



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       __lowercase  (					a__				,  a__				,  a__				,  a__				,  a__  )							->					Optional[Any]:





							with open(SCREAMING_SNAKE_CASE_  ) as metadata_file:
														__SCREAMING_SNAKE_CASE										=   json.load(SCREAMING_SNAKE_CASE_  )
							__SCREAMING_SNAKE_CASE										=   LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE_				,  **metadata['model_config']  )

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

							# Load the entity vocab file
							__SCREAMING_SNAKE_CASE										=   load_original_entity_vocab(SCREAMING_SNAKE_CASE_  )
							# 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=SCREAMING_SNAKE_CASE_				,  rstrip=SCREAMING_SNAKE_CASE_  )
							__SCREAMING_SNAKE_CASE										=   AddedToken('<ent2>'				,  lstrip=SCREAMING_SNAKE_CASE_				,  rstrip=SCREAMING_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(SCREAMING_SNAKE_CASE_  )
							with open(os.path.join(SCREAMING_SNAKE_CASE_				,  'tokenizer_config.json'  )				,  'r'  ) as f:
														__SCREAMING_SNAKE_CASE										=   json.load(SCREAMING_SNAKE_CASE_  )
							__SCREAMING_SNAKE_CASE										=   'MLukeTokenizer'
							with open(os.path.join(SCREAMING_SNAKE_CASE_				,  'tokenizer_config.json'  )				,  'w'  ) as f:
														json.dump(SCREAMING_SNAKE_CASE_				,  SCREAMING_SNAKE_CASE_  )

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

							__SCREAMING_SNAKE_CASE										=   MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_  )

							# 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=SCREAMING_SNAKE_CASE_  ).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(SCREAMING_SNAKE_CASE_				,  strict=SCREAMING_SNAKE_CASE_  )

							if set(SCREAMING_SNAKE_CASE_  ) != {"luke.embeddings.position_ids"}:
														raise ValueError(f"""Unexpected unexpected_keys: {unexpected_keys}"""  )
							if set(SCREAMING_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
							__SCREAMING_SNAKE_CASE										=   MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_				,  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(SCREAMING_SNAKE_CASE_				,  entity_spans=[span]				,  return_tensors='pt'  )

							__SCREAMING_SNAKE_CASE										=   model(**SCREAMING_SNAKE_CASE_  )

							# 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([[0.0892, 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]				,  SCREAMING_SNAKE_CASE_				,  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([[-0.1482, 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]				,  SCREAMING_SNAKE_CASE_				,  atol=1E-4  ):
														raise ValueError

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

							__SCREAMING_SNAKE_CASE										=   model(**SCREAMING_SNAKE_CASE_  )

							__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(SCREAMING_SNAKE_CASE_  )

							__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(SCREAMING_SNAKE_CASE_  )  )
							model.save_pretrained(SCREAMING_SNAKE_CASE_  )






def       __lowercase  (					a__  )							->					Tuple:
							__SCREAMING_SNAKE_CASE										=   ['[MASK]', '[PAD]', '[UNK]']

							__SCREAMING_SNAKE_CASE										=   [json.loads(SCREAMING_SNAKE_CASE_  ) for line in open(SCREAMING_SNAKE_CASE_  )]

							__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__						:	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.'''
			)
			lowerCAmelCase__						:	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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				str								=       "gpt_bigcode"
 __lowerCamelCase    :				Optional[int]								=       ["past_key_values"]
 __lowerCamelCase    :				List[str]								=       {
     "hidden_size": "n_embd",
     "max_position_embeddings": "n_positions",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }




 def __init__(						self      ,  _lowerCAmelCase=50257      ,  _lowerCAmelCase=1024      ,  _lowerCAmelCase=768      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase="gelu_pytorch_tanh"      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=1E-5      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=50256      ,  _lowerCAmelCase=50256      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  **_lowerCAmelCase      ,  )     ->				List[Any]:
     _lowerCAmelCase						      =   vocab_size
     _lowerCAmelCase						      =   n_positions
     _lowerCAmelCase						      =   n_embd
     _lowerCAmelCase						      =   n_layer
     _lowerCAmelCase						      =   n_head
     _lowerCAmelCase						      =   n_inner
     _lowerCAmelCase						      =   activation_function
     _lowerCAmelCase						      =   resid_pdrop
     _lowerCAmelCase						      =   embd_pdrop
     _lowerCAmelCase						      =   attn_pdrop
     _lowerCAmelCase						      =   layer_norm_epsilon
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   scale_attn_weights
     _lowerCAmelCase						      =   use_cache
     _lowerCAmelCase						      =   attention_softmax_in_fpaa
     _lowerCAmelCase						      =   scale_attention_softmax_in_fpaa
     _lowerCAmelCase						      =   multi_query

     _lowerCAmelCase						      =   bos_token_id
     _lowerCAmelCase						      =   eos_token_id

     super().__init__(bos_token_id=_lowerCAmelCase      ,  eos_token_id=_lowerCAmelCase      ,  **_lowerCAmelCase		)


