
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   (       __lowerCamelCase	:					Any							,   __lowerCamelCase	:					Union[str, Any]							,   __lowerCamelCase	:					Dict							,   __lowerCamelCase	:					List[Any]							,   __lowerCamelCase	:					Any       ):
       # Load configuration defined in the metadata file
       with open(__lowerCamelCase       ) as metadata_file:
              snake_case			:  str     							= json.load(__lowerCamelCase       )
       snake_case			:  Optional[Any]     							= LukeConfig(use_entity_aware_attention=__lowerCamelCase							,   **metadata["model_config"]       )

       # Load in the weights from the checkpoint_path
       snake_case			:  int     							= torch.load(__lowerCamelCase							,   map_location="cpu"       )

       # Load the entity vocab file
       snake_case			:  Optional[int]     							= load_entity_vocab(__lowerCamelCase       )

       snake_case			:  List[Any]     							= RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]       )

       # Add special tokens to the token vocabulary for downstream tasks
       snake_case			:  Tuple     							= AddedToken("<ent>"							,   lstrip=__lowerCamelCase							,   rstrip=__lowerCamelCase       )
       snake_case			:  Union[str, Any]     							= 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       )

       snake_case			:  Any     							= LukeTokenizer.from_pretrained(__lowerCamelCase       )

       # Initialize the embeddings of the special tokens
       snake_case			:  str     							= state_dict["embeddings.word_embeddings.weight"]
       snake_case			:  Optional[Any]     							= word_emb[tokenizer.convert_tokens_to_ids(["@"]       )[0]].unsqueeze(0       )
       snake_case			:  Union[str, Any]     							= word_emb[tokenizer.convert_tokens_to_ids(["#"]       )[0]].unsqueeze(0       )
       snake_case			:  Optional[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"]:
                     snake_case			:  Union[str, Any]     							= f"""encoder.layer.{layer_index}.attention.self."""
                     snake_case			:  str     							= state_dict[prefix + matrix_name]
                     snake_case			:  List[Any]     							= state_dict[prefix + matrix_name]
                     snake_case			:  List[str]     							= state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
       snake_case			:  Optional[Any]     							= state_dict["entity_embeddings.entity_embeddings.weight"]
       snake_case			:  Union[str, Any]     							= entity_emb[entity_vocab["[MASK]"]]

       snake_case			:  Dict     							= LukeModel(config=__lowerCamelCase       ).eval()

       snake_case							,   snake_case			:  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
       snake_case			:  int     							= LukeTokenizer.from_pretrained(__lowerCamelCase							,   task="entity_classification"       )

       snake_case			:  List[Any]     							= (
           "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 ."
       )
       snake_case			:  Optional[Any]     							= (39, 42)
       snake_case			:  str     							= tokenizer(__lowerCamelCase							,   entity_spans=[span]							,   add_prefix_space=__lowerCamelCase							,   return_tensors="pt"       )

       snake_case			:  Any     							= model(**__lowerCamelCase       )

       # Verify word hidden states
       if model_size == "large":
              snake_case			:  List[Any]     							= torch.Size((1, 42, 1024)       )
              snake_case			:  Optional[Any]     							= torch.tensor(
                  [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]       )
       else:  # base
              snake_case			:  Dict     							= torch.Size((1, 42, 768)       )
              snake_case			:  Optional[int]     							= 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":
              snake_case			:  List[str]     							= torch.Size((1, 1, 1024)       )
              snake_case			:  Union[str, Any]     							= torch.tensor([[0.0466, -0.0106, -0.0179]]       )
       else:  # base
              snake_case			:  Optional[int]     							= torch.Size((1, 1, 768)       )
              snake_case			:  Union[str, Any]     							= 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      UpperCamelCase   (       __lowerCamelCase	:					Tuple       ):
       snake_case			:  Dict     							= {}
       with open(__lowerCamelCase							,   "r"							,   encoding="utf-8"       ) as f:
              for index, line in enumerate(__lowerCamelCase       ):
                     snake_case							,   snake_case			:  Optional[int]     							= line.rstrip().split("\t"       )
                     snake_case			:  int     							= index

       return entity_vocab


if __name__ == "__main__":
   __lowerCamelCase				=			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."""
   )
   __lowerCamelCase				=			parser.parse_args()
   convert_luke_checkpoint(
       args.checkpoint_path,
       args.metadata_path,
       args.entity_vocab_path,
       args.pytorch_dump_folder_path,
       args.model_size,
   )







import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


__lowerCamelCase				=			"""\
@article{hendrycksmath2021,
  title={Measuring Mathematical Problem Solving With the MATH Dataset},
  author={Dan Hendrycks
    and Collin Burns
    and Saurav Kadavath
    and Akul Arora
    and Steven Basart
    and Eric Tang
    and Dawn Song
    and Jacob Steinhardt},
  journal={arXiv preprint arXiv:2103.03874},
  year={2021}
}
"""


__lowerCamelCase				=			"""\
This metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.
It first canonicalizes the inputs (e.g., converting \"1/2\" to \"\\frac{1}{2}\") and then computes accuracy.
"""


__lowerCamelCase				=			R"""
Calculates accuracy after canonicalizing inputs.

Args:
    predictions: list of predictions to score. Each prediction
        is a string that contains natural language and LaTex.
    references: list of reference for each prediction. Each
        reference is a string that contains natural language
        and LaTex.
Returns:
    accuracy: accuracy after canonicalizing inputs
        (e.g., converting \"1/2\" to \"\\frac{1}{2}\")

Examples:
    >>> metric = datasets.load_metric(\"competition_math\")
    >>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])
    >>> print(results)
    {'accuracy': 1.0}
"""






@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION			,_KWARGS_DESCRIPTION					)
class     UpperCAmelCase						(		datasets.Metric					):



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]					)						->      Optional[Any]:


          '''simple docstring'''


          return datasets.MetricInfo(
              description=_DESCRIPTION       ,		citation=_CITATION       ,		inputs_description=_KWARGS_DESCRIPTION       ,		features=datasets.Features(
                  {
                      "predictions": datasets.Value("string"					),
                      "references": datasets.Value("string"					),
                  }					)       ,		homepage="https://github.com/hendrycks/math"       ,		codebase_urls=["https://github.com/hendrycks/math"]       ,		)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]       ,		snake_case__						:   str       ,		snake_case__						:   List[Any]					)						->      Optional[Any]:


          '''simple docstring'''


          snake_case			:  Optional[int]     							= 0.0
          for i, j in zip(snake_case__       ,		snake_case__					):
                 n_correct += 1.0 if math_equivalence.is_equiv(snake_case__       ,		snake_case__					) else 0.0
          snake_case			:  Optional[int]     							= n_correct / len(snake_case__					)
          return {
              "accuracy": accuracy,
          }





