


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 :			List[Any]  ,    __A :			Optional[Any]  ,    __A :			int  ,    __A :			List[Any]  ,    __A :			Union[str, Any]				)	->		str:

       """simple docstring"""

       with open(__A				) as metadata_file:
              a_					:		Optional[int]							=  json.load(__A				)
       a_					:		Any							=  LukeConfig(use_entity_aware_attention=__A  ,    **metadata['model_config']				)

       # Load in the weights from the checkpoint_path
       a_					:		List[Any]							=  torch.load(__A  ,    map_location='cpu'				)['module']

       # Load the entity vocab file
       a_					:		List[str]							=  load_original_entity_vocab(__A				)
       # add an entry for [MASK2]
       a_					:		Tuple							=  max(entity_vocab.values()				) + 1
       config.entity_vocab_size += 1

       a_					:		Dict							=  XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']				)

       # Add special tokens to the token vocabulary for downstream tasks
       a_					:		Optional[Any]							=  AddedToken('<ent>'  ,    lstrip=__A  ,    rstrip=__A				)
       a_					:		List[str]							=  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:
              a_					:		Optional[int]							=  json.load(__A				)
       a_					:		Union[str, Any]							=  '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				)

       a_					:		Union[str, Any]							=  MLukeTokenizer.from_pretrained(__A				)

       # Initialize the embeddings of the special tokens
       a_					:		Optional[int]							=  tokenizer.convert_tokens_to_ids(['@']				)[0]
       a_					:		Optional[int]							=  tokenizer.convert_tokens_to_ids(['#']				)[0]

       a_					:		Any							=  state_dict['embeddings.word_embeddings.weight']
       a_					:		Dict							=  word_emb[ent_init_index].unsqueeze(0				)
       a_					:		Dict							=  word_emb[enta_init_index].unsqueeze(0				)
       a_					:		Optional[int]							=  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"]:
              a_					:		Union[str, Any]							=  state_dict[bias_name]
              a_					:		Tuple							=  decoder_bias[ent_init_index].unsqueeze(0				)
              a_					:		Optional[int]							=  decoder_bias[enta_init_index].unsqueeze(0				)
              a_					:		List[str]							=  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"]:
                     a_					:		List[str]							=  F"""encoder.layer.{layer_index}.attention.self."""
                     a_					:		List[str]							=  state_dict[prefix + matrix_name]
                     a_					:		Optional[Any]							=  state_dict[prefix + matrix_name]
                     a_					:		Tuple							=  state_dict[prefix + matrix_name]

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

       a_					:		str							=  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'				)
       a_					:		List[Any]							=  OrderedDict()
       for key, value in state_dict.items():
              if not (key.startswith('lm_head'				) or key.startswith('entity_predictions'				)):
                     a_					:		Any							=  state_dict[key]
              else:
                     a_					:		Dict							=  state_dict[key]

       a_							,		a_					:		Dict							=  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
       a_					:		Optional[int]							=  MLukeTokenizer.from_pretrained(__A  ,    task='entity_classification'				)

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

       a_					:		int							=  model(**__A				)

       # Verify word hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              a_					:		Optional[Any]							=  torch.Size((1, 33, 7_68)				)
              a_					:		Dict							=  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
              a_					:		str							=  torch.Size((1, 1, 7_68)				)
              a_					:		Optional[int]							=  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
       a_					:		Union[str, Any]							=  MLukeTokenizer.from_pretrained(__A				)
       a_					:		Union[str, Any]							=  'Tokyo is the capital of <mask>.'
       a_					:		Dict							=  (24, 30)
       a_					:		Tuple							=  tokenizer(__A  ,    entity_spans=[span]  ,    return_tensors='pt'				)

       a_					:		str							=  model(**__A				)

       a_					:		List[Any]							=  encoding['input_ids'][0].tolist()
       a_					:		List[Any]							=  input_ids.index(tokenizer.convert_tokens_to_ids('<mask>'				)				)
       a_					:		List[str]							=  outputs.logits[0][mask_position_id].argmax(dim=-1				)
       assert "Japan" == tokenizer.decode(__A				)

       a_					:		Any							=  outputs.entity_logits[0][0].argmax().item()
       a_					:		Tuple							=  [
           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        SCREAMING_SNAKE_CASE_						(			__A :			Optional[int]				)	->		Tuple:

       """simple docstring"""

       a_					:		List[str]							=  ['[MASK]', '[PAD]', '[UNK]']

       a_					:		Any							=  [json.loads(__A				) for line in open(__A				)]

       a_					:		Optional[Any]							=  {}
       for entry in data:
              a_					:		List[str]							=  entry['id']
              for entity_name, language in entry["entities"]:
                     if entity_name in SPECIAL_TOKENS:
                            a_					:		Optional[int]							=  entity_id
                            break
                     a_					:		List[Any]							=  F"""{language}:{entity_name}"""
                     a_					:		str							=  entity_id
       return new_mapping


if __name__ == "__main__":
   UpperCAmelCase_      : Tuple  					=       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.'
   )
   UpperCAmelCase_      : 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,
   )








import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


UpperCAmelCase_									=	logging.get_logger(__name__)





class     lowerCamelCase__:
      def __init__(				self: Any    ,       UpperCamelCase_: str    ,       UpperCamelCase_: Dict   ):
             __lowerCamelCase   =						question_encoder
             __lowerCamelCase   =						generator
             __lowerCamelCase   =						self.question_encoder
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: Optional[Any]   ):
             if os.path.isfile(UpperCamelCase_   ):
                    raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'   )
             os.makedirs(UpperCamelCase_    ,       exist_ok=UpperCamelCase_   )
             __lowerCamelCase   =						os.path.join(UpperCamelCase_    ,       """question_encoder_tokenizer"""   )
             __lowerCamelCase   =						os.path.join(UpperCamelCase_    ,       """generator_tokenizer"""   )
             self.question_encoder.save_pretrained(UpperCamelCase_   )
             self.generator.save_pretrained(UpperCamelCase_   )
      @classmethod
      def 			lowerCAmelCase__						(				cls: List[Any]    ,       UpperCamelCase_: Dict    ,       **UpperCamelCase_: Union[str, Any]   ):
             # dynamically import AutoTokenizer
             from ..auto.tokenization_auto import AutoTokenizer

             __lowerCamelCase   =						kwargs.pop("""config"""    ,       UpperCamelCase_   )

             if config is None:
                    __lowerCamelCase   =						RagConfig.from_pretrained(UpperCamelCase_   )

             __lowerCamelCase   =						AutoTokenizer.from_pretrained(
                 UpperCamelCase_    ,       config=config.question_encoder    ,       subfolder="""question_encoder_tokenizer"""   )
             __lowerCamelCase   =						AutoTokenizer.from_pretrained(
                 UpperCamelCase_    ,       config=config.generator    ,       subfolder="""generator_tokenizer"""   )
             return cls(question_encoder=UpperCamelCase_    ,       generator=UpperCamelCase_   )
      def __call__(				self: Tuple    ,       *UpperCamelCase_: int    ,       **UpperCamelCase_: int   ):
             return self.current_tokenizer(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Tuple    ,       *UpperCamelCase_: List[Any]    ,       **UpperCamelCase_: List[Any]   ):
             return self.generator.batch_decode(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Optional[Any]    ,       *UpperCamelCase_: str    ,       **UpperCamelCase_: Union[str, Any]   ):
             return self.generator.decode(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: str   ):
             __lowerCamelCase   =						self.question_encoder
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						self.generator




      def 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: List[str]    ,       UpperCamelCase_: Optional[List[str]] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: str = "longest"    ,       UpperCamelCase_: str = None    ,       UpperCamelCase_: bool = True    ,       **UpperCamelCase_: int    ,       ):
             warnings.warn(
                 """`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
                 """regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
                 """context manager to prepare your targets. See the documentation of your specific tokenizer for more """
                 """details"""    ,       UpperCamelCase_    ,       )
             if max_length is None:
                    __lowerCamelCase   =						self.current_tokenizer.model_max_length
             __lowerCamelCase   =						self(
                 UpperCamelCase_    ,       add_special_tokens=UpperCamelCase_    ,       return_tensors=UpperCamelCase_    ,       max_length=UpperCamelCase_    ,       padding=UpperCamelCase_    ,       truncation=UpperCamelCase_    ,       **UpperCamelCase_    ,       )
             if tgt_texts is None:
                    return model_inputs
             # Process tgt_texts
             if max_target_length is None:
                    __lowerCamelCase   =						self.current_tokenizer.model_max_length
             __lowerCamelCase   =						self(
                 text_target=UpperCamelCase_    ,       add_special_tokens=UpperCamelCase_    ,       return_tensors=UpperCamelCase_    ,       padding=UpperCamelCase_    ,       max_length=UpperCamelCase_    ,       truncation=UpperCamelCase_    ,       **UpperCamelCase_    ,       )
             __lowerCamelCase   =						labels["""input_ids"""]
             return model_inputs

