



'''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 							lowerCamelCase	(					_snake_case	:    Union[str, Any]  ,_snake_case	:    Dict  ,_snake_case	:    Any  ,_snake_case	:    Optional[Any]  ,_snake_case	:    List[Any]							):





    '''simple docstring'''


    with open(_snake_case							) as metadata_file:
        lowercase__									=    json.load(_snake_case							)
    lowercase__									=    LukeConfig(use_entity_aware_attention=_snake_case  ,**metadata["model_config"]							)

    # Load in the weights from the checkpoint_path
    lowercase__									=    torch.load(_snake_case  ,map_location="cpu"							)

    # Load the entity vocab file
    lowercase__									=    load_entity_vocab(_snake_case							)

    lowercase__									=    RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]							)

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

    lowercase__									=    LukeTokenizer.from_pretrained(_snake_case							)

    # Initialize the embeddings of the special tokens
    lowercase__									=    state_dict["embeddings.word_embeddings.weight"]
    lowercase__									=    word_emb[tokenizer.convert_tokens_to_ids(["@"]							)[0]].unsqueeze(0							)
    lowercase__									=    word_emb[tokenizer.convert_tokens_to_ids(["#"]							)[0]].unsqueeze(0							)
    lowercase__									=    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"]:
            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]"]]

    lowercase__									=    LukeModel(config=_snake_case							).eval()

    lowercase__							,							lowercase__									=    model.load_state_dict(_snake_case  ,strict=_snake_case							)
    if not (len(_snake_case							) == 1 and missing_keys[0] == "embeddings.position_ids"):
        raise ValueError(f'''Missing keys {', '.join(_snake_case							)}. 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
    lowercase__									=    LukeTokenizer.from_pretrained(_snake_case  ,task="entity_classification"							)

    lowercase__									=    (
        "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 ."
    )
    lowercase__									=    (39, 42)
    lowercase__									=    tokenizer(_snake_case  ,entity_spans=[span]  ,add_prefix_space=_snake_case  ,return_tensors="pt"							)

    lowercase__									=    model(**_snake_case							)

    # Verify word hidden states
    if model_size == "large":
        lowercase__									=    torch.Size((1, 42, 1_024)							)
        lowercase__									=    torch.tensor(
            [[0.0_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]]							)
    else:  # base
        lowercase__									=    torch.Size((1, 42, 768)							)
        lowercase__									=    torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]]							)

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

    # Verify entity hidden states
    if model_size == "large":
        lowercase__									=    torch.Size((1, 1, 1_024)							)
        lowercase__									=    torch.tensor([[0.0_466, -0.0_106, -0.0_179]]							)
    else:  # base
        lowercase__									=    torch.Size((1, 1, 768)							)
        lowercase__									=    torch.tensor([[0.1_457, 0.1_044, 0.0_174]]							)

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

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




def 							lowerCamelCase	(					_snake_case	:    Union[str, Any]							):





    '''simple docstring'''


    lowercase__									=    {}
    with open(_snake_case  ,"r"  ,encoding="utf-8"							) as f:
        for index, line in enumerate(_snake_case							):
            lowercase__							,							lowercase__									=    line.rstrip().split("\t"							)
            lowercase__									=    index

    return entity_vocab


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__          =  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."
 )
 SCREAMING_SNAKE_CASE__          =  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'''





def 							lowerCamelCase	(					_snake_case	:    int = 50_000_000							):





    '''simple docstring'''


    lowercase__									=    set()
    lowercase__									=    int((limit - 24) ** (1 / 2)							)

    lowercase__									=    set(range(3  ,prime_square_limit + 1  ,2							)							)
    primes.add(2							)
    for p in range(3  ,prime_square_limit + 1  ,2							):
        if p not in primes:
            continue
        primes.difference_update(set(range(p * p  ,prime_square_limit + 1  ,_snake_case							)							)							)

    for primea in primes:
        lowercase__									=    primea * primea
        for primea in primes:
            lowercase__									=    primea * primea * primea
            if square + cube >= limit - 16:
                break
            for primea in primes:
                lowercase__									=    primea * primea * primea * primea
                lowercase__									=    square + cube + tetr
                if total >= limit:
                    break
                ret.add(_snake_case							)

    return len(_snake_case							)


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




