING_SNAKE_CASE__						,	return_tensors="pt"		)
				A__											=					self._large_tokenizer(
				    text_target=SCREAMING_SNAKE_CASE__						,	max_length=5						,	padding=SCREAMING_SNAKE_CASE__						,	truncation=SCREAMING_SNAKE_CASE__						,	return_tensors="pt"		)

				assert batch.input_ids.shape == (2, 4096)
				assert batch.attention_mask.shape == (2, 4096)
				assert targets["input_ids"].shape == (2, 5)
				assert len(SCREAMING_SNAKE_CASE__		) == 2  # input_ids, attention_mask.







			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					(
				    "This is an example string that is used to test the original TF implementation against the HF"
				    " implementation"
				)

				A__											=					self._large_tokenizer(SCREAMING_SNAKE_CASE__		).input_ids

				self.assertListEqual(
				    SCREAMING_SNAKE_CASE__						,	[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1]						,	)






"""simple docstring"""




import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      int   			=  BigBirdConfig.from_json_file(_UpperCamelCase    )
  print(f'''Building PyTorch model from configuration: {config}'''    )

  if is_trivia_qa:
    _lowercase:      Union[str, Any]   			=  BigBirdForQuestionAnswering(_UpperCamelCase    )
  else:
    _lowercase:      Dict   			=  BigBirdForPreTraining(_UpperCamelCase    )

  # Load weights from tf checkpoint
  load_tf_weights_in_big_bird(_UpperCamelCase			,					_UpperCamelCase			,					is_trivia_qa=_UpperCamelCase    )

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


if __name__ == "__main__":
  A__ :       List[Any]				= 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(
      '--big_bird_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained BERT model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  parser.add_argument(
      '--is_trivia_qa', action='store_true', help='Whether to convert a model with a trivia_qa head.'
  )
  A__ :       Optional[Any]				= parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
  )
