

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   (    A__			:     int   ,     A__			:     Optional[int]   ,     A__			:     Any   ,     A__			:     Any   ,     A__			:     str  )					->   int:





      """simple docstring"""



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

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

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

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

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

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

      _lowercase			 =MLukeTokenizer.from_pretrained(lowerCamelCase_  )

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

      _lowercase			 =state_dict["""embeddings.word_embeddings.weight"""]
      _lowercase			 =word_emb[ent_init_index].unsqueeze(0  )
      _lowercase			 =word_emb[enta_init_index].unsqueeze(0  )
      _lowercase			 =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"]:
            _lowercase			 =state_dict[bias_name]
            _lowercase			 =decoder_bias[ent_init_index].unsqueeze(0  )
            _lowercase			 =decoder_bias[enta_init_index].unsqueeze(0  )
            _lowercase			 =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"]:
                  _lowercase			 =F'''encoder.layer.{layer_index}.attention.self.'''
                  _lowercase			 =state_dict[prefix + matrix_name]
                  _lowercase			 =state_dict[prefix + matrix_name]
                  _lowercase			 =state_dict[prefix + matrix_name]

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

      _lowercase			 =LukeForMaskedLM(config=lowerCamelCase_  ).eval()

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

      _lowercase			 =model.load_state_dict(lowerCamelCase_   ,     strict=lowerCamelCase_  )

      if set(lowerCamelCase_  ) != {"luke.embeddings.position_ids"}:
            raise ValueError(F'''Unexpected unexpected_keys: {unexpected_keys}'''  )
      if set(lowerCamelCase_  ) != {
          "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
      _lowercase			 =MLukeTokenizer.from_pretrained(lowerCamelCase_   ,     task='entity_classification'  )

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

      _lowercase			 =model(**lowerCamelCase_  )

      # Verify word hidden states
      if model_size == "large":
            raise NotImplementedError
      else:  # base
            _lowercase			 =torch.Size((1, 33, 768)  )
            _lowercase			 =torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]  )

      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":
            raise NotImplementedError
      else:  # base
            _lowercase			 =torch.Size((1, 1, 768)  )
            _lowercase			 =torch.tensor([[-0.1482, 0.0609, 0.0322]]  )

      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

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

      _lowercase			 =model(**lowerCamelCase_  )

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

      _lowercase			 =outputs.entity_logits[0][0].argmax().item()
      _lowercase			 =[
          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(lowerCamelCase_  )  )
      model.save_pretrained(lowerCamelCase_  )
def   a   (    A__			:     Optional[Any]  )					->   Any:





      """simple docstring"""



      _lowercase			 =["""[MASK]""", """[PAD]""", """[UNK]"""]

      _lowercase			 =[json.loads(lowerCamelCase_  ) for line in open(lowerCamelCase_  )]

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


if __name__ == "__main__":
  lowercase_             = 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.'
  )
  lowercase_             = 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 requests

lowercase_             =     'YOUR API KEY'
def   a   (    A__			:     str   ,     A__			:     str = giphy_api_key  )					->   list:





      """simple docstring"""



      _lowercase			 ='+'.join(query.split()  )
      _lowercase			 =F'''https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}'''
      _lowercase			 =requests.get(A__  ).json()['data']
      return [gif["url"] for gif in gifs]


if __name__ == "__main__":
  print('\n'.join(get_gifs('space ship')))


