

"""simple docstring"""
import argparse
import os
import re

import packaging.version


_A							=  """examples/"""
_A							=  {
    """examples""": (re.compile(R"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
    """init""": (re.compile(R"""^__version__\s+=\s+\"([^\"]+)\"\s*$""", re.MULTILINE), """__version__ = \"VERSION\"\n"""),
    """setup""": (re.compile(R"""^(\s*)version\s*=\s*\"[^\"]+\",""", re.MULTILINE), R"""\1version=\"VERSION\","""),
    """doc""": (re.compile(R"""^(\s*)release\s*=\s*\"[^\"]+\"$""", re.MULTILINE), """release = \"VERSION\"\n"""),
}
_A							=  {
    """init""": """src/transformers/__init__.py""",
    """setup""": """setup.py""",
}
_A							=  """README.md"""




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> int:
      with open(lowerCAmelCase			,	"""r"""			,	encoding="""utf-8"""			,	newline="""\n"""    ) as f:
            UpperCAmelCase__			:							List[Any]							=    f.read()
      UpperCAmelCase__					,		UpperCAmelCase__			:							Optional[Any]							=    REPLACE_PATTERNS[pattern]
      UpperCAmelCase__			:							Optional[int]							=    replace.replace("""VERSION"""			,	lowerCAmelCase    )
      UpperCAmelCase__			:							List[Any]							=    re_pattern.sub(lowerCAmelCase			,	lowerCAmelCase    )
      with open(lowerCAmelCase			,	"""w"""			,	encoding="""utf-8"""			,	newline="""\n"""    ) as f:
            f.write(lowerCAmelCase    )




def 					a__						(						lowerCAmelCase    )	-> str:
      for folder, directories, fnames in os.walk(lowerCAmelCase    ):
            # Removing some of the folders with non-actively maintained examples from the walk
            if "research_projects" in directories:
                  directories.remove("""research_projects"""    )
            if "legacy" in directories:
                  directories.remove("""legacy"""    )
            for fname in fnames:
                  if fname.endswith(""".py"""    ):
                        update_version_in_file(os.path.join(lowerCAmelCase			,	lowerCAmelCase    )			,	lowerCAmelCase			,	pattern="""examples"""    )




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase=False    )	-> List[Any]:
      for pattern, fname in REPLACE_FILES.items():
            update_version_in_file(lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )
      if not patch:
            update_version_in_examples(lowerCAmelCase    )




def 					a__						(						)	-> List[Any]:
      UpperCAmelCase__			:							Optional[int]							=    """🤗 Transformers currently provides the following architectures"""
      UpperCAmelCase__			:							Union[str, Any]							=    """1. Want to contribute a new model?"""
      with open(lowerCAmelCase			,	"""r"""			,	encoding="""utf-8"""			,	newline="""\n"""    ) as f:
            UpperCAmelCase__			:							Dict							=    f.readlines()

      # Find the start of the list.
      UpperCAmelCase__			:							Union[str, Any]							=    0
      while not lines[start_index].startswith(_start_prompt    ):
            start_index += 1
      start_index += 1

      UpperCAmelCase__			:							Dict							=    start_index
      # Update the lines in the model list.
      while not lines[index].startswith(_end_prompt    ):
            if lines[index].startswith("""1."""    ):
                  UpperCAmelCase__			:							Optional[int]							=    lines[index].replace(
                      """https://huggingface.co/docs/transformers/main/model_doc"""			,	"""https://huggingface.co/docs/transformers/model_doc"""			,	)
            index += 1

      with open(lowerCAmelCase			,	"""w"""			,	encoding="""utf-8"""			,	newline="""\n"""    ) as f:
            f.writelines(lowerCAmelCase    )




def 					a__						(						)	-> List[Any]:
      with open(REPLACE_FILES["""init"""]			,	"""r"""    ) as f:
            UpperCAmelCase__			:							List[str]							=    f.read()
      UpperCAmelCase__			:							int							=    REPLACE_PATTERNS["""init"""][0].search(lowerCAmelCase    ).groups()[0]
      return packaging.version.parse(lowerCAmelCase    )




def 					a__						(						lowerCAmelCase=False    )	-> List[str]:
      UpperCAmelCase__			:							Any							=    get_version()
      if patch and default_version.is_devrelease:
            raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!"""    )
      if default_version.is_devrelease:
            UpperCAmelCase__			:							Any							=    default_version.base_version
      elif patch:
            UpperCAmelCase__			:							str							=    F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
      else:
            UpperCAmelCase__			:							List[Any]							=    F"""{default_version.major}.{default_version.minor + 1}.0"""

      # Now let's ask nicely if that's the right one.
      UpperCAmelCase__			:							List[Any]							=    input(F"""Which version are you releasing? [{default_version}]"""    )
      if len(lowerCAmelCase    ) == 0:
            UpperCAmelCase__			:							Tuple							=    default_version

      print(F"""Updating version to {version}."""    )
      global_version_update(lowerCAmelCase			,	patch=lowerCAmelCase    )
      if not patch:
            print("""Cleaning main README, don't forget to run `make fix-copies`."""    )
            clean_main_ref_in_model_list()




def 					a__						(						)	-> Any:
      UpperCAmelCase__			:							Union[str, Any]							=    get_version()
      UpperCAmelCase__			:							Tuple							=    F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
      UpperCAmelCase__			:							Any							=    current_version.base_version

      # Check with the user we got that right.
      UpperCAmelCase__			:							Tuple							=    input(F"""Which version are we developing now? [{dev_version}]"""    )
      if len(lowerCAmelCase    ) == 0:
            UpperCAmelCase__			:							Optional[Any]							=    dev_version

      print(F"""Updating version to {version}."""    )
      global_version_update(lowerCAmelCase    )
      print("""Cleaning main README, don't forget to run `make fix-copies`."""    )
      clean_main_ref_in_model_list()


if __name__ == "__main__":
    _A							=  argparse.ArgumentParser()
    parser.add_argument("""--post_release""", action="""store_true""", help="""Whether this is pre or post release.""")
    parser.add_argument("""--patch""", action="""store_true""", help="""Whether or not this is a patch release.""")
    _A							=  parser.parse_args()
    if not args.post_release:
        pre_release_work(patch=args.patch)
    elif args.patch:
        print("""Nothing to do after a patch :-)""")
    else:
        post_release_work()




"""simple docstring"""
import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken




@torch.no_grad()
def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> Tuple:
      # Load configuration defined in the metadata file
      with open(lowerCAmelCase    ) as metadata_file:
            UpperCAmelCase__			:							Optional[int]							=    json.load(lowerCAmelCase    )
      UpperCAmelCase__			:							Optional[int]							=    LukeConfig(use_entity_aware_attention=lowerCAmelCase			,	**metadata["""model_config"""]    )

      # Load in the weights from the checkpoint_path
      UpperCAmelCase__			:							Dict							=    torch.load(lowerCAmelCase			,	map_location="""cpu"""    )

      # Load the entity vocab file
      UpperCAmelCase__			:							List[str]							=    load_entity_vocab(lowerCAmelCase    )

      UpperCAmelCase__			:							Union[str, Any]							=    RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]    )

      # Add special tokens to the token vocabulary for downstream tasks
      UpperCAmelCase__			:							Any							=    AddedToken("""<ent>"""			,	lstrip=lowerCAmelCase			,	rstrip=lowerCAmelCase    )
      UpperCAmelCase__			:							Tuple							=    AddedToken("""<ent2>"""			,	lstrip=lowerCAmelCase			,	rstrip=lowerCAmelCase    )
      tokenizer.add_special_tokens({"""additional_special_tokens""": [entity_token_a, entity_token_a]}    )
      config.vocab_size += 2

      print(F"""Saving tokenizer to {pytorch_dump_folder_path}"""    )
      tokenizer.save_pretrained(lowerCAmelCase    )
      with open(os.path.join(lowerCAmelCase			,	LukeTokenizer.vocab_files_names["""entity_vocab_file"""]    )			,	"""w"""    ) as f:
            json.dump(lowerCAmelCase			,	lowerCAmelCase    )

      UpperCAmelCase__			:							int							=    LukeTokenizer.from_pretrained(lowerCAmelCase    )

      # Initialize the embeddings of the special tokens
      UpperCAmelCase__			:							Any							=    state_dict["""embeddings.word_embeddings.weight"""]
      UpperCAmelCase__			:							Any							=    word_emb[tokenizer.convert_tokens_to_ids(["""@"""]    )[0]].unsqueeze(0    )
      UpperCAmelCase__			:							List[Any]							=    word_emb[tokenizer.convert_tokens_to_ids(["""#"""]    )[0]].unsqueeze(0    )
      UpperCAmelCase__			:							List[Any]							=    torch.cat([word_emb, ent_emb, enta_emb]    )

      # Initialize the query layers of the entity-aware self-attention mechanism
      for layer_index in range(config.num_hidden_layers    ):
            for matrix_name in ["query.weight", "query.bias"]:
                  UpperCAmelCase__			:							Any							=    F"""encoder.layer.{layer_index}.attention.self."""
                  UpperCAmelCase__			:							List[Any]							=    state_dict[prefix + matrix_name]
                  UpperCAmelCase__			:							Dict							=    state_dict[prefix + matrix_name]
                  UpperCAmelCase__			:							Optional[int]							=    state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
      UpperCAmelCase__			:							List[str]							=    state_dict["""entity_embeddings.entity_embeddings.weight"""]
      UpperCAmelCase__			:							int							=    entity_emb[entity_vocab["""[MASK]"""]]

      UpperCAmelCase__			:							Tuple							=    LukeModel(config=lowerCAmelCase    ).eval()

      UpperCAmelCase__					,		UpperCAmelCase__			:							Optional[Any]							=    model.load_state_dict(lowerCAmelCase			,	strict=lowerCAmelCase    )
      if not (len(lowerCAmelCase    ) == 1 and missing_keys[0] == "embeddings.position_ids"):
            raise ValueError(F"""Missing keys {', '.join(lowerCAmelCase    )}. Expected only missing embeddings.position_ids"""    )
      if not (all(key.startswith("""entity_predictions"""    ) or key.startswith("""lm_head"""    ) for key in unexpected_keys    )):
            raise ValueError(
                """Unexpected keys"""
                F""" {', '.join([key for key in unexpected_keys if not (key.startswith('entity_predictions'    ) or key.startswith('lm_head'    ))]    )}"""    )

      # Check outputs
      UpperCAmelCase__			:							Optional[Any]							=    LukeTokenizer.from_pretrained(lowerCAmelCase			,	task="""entity_classification"""    )

      UpperCAmelCase__			:							Dict							=    (
          """Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"""
          """ new world number one avoid a humiliating second- round exit at Wimbledon ."""
      )
      UpperCAmelCase__			:							List[Any]							=    (39, 42)
      UpperCAmelCase__			:							str							=    tokenizer(lowerCAmelCase			,	entity_spans=[span]			,	add_prefix_space=lowerCAmelCase			,	return_tensors="""pt"""    )

      UpperCAmelCase__			:							List[str]							=    model(**lowerCAmelCase    )

      # Verify word hidden states
      if model_size == "large":
            UpperCAmelCase__			:							Optional[Any]							=    torch.Size((1, 42, 10_24)    )
            UpperCAmelCase__			:							Union[str, Any]							=    torch.tensor(
                [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]    )
      else:  # base
            UpperCAmelCase__			:							Optional[int]							=    torch.Size((1, 42, 7_68)    )
            UpperCAmelCase__			:							Optional[Any]							=    torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]    )

      if not (outputs.last_hidden_state.shape == expected_shape):
            raise ValueError(
                F"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"""    )
      if not torch.allclose(outputs.last_hidden_state[0, :3, :3]			,	lowerCAmelCase			,	atol=1E-4    ):
            raise ValueError

      # Verify entity hidden states
      if model_size == "large":
            UpperCAmelCase__			:							Union[str, Any]							=    torch.Size((1, 1, 10_24)    )
            UpperCAmelCase__			:							str							=    torch.tensor([[0.0466, -0.0106, -0.0179]]    )
      else:  # base
            UpperCAmelCase__			:							Tuple							=    torch.Size((1, 1, 7_68)    )
            UpperCAmelCase__			:							List[str]							=    torch.tensor([[0.1457, 0.1044, 0.0174]]    )

      if not (outputs.entity_last_hidden_state.shape != expected_shape):
            raise ValueError(
                F"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
                F""" {expected_shape}"""    )
      if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]			,	lowerCAmelCase			,	atol=1E-4    ):
            raise ValueError

      # Finally, save our PyTorch model and tokenizer
      print("""Saving PyTorch model to {}""".format(lowerCAmelCase    )    )
      model.save_pretrained(lowerCAmelCase    )




def 					a__						(						lowerCAmelCase    )	-> Union[str, Any]:
      UpperCAmelCase__			:							List[str]							=    {}
      with open(lowerCAmelCase			,	"""r"""			,	encoding="""utf-8"""    ) as f:
            for index, line in enumerate(lowerCAmelCase    ):
                  UpperCAmelCase__					,		UpperCAmelCase__			:							Tuple							=    line.rstrip().split("""\t"""    )
                  UpperCAmelCase__			:							int							=    index

      return entity_vocab


if __name__ == "__main__":
    _A							=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument("""--checkpoint_path""", type=str, help="""Path to a pytorch_model.bin file.""")
    parser.add_argument(
        """--metadata_path""", default=None, type=str, help="""Path to a metadata.json file, defining the configuration."""
    )
    parser.add_argument(
        """--entity_vocab_path""",
        default=None,
        type=str,
        help="""Path to an entity_vocab.tsv file, containing the entity vocabulary.""",
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to where to dump the output PyTorch model."""
    )
    parser.add_argument(
        """--model_size""", default="""base""", type=str, choices=["""base""", """large"""], help="""Size of the model to be converted."""
    )
    _A							=  parser.parse_args()
    convert_luke_checkpoint(
        args.checkpoint_path,
        args.metadata_path,
        args.entity_vocab_path,
        args.pytorch_dump_folder_path,
        args.model_size,
    )

