





'''simple docstring'''
import fire

from utils import calculate_rouge, save_json
def 				lowercase__   (   __lowercase						:  List[str]				,  __lowercase						:  Union[str, Any]				,  __lowercase						:  Union[str, Any]=None				,  **__lowercase						:  Dict     ) ->      Optional[int]:



       """simple docstring"""






       __UpperCamelCase       =  [x.strip() for x in open(__lowercase     ).readlines()]
       __UpperCamelCase       =  [x.strip() for x in open(__lowercase     ).readlines()][: len(__lowercase     )]
       __UpperCamelCase       =  calculate_rouge(__lowercase				,  __lowercase				,  **__lowercase     )
       if save_path is not None:
              save_json(__lowercase				,  __lowercase				,  indent=__lowercase     )
       return metrics  # these print nicely


if __name__ == "__main__":
    fire.Fire(calculate_rouge_path)







'''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 				lowercase__   (   __lowercase						:  int				,  __lowercase						:  int				,  __lowercase						:  Union[str, Any]				,  __lowercase						:  Union[str, Any]				,  __lowercase						:  Any     ) ->      Optional[Any]:



       """simple docstring"""






       with open(__lowercase     ) as metadata_file:
              __UpperCamelCase       =  json.load(__lowercase     )
       __UpperCamelCase       =  LukeConfig(use_entity_aware_attention=__lowercase				,  **metadata['model_config']     )

       # Load in the weights from the checkpoint_path
       __UpperCamelCase       =  torch.load(__lowercase				,  map_location='cpu'     )

       # Load the entity vocab file
       __UpperCamelCase       =  load_entity_vocab(__lowercase     )

       __UpperCamelCase       =  RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']     )

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

       __UpperCamelCase       =  LukeTokenizer.from_pretrained(__lowercase     )

       # 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=__lowercase     ).eval()

       __UpperCamelCase       ,						__UpperCamelCase       =  model.load_state_dict(__lowercase				,  strict=__lowercase     )
       if not (len(__lowercase     ) == 1 and missing_keys[0] == "embeddings.position_ids"):
              raise ValueError(F'''Missing keys {', '.join(__lowercase     )}. 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(__lowercase				,  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(__lowercase				,  entity_spans=[span]				,  add_prefix_space=__lowercase				,  return_tensors='pt'     )

       __UpperCamelCase       =  model(**__lowercase     )

       # Verify word hidden states
       if model_size == "large":
              __UpperCamelCase       =  torch.Size((1, 42, 1024)     )
              __UpperCamelCase       =  torch.tensor(
                  [[0.0_1_3_3, 0.0_8_6_5, 0.0_0_9_5], [0.3_0_9_3, -0.2_5_7_6, -0.7_4_1_8], [-0.1_7_2_0, -0.2_1_1_7, -0.2_8_6_9]]     )
       else:  # base
              __UpperCamelCase       =  torch.Size((1, 42, 768)     )
              __UpperCamelCase       =  torch.tensor([[0.0_0_3_7, 0.1_3_6_8, -0.0_0_9_1], [0.1_0_9_9, 0.3_3_2_9, -0.1_0_9_5], [0.0_7_6_5, 0.5_3_3_5, 0.1_1_7_9]]     )

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

       # Verify entity hidden states
       if model_size == "large":
              __UpperCamelCase       =  torch.Size((1, 1, 1024)     )
              __UpperCamelCase       =  torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]     )
       else:  # base
              __UpperCamelCase       =  torch.Size((1, 1, 768)     )
              __UpperCamelCase       =  torch.tensor([[0.1_4_5_7, 0.1_0_4_4, 0.0_1_7_4]]     )

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

       # Finally, save our PyTorch model and tokenizer
       print('Saving PyTorch model to {}'.format(__lowercase     )     )
       model.save_pretrained(__lowercase     )
def 				lowercase__   (   __lowercase						:  Dict     ) ->      List[str]:



       """simple docstring"""






       __UpperCamelCase       =  {}
       with open(__lowercase				,  'r'				,  encoding='utf-8'     ) as f:
              for index, line in enumerate(__lowercase     ):
                     __UpperCamelCase       ,						__UpperCamelCase       =  line.rstrip().split('\t'     )
                     __UpperCamelCase       =  index

       return entity_vocab


if __name__ == "__main__":
    a__							:							Any =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__							:							str =parser.parse_args()
    convert_luke_checkpoint(
        args.checkpoint_path,
        args.metadata_path,
        args.entity_vocab_path,
        args.pytorch_dump_folder_path,
        args.model_size,
    )
