

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 	__a     (      A__			:  Tuple			,  A__			:  Any			,  A__			:  List[Any]			,  A__			:  Any			,  A__			:  Optional[int]       ):
    # Load configuration defined in the metadata file
    with open(A__       ) as metadata_file:
        SCREAMING_SNAKE_CASE										=   json.load(A__       )
    SCREAMING_SNAKE_CASE										=   LukeConfig(use_entity_aware_attention=A__			,  **metadata["model_config"]       )

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

    # Load the entity vocab file
    SCREAMING_SNAKE_CASE										=   load_entity_vocab(A__       )

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

    # Add special tokens to the token vocabulary for downstream tasks
    SCREAMING_SNAKE_CASE										=   AddedToken("<ent>"			,  lstrip=A__			,  rstrip=A__       )
    SCREAMING_SNAKE_CASE										=   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__			,  LukeTokenizer.vocab_files_names["entity_vocab_file"]       )			,  "w"       ) as f:
        json.dump(A__			,  A__       )

    SCREAMING_SNAKE_CASE										=   LukeTokenizer.from_pretrained(A__       )

    # Initialize the embeddings of the special tokens
    SCREAMING_SNAKE_CASE										=   state_dict["embeddings.word_embeddings.weight"]
    SCREAMING_SNAKE_CASE										=   word_emb[tokenizer.convert_tokens_to_ids(["@"]       )[0]].unsqueeze(0       )
    SCREAMING_SNAKE_CASE										=   word_emb[tokenizer.convert_tokens_to_ids(["#"]       )[0]].unsqueeze(0       )
    SCREAMING_SNAKE_CASE										=   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"]:
            SCREAMING_SNAKE_CASE										=   F"encoder.layer.{layer_index}.attention.self."
            SCREAMING_SNAKE_CASE										=   state_dict[prefix + matrix_name]
            SCREAMING_SNAKE_CASE										=   state_dict[prefix + matrix_name]
            SCREAMING_SNAKE_CASE										=   state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
    SCREAMING_SNAKE_CASE										=   state_dict["entity_embeddings.entity_embeddings.weight"]
    SCREAMING_SNAKE_CASE										=   entity_emb[entity_vocab["[MASK]"]]

    SCREAMING_SNAKE_CASE										=   LukeModel(config=A__       ).eval()

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

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

    SCREAMING_SNAKE_CASE										=   model(**A__       )

    # Verify word hidden states
    if model_size == "large":
        SCREAMING_SNAKE_CASE										=   torch.Size((1, 42, 1024)       )
        SCREAMING_SNAKE_CASE										=   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
        SCREAMING_SNAKE_CASE										=   torch.Size((1, 42, 768)       )
        SCREAMING_SNAKE_CASE										=   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]			,  A__			,  atol=1E-4       ):
        raise ValueError

    # Verify entity hidden states
    if model_size == "large":
        SCREAMING_SNAKE_CASE										=   torch.Size((1, 1, 1024)       )
        SCREAMING_SNAKE_CASE										=   torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]       )
    else:  # base
        SCREAMING_SNAKE_CASE										=   torch.Size((1, 1, 768)       )
        SCREAMING_SNAKE_CASE										=   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]			,  A__			,  atol=1E-4       ):
        raise ValueError

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





def 	__a     (      A__			:  List[Any]       ):
    SCREAMING_SNAKE_CASE										=   {}
    with open(A__			,  "r"			,  encoding="utf-8"       ) as f:
        for index, line in enumerate(A__       ):
            SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   line.rstrip().split("\t"       )
            SCREAMING_SNAKE_CASE										=   index

    return entity_vocab


if __name__ == "__main__":
    __A			:					Optional[int]						=			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			:					Optional[int]						=			parser.parse_args()
    convert_luke_checkpoint(
        args.checkpoint_path,
        args.metadata_path,
        args.entity_vocab_path,
        args.pytorch_dump_folder_path,
        args.model_size,
    )


from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A			:					Optional[int]						=			logging.get_logger(__name__)

__A			:					Optional[Any]						=			{
    'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      "gpt_neox"





   def __init__(       self					:			Optional[int]     , __lowerCamelCase					:			List[str]=50432     , __lowerCamelCase					:			int=6144     , __lowerCamelCase					:			Optional[Any]=44     , __lowerCamelCase					:			Tuple=64     , __lowerCamelCase					:			Optional[int]=24576     , __lowerCamelCase					:			List[str]="gelu"     , __lowerCamelCase					:			Any=0.25     , __lowerCamelCase					:			List[Any]=10000     , __lowerCamelCase					:			List[Any]=0.0     , __lowerCamelCase					:			int=0.0     , __lowerCamelCase					:			str=0.1     , __lowerCamelCase					:			List[Any]=2048     , __lowerCamelCase					:			Tuple=0.02     , __lowerCamelCase					:			Tuple=1e-5     , __lowerCamelCase					:			Dict=True     , __lowerCamelCase					:			int=0     , __lowerCamelCase					:			Optional[Any]=2     , __lowerCamelCase					:			List[str]=False     , __lowerCamelCase					:			Optional[Any]=True     , __lowerCamelCase					:			Optional[int]=None     , **__lowerCamelCase					:			str     , ):
       super().__init__(bos_token_id=__lowerCamelCase     , eos_token_id=__lowerCamelCase     , **__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   vocab_size
       SCREAMING_SNAKE_CASE										=   max_position_embeddings
       SCREAMING_SNAKE_CASE										=   hidden_size
       SCREAMING_SNAKE_CASE										=   num_hidden_layers
       SCREAMING_SNAKE_CASE										=   num_attention_heads
       SCREAMING_SNAKE_CASE										=   intermediate_size
       SCREAMING_SNAKE_CASE										=   hidden_act
       SCREAMING_SNAKE_CASE										=   rotary_pct
       SCREAMING_SNAKE_CASE										=   rotary_emb_base
       SCREAMING_SNAKE_CASE										=   attention_dropout
       SCREAMING_SNAKE_CASE										=   hidden_dropout
       SCREAMING_SNAKE_CASE										=   classifier_dropout
       SCREAMING_SNAKE_CASE										=   initializer_range
       SCREAMING_SNAKE_CASE										=   layer_norm_eps
       SCREAMING_SNAKE_CASE										=   use_cache
       SCREAMING_SNAKE_CASE										=   tie_word_embeddings
       SCREAMING_SNAKE_CASE										=   use_parallel_residual
       SCREAMING_SNAKE_CASE										=   rope_scaling
       self._rope_scaling_validation()

       if self.hidden_size % self.num_attention_heads != 0:
           raise ValueError(
               "The hidden size is not divisble by the number of attention heads! Make sure to update them!"			)





   def      _snake_case		(       self					:			Union[str, Any]			):
       if self.rope_scaling is None:
           return

       if not isinstance(self.rope_scaling     , __lowerCamelCase			) or len(self.rope_scaling			) != 2:
           raise ValueError(
               "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
               f"got {self.rope_scaling}"			)
       SCREAMING_SNAKE_CASE										=   self.rope_scaling.get("type"     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   self.rope_scaling.get("factor"     , __lowerCamelCase			)
       if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
           raise ValueError(
               f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"			)
       if rope_scaling_factor is None or not isinstance(__lowerCamelCase     , __lowerCamelCase			) or rope_scaling_factor <= 1.0:
           raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"			)