
"""simple docstring"""




import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin


A__      :	List[str]          =    get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model""")







@require_sentencepiece
@require_tokenizers
class   _lowercase						(       lowerCAmelCase_	,   unittest.TestCase				):


     '''simple docstring'''





     _A								=							GPTSwaTokenizer
     _A								=							False
     _A								=							True
     _A								=							False



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

          # We have a SentencePiece fixture for testing
          UpperCAmelCase__							: Any 							=						GPTSwaTokenizer(__a ,			eos_token="<unk>" ,			bos_token="<unk>" ,			pad_token="<unk>"  )

          tokenizer.save_pretrained(self.tmpdirname  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Union[str, Any]:
          UpperCAmelCase__							: int 							=						"This is a test"
          UpperCAmelCase__							: Union[str, Any] 							=						"This is a test"
          return input_text, output_text



     def  lowerCAmelCase__       (      self  )->					List[str]:
          UpperCAmelCase__							: Any 							=						"<s>"
          UpperCAmelCase__							: 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  lowerCAmelCase__       (      self  )->					Any:
          UpperCAmelCase__							: Union[str, Any] 							=						list(self.get_tokenizer().get_vocab().keys()  )

          self.assertEqual(vocab_keys[0] ,			"<unk>"  )
          self.assertEqual(vocab_keys[1] ,			"<s>"  )
          self.assertEqual(vocab_keys[-1] ,			"j"  )
          self.assertEqual(len(__a  ) ,			20_00  )



     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          self.assertEqual(self.get_tokenizer().vocab_size ,			20_00  )



     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          UpperCAmelCase__							: str 							=						GPTSwaTokenizer(__a  )

          UpperCAmelCase__							: Union[str, Any] 							=						tokenizer.tokenize("This is a test"  )
          self.assertListEqual(__a ,			["▁This", "▁is", "▁a", "▁t", "est"]  )

          self.assertListEqual(tokenizer.convert_tokens_to_ids(__a  ) ,			[4_65, 2_87, 2_65, 6_31, 8_42]  )

          UpperCAmelCase__							: Tuple 							=						tokenizer.tokenize("I was born in 92000, and this is falsé."  )
          # fmt: off
          self.assertListEqual(
              __a ,			["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."] ,			)
          # fmt: on

          UpperCAmelCase__							: Dict 							=						tokenizer.convert_tokens_to_ids(__a  )
          self.assertListEqual(
              __a ,			[2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60] ,			)

          UpperCAmelCase__							: Optional[Any] 							=						tokenizer.convert_ids_to_tokens(__a  )
          # fmt: off
          self.assertListEqual(
              __a ,			["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."]  )
          # fmt: on



     def  lowerCAmelCase__       (      self  )->					Dict:
          UpperCAmelCase__							: Tuple 							=						GPTSwaTokenizer(__a  )
          UpperCAmelCase__							: Dict 							=						["This is a test", "I was born in 92000, and this is falsé."]
          UpperCAmelCase__							: Union[str, Any] 							=						[
              [4_65, 2_87, 2_65, 6_31, 8_42],
              [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60],
          ]

          # Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
          for text, expected_ids in zip(__a ,			__a  ):
               self.assertListEqual(tokenizer.encode_fast(__a  ) ,			__a  )

          # Test that decode_fast returns the input text
          for text, token_ids in zip(__a ,			__a  ):
               self.assertEqual(tokenizer.decode_fast(__a  ) ,			__a  )



     @slow
     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          UpperCAmelCase__							: Optional[int] 							=						[
              "<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')",
              "Hey there, how are you doing this fine day?",
              "This is a text with a trailing spaces followed by a dot     .",
              "Häj sväjs lillebrör! =)",
              "Det är inget fel på Mr. Cool",
          ]

          # fmt: off
          UpperCAmelCase__							: str 							=						{"input_ids": [[6_34_23, 5, 68_11, 1_49_54, 2_82, 8_16, 38_21, 6_34_66, 6_34_25, 6_34_62, 18, 6_39_78, 6_78, 3_01, 13_20, 6_34_23, 6_34_55, 6_34_58, 18, 6_39_82, 42_46, 39_40, 19_01, 4_77_89, 55_47, 1_89_94], [1_96_30, 11_00, 6_34_46, 13_42, 6_33, 5_44, 44_88, 5_93, 51_02, 24_16, 6_34_95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [16_52, 4_28, 2_68, 19_36, 5_15, 2_68, 5_85_93, 2_24_13, 91_06, 5_46, 2_68, 3_32_13, 6_39_79, 6_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_51_30, 6_34_50, 9_24, 6_34_49, 22_49, 40_62, 15_58, 3_18, 6_35_04, 2_14_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_09, 3_77, 28_27, 25_59, 3_32, 65_75, 6_34_43, 2_68_01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 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, 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, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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], [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]]}
          # fmt: on
          self.tokenizer_integration_test_util(
              expected_encoding=__a ,			model_name="AI-Sweden/gpt-sw3-126m" ,			sequences=__a ,			)







"""simple docstring"""




import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def 							a__ (				lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       List[Any]		):






     '''simple docstring'''
     # Construct model
     if gpta_config_file == "":
          UpperCAmelCase__							: Optional[int] 							=						GPTaConfig()
     else:
          UpperCAmelCase__							: Dict 							=						GPTaConfig.from_json_file(lowerCAmelCase		)
     UpperCAmelCase__							: Optional[Any] 							=						GPTaModel(lowerCAmelCase		)

     # Load weights from numpy
     load_tf_weights_in_gpta(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase		)

     # Save pytorch-model
     UpperCAmelCase__							: Optional[int] 							=						pytorch_dump_folder_path + "/" + WEIGHTS_NAME
     UpperCAmelCase__							: Any 							=						pytorch_dump_folder_path + "/" + CONFIG_NAME
     print(F"Save PyTorch model to {pytorch_weights_dump_path}"		)
     torch.save(model.state_dict()       ,    lowerCAmelCase		)
     print(F"Save configuration file to {pytorch_config_dump_path}"		)
     with open(lowerCAmelCase       ,    "w"       ,    encoding="utf-8"		) as f:
          f.write(config.to_json_string()		)


if __name__ == "__main__":
    A__      :	Optional[int]          =    argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--gpt2_config_file""",
        default="""""",
        type=str,
        help=(
            """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
            """This specifies the model architecture."""
        ),
    )
    A__      :	Optional[Any]          =    parser.parse_args()
    convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)



