



import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken





@torch.no_grad()
def 		a__      (		_UpperCamelCase      :			Tuple			,_UpperCamelCase      :			Dict			,_UpperCamelCase      :			List[str]			,_UpperCamelCase      :			Optional[Any]			,_UpperCamelCase      :			Tuple				):

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

   # Load in the weights from the checkpoint_path
   __lowerCamelCase    				=     torch.load(__a			,map_location='''cpu'''				)['''module''']

   # Load the entity vocab file
   __lowerCamelCase    				=     load_original_entity_vocab(__a				)
   # add an entry for [MASK2]
   __lowerCamelCase    				=     max(entity_vocab.values()				) + 1
   config.entity_vocab_size += 1

   __lowerCamelCase    				=     XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']				)

   # Add special tokens to the token vocabulary for downstream tasks
   __lowerCamelCase    				=     AddedToken('''<ent>'''			,lstrip=__a			,rstrip=__a				)
   __lowerCamelCase    				=     AddedToken('''<ent2>'''			,lstrip=__a			,rstrip=__a				)
   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(__a				)
   with open(os.path.join(__a			,'''tokenizer_config.json'''				)			,'''r'''				) as f:
      __lowerCamelCase    				=     json.load(__a				)
   __lowerCamelCase    				=     '''MLukeTokenizer'''
   with open(os.path.join(__a			,'''tokenizer_config.json'''				)			,'''w'''				) as f:
      json.dump(__a			,__a				)

   with open(os.path.join(__a			,MLukeTokenizer.vocab_files_names['''entity_vocab_file''']				)			,'''w'''				) as f:
      json.dump(__a			,__a				)

   __lowerCamelCase    				=     MLukeTokenizer.from_pretrained(__a				)

   # Initialize the embeddings of the special tokens
   __lowerCamelCase    				=     tokenizer.convert_tokens_to_ids(['''@''']				)[0]
   __lowerCamelCase    				=     tokenizer.convert_tokens_to_ids(['''#''']				)[0]

   __lowerCamelCase    				=     state_dict['''embeddings.word_embeddings.weight''']
   __lowerCamelCase    				=     word_emb[ent_init_index].unsqueeze(0				)
   __lowerCamelCase    				=     word_emb[enta_init_index].unsqueeze(0				)
   __lowerCamelCase    				=     torch.cat([word_emb, ent_emb, enta_emb]				)
   # add special tokens for 'entity_predictions.bias'
   for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
      __lowerCamelCase    				=     state_dict[bias_name]
      __lowerCamelCase    				=     decoder_bias[ent_init_index].unsqueeze(0				)
      __lowerCamelCase    				=     decoder_bias[enta_init_index].unsqueeze(0				)
      __lowerCamelCase    				=     torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]				)

   # 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"]:
         __lowerCamelCase    				=     F"""encoder.layer.{layer_index}.attention.self."""
         __lowerCamelCase    				=     state_dict[prefix + matrix_name]
         __lowerCamelCase    				=     state_dict[prefix + matrix_name]
         __lowerCamelCase    				=     state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
   __lowerCamelCase    				=     state_dict['''entity_embeddings.entity_embeddings.weight''']
   __lowerCamelCase    				=     entity_emb[entity_vocab['''[MASK]''']].unsqueeze(0				)
   __lowerCamelCase    				=     torch.cat([entity_emb, entity_mask_emb]				)
   # add [MASK2] for 'entity_predictions.bias'
   __lowerCamelCase    				=     state_dict['''entity_predictions.bias''']
   __lowerCamelCase    				=     entity_prediction_bias[entity_vocab['''[MASK]''']].unsqueeze(0				)
   __lowerCamelCase    				=     torch.cat([entity_prediction_bias, entity_mask_bias]				)

   __lowerCamelCase    				=     LukeForMaskedLM(config=__a				).eval()

   state_dict.pop('''entity_predictions.decoder.weight'''				)
   state_dict.pop('''lm_head.decoder.weight'''				)
   state_dict.pop('''lm_head.decoder.bias'''				)
   __lowerCamelCase    				=     OrderedDict()
   for key, value in state_dict.items():
      if not (key.startswith('''lm_head'''				) or key.startswith('''entity_predictions'''				)):
         __lowerCamelCase    				=     state_dict[key]
      else:
         __lowerCamelCase    				=     state_dict[key]

   __lowerCamelCase    				=     model.load_state_dict(__a			,strict=__a				)

   if set(__a				) != {"luke.embeddings.position_ids"}:
      raise ValueError(F"""Unexpected unexpected_keys: {unexpected_keys}"""				)
   if set(__a				) != {
       "lm_head.decoder.weight",
       "lm_head.decoder.bias",
       "entity_predictions.decoder.weight",
   }:
      raise ValueError(F"""Unexpected missing_keys: {missing_keys}"""				)

   model.tie_weights()
   assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
   assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

   # Check outputs
   __lowerCamelCase    				=     MLukeTokenizer.from_pretrained(__a			,task='''entity_classification'''				)

   __lowerCamelCase    				=     '''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
   __lowerCamelCase    				=     (0, 9)
   __lowerCamelCase    				=     tokenizer(__a			,entity_spans=[span]			,return_tensors='''pt'''				)

   __lowerCamelCase    				=     model(**__a				)

   # Verify word hidden states
   if model_size == "large":
      raise NotImplementedError
   else:  # base
      __lowerCamelCase    				=     torch.Size((1, 33, 7_68)				)
      __lowerCamelCase    				=     torch.tensor([[0.0_892, 0.0_596, -0.2_819], [0.0_134, 0.1_199, 0.0_573], [-0.0_169, 0.0_927, 0.0_644]]				)

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

   # Verify entity hidden states
   if model_size == "large":
      raise NotImplementedError
   else:  # base
      __lowerCamelCase    				=     torch.Size((1, 1, 7_68)				)
      __lowerCamelCase    				=     torch.tensor([[-0.1_482, 0.0_609, 0.0_322]]				)

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

   # Verify masked word/entity prediction
   __lowerCamelCase    				=     MLukeTokenizer.from_pretrained(__a				)
   __lowerCamelCase    				=     '''Tokyo is the capital of <mask>.'''
   __lowerCamelCase    				=     (24, 30)
   __lowerCamelCase    				=     tokenizer(__a			,entity_spans=[span]			,return_tensors='''pt'''				)

   __lowerCamelCase    				=     model(**__a				)

   __lowerCamelCase    				=     encoding['''input_ids'''][0].tolist()
   __lowerCamelCase    				=     input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>'''				)				)
   __lowerCamelCase    				=     outputs.logits[0][mask_position_id].argmax(dim=-1				)
   assert "Japan" == tokenizer.decode(__a				)

   __lowerCamelCase    				=     outputs.entity_logits[0][0].argmax().item()
   __lowerCamelCase    				=     [
       entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
   ]
   assert [e for e in multilingual_predicted_entities if e.startswith('''en:'''				)][0] == "en:Japan"

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





def 		a__      (		_UpperCamelCase      :			List[Any]				):
   __lowerCamelCase    				=     ['''[MASK]''', '''[PAD]''', '''[UNK]''']

   __lowerCamelCase    				=     [json.loads(__a				) for line in open(__a				)]

   __lowerCamelCase    				=     {}
   for entry in data:
      __lowerCamelCase    				=     entry['''id''']
      for entity_name, language in entry["entities"]:
         if entity_name in SPECIAL_TOKENS:
            __lowerCamelCase    				=     entity_id
            break
         __lowerCamelCase    				=     F"""{language}:{entity_name}"""
         __lowerCamelCase    				=     entity_id
   return new_mapping


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,
  )









def   __UpperCAmelCase					(      __a :     str      )     ->	list:

					"""simple docstring"""

					if n_term == "":
										return []
					_a      :       list   =						[]
					for temp in range(int(__a      )      ):
										series.append(F"""1/{temp + 1}""" if series else '''1'''      )
					return series


if __name__ == "__main__":
			a__												=						input('''Enter the last number (nth term) of the Harmonic Series''')
			print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
			print(harmonic_series(nth_term))



