






"""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     _UpperCamelCase      (	A     ,							A     ,							A     ,							A     ,							A      ):







   with open(__snake_case      ) as metadata_file:
      UpperCamelCase_			     =json.load(__snake_case      )
   UpperCamelCase_			     =LukeConfig(use_entity_aware_attention=__snake_case     ,							**metadata["model_config"]      )

   # Load in the weights from the checkpoint_path
   UpperCamelCase_			     =torch.load(__snake_case     ,							map_location="cpu"      )

   # Load the entity vocab file
   UpperCamelCase_			     =load_entity_vocab(__snake_case      )

   UpperCamelCase_			     =RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]      )

   # Add special tokens to the token vocabulary for downstream tasks
   UpperCamelCase_			     =AddedToken("<ent>"     ,							lstrip=__snake_case     ,							rstrip=__snake_case      )
   UpperCamelCase_			     =AddedToken("<ent2>"     ,							lstrip=__snake_case     ,							rstrip=__snake_case      )
   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(__snake_case      )
   with open(os.path.join(__snake_case     ,							LukeTokenizer.vocab_files_names["entity_vocab_file"]      )     ,							"w"      ) as f:
      json.dump(__snake_case     ,							__snake_case      )

   UpperCamelCase_			     =LukeTokenizer.from_pretrained(__snake_case      )

   # Initialize the embeddings of the special tokens
   UpperCamelCase_			     =state_dict["""embeddings.word_embeddings.weight"""]
   UpperCamelCase_			     =word_emb[tokenizer.convert_tokens_to_ids(["@"]      )[0]].unsqueeze(0      )
   UpperCamelCase_			     =word_emb[tokenizer.convert_tokens_to_ids(["#"]      )[0]].unsqueeze(0      )
   UpperCamelCase_			     =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_			     =f"""encoder.layer.{layer_index}.attention.self."""
         UpperCamelCase_			     =state_dict[prefix + matrix_name]
         UpperCamelCase_			     =state_dict[prefix + matrix_name]
         UpperCamelCase_			     =state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
   UpperCamelCase_			     =state_dict["""entity_embeddings.entity_embeddings.weight"""]
   UpperCamelCase_			     =entity_emb[entity_vocab["""[MASK]"""]]

   UpperCamelCase_			     =LukeModel(config=__snake_case      ).eval()

   UpperCamelCase_			     =model.load_state_dict(__snake_case     ,							strict=__snake_case      )
   if not (len(__snake_case      ) == 1 and missing_keys[0] == "embeddings.position_ids"):
      raise ValueError(f"""Missing keys {", ".join(__snake_case      )}. 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_			     =LukeTokenizer.from_pretrained(__snake_case     ,							task="entity_classification"      )

   UpperCamelCase_			     =(
       """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_			     =(39, 42)
   UpperCamelCase_			     =tokenizer(__snake_case     ,							entity_spans=[span]     ,							add_prefix_space=__snake_case     ,							return_tensors="pt"      )

   UpperCamelCase_			     =model(**__snake_case      )

   # Verify word hidden states
   if model_size == "large":
      UpperCamelCase_			     =torch.Size((1, 42, 1_024)      )
      UpperCamelCase_			     =torch.tensor(
          [[0.01_33, 0.08_65, 0.00_95], [0.30_93, -0.25_76, -0.74_18], [-0.17_20, -0.21_17, -0.28_69]]      )
   else:  # base
      UpperCamelCase_			     =torch.Size((1, 42, 768)      )
      UpperCamelCase_			     =torch.tensor([[0.00_37, 0.13_68, -0.00_91], [0.10_99, 0.33_29, -0.10_95], [0.07_65, 0.53_35, 0.11_79]]      )

   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]     ,							__snake_case     ,							atol=1e-4      ):
      raise ValueError

   # Verify entity hidden states
   if model_size == "large":
      UpperCamelCase_			     =torch.Size((1, 1, 1_024)      )
      UpperCamelCase_			     =torch.tensor([[0.04_66, -0.01_06, -0.01_79]]      )
   else:  # base
      UpperCamelCase_			     =torch.Size((1, 1, 768)      )
      UpperCamelCase_			     =torch.tensor([[0.14_57, 0.10_44, 0.01_74]]      )

   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]     ,							__snake_case     ,							atol=1e-4      ):
      raise ValueError

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




def     _UpperCamelCase      (	A      ):
   UpperCamelCase_			     ={}
   with open(__snake_case     ,							"r"     ,							encoding="utf-8"      ) as f:
      for index, line in enumerate(__snake_case      ):
         UpperCamelCase_			     =line.rstrip().split("\t"      )
         UpperCamelCase_			     =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,
    )




"""simple docstring"""
import mpmath  # for roots of unity
import numpy as np




class 	lowercase__				:





   def __init__(							self      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE=None)  ->  Tuple:
      # Input as list
      _lowerCamelCase	:					Any   =		list(poly_a or [0])[:]
      _lowerCamelCase	:					Optional[Any]   =		list(poly_b or [0])[:]

      # Remove leading zero coefficients
      while self.polyA[-1] == 0:
         self.polyA.pop()
      _lowerCamelCase	:					int   =		len(self.polyA)

      while self.polyB[-1] == 0:
         self.polyB.pop()
      _lowerCamelCase	:					Union[str, Any]   =		len(self.polyB)

      # Add 0 to make lengths equal a power of 2
      _lowerCamelCase	:					List[Any]   =		int(
          2 ** np.ceil(np.loga(len(self.polyA) + len(self.polyB) - 1)))

      while len(self.polyA) < self.c_max_length:
         self.polyA.append(0)
      while len(self.polyB) < self.c_max_length:
         self.polyB.append(0)
      # A complex root used for the fourier transform
      _lowerCamelCase	:					Optional[Any]   =		complex(mpmath.root(x=1      ,   n=self.c_max_length      ,   k=1))

      # The product
      _lowerCamelCase	:					int   =		self.__multiply()





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      _lowerCamelCase	:					Dict   =		[[x] for x in self.polyA] if which == """A""" else [[x] for x in self.polyB]
      # Corner case
      if len(SCREAMING_SNAKE_CASE) <= 1:
         return dft[0]
      #
      _lowerCamelCase	:					str   =		self.c_max_length // 2
      while next_ncol > 0:
         _lowerCamelCase	:					Dict   =		[[] for i in range(SCREAMING_SNAKE_CASE)]
         _lowerCamelCase	:					Tuple   =		self.root**next_ncol

         # First half of next step
         _lowerCamelCase	:					int   =		1
         for j in range(self.c_max_length // (next_ncol * 2)):
            for i in range(SCREAMING_SNAKE_CASE):
               new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j])
            current_root *= root
         # Second half of next step
         _lowerCamelCase	:					Optional[int]   =		1
         for j in range(self.c_max_length // (next_ncol * 2)):
            for i in range(SCREAMING_SNAKE_CASE):
               new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j])
            current_root *= root
         # Update
         _lowerCamelCase	:					Union[str, Any]   =		new_dft
         _lowerCamelCase	:					List[str]   =		next_ncol // 2
      return dft[0]





   def 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase	:					Optional[Any]   =		self.__dft("""A""")
      _lowerCamelCase	:					List[str]   =		self.__dft("""B""")
      _lowerCamelCase	:					List[Any]   =		[[dft_a[i] * dft_b[i] for i in range(self.c_max_length)]]
      del dft_a
      del dft_b

      # Corner Case
      if len(inverce_c[0]) <= 1:
         return inverce_c[0]
      # Inverse DFT
      _lowerCamelCase	:					List[str]   =		2
      while next_ncol <= self.c_max_length:
         _lowerCamelCase	:					Any   =		[[] for i in range(SCREAMING_SNAKE_CASE)]
         _lowerCamelCase	:					List[Any]   =		self.root ** (next_ncol // 2)
         _lowerCamelCase	:					str   =		1
         # First half of next step
         for j in range(self.c_max_length // next_ncol):
            for i in range(next_ncol // 2):
               # Even positions
               new_inverse_c[i].append(
                   (
                       inverce_c[i][j]
                       + inverce_c[i][j + self.c_max_length // next_ncol]
                   )
                   / 2)
               # Odd positions
               new_inverse_c[i + next_ncol // 2].append(
                   (
                       inverce_c[i][j]
                       - inverce_c[i][j + self.c_max_length // next_ncol]
                   )
                   / (2 * current_root))
            current_root *= root
         # Update
         _lowerCamelCase	:					Any   =		new_inverse_c
         next_ncol *= 2
      # Unpack
      _lowerCamelCase	:					Optional[Any]   =		[round(x[0].real      ,   8) + round(x[0].imag      ,   8) * 1j for x in inverce_c]

      # Remove leading 0's
      while inverce_c[-1] == 0:
         inverce_c.pop()
      return inverce_c





   def __str__(							self)  ->  Any:
      _lowerCamelCase	:					Dict   =		"""A = """ + """ + """.join(
          F'{coef}*x^{i}' for coef, i in enumerate(self.polyA[: self.len_A]))
      _lowerCamelCase	:					List[Any]   =		"""B = """ + """ + """.join(
          F'{coef}*x^{i}' for coef, i in enumerate(self.polyB[: self.len_B]))
      _lowerCamelCase	:					int   =		"""A*B = """ + """ + """.join(
          F'{coef}*x^{i}' for coef, i in enumerate(self.product))

      return F'{a}\n{b}\n{c}'


# Unit tests
if __name__ == "__main__":
 import doctest

 doctest.testmod()

