"""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       (     snake_case,      snake_case,      snake_case,      snake_case,      snake_case):
     # Load configuration defined in the metadata file
     with open(snake_case) as metadata_file:
          __snake_case				=							json.load(snake_case)
     __snake_case				=							LukeConfig(use_entity_aware_attention=snake_case,      **metadata['''model_config'''])

     # Load in the weights from the checkpoint_path
     __snake_case				=							torch.load(snake_case,      map_location='''cpu''')['''module''']

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

     __snake_case				=							XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''])

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

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

     __snake_case				=							MLukeTokenizer.from_pretrained(snake_case)

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

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

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

     __snake_case				=							LukeForMaskedLM(config=snake_case).eval()

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

     __snake_case		,	__snake_case				=							model.load_state_dict(snake_case,      strict=snake_case)

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

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

     __snake_case				=							model(**snake_case)

     # Verify word hidden states
     if model_size == "large":
          raise NotImplementedError
     else:  # base
          __snake_case				=							torch.Size((1, 33, 7_68))
          __snake_case				=							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],      snake_case,      atol=1E-4):
          raise ValueError

     # Verify entity hidden states
     if model_size == "large":
          raise NotImplementedError
     else:  # base
          __snake_case				=							torch.Size((1, 1, 7_68))
          __snake_case				=							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],      snake_case,      atol=1E-4):
          raise ValueError

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

     __snake_case				=							model(**snake_case)

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

     __snake_case				=							outputs.entity_logits[0][0].argmax().item()
     __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(snake_case))
     model.save_pretrained(snake_case)





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							['''[MASK]''', '''[PAD]''', '''[UNK]''']

     __snake_case				=							[json.loads(snake_case) for line in open(snake_case)]

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


if __name__ == "__main__":
       __lowercase		:		Union[str, 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."
       )
       __lowercase		:		Union[str, Any]														=							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_torch_available,
)


__lowercase		:		Optional[Any]														=							{
    "configuration_falcon": ["FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP", "FalconConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase		:		str														=							[
           "FALCON_PRETRAINED_MODEL_ARCHIVE_LIST",
           "FalconForCausalLM",
           "FalconModel",
           "FalconPreTrainedModel",
           "FalconForSequenceClassification",
           "FalconForTokenClassification",
           "FalconForQuestionAnswering",
       ]


if TYPE_CHECKING:
       from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_falcon import (
                  FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FalconForCausalLM,
                  FalconForQuestionAnswering,
                  FalconForSequenceClassification,
                  FalconForTokenClassification,
                  FalconModel,
                  FalconPreTrainedModel,
              )


else:
       import sys

       __lowercase		:		List[Any]														=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)