





'''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


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

@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase_		(		__magic_name__    ,unittest.TestCase		):
 __lowerCamelCase    :				int								=       PegasusTokenizer
 __lowerCamelCase    :				Union[str, Any]								=       PegasusTokenizerFast
 __lowerCamelCase    :				Tuple								=       True
 __lowerCamelCase    :				Dict								=       True




 def 				_snake_case (						self		)     ->				Tuple:
     super().setUp()

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




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




 def 				_snake_case (						self      ,  **_lowerCAmelCase		)     ->				PegasusTokenizer:
     return PegasusTokenizer.from_pretrained(self.tmpdirname      ,  **_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				str:
     return ("This is a test", "This is a test")




 def 				_snake_case (						self		)     ->				str:
     _lowerCAmelCase						      =   "</s>"
     _lowerCAmelCase						      =   1

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




 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase						      =   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(_lowerCAmelCase		)      ,  1103		)




 def 				_snake_case (						self		)     ->				Optional[int]:
     self.assertEqual(self.get_tokenizer().vocab_size      ,  1103		)




 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase						      =   self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
     _lowerCAmelCase						      =   self.tokenizer_class.from_pretrained(self.tmpdirname		)
     _lowerCAmelCase						      =   (
         "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>"
     )
     _lowerCAmelCase						      =   rust_tokenizer([raw_input_str]      ,  return_tensors=_lowerCAmelCase      ,  add_special_tokens=_lowerCAmelCase		).input_ids[0]
     _lowerCAmelCase						      =   py_tokenizer([raw_input_str]      ,  return_tensors=_lowerCAmelCase      ,  add_special_tokens=_lowerCAmelCase		).input_ids[0]
     self.assertListEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




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




 def 				_snake_case (						self		)     ->				int:
     _lowerCAmelCase						      =   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
     _lowerCAmelCase						      =   "To ensure a smooth flow of bank resolutions."
     _lowerCAmelCase						      =   [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
     _lowerCAmelCase						      =   tokenizer([raw_input_str]      ,  return_tensors=_lowerCAmelCase		).input_ids[0]
     self.assertListEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)
     assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]		) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]




 @require_torch
 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   ["This is going to be way too long." * 150, "short example"]
     _lowerCAmelCase						      =   ["not super long but more than 5 tokens", "tiny"]
     _lowerCAmelCase						      =   self._large_tokenizer(_lowerCAmelCase      ,  padding=_lowerCAmelCase      ,  truncation=_lowerCAmelCase      ,  return_tensors="pt"		)
     _lowerCAmelCase						      =   self._large_tokenizer(
         text_target=_lowerCAmelCase      ,  max_length=5      ,  padding=_lowerCAmelCase      ,  truncation=_lowerCAmelCase      ,  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(_lowerCAmelCase		) == 2  # input_ids, attention_mask.





 @slow
 def 				_snake_case (						self		)     ->				Dict:
     # fmt: off
     _lowerCAmelCase						      =   {"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=_lowerCAmelCase      ,  model_name="google/bigbird-pegasus-large-arxiv"      ,  revision="ba85d0851d708441f91440d509690f1ab6353415"      ,  )







@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase_		(		__magic_name__    ,unittest.TestCase		):
 __lowerCamelCase    :				Optional[Any]								=       PegasusTokenizer
 __lowerCamelCase    :				str								=       PegasusTokenizerFast
 __lowerCamelCase    :				List[Any]								=       True
 __lowerCamelCase    :				int								=       True




 def 				_snake_case (						self		)     ->				Optional[Any]:
     super().setUp()

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




 @cached_property
 def 				_snake_case (						self		)     ->				List[Any]:
     return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv"		)




 def 				_snake_case (						self      ,  **_lowerCAmelCase		)     ->				PegasusTokenizer:
     return PegasusTokenizer.from_pretrained(self.tmpdirname      ,  **_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Dict:
     return ("This is a test", "This is a test")




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




 @require_torch
 def 				_snake_case (						self		)     ->				str:
     _lowerCAmelCase						      =   ["This is going to be way too long." * 1000, "short example"]
     _lowerCAmelCase						      =   ["not super long but more than 5 tokens", "tiny"]
     _lowerCAmelCase						      =   self._large_tokenizer(_lowerCAmelCase      ,  padding=_lowerCAmelCase      ,  truncation=_lowerCAmelCase      ,  return_tensors="pt"		)
     _lowerCAmelCase						      =   self._large_tokenizer(
         text_target=_lowerCAmelCase      ,  max_length=5      ,  padding=_lowerCAmelCase      ,  truncation=_lowerCAmelCase      ,  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(_lowerCAmelCase		) == 2  # input_ids, attention_mask.





 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   (
         "This is an example string that is used to test the original TF implementation against the HF"
         " implementation"
     )

     _lowerCAmelCase						      =   self._large_tokenizer(_lowerCAmelCase		).input_ids

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









import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()


def       UpperCamelCase__(			UpperCamelCase__     :			Any					,	UpperCamelCase__     :			int					,	UpperCamelCase__     :			List[Any]    )->Optional[int]:
       # Initialise PyTorch model
       A__		   =		RemBertConfig.from_json_file(UpperCamelCase__    )
       print('''Building PyTorch model from configuration: {}'''.format(str(UpperCamelCase__    )    )    )
       A__		   =		RemBertModel(UpperCamelCase__    )

       # Load weights from tf checkpoint
       load_tf_weights_in_rembert(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__    )

       # Save pytorch-model
       print('''Save PyTorch model to {}'''.format(UpperCamelCase__    )    )
       torch.save(model.state_dict()					,	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(
           '--rembert_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained RemBERT 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.'
       )
       a__: Union[str, Any]							   =						parser.parse_args()
       convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)





