






'''simple docstring'''

import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
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__						:Optional[Any]          =			"""▁"""

UpperCAmelCase__						:Optional[int]          =			get_tests_dir("""fixtures/test_sentencepiece.model""")







@require_sentencepiece
@require_tokenizers
class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_    ,   unittest.TestCase  ):
       snake_case__    :     Union[str, Any]   		=			BigBirdTokenizer
       snake_case__    :     int   		=			BigBirdTokenizerFast
       snake_case__    :     Tuple   		=			True
       snake_case__    :     List[Any]   		=			True




       def 					a_					(					self      : Optional[Any]					):


            """simple docstring"""







            super().setUp()

            __lowerCamelCase						:       Optional[Any]				   =				self.tokenizer_class(A__	,    keep_accents=A__					)
            tokenizer.save_pretrained(self.tmpdirname					)




       def 					a_					(					self      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       str				   =				"""<s>"""
            __lowerCamelCase						:       Optional[int]				   =				1

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




       def 					a_					(					self      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				list(self.get_tokenizer().get_vocab().keys()					)

            self.assertEqual(vocab_keys[0]	,    """<unk>"""					)
            self.assertEqual(vocab_keys[1]	,    """<s>"""					)
            self.assertEqual(vocab_keys[-1]	,    """[MASK]"""					)
            self.assertEqual(len(A__					)	,    1004					)




       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            self.assertEqual(self.get_tokenizer().vocab_size	,    1000					)




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


            """simple docstring"""







            if not self.test_rust_tokenizer:
                 return

            __lowerCamelCase						:       Any				   =				self.get_tokenizer()
            __lowerCamelCase						:       Union[str, Any]				   =				self.get_rust_tokenizer()

            __lowerCamelCase						:       Tuple				   =				"""I was born in 92000, and this is falsé."""

            __lowerCamelCase						:       Any				   =				tokenizer.tokenize(A__					)
            __lowerCamelCase						:       Optional[int]				   =				rust_tokenizer.tokenize(A__					)
            self.assertListEqual(A__	,    A__					)

            __lowerCamelCase						:       Optional[Any]				   =				tokenizer.encode(A__	,    add_special_tokens=A__					)
            __lowerCamelCase						:       Optional[int]				   =				rust_tokenizer.encode(A__	,    add_special_tokens=A__					)
            self.assertListEqual(A__	,    A__					)

            __lowerCamelCase						:       Tuple				   =				self.get_rust_tokenizer()
            __lowerCamelCase						:       Optional[Any]				   =				tokenizer.encode(A__					)
            __lowerCamelCase						:       int				   =				rust_tokenizer.encode(A__					)
            self.assertListEqual(A__	,    A__					)




       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				BigBirdTokenizer(A__	,    keep_accents=A__					)

            __lowerCamelCase						:       List[str]				   =				tokenizer.tokenize("""This is a test"""					)
            self.assertListEqual(A__	,    ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]					)

            self.assertListEqual(
                tokenizer.convert_tokens_to_ids(A__					)	,    [285, 46, 10, 170, 382]	,    )

            __lowerCamelCase						:       List[Any]				   =				tokenizer.tokenize("""I was born in 92000, and this is falsé."""					)
            self.assertListEqual(
                A__	,    [
                    SPIECE_UNDERLINE + """I""",
                    SPIECE_UNDERLINE + """was""",
                    SPIECE_UNDERLINE + """b""",
                    """or""",
                    """n""",
                    SPIECE_UNDERLINE + """in""",
                    SPIECE_UNDERLINE + """""",
                    """9""",
                    """2""",
                    """0""",
                    """0""",
                    """0""",
                    """,""",
                    SPIECE_UNDERLINE + """and""",
                    SPIECE_UNDERLINE + """this""",
                    SPIECE_UNDERLINE + """is""",
                    SPIECE_UNDERLINE + """f""",
                    """al""",
                    """s""",
                    """é""",
                    """.""",
                ]	,    )
            __lowerCamelCase						:       Tuple				   =				tokenizer.convert_tokens_to_ids(A__					)
            self.assertListEqual(
                A__	,    [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]	,    )

            __lowerCamelCase						:       List[Any]				   =				tokenizer.convert_ids_to_tokens(A__					)
            self.assertListEqual(
                A__	,    [
                    SPIECE_UNDERLINE + """I""",
                    SPIECE_UNDERLINE + """was""",
                    SPIECE_UNDERLINE + """b""",
                    """or""",
                    """n""",
                    SPIECE_UNDERLINE + """in""",
                    SPIECE_UNDERLINE + """""",
                    """<unk>""",
                    """2""",
                    """0""",
                    """0""",
                    """0""",
                    """,""",
                    SPIECE_UNDERLINE + """and""",
                    SPIECE_UNDERLINE + """this""",
                    SPIECE_UNDERLINE + """is""",
                    SPIECE_UNDERLINE + """f""",
                    """al""",
                    """s""",
                    """<unk>""",
                    """.""",
                ]	,    )




       @cached_property
       def 					a_					(					self      : Dict					):


            """simple docstring"""







            return BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base"""					)




       @slow
       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				"""Hello World!"""
            __lowerCamelCase						:       Any				   =				[65, 18536, 2260, 101, 66]

            self.assertListEqual(A__	,    self.big_tokenizer.encode(A__					)					)




       @slow
       def 					a_					(					self      : int					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				(
                """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
                """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
            )
            # fmt: off
            __lowerCamelCase						:       Optional[int]				   =				[65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 34324, 497, 391, 408, 11342, 1244, 385, 100, 938, 985, 456, 574, 362, 12597, 3200, 3129, 1172, 66]  # noqa: E231
            # fmt: on
            self.assertListEqual(A__	,    self.big_tokenizer.encode(A__					)					)




       @require_torch
       @slow
       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            import torch

            from transformers import BigBirdConfig, BigBirdModel

            # Build sequence
            __lowerCamelCase						:       Optional[Any]				   =				list(self.big_tokenizer.get_vocab().keys()					)[:10]
            __lowerCamelCase						:       Optional[Any]				   =				""" """.join(A__					)
            __lowerCamelCase						:       Tuple				   =				self.big_tokenizer.encode_plus(A__	,    return_tensors="""pt"""	,    return_token_type_ids=A__					)
            __lowerCamelCase						:       Any				   =				self.big_tokenizer.batch_encode_plus(
                [sequence + """ """ + sequence]	,    return_tensors="""pt"""	,    return_token_type_ids=A__					)

            __lowerCamelCase						:       Any				   =				BigBirdConfig(attention_type="""original_full"""					)
            __lowerCamelCase						:       Optional[Any]				   =				BigBirdModel(A__					)

            assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

            with torch.no_grad():
                 model(**A__					)
                 model(**A__					)




       @slow
       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[Any]				   =				BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base"""					)
            __lowerCamelCase						:       Optional[Any]				   =				tokenizer.decode(tokenizer("""Paris is the [MASK]."""					).input_ids					)

            self.assertTrue(decoded_text == """[CLS] Paris is the[MASK].[SEP]"""					)




       @slow
       def 					a_					(					self      : int					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				{"""input_ids""": [[65, 39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114, 66], [65, 448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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, 0, 0, 0, 0, 0, 0, 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=A__	,    model_name="""google/bigbird-roberta-base"""	,    revision="""215c99f1600e06f83acce68422f2035b2b5c3510"""	,    )










'''simple docstring'''

import argparse

import torch

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


logging.set_verbosity_info()
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     # Initialise PyTorch model
     __lowerCamelCase						:       str				   =				RemBertConfig.from_json_file(_lowercase     )
     print("""Building PyTorch model from configuration: {}""".format(str(_lowercase     )     )     )
     __lowerCamelCase						:       List[Any]				   =				RemBertModel(_lowercase     )

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

     # Save pytorch-model
     print("""Save PyTorch model to {}""".format(_lowercase     )     )
     torch.save(model.state_dict(),						_lowercase     )


if __name__ == "__main__":
   UpperCAmelCase__						:Tuple          =			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."""
   )
   UpperCAmelCase__						:List[Any]          =			parser.parse_args()
   convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)



