



"""simple docstring"""





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__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
						# Load configuration defined in the metadata file
						with open(lowerCAmelCase__   ) as metadata_file:
												UpperCAmelCase_    				=							json.load(lowerCAmelCase__   )
						UpperCAmelCase_    				=							LukeConfig(use_entity_aware_attention=lowerCAmelCase__			,    **metadata["model_config"]   )

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

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

						UpperCAmelCase_    				=							XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]   )

						# Add special tokens to the token vocabulary for downstream tasks
						UpperCAmelCase_    				=							AddedToken("<ent>"			,    lstrip=lowerCAmelCase__			,    rstrip=lowerCAmelCase__   )
						UpperCAmelCase_    				=							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:
												UpperCAmelCase_    				=							json.load(lowerCAmelCase__   )
						UpperCAmelCase_    				=							"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__   )

						UpperCAmelCase_    				=							MLukeTokenizer.from_pretrained(lowerCAmelCase__   )

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

						UpperCAmelCase_    				=							state_dict["embeddings.word_embeddings.weight"]
						UpperCAmelCase_    				=							word_emb[ent_init_index].unsqueeze(0   )
						UpperCAmelCase_    				=							word_emb[enta_init_index].unsqueeze(0   )
						UpperCAmelCase_    				=							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"]:
												UpperCAmelCase_    				=							state_dict[bias_name]
												UpperCAmelCase_    				=							decoder_bias[ent_init_index].unsqueeze(0   )
												UpperCAmelCase_    				=							decoder_bias[enta_init_index].unsqueeze(0   )
												UpperCAmelCase_    				=							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"]:
																		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]"]].unsqueeze(0   )
						UpperCAmelCase_    				=							torch.cat([entity_emb, entity_mask_emb]   )
						# add [MASK2] for 'entity_predictions.bias'
						UpperCAmelCase_    				=							state_dict["entity_predictions.bias"]
						UpperCAmelCase_    				=							entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0   )
						UpperCAmelCase_    				=							torch.cat([entity_prediction_bias, entity_mask_bias]   )

						UpperCAmelCase_    				=							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"   )
						UpperCAmelCase_    				=							OrderedDict()
						for key, value in state_dict.items():
												if not (key.startswith("lm_head"   ) or key.startswith("entity_predictions"   )):
																		UpperCAmelCase_    				=							state_dict[key]
												else:
																		UpperCAmelCase_    				=							state_dict[key]

						UpperCAmelCase_				,					UpperCAmelCase_    				=							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
						UpperCAmelCase_    				=							MLukeTokenizer.from_pretrained(lowerCAmelCase__			,    task="entity_classification"   )

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

						UpperCAmelCase_    				=							model(**lowerCAmelCase__   )

						# Verify word hidden states
						if model_size == "large":
												raise NotImplementedError
						else:  # base
												UpperCAmelCase_    				=							torch.Size((1, 33, 768)   )
												UpperCAmelCase_    				=							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
												UpperCAmelCase_    				=							torch.Size((1, 1, 768)   )
												UpperCAmelCase_    				=							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
						UpperCAmelCase_    				=							MLukeTokenizer.from_pretrained(lowerCAmelCase__   )
						UpperCAmelCase_    				=							"Tokyo is the capital of <mask>."
						UpperCAmelCase_    				=							(24, 30)
						UpperCAmelCase_    				=							tokenizer(lowerCAmelCase__			,    entity_spans=[span]			,    return_tensors="pt"   )

						UpperCAmelCase_    				=							model(**lowerCAmelCase__   )

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

						UpperCAmelCase_    				=							outputs.entity_logits[0][0].argmax().item()
						UpperCAmelCase_    				=							[
						    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__			(   lowerCAmelCase__   ):
						UpperCAmelCase_    				=							["[MASK]", "[PAD]", "[UNK]"]

						UpperCAmelCase_    				=							[json.loads(lowerCAmelCase__   ) for line in open(lowerCAmelCase__   )]

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


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,
		)









"""simple docstring"""





from __future__ import annotations

import math
def      a__			(   lowerCAmelCase__   ):

      if 1 < number < 4:
            # 2 and 3 are primes
            return True
      elif number < 2 or number % 2 == 0 or number % 3 == 0:
            # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
            return False

      # All primes number are in format of 6k +/- 1
      for i in range(5			,    int(math.sqrt(lowerCAmelCase__   ) + 1   )			,    6   ):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True


lowerCamelCase      =		[num for num in range(3, 100_001, 2) if not is_prime(num)]
def      a__			(   lowerCAmelCase__   ):
      if not isinstance(lowerCAmelCase__			,    lowerCAmelCase__   ):
            raise ValueError("n must be an integer"   )
      if n <= 0:
            raise ValueError("n must be >= 0"   )

      UpperCAmelCase_    				=							[]
      for num in range(len(lowerCAmelCase__   )   ):
            UpperCAmelCase_    				=							0
            while 2 * i * i <= odd_composites[num]:
                  UpperCAmelCase_    				=							odd_composites[num] - 2 * i * i
                  if is_prime(lowerCAmelCase__   ):
                        break
                  i += 1
            else:
                  list_nums.append(odd_composites[num]   )
                  if len(lowerCAmelCase__   ) == n:
                        return list_nums

      return []
def      a__			(   ):
      return compute_nums(1   )[0]


if __name__ == "__main__":
  print(F"{solution() = }")




