"""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 					__a   (		A    ,				A    ,				A    ,				A    ,				A      ):






     '''simple docstring'''



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

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

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

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

     # Add special tokens to the token vocabulary for downstream tasks
     lowercase__				 =							AddedToken("<ent>"    ,				lstrip=A    ,				rstrip=A      )
     lowercase__				 =							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      )

     lowercase__				 =							LukeTokenizer.from_pretrained(A      )

     # 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=A      ).eval()

     lowercase__ ,						lowercase__				 =							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
     lowercase__				 =							LukeTokenizer.from_pretrained(A    ,				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(A    ,				entity_spans=[span]    ,				add_prefix_space=A    ,				return_tensors="pt"      )

     lowercase__				 =							model(**A      )

     # Verify word hidden states
     if model_size == "large":
          lowercase__				 =							torch.Size((1, 42, 10_24)      )
          lowercase__				 =							torch.tensor(
              [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]      )
     else:  # base
          lowercase__				 =							torch.Size((1, 42, 7_68)      )
          lowercase__				 =							torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]      )

     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":
          lowercase__				 =							torch.Size((1, 1, 10_24)      )
          lowercase__				 =							torch.tensor([[0.0466, -0.0106, -0.0179]]      )
     else:  # base
          lowercase__				 =							torch.Size((1, 1, 7_68)      )
          lowercase__				 =							torch.tensor([[0.1457, 0.1044, 0.0174]]      )

     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      ):






     '''simple docstring'''



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

     return entity_vocab


if __name__ == "__main__":
 lowerCAmelCase_:					List[Any]     =					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_:					str     =					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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_:					str     =					logging.get_logger(__name__)

lowerCAmelCase_:					List[Any]     =					{
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}






class    a__    (   _a	):
   snake_case_							=      "data2vec-vision"


   def __init__(     self,				_UpperCAmelCase=768,				_UpperCAmelCase=12,				_UpperCAmelCase=12,				_UpperCAmelCase=3072,				_UpperCAmelCase="gelu",				_UpperCAmelCase=0.0,				_UpperCAmelCase=0.0,				_UpperCAmelCase=0.02,				_UpperCAmelCase=1E-12,				_UpperCAmelCase=224,				_UpperCAmelCase=16,				_UpperCAmelCase=3,				_UpperCAmelCase=False,				_UpperCAmelCase=False,				_UpperCAmelCase=False,				_UpperCAmelCase=False,				_UpperCAmelCase=0.1,				_UpperCAmelCase=0.1,				_UpperCAmelCase=True,				_UpperCAmelCase=[3, 5, 7, 11],				_UpperCAmelCase=[1, 2, 3, 6],				_UpperCAmelCase=True,				_UpperCAmelCase=0.4,				_UpperCAmelCase=256,				_UpperCAmelCase=1,				_UpperCAmelCase=False,				_UpperCAmelCase=255,				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(**_UpperCAmelCase  )

        lowercase__				 =							hidden_size
        lowercase__				 =							num_hidden_layers
        lowercase__				 =							num_attention_heads
        lowercase__				 =							intermediate_size
        lowercase__				 =							hidden_act
        lowercase__				 =							hidden_dropout_prob
        lowercase__				 =							attention_probs_dropout_prob
        lowercase__				 =							initializer_range
        lowercase__				 =							layer_norm_eps

        lowercase__				 =							image_size
        lowercase__				 =							patch_size
        lowercase__				 =							num_channels
        lowercase__				 =							use_mask_token
        lowercase__				 =							use_absolute_position_embeddings
        lowercase__				 =							use_relative_position_bias
        lowercase__				 =							use_shared_relative_position_bias
        lowercase__				 =							layer_scale_init_value
        lowercase__				 =							drop_path_rate
        lowercase__				 =							use_mean_pooling
        # decode head attributes (semantic segmentation)
        lowercase__				 =							out_indices
        lowercase__				 =							pool_scales
        # auxiliary head attributes (semantic segmentation)
        lowercase__				 =							use_auxiliary_head
        lowercase__				 =							auxiliary_loss_weight
        lowercase__				 =							auxiliary_channels
        lowercase__				 =							auxiliary_num_convs
        lowercase__				 =							auxiliary_concat_input
        lowercase__				 =							semantic_loss_ignore_index






class    a__    (   _a	):
   snake_case_							=      version.parse("1.11"	)


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return OrderedDict(
            [
                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
            ]  )


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return 1E-4




