




'''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 			SCREAMING_SNAKE_CASE_			(							__A    :			int	,					__A    :			List[Any]	,					__A    :			Any	,					__A    :			int	,					__A    :			str  )			->			Union[str, Any]:


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

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

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

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

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

   _SCREAMING_SNAKE_CASE	=				MLukeTokenizer.from_pretrained(lowerCAmelCase__  )

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

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

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

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

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

   _SCREAMING_SNAKE_CASE	=				model(**lowerCAmelCase__  )

   # Verify word hidden states
   if model_size == "large":
      raise NotImplementedError
   else:  # base
      _SCREAMING_SNAKE_CASE	=				torch.Size((1, 33, 7_68)  )
      _SCREAMING_SNAKE_CASE	=				torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]]  )

   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
      _SCREAMING_SNAKE_CASE	=				torch.Size((1, 1, 7_68)  )
      _SCREAMING_SNAKE_CASE	=				torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]]  )

   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
   _SCREAMING_SNAKE_CASE	=				MLukeTokenizer.from_pretrained(lowerCAmelCase__  )
   _SCREAMING_SNAKE_CASE	=				"Tokyo is the capital of <mask>."
   _SCREAMING_SNAKE_CASE	=				(24, 30)
   _SCREAMING_SNAKE_CASE	=				tokenizer(lowerCAmelCase__	,					entity_spans=[span]	,					return_tensors="pt"  )

   _SCREAMING_SNAKE_CASE	=				model(**lowerCAmelCase__  )

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

   _SCREAMING_SNAKE_CASE	=				outputs.entity_logits[0][0].argmax().item()
   _SCREAMING_SNAKE_CASE	=				[
       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 			SCREAMING_SNAKE_CASE_			(							__A    :			Union[str, Any]  )			->			Tuple:
   _SCREAMING_SNAKE_CASE	=				["[MASK]", "[PAD]", "[UNK]"]

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

   _SCREAMING_SNAKE_CASE	=				{}
   for entry in data:
      _SCREAMING_SNAKE_CASE	=				entry["id"]
      for entity_name, language in entry["entities"]:
         if entity_name in SPECIAL_TOKENS:
            _SCREAMING_SNAKE_CASE	=				entity_id
            break
         _SCREAMING_SNAKE_CASE	=				f"""{language}:{entity_name}"""
         _SCREAMING_SNAKE_CASE	=				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 collections import OrderedDict
from typing import List, Mapping

from packaging import version

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


lowerCamelCase_			=						logging.get_logger(__name__)

lowerCamelCase_			=						{
    'google/efficientnet-b7': 'https://huggingface.co/google/efficientnet-b7/resolve/main/config.json',
}







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						'''efficientnet'''



   def __init__(   self :     Optional[Any]  ,				__lowerCamelCase :     int = 3  ,				__lowerCamelCase :     int = 6_0_0  ,				__lowerCamelCase :     float = 2.0  ,				__lowerCamelCase :     float = 3.1  ,				__lowerCamelCase :     int = 8  ,				__lowerCamelCase :     List[int] = [3, 3, 5, 3, 5, 5, 3]  ,				__lowerCamelCase :     List[int] = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]  ,				__lowerCamelCase :     List[int] = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]  ,				__lowerCamelCase :     List[int] = []  ,				__lowerCamelCase :     List[int] = [1, 2, 2, 2, 1, 2, 1]  ,				__lowerCamelCase :     List[int] = [1, 2, 2, 3, 3, 4, 1]  ,				__lowerCamelCase :     List[int] = [1, 6, 6, 6, 6, 6, 6]  ,				__lowerCamelCase :     float = 0.2_5  ,				__lowerCamelCase :     str = "swish"  ,				__lowerCamelCase :     int = 2_5_6_0  ,				__lowerCamelCase :     str = "mean"  ,				__lowerCamelCase :     float = 0.0_2  ,				__lowerCamelCase :     float = 0.0_0_1  ,				__lowerCamelCase :     float = 0.9_9  ,				__lowerCamelCase :     float = 0.5  ,				__lowerCamelCase :     float = 0.2  ,				**__lowerCamelCase :     Tuple  ,				):

      """simple docstring"""
      super().__init__(**__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				num_channels
      _SCREAMING_SNAKE_CASE	=				image_size
      _SCREAMING_SNAKE_CASE	=				width_coefficient
      _SCREAMING_SNAKE_CASE	=				depth_coefficient
      _SCREAMING_SNAKE_CASE	=				depth_divisor
      _SCREAMING_SNAKE_CASE	=				kernel_sizes
      _SCREAMING_SNAKE_CASE	=				in_channels
      _SCREAMING_SNAKE_CASE	=				out_channels
      _SCREAMING_SNAKE_CASE	=				depthwise_padding
      _SCREAMING_SNAKE_CASE	=				strides
      _SCREAMING_SNAKE_CASE	=				num_block_repeats
      _SCREAMING_SNAKE_CASE	=				expand_ratios
      _SCREAMING_SNAKE_CASE	=				squeeze_expansion_ratio
      _SCREAMING_SNAKE_CASE	=				hidden_act
      _SCREAMING_SNAKE_CASE	=				hidden_dim
      _SCREAMING_SNAKE_CASE	=				pooling_type
      _SCREAMING_SNAKE_CASE	=				initializer_range
      _SCREAMING_SNAKE_CASE	=				batch_norm_eps
      _SCREAMING_SNAKE_CASE	=				batch_norm_momentum
      _SCREAMING_SNAKE_CASE	=				dropout_rate
      _SCREAMING_SNAKE_CASE	=				drop_connect_rate
      _SCREAMING_SNAKE_CASE	=				sum(__lowerCamelCase   ) * 4







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						version.parse('''1.11'''       )



   @property
   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

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



   @property
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      return 1e-5

