
'''simple docstring'''



import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_lowercase					    =		get_tests_dir("""fixtures/spiece.model""")




@require_sentencepiece
@require_tokenizers
class   UpperCAmelCase_     (		_lowerCamelCase       ,							unittest.TestCase ):






						'''simple docstring'''






						_lowercase		:      Optional[Any]									=   ErnieMTokenizer
						_lowercase		:      Union[str, Any]									=   False
						_lowercase		:      Dict									=   True
						_lowercase		:      Dict									=   False
						_lowercase		:      List[Any]									=   False




						def 					_lowercase	(					self      ):

												"""simple docstring"""



												super().setUp()

												# We have a SentencePiece fixture for testing
												_lowerCAmelCase  						= ErnieMTokenizer(A__    ,   unk_token="""<unk>"""    ,   pad_token="""<pad>"""      )
												tokenizer.save_pretrained(self.tmpdirname      )




						def 					_lowercase	(					self    ,   _lowercase      ):

												"""simple docstring"""



												_lowerCAmelCase  						= """this is a test"""
												_lowerCAmelCase  						= """this is a test"""
												return input_text, output_text




						def 					_lowercase	(					self      ):

												"""simple docstring"""



												_lowerCAmelCase  						= """<pad>"""
												_lowerCAmelCase  						= 0

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




						def 					_lowercase	(					self      ):

												"""simple docstring"""



												_lowerCAmelCase  						= list(self.get_tokenizer().get_vocab().keys()      )

												self.assertEqual(vocab_keys[0]    ,   """<pad>"""      )
												self.assertEqual(vocab_keys[1]    ,   """<unk>"""      )
												self.assertEqual(vocab_keys[-1]    ,   """▁eloquent"""      )
												self.assertEqual(len(A__      )    ,   30_000      )




						def 					_lowercase	(					self      ):

												"""simple docstring"""



												self.assertEqual(self.get_tokenizer().vocab_size    ,   30_000      )




						def 					_lowercase	(					self      ):

												"""simple docstring"""



												if not self.test_rust_tokenizer:
																		return

												_lowerCAmelCase  						= self.get_tokenizer()
												_lowerCAmelCase  						= self.get_rust_tokenizer()

												_lowerCAmelCase  						= """I was born in 92000, and this is falsé."""

												_lowerCAmelCase  						= tokenizer.tokenize(A__      )
												_lowerCAmelCase  						= rust_tokenizer.tokenize(A__      )
												self.assertListEqual(A__    ,   A__      )

												_lowerCAmelCase  						= tokenizer.encode(A__    ,   add_special_tokens=A__      )
												_lowerCAmelCase  						= rust_tokenizer.encode(A__    ,   add_special_tokens=A__      )
												self.assertListEqual(A__    ,   A__      )

												_lowerCAmelCase  						= self.get_rust_tokenizer()
												_lowerCAmelCase  						= tokenizer.encode(A__      )
												_lowerCAmelCase  						= rust_tokenizer.encode(A__      )
												self.assertListEqual(A__    ,   A__      )




						def 					_lowercase	(					self      ):

												"""simple docstring"""



												_lowerCAmelCase  						= ErnieMTokenizer(A__    ,   do_lower_case=A__    ,   unk_token="""<unk>"""    ,   pad_token="""<pad>"""      )

												_lowerCAmelCase  						= tokenizer.tokenize("""This is a test"""      )
												self.assertListEqual(A__    ,   ["""▁this""", """▁is""", """▁a""", """▁test"""]      )

												self.assertListEqual(tokenizer.convert_tokens_to_ids(A__      )    ,   [48, 25, 21, 1_289]      )

												_lowerCAmelCase  						= tokenizer.tokenize("""I was born in 92000, and this is falsé."""      )
												# ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
												self.assertListEqual(
												    A__    ,   ["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""]      )
												_lowerCAmelCase  						= tokenizer.convert_tokens_to_ids(A__      )
												self.assertListEqual(A__    ,   [31, 23, 386, 19, 518, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]      )

												_lowerCAmelCase  						= tokenizer.convert_ids_to_tokens(A__      )
												self.assertListEqual(
												    A__    ,   ["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""]    ,   )




						def 					_lowercase	(					self      ):

												"""simple docstring"""



												_lowerCAmelCase  						= ErnieMTokenizer(A__    ,   unk_token="""<unk>"""    ,   pad_token="""<pad>"""      )

												_lowerCAmelCase  						= tokenizer.encode("""sequence builders"""      )
												_lowerCAmelCase  						= tokenizer.encode("""multi-sequence build"""      )

												_lowerCAmelCase  						= tokenizer.build_inputs_with_special_tokens(A__      )
												_lowerCAmelCase  						= tokenizer.build_inputs_with_special_tokens(A__    ,   A__      )

												assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
												assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
												    tokenizer.sep_token_id
												] + text_a + [tokenizer.sep_token_id]




						@slow
						def 					_lowercase	(					self      ):

												"""simple docstring"""



												_lowerCAmelCase  						= {"""input_ids""": [[0, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 9, 304, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 5, 5, 5, 16, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 6_460, 1_328, 4_589, 42, 122_009, 115_774, 23, 3_559, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [0, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 581, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
												# fmt: on

												self.tokenizer_integration_test_util(
												    expected_encoding=A__    ,   model_name="""susnato/ernie-m-base_pytorch"""    ,   sequences=[
												        """Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides """
												        """general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural """
												        """Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained """
												        """models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.""",
												        """BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly """
												        """conditioning on both left and right context in all layers.""",
												        """The quick brown fox jumps over the lazy dog.""",
												    ]    ,   )


'''simple docstring'''



import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Any:




   """simple docstring"""
   # Initialise PyTorch model
   _SCREAMING_SNAKE_CASE          = BertConfig.from_pretrained(
       """bert-large-cased"""			,					vocab_size=SCREAMING_SNAKE_CASE_			,					max_position_embeddings=5_12			,					is_decoder=SCREAMING_SNAKE_CASE_			,					add_cross_attention=SCREAMING_SNAKE_CASE_			,					)
   _SCREAMING_SNAKE_CASE          = bert_config.to_dict()
   del bert_config_dict["type_vocab_size"]
   _SCREAMING_SNAKE_CASE          = BertGenerationConfig(**SCREAMING_SNAKE_CASE_  )
   if is_encoder:
      _SCREAMING_SNAKE_CASE          = BertGenerationEncoder(SCREAMING_SNAKE_CASE_  )
   else:
      _SCREAMING_SNAKE_CASE          = BertGenerationDecoder(SCREAMING_SNAKE_CASE_  )
   print(F"Building PyTorch model from configuration: {config}"  )

   # Load weights from tf checkpoint
   load_tf_weights_in_bert_generation(
       SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					model_class="""bert"""			,					is_encoder_named_decoder=SCREAMING_SNAKE_CASE_			,					is_encoder=SCREAMING_SNAKE_CASE_			,					)

   # Save pytorch-model
   print(F"Save PyTorch model and config to {pytorch_dump_path}"  )
   model.save_pretrained(SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
     UpperCamelCase__					:						int											=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
     )
     parser.add_argument(
         "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
     )
     parser.add_argument(
         "--is_encoder_named_decoder",
         action="store_true",
         help="If decoder has to be renamed to encoder in PyTorch model.",
     )
     parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
     parser.add_argument("--vocab_size", default=50_358, type=int, help="Vocab size of model")
     UpperCamelCase__					:						int											=     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(
         args.tf_hub_path,
         args.pytorch_dump_path,
         args.is_encoder_named_decoder,
         args.vocab_size,
         is_encoder=args.is_encoder,
     )






