

"""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 	UpperCAmelCase__   (				A__	,    A__	,    A__	,    A__	,    A__   )      ->      Tuple:




   """simple docstring"""
   # Load configuration defined in the metadata file
   with open(A__   ) as metadata_file:
      lowerCamelCase__						=			json.load(A__   )
   lowerCamelCase__						=			LukeConfig(use_entity_aware_attention=A__	,    **metadata["model_config"]   )

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

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

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

   # Add special tokens to the token vocabulary for downstream tasks
   lowerCamelCase__						=			AddedToken("<ent>"	,    lstrip=A__	,    rstrip=A__   )
   lowerCamelCase__						=			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__	,    "tokenizer_config.json"   )	,    "r"   ) as f:
      lowerCamelCase__						=			json.load(A__   )
   lowerCamelCase__						=			"MLukeTokenizer"
   with open(os.path.join(A__	,    "tokenizer_config.json"   )	,    "w"   ) as f:
      json.dump(A__	,    A__   )

   with open(os.path.join(A__	,    MLukeTokenizer.vocab_files_names["entity_vocab_file"]   )	,    "w"   ) as f:
      json.dump(A__	,    A__   )

   lowerCamelCase__						=			MLukeTokenizer.from_pretrained(A__   )

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

   lowerCamelCase__						=			state_dict["embeddings.word_embeddings.weight"]
   lowerCamelCase__						=			word_emb[ent_init_index].unsqueeze(0   )
   lowerCamelCase__						=			word_emb[enta_init_index].unsqueeze(0   )
   lowerCamelCase__						=			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"]:
      lowerCamelCase__						=			state_dict[bias_name]
      lowerCamelCase__						=			decoder_bias[ent_init_index].unsqueeze(0   )
      lowerCamelCase__						=			decoder_bias[enta_init_index].unsqueeze(0   )
      lowerCamelCase__						=			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"]:
         lowerCamelCase__						=			f'encoder.layer.{layer_index}.attention.self.'
         lowerCamelCase__						=			state_dict[prefix + matrix_name]
         lowerCamelCase__						=			state_dict[prefix + matrix_name]
         lowerCamelCase__						=			state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
   lowerCamelCase__						=			state_dict["entity_embeddings.entity_embeddings.weight"]
   lowerCamelCase__						=			entity_emb[entity_vocab["[MASK]"]].unsqueeze(0   )
   lowerCamelCase__						=			torch.cat([entity_emb, entity_mask_emb]   )
   # add [MASK2] for 'entity_predictions.bias'
   lowerCamelCase__						=			state_dict["entity_predictions.bias"]
   lowerCamelCase__						=			entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0   )
   lowerCamelCase__						=			torch.cat([entity_prediction_bias, entity_mask_bias]   )

   lowerCamelCase__						=			LukeForMaskedLM(config=A__   ).eval()

   state_dict.pop("entity_predictions.decoder.weight"   )
   state_dict.pop("lm_head.decoder.weight"   )
   state_dict.pop("lm_head.decoder.bias"   )
   lowerCamelCase__						=			OrderedDict()
   for key, value in state_dict.items():
      if not (key.startswith("lm_head"   ) or key.startswith("entity_predictions"   )):
         lowerCamelCase__						=			state_dict[key]
      else:
         lowerCamelCase__						=			state_dict[key]

   lowerCamelCase__					,       lowerCamelCase__						=			model.load_state_dict(A__	,    strict=A__   )

   if set(A__   ) != {"luke.embeddings.position_ids"}:
      raise ValueError(f'Unexpected unexpected_keys: {unexpected_keys}'   )
   if set(A__   ) != {
       "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
   lowerCamelCase__						=			MLukeTokenizer.from_pretrained(A__	,    task="entity_classification"   )

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

   lowerCamelCase__						=			model(**A__   )

   # Verify word hidden states
   if model_size == "large":
      raise NotImplementedError
   else:  # base
      lowerCamelCase__						=			torch.Size((1, 33, 768)   )
      lowerCamelCase__						=			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]	,    A__	,    atol=1E-4   ):
      raise ValueError

   # Verify entity hidden states
   if model_size == "large":
      raise NotImplementedError
   else:  # base
      lowerCamelCase__						=			torch.Size((1, 1, 768)   )
      lowerCamelCase__						=			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]	,    A__	,    atol=1E-4   ):
      raise ValueError

   # Verify masked word/entity prediction
   lowerCamelCase__						=			MLukeTokenizer.from_pretrained(A__   )
   lowerCamelCase__						=			"Tokyo is the capital of <mask>."
   lowerCamelCase__						=			(24, 30)
   lowerCamelCase__						=			tokenizer(A__	,    entity_spans=[span]	,    return_tensors="pt"   )

   lowerCamelCase__						=			model(**A__   )

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

   lowerCamelCase__						=			outputs.entity_logits[0][0].argmax().item()
   lowerCamelCase__						=			[
       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(A__   )   )
   model.save_pretrained(A__   )



def 	UpperCAmelCase__   (				A__   )      ->      Any:




   """simple docstring"""
   lowerCamelCase__						=			["[MASK]", "[PAD]", "[UNK]"]

   lowerCamelCase__						=			[json.loads(A__   ) for line in open(A__   )]

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


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_     :			List[str]	  =			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_     :			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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_     :			List[Any]	  =			{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_     :			Union[str, Any]	  =			[
         '''RagModel''',
         '''RagPreTrainedModel''',
         '''RagSequenceForGeneration''',
         '''RagTokenForGeneration''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_     :			str	  =			[
         '''TFRagModel''',
         '''TFRagPreTrainedModel''',
         '''TFRagSequenceForGeneration''',
         '''TFRagTokenForGeneration''',
     ]


if TYPE_CHECKING:
     from .configuration_rag import RagConfig
     from .retrieval_rag import RagRetriever
     from .tokenization_rag import RagTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_rag import (
              TFRagModel,
              TFRagPreTrainedModel,
              TFRagSequenceForGeneration,
              TFRagTokenForGeneration,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE_     :			List[str]	  =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




