
"""simple docstring"""





import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM



def  lowerCamelCase__						(     __snake_case      )				->						Optional[int]:


					"""simple docstring"""





					if "model" in orig_key:
										_UpperCamelCase									=    orig_key.replace('''model.''',	''''''      )
					if "norm1" in orig_key:
										_UpperCamelCase									=    orig_key.replace('''norm1''',	'''attention.output.LayerNorm'''      )
					if "norm2" in orig_key:
										_UpperCamelCase									=    orig_key.replace('''norm2''',	'''output.LayerNorm'''      )
					if "norm" in orig_key:
										_UpperCamelCase									=    orig_key.replace('''norm''',	'''LayerNorm'''      )
					if "transformer" in orig_key:
										_UpperCamelCase									=    orig_key.split('''.'''      )[0].split('''_'''      )[-1]
										_UpperCamelCase									=    orig_key.replace(F'''transformer_{layer_num}''',	F'''encoder.layer.{layer_num}'''      )
					if "mha.attn" in orig_key:
										_UpperCamelCase									=    orig_key.replace('''mha.attn''',	'''attention.self'''      )
					if "mha" in orig_key:
										_UpperCamelCase									=    orig_key.replace('''mha''',	'''attention'''      )
					if "W_q" in orig_key:
										_UpperCamelCase									=    orig_key.replace('''W_q''',	'''self.query'''      )
					if "W_k" in orig_key:
										_UpperCamelCase									=    orig_key.replace('''W_k''',	'''self.key'''      )
					if "W_v" in orig_key:
										_UpperCamelCase									=    orig_key.replace('''W_v''',	'''self.value'''      )
					if "ff1" in orig_key:
										_UpperCamelCase									=    orig_key.replace('''ff1''',	'''intermediate.dense'''      )
					if "ff2" in orig_key:
										_UpperCamelCase									=    orig_key.replace('''ff2''',	'''output.dense'''      )
					if "ff" in orig_key:
										_UpperCamelCase									=    orig_key.replace('''ff''',	'''output.dense'''      )
					if "mlm_class" in orig_key:
										_UpperCamelCase									=    orig_key.replace('''mlm.mlm_class''',	'''cls.predictions.decoder'''      )
					if "mlm" in orig_key:
										_UpperCamelCase									=    orig_key.replace('''mlm''',	'''cls.predictions.transform'''      )
					if "cls" not in orig_key:
										_UpperCamelCase									=    """yoso.""" + orig_key

					return orig_key



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						Union[str, Any]:


					"""simple docstring"""





					for key in orig_state_dict.copy().keys():
										_UpperCamelCase									=    orig_state_dict.pop(snake_case__      )

										if ("pooler" in key) or ("sen_class" in key):
															continue
										else:
															_UpperCamelCase									=    val

					_UpperCamelCase									=    orig_state_dict["""cls.predictions.decoder.bias"""]
					_UpperCamelCase									=    torch.arange(snake_case__      ).expand((1, -1)      ) + 2

					return orig_state_dict



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						str:


					"""simple docstring"""





					_UpperCamelCase									=    torch.load(snake_case__,	map_location='''cpu'''      )["""model_state_dict"""]
					_UpperCamelCase									=    YosoConfig.from_json_file(snake_case__      )
					_UpperCamelCase									=    YosoForMaskedLM(snake_case__      )

					_UpperCamelCase									=    convert_checkpoint_helper(config.max_position_embeddings,	snake_case__      )

					print(model.load_state_dict(snake_case__      )      )
					model.eval()
					model.save_pretrained(snake_case__      )

					print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''      )


if __name__ == "__main__":
				_a								=						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to YOSO pytorch checkpoint."""
				)
				parser.add_argument(
				    """--config_file""",
				    default=None,
				    type=str,
				    required=True,
				    help="""The json file for YOSO model config.""",
				)
				parser.add_argument(
				    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
				)
				_a								=						parser.parse_args()
				convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)














import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class       snake_case__ (				lowercase_						,     unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase				:					List[str]								=			GPTaTokenizer
       lowerCamelCase				:					Optional[int]								=			GPTaTokenizerFast
       lowerCamelCase				:					List[Any]								=			True
       lowerCamelCase				:					List[str]								=			{"add_prefix_space": True}
       lowerCamelCase				:					Optional[int]								=			False



       def        __lowercase    (				self     )			->   int:






            '''simple docstring'''





            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            __snake_case      :Optional[Any] 							=    [
                """l""",
                """o""",
                """w""",
                """e""",
                """r""",
                """s""",
                """t""",
                """i""",
                """d""",
                """n""",
                """\u0120""",
                """\u0120l""",
                """\u0120n""",
                """\u0120lo""",
                """\u0120low""",
                """er""",
                """\u0120lowest""",
                """\u0120newer""",
                """\u0120wider""",
                """<unk>""",
                """<|endoftext|>""",
            ]
            __snake_case      :Dict 							=    dict(zip(a__      ,    range(len(a__     )     )     )     )
            __snake_case      :List[Any] 							=    ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
            __snake_case      :List[str] 							=    {"""unk_token""": """<unk>"""}

            __snake_case      :Dict 							=    os.path.join(self.tmpdirname      ,    VOCAB_FILES_NAMES["""vocab_file"""]     )
            __snake_case      :Optional[Any] 							=    os.path.join(self.tmpdirname      ,    VOCAB_FILES_NAMES["""merges_file"""]     )
            with open(self.vocab_file      ,    """w"""      ,    encoding="""utf-8"""     ) as fp:
                 fp.write(json.dumps(a__     ) + """\n"""     )
            with open(self.merges_file      ,    """w"""      ,    encoding="""utf-8"""     ) as fp:
                 fp.write("""\n""".join(a__     )     )



       def        __lowercase    (				self      ,    **a__     )			->   Any:






            '''simple docstring'''





            kwargs.update(self.special_tokens_map     )
            return GPTaTokenizer.from_pretrained(self.tmpdirname      ,    **a__     )



       def        __lowercase    (				self      ,    **a__     )			->   Tuple:






            '''simple docstring'''





            kwargs.update(self.special_tokens_map     )
            return GPTaTokenizerFast.from_pretrained(self.tmpdirname      ,    **a__     )



       def        __lowercase    (				self      ,    a__     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    """lower newer"""
            __snake_case      :Any 							=    """lower newer"""
            return input_text, output_text



       def        __lowercase    (				self     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    GPTaTokenizer(self.vocab_file      ,    self.merges_file      ,    **self.special_tokens_map     )
            __snake_case      :List[Any] 							=    """lower newer"""
            __snake_case      :int 							=    ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
            __snake_case      :Any 							=    tokenizer.tokenize(a__      ,    add_prefix_space=a__     )
            self.assertListEqual(a__      ,    a__     )

            __snake_case      :List[Any] 							=    tokens + [tokenizer.unk_token]
            __snake_case      :List[Any] 							=    [14, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(a__     )      ,    a__     )



       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            if not self.test_rust_tokenizer:
                 return

            __snake_case      :Union[str, Any] 							=    self.get_tokenizer()
            __snake_case      :Optional[Any] 							=    self.get_rust_tokenizer(add_prefix_space=a__     )

            __snake_case      :Optional[int] 							=    """lower newer"""

            # Testing tokenization
            __snake_case      :List[Any] 							=    tokenizer.tokenize(a__      ,    add_prefix_space=a__     )
            __snake_case      :int 							=    rust_tokenizer.tokenize(a__     )
            self.assertListEqual(a__      ,    a__     )

            # Testing conversion to ids without special tokens
            __snake_case      :List[str] 							=    tokenizer.encode(a__      ,    add_special_tokens=a__      ,    add_prefix_space=a__     )
            __snake_case      :int 							=    rust_tokenizer.encode(a__      ,    add_special_tokens=a__     )
            self.assertListEqual(a__      ,    a__     )

            # Testing conversion to ids with special tokens
            __snake_case      :Union[str, Any] 							=    self.get_rust_tokenizer(add_prefix_space=a__     )
            __snake_case      :Optional[int] 							=    tokenizer.encode(a__      ,    add_prefix_space=a__     )
            __snake_case      :Optional[int] 							=    rust_tokenizer.encode(a__     )
            self.assertListEqual(a__      ,    a__     )

            # Testing the unknown token
            __snake_case      :Dict 							=    tokens + [rust_tokenizer.unk_token]
            __snake_case      :Optional[int] 							=    [14, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(a__     )      ,    a__     )



       def        __lowercase    (				self      ,    *a__      ,    **a__     )			->   Any:






            '''simple docstring'''





            pass



       def        __lowercase    (				self      ,    a__=15     )			->   Optional[Any]:






            '''simple docstring'''





            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
                      __snake_case      :Optional[int] 							=    self.rust_tokenizer_class.from_pretrained(a__      ,    **a__     )

                      # Simple input
                      __snake_case      :List[str] 							=    """This is a simple input"""
                      __snake_case      :List[str] 							=    ["""This is a simple input 1""", """This is a simple input 2"""]
                      __snake_case      :Tuple 							=    ("""This is a simple input""", """This is a pair""")
                      __snake_case      :Union[str, Any] 							=    [
                          ("""This is a simple input 1""", """This is a simple input 2"""),
                          ("""This is a simple pair 1""", """This is a simple pair 2"""),
                      ]

                      # Simple input tests
                      self.assertRaises(a__      ,    tokenizer_r.encode      ,    a__      ,    max_length=a__      ,    padding="""max_length"""     )

                      # Simple input
                      self.assertRaises(a__      ,    tokenizer_r.encode_plus      ,    a__      ,    max_length=a__      ,    padding="""max_length"""     )

                      # Simple input
                      self.assertRaises(
                          a__      ,    tokenizer_r.batch_encode_plus      ,    a__      ,    max_length=a__      ,    padding="""max_length"""      ,    )

                      # Pair input
                      self.assertRaises(a__      ,    tokenizer_r.encode      ,    a__      ,    max_length=a__      ,    padding="""max_length"""     )

                      # Pair input
                      self.assertRaises(a__      ,    tokenizer_r.encode_plus      ,    a__      ,    max_length=a__      ,    padding="""max_length"""     )

                      # Pair input
                      self.assertRaises(
                          a__      ,    tokenizer_r.batch_encode_plus      ,    a__      ,    max_length=a__      ,    padding="""max_length"""      ,    )



       def        __lowercase    (				self     )			->   int:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    GPTaTokenizer.from_pretrained(self.tmpdirname      ,    pad_token="""<pad>"""     )

            # Simple input
            __snake_case      :Tuple 							=    """This is a simple input"""
            __snake_case      :Optional[int] 							=    ["""This is a simple input looooooooong""", """This is a simple input"""]
            __snake_case      :List[str] 							=    ("""This is a simple input""", """This is a pair""")
            __snake_case      :List[str] 							=    [
                ("""This is a simple input loooooong""", """This is a simple input"""),
                ("""This is a simple pair loooooong""", """This is a simple pair"""),
            ]

            __snake_case      :Tuple 							=    tokenizer.pad_token_id

            __snake_case      :int 							=    tokenizer(a__      ,    padding="""max_length"""      ,    max_length=30      ,    return_tensors="""np"""     )
            __snake_case      :Optional[Any] 							=    tokenizer(a__      ,    padding=a__      ,    truncate=a__      ,    return_tensors="""np"""     )
            __snake_case      :List[Any] 							=    tokenizer(*a__      ,    padding="""max_length"""      ,    max_length=60      ,    return_tensors="""np"""     )
            __snake_case      :Optional[int] 							=    tokenizer(a__      ,    padding=a__      ,    truncate=a__      ,    return_tensors="""np"""     )

            # s
            # test single string max_length padding
            self.assertEqual(out_s["""input_ids"""].shape[-1]      ,    30     )
            self.assertTrue(pad_token_id in out_s["""input_ids"""]     )
            self.assertTrue(0 in out_s["""attention_mask"""]     )

            # s2
            # test automatic padding
            self.assertEqual(out_sa["""input_ids"""].shape[-1]      ,    33     )
            # long slice doesn't have padding
            self.assertFalse(pad_token_id in out_sa["""input_ids"""][0]     )
            self.assertFalse(0 in out_sa["""attention_mask"""][0]     )
            # short slice does have padding
            self.assertTrue(pad_token_id in out_sa["""input_ids"""][1]     )
            self.assertTrue(0 in out_sa["""attention_mask"""][1]     )

            # p
            # test single pair max_length padding
            self.assertEqual(out_p["""input_ids"""].shape[-1]      ,    60     )
            self.assertTrue(pad_token_id in out_p["""input_ids"""]     )
            self.assertTrue(0 in out_p["""attention_mask"""]     )

            # p2
            # test automatic padding pair
            self.assertEqual(out_pa["""input_ids"""].shape[-1]      ,    52     )
            # long slice pair doesn't have padding
            self.assertFalse(pad_token_id in out_pa["""input_ids"""][0]     )
            self.assertFalse(0 in out_pa["""attention_mask"""][0]     )
            # short slice pair does have padding
            self.assertTrue(pad_token_id in out_pa["""input_ids"""][1]     )
            self.assertTrue(0 in out_pa["""attention_mask"""][1]     )



       def        __lowercase    (				self     )			->   List[str]:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    """$$$"""
            __snake_case      :Optional[Any] 							=    GPTaTokenizer.from_pretrained(self.tmpdirname      ,    bos_token=a__      ,    add_bos_token=a__     )

            __snake_case      :List[Any] 							=    """This is a simple input"""
            __snake_case      :Tuple 							=    ["""This is a simple input 1""", """This is a simple input 2"""]

            __snake_case      :Tuple 							=    tokenizer.bos_token_id

            __snake_case      :List[str] 							=    tokenizer(a__     )
            __snake_case      :Any 							=    tokenizer(a__     )

            self.assertEqual(out_s.input_ids[0]      ,    a__     )
            self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids     )     )

            __snake_case      :int 							=    tokenizer.decode(out_s.input_ids     )
            __snake_case      :Tuple 							=    tokenizer.batch_decode(out_sa.input_ids     )

            self.assertEqual(decode_s.split()[0]      ,    a__     )
            self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa     )     )



       def        __lowercase    (				self     )			->   str:






            '''simple docstring'''





            pass



       def        __lowercase    (				self     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :List[str] 							=    [self.get_tokenizer(do_lower_case=a__      ,    add_bos_token=a__     )]
            for tokenizer in tokenizers:
                 with self.subTest(F'''{tokenizer.__class__.__name__}'''     ):
                      __snake_case      :Tuple 							=    """Encode this."""
                      __snake_case      :Tuple 							=    """This one too please."""
                      __snake_case      :Union[str, Any] 							=    tokenizer.encode(a__      ,    add_special_tokens=a__     )
                      encoded_sequence += tokenizer.encode(a__      ,    add_special_tokens=a__     )
                      __snake_case      :List[str] 							=    tokenizer.encode_plus(
                          a__      ,    a__      ,    add_special_tokens=a__      ,    return_special_tokens_mask=a__      ,    )
                      __snake_case      :Union[str, Any] 							=    encoded_sequence_dict["""input_ids"""]
                      __snake_case      :Optional[Any] 							=    encoded_sequence_dict["""special_tokens_mask"""]
                      self.assertEqual(len(a__     )      ,    len(a__     )     )

                      __snake_case      :Optional[int] 							=    [
                          (x if not special_tokens_mask[i] else None) for i, x in enumerate(a__     )
                      ]
                      __snake_case      :str 							=    [x for x in filtered_sequence if x is not None]
                      self.assertEqual(a__      ,    a__     )




@require_tokenizers
class       snake_case__ (				unittest.TestCase):

       '''simple docstring'''



       def        __lowercase    (				self     )			->   Any:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    AutoTokenizer.from_pretrained("""facebook/opt-350m"""      ,    from_slow=a__     )
            __snake_case      :Union[str, Any] 							=    """A photo of a cat"""

            __snake_case      :int 							=    tokenizer.encode(
                a__      ,    )
            self.assertEqual(a__      ,    [2, 2_50, 13_45, 9, 10, 47_58]     )
            tokenizer.save_pretrained("""test_opt"""     )

            __snake_case      :int 							=    AutoTokenizer.from_pretrained("""./test_opt"""     )
            __snake_case      :Tuple 							=    tokenizer.encode(
                a__      ,    )
            self.assertEqual(a__      ,    [2, 2_50, 13_45, 9, 10, 47_58]     )



       def        __lowercase    (				self     )			->   Any:






            '''simple docstring'''





            __snake_case      :Tuple 							=    AutoTokenizer.from_pretrained("""facebook/opt-350m"""      ,    use_slow=a__     )
            __snake_case      :str 							=    """A photo of a cat"""

            __snake_case      :List[str] 							=    tokenizer.encode(
                a__      ,    )
            # Same as above
            self.assertEqual(a__      ,    [2, 2_50, 13_45, 9, 10, 47_58]     )



       @unittest.skip("""This test is failing because of a bug in the fast tokenizer"""     )
       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            __snake_case      :int 							=    AutoTokenizer.from_pretrained("""facebook/opt-350m"""      ,    from_slow=a__     )

            __snake_case      :Optional[Any] 							=    """bos"""
            __snake_case      :int 							=    tokenizer.get_vocab()["""bos"""]

            __snake_case      :str 							=    """A photo of a cat"""
            __snake_case      :int 							=    tokenizer.encode(
                a__      ,    )
            # We changed the bos token
            self.assertEqual(a__      ,    [3_19_57, 2_50, 13_45, 9, 10, 47_58]     )
            tokenizer.save_pretrained("""./tok"""     )
            __snake_case      :Dict 							=    AutoTokenizer.from_pretrained("""./tok"""     )
            self.assertTrue(tokenizer.is_fast     )
            __snake_case      :List[Any] 							=    tokenizer.encode(
                a__      ,    )
            self.assertEqual(a__      ,    [3_19_57, 2_50, 13_45, 9, 10, 47_58]     )


