

"""simple docstring"""
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
    import transformers




def 					a__						(						lowerCAmelCase    )	-> str:
      random.seed(lowerCAmelCase    )
      np.random.seed(lowerCAmelCase    )
      torch.manual_seed(lowerCAmelCase    )
      torch.cuda.manual_seed_all(lowerCAmelCase    )
      # ^^ safe to call this function even if cuda is not available





class       lowerCamelCase	:


  '''simple docstring'''







  def __init__(self							,       _lowerCamelCase							,       _lowerCamelCase = 0.9_999							,       _lowerCamelCase = 0.0							,       _lowerCamelCase = 0							,       _lowerCamelCase = False							,       _lowerCamelCase = 1.0							,       _lowerCamelCase = 2 / 3							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       **_lowerCamelCase							,       ):


        """simple docstring"""

        if isinstance(_lowerCamelCase							,       torch.nn.Module	):
              UpperCAmelCase__			:							Any							=    (
                  """Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. """
                  """Please pass the parameters of the module instead."""
              )
              deprecate(
                  """passing a `torch.nn.Module` to `ExponentialMovingAverage`"""							,       """1.0.0"""							,       _lowerCamelCase							,       standard_warn=_lowerCamelCase							,       )
              UpperCAmelCase__			:							str							=    parameters.parameters()

              # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
              UpperCAmelCase__			:							List[str]							=    True

        if kwargs.get("""max_value"""							,       _lowerCamelCase	) is not None:
              UpperCAmelCase__			:							Dict							=    """The `max_value` argument is deprecated. Please use `decay` instead."""
              deprecate("""max_value"""							,       """1.0.0"""							,       _lowerCamelCase							,       standard_warn=_lowerCamelCase	)
              UpperCAmelCase__			:							Dict							=    kwargs["""max_value"""]

        if kwargs.get("""min_value"""							,       _lowerCamelCase	) is not None:
              UpperCAmelCase__			:							List[Any]							=    """The `min_value` argument is deprecated. Please use `min_decay` instead."""
              deprecate("""min_value"""							,       """1.0.0"""							,       _lowerCamelCase							,       standard_warn=_lowerCamelCase	)
              UpperCAmelCase__			:							Dict							=    kwargs["""min_value"""]

        UpperCAmelCase__			:							Tuple							=    list(_lowerCamelCase	)
        UpperCAmelCase__			:							int							=    [p.clone().detach() for p in parameters]

        if kwargs.get("""device"""							,       _lowerCamelCase	) is not None:
              UpperCAmelCase__			:							Any							=    """The `device` argument is deprecated. Please use `to` instead."""
              deprecate("""device"""							,       """1.0.0"""							,       _lowerCamelCase							,       standard_warn=_lowerCamelCase	)
              self.to(device=kwargs["""device"""]	)

        UpperCAmelCase__			:							List[Any]							=    None

        UpperCAmelCase__			:							int							=    decay
        UpperCAmelCase__			:							List[str]							=    min_decay
        UpperCAmelCase__			:							str							=    update_after_step
        UpperCAmelCase__			:							int							=    use_ema_warmup
        UpperCAmelCase__			:							List[Any]							=    inv_gamma
        UpperCAmelCase__			:							Tuple							=    power
        UpperCAmelCase__			:							Optional[int]							=    0
        UpperCAmelCase__			:							Union[str, Any]							=    None  # set in `step()`

        UpperCAmelCase__			:							Tuple							=    model_cls
        UpperCAmelCase__			:							List[str]							=    model_config



  @classmethod
  def  _a      (cls							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__					,		UpperCAmelCase__			:							int							=    model_cls.load_config(_lowerCamelCase							,       return_unused_kwargs=_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[int]							=    model_cls.from_pretrained(_lowerCamelCase	)

        UpperCAmelCase__			:							Tuple							=    cls(model.parameters()							,       model_cls=_lowerCamelCase							,       model_config=model.config	)

        ema_model.load_state_dict(_lowerCamelCase	)
        return ema_model



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        if self.model_cls is None:
              raise ValueError("""`save_pretrained` can only be used if `model_cls` was defined at __init__."""	)

        if self.model_config is None:
              raise ValueError("""`save_pretrained` can only be used if `model_config` was defined at __init__."""	)

        UpperCAmelCase__			:							Optional[Any]							=    self.model_cls.from_config(self.model_config	)
        UpperCAmelCase__			:							List[str]							=    self.state_dict()
        state_dict.pop("""shadow_params"""							,       _lowerCamelCase	)

        model.register_to_config(**_lowerCamelCase	)
        self.copy_to(model.parameters()	)
        model.save_pretrained(_lowerCamelCase	)



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    max(0							,       optimization_step - self.update_after_step - 1	)

        if step <= 0:
              return 0.0

        if self.use_ema_warmup:
              UpperCAmelCase__			:							Union[str, Any]							=    1 - (1 + step / self.inv_gamma) ** -self.power
        else:
              UpperCAmelCase__			:							Union[str, Any]							=    (1 + step) / (10 + step)

        UpperCAmelCase__			:							Optional[int]							=    min(_lowerCamelCase							,       self.decay	)
        # make sure decay is not smaller than min_decay
        UpperCAmelCase__			:							Any							=    max(_lowerCamelCase							,       self.min_decay	)
        return cur_decay_value



  @torch.no_grad()
  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        if isinstance(_lowerCamelCase							,       torch.nn.Module	):
              UpperCAmelCase__			:							int							=    (
                  """Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. """
                  """Please pass the parameters of the module instead."""
              )
              deprecate(
                  """passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"""							,       """1.0.0"""							,       _lowerCamelCase							,       standard_warn=_lowerCamelCase							,       )
              UpperCAmelCase__			:							Any							=    parameters.parameters()

        UpperCAmelCase__			:							List[str]							=    list(_lowerCamelCase	)

        self.optimization_step += 1

        # Compute the decay factor for the exponential moving average.
        UpperCAmelCase__			:							Optional[int]							=    self.get_decay(self.optimization_step	)
        UpperCAmelCase__			:							Optional[Any]							=    decay
        UpperCAmelCase__			:							int							=    1 - decay

        UpperCAmelCase__			:							List[Any]							=    contextlib.nullcontext
        if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
              import deepspeed

        for s_param, param in zip(self.shadow_params							,       _lowerCamelCase	):
              if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                    UpperCAmelCase__			:							int							=    deepspeed.zero.GatheredParameters(_lowerCamelCase							,       modifier_rank=_lowerCamelCase	)

              with context_manager():
                    if param.requires_grad:
                          s_param.sub_(one_minus_decay * (s_param - param)	)
                    else:
                          s_param.copy_(_lowerCamelCase	)



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    list(_lowerCamelCase	)
        for s_param, param in zip(self.shadow_params							,       _lowerCamelCase	):
              param.data.copy_(s_param.to(param.device	).data	)



  def  _a      (self							,       _lowerCamelCase=None							,       _lowerCamelCase=None	):


        """simple docstring"""

        UpperCAmelCase__			:							Any							=    [
            p.to(device=_lowerCamelCase							,       dtype=_lowerCamelCase	) if p.is_floating_point() else p.to(device=_lowerCamelCase	)
            for p in self.shadow_params
        ]



  def  _a      (self	):


        """simple docstring"""

        return {
            "decay": self.decay,
            "min_decay": self.min_decay,
            "optimization_step": self.optimization_step,
            "update_after_step": self.update_after_step,
            "use_ema_warmup": self.use_ema_warmup,
            "inv_gamma": self.inv_gamma,
            "power": self.power,
            "shadow_params": self.shadow_params,
        }



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    [param.detach().cpu().clone() for param in parameters]



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        if self.temp_stored_params is None:
              raise RuntimeError("""This ExponentialMovingAverage has no `store()`ed weights """ """to `restore()`"""	)
        for c_param, param in zip(self.temp_stored_params							,       _lowerCamelCase	):
              param.data.copy_(c_param.data	)

        # Better memory-wise.
        UpperCAmelCase__			:							Any							=    None




  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							Any							=    copy.deepcopy(_lowerCamelCase	)

        UpperCAmelCase__			:							Optional[Any]							=    state_dict.get("""decay"""							,       self.decay	)
        if self.decay < 0.0 or self.decay > 1.0:
              raise ValueError("""Decay must be between 0 and 1"""	)

        UpperCAmelCase__			:							Optional[int]							=    state_dict.get("""min_decay"""							,       self.min_decay	)
        if not isinstance(self.min_decay							,       _lowerCamelCase	):
              raise ValueError("""Invalid min_decay"""	)

        UpperCAmelCase__			:							List[Any]							=    state_dict.get("""optimization_step"""							,       self.optimization_step	)
        if not isinstance(self.optimization_step							,       _lowerCamelCase	):
              raise ValueError("""Invalid optimization_step"""	)

        UpperCAmelCase__			:							int							=    state_dict.get("""update_after_step"""							,       self.update_after_step	)
        if not isinstance(self.update_after_step							,       _lowerCamelCase	):
              raise ValueError("""Invalid update_after_step"""	)

        UpperCAmelCase__			:							Tuple							=    state_dict.get("""use_ema_warmup"""							,       self.use_ema_warmup	)
        if not isinstance(self.use_ema_warmup							,       _lowerCamelCase	):
              raise ValueError("""Invalid use_ema_warmup"""	)

        UpperCAmelCase__			:							Optional[Any]							=    state_dict.get("""inv_gamma"""							,       self.inv_gamma	)
        if not isinstance(self.inv_gamma							,       (float, int)	):
              raise ValueError("""Invalid inv_gamma"""	)

        UpperCAmelCase__			:							Any							=    state_dict.get("""power"""							,       self.power	)
        if not isinstance(self.power							,       (float, int)	):
              raise ValueError("""Invalid power"""	)

        UpperCAmelCase__			:							Union[str, Any]							=    state_dict.get("""shadow_params"""							,       _lowerCamelCase	)
        if shadow_params is not None:
              UpperCAmelCase__			:							str							=    shadow_params
              if not isinstance(self.shadow_params							,       _lowerCamelCase	):
                    raise ValueError("""shadow_params must be a list"""	)
              if not all(isinstance(_lowerCamelCase							,       torch.Tensor	) for p in self.shadow_params	):
                    raise ValueError("""shadow_params must all be Tensors"""	)




"""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,
    )

