




import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


UpperCAmelCase_		:				Union[str, Any]									=     logging.get_logger(__name__)

UpperCAmelCase_		:				Dict									=     {"vocab_file": "vocab.txt"}

UpperCAmelCase_		:				int									=     {
    "vocab_file": {
        "facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
        "facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
    },
}

UpperCAmelCase_		:				List[str]									=     {
    "facebook/esm2_t6_8M_UR50D": 1024,
    "facebook/esm2_t12_35M_UR50D": 1024,
}




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				with open(lowerCamelCase	,     """r"""   ) as f:
								__magic_name__	:	Dict								=f.read().splitlines()
								return [l.strip() for l in lines]




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					VOCAB_FILES_NAMES
						UpperCamelCase     						=					PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase     						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase     						=					["""input_ids""", """attention_mask"""]

						def __init__(		self   :Dict    , __snake_case   :List[Any]    , __snake_case   :Any="<unk>"    , __snake_case   :Dict="<cls>"    , __snake_case   :List[str]="<pad>"    , __snake_case   :List[Any]="<mask>"    , __snake_case   :List[Any]="<eos>"    , **__snake_case   :Dict    , ):
										'''simple docstring'''

										super().__init__(**__snake_case )
										__magic_name__	:	Any								=load_vocab_file(__snake_case )
										__magic_name__	:	Any								=dict(enumerate(self.all_tokens ) )
										__magic_name__	:	Optional[int]								={tok: ind for ind, tok in enumerate(self.all_tokens )}
										__magic_name__	:	List[str]								=unk_token
										__magic_name__	:	Optional[int]								=cls_token
										__magic_name__	:	Union[str, Any]								=pad_token
										__magic_name__	:	str								=mask_token
										__magic_name__	:	Dict								=eos_token
										__magic_name__	:	List[Any]								=self.all_tokens
										self._create_trie(self.unique_no_split_tokens )

						def     A__						(		self   :List[Any]    , __snake_case   :int ):
										'''simple docstring'''

										return self._id_to_token.get(__snake_case    , self.unk_token )

						def     A__						(		self   :List[str]    , __snake_case   :str ):
										'''simple docstring'''

										return self._token_to_id.get(__snake_case    , self._token_to_id.get(self.unk_token ) )

						def     A__						(		self   :Tuple    , __snake_case   :str    , **__snake_case   :List[Any] ):
										'''simple docstring'''

										return text.split()

						def     A__						(		self   :List[Any]    , __snake_case   :List[Any]=False ):
										'''simple docstring'''

										return len(self._id_to_token )

						def     A__						(		self   :Dict ):
										'''simple docstring'''

										return {token: i for i, token in enumerate(self.all_tokens )}

						def     A__						(		self   :Tuple    , __snake_case   :str ):
										'''simple docstring'''

										return self._token_to_id.get(__snake_case    , self._token_to_id.get(self.unk_token ) )

						def     A__						(		self   :Optional[Any]    , __snake_case   :int ):
										'''simple docstring'''

										return self._id_to_token.get(__snake_case    , self.unk_token )

						def     A__						(		self   :Union[str, Any]    , __snake_case   :List[int]    , __snake_case   :Optional[List[int]] = None ):
										'''simple docstring'''

										__magic_name__	:	int								=[self.cls_token_id]
										__magic_name__	:	Optional[int]								=[self.eos_token_id]  # No sep token in ESM vocabulary
										if token_ids_a is None:
														if self.eos_token_id is None:
																		return cls + token_ids_a
														else:
																		return cls + token_ids_a + sep
										elif self.eos_token_id is None:
														raise ValueError("""Cannot tokenize multiple sequences when EOS token is not set!""" )
										return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token

						def     A__						(		self   :Dict    , __snake_case   :List    , __snake_case   :Optional[List] = None    , __snake_case   :bool = False ):
										'''simple docstring'''

										if already_has_special_tokens:
														if token_ids_a is not None:
																		raise ValueError(
																		    """You should not supply a second sequence if the provided sequence of """
																		    """ids is already formatted with special tokens for the model.""" )

														return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
										__magic_name__	:	Tuple								=[1] + ([0] * len(__snake_case )) + [1]
										if token_ids_a is not None:
														mask += [0] * len(__snake_case ) + [1]
										return mask

						def     A__						(		self   :Any    , __snake_case   :int    , __snake_case   :int ):
										'''simple docstring'''

										__magic_name__	:	int								=os.path.join(__snake_case    , (filename_prefix + """-""" if filename_prefix else """""") + """vocab.txt""" )
										with open(__snake_case    , """w""" ) as f:
														f.write("""\n""".join(self.all_tokens ) )
										return (vocab_file,)

						@property
						def     A__						(		self   :Dict ):
										'''simple docstring'''

										return self.get_vocab_size(with_added_tokens=__snake_case )


						def     A__						(		self   :str    , __snake_case   :Union[List[str], List[AddedToken]]    , __snake_case   :bool = False ):
										'''simple docstring'''

										return super()._add_tokens(__snake_case    , special_tokens=__snake_case )










import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken




@torch.no_grad()
def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				# Load configuration defined in the metadata file
				with open(lowerCamelCase   ) as metadata_file:
								__magic_name__	:	List[Any]								=json.load(lowerCamelCase   )
				__magic_name__	:	Optional[int]								=LukeConfig(use_entity_aware_attention=lowerCamelCase	,     **metadata["""model_config"""]   )

				# Load in the weights from the checkpoint_path
				__magic_name__	:	List[str]								=torch.load(lowerCamelCase	,     map_location="""cpu"""   )

				# Load the entity vocab file
				__magic_name__	:	Dict								=load_entity_vocab(lowerCamelCase   )

				__magic_name__	:	Union[str, Any]								=RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]   )

				# Add special tokens to the token vocabulary for downstream tasks
				__magic_name__	:	List[str]								=AddedToken("""<ent>"""	,     lstrip=lowerCamelCase	,     rstrip=lowerCamelCase   )
				__magic_name__	:	Union[str, Any]								=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	,     LukeTokenizer.vocab_files_names["""entity_vocab_file"""]   )	,     """w"""   ) as f:
								json.dump(lowerCamelCase	,     lowerCamelCase   )

				__magic_name__	:	Optional[Any]								=LukeTokenizer.from_pretrained(lowerCamelCase   )

				# Initialize the embeddings of the special tokens
				__magic_name__	:	List[str]								=state_dict["""embeddings.word_embeddings.weight"""]
				__magic_name__	:	Optional[int]								=word_emb[tokenizer.convert_tokens_to_ids(["""@"""]   )[0]].unsqueeze(0   )
				__magic_name__	:	Optional[int]								=word_emb[tokenizer.convert_tokens_to_ids(["""#"""]   )[0]].unsqueeze(0   )
				__magic_name__	:	Tuple								=torch.cat([word_emb, ent_emb, enta_emb]   )

				# 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"]:
												__magic_name__	:	Optional[Any]								=F"encoder.layer.{layer_index}.attention.self."
												__magic_name__	:	Optional[Any]								=state_dict[prefix + matrix_name]
												__magic_name__	:	Any								=state_dict[prefix + matrix_name]
												__magic_name__	:	Dict								=state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
				__magic_name__	:	str								=state_dict["""entity_embeddings.entity_embeddings.weight"""]
				__magic_name__	:	Union[str, Any]								=entity_emb[entity_vocab["""[MASK]"""]]

				__magic_name__	:	Optional[int]								=LukeModel(config=lowerCamelCase   ).eval()

				__magic_name__				, __magic_name__	:	Any								=model.load_state_dict(lowerCamelCase	,     strict=lowerCamelCase   )
				if not (len(lowerCamelCase   ) == 1 and missing_keys[0] == "embeddings.position_ids"):
								raise ValueError(F"Missing keys {', '.join(lowerCamelCase   )}. Expected only missing embeddings.position_ids"   )
				if not (all(key.startswith("""entity_predictions"""   ) or key.startswith("""lm_head"""   ) for key in unexpected_keys   )):
								raise ValueError(
								    """Unexpected keys"""
								    F" {', '.join([key for key in unexpected_keys if not (key.startswith('entity_predictions'   ) or key.startswith('lm_head'   ))]   )}"   )

				# Check outputs
				__magic_name__	:	Dict								=LukeTokenizer.from_pretrained(lowerCamelCase	,     task="""entity_classification"""   )

				__magic_name__	:	int								=(
				    """Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"""
				    """ new world number one avoid a humiliating second- round exit at Wimbledon ."""
				)
				__magic_name__	:	Optional[Any]								=(39, 42)
				__magic_name__	:	Dict								=tokenizer(lowerCamelCase	,     entity_spans=[span]	,     add_prefix_space=lowerCamelCase	,     return_tensors="""pt"""   )

				__magic_name__	:	str								=model(**lowerCamelCase   )

				# Verify word hidden states
				if model_size == "large":
								__magic_name__	:	Tuple								=torch.Size((1, 42, 1024)   )
								__magic_name__	:	int								=torch.tensor(
								    [[0.0_1_3_3, 0.0_8_6_5, 0.0_0_9_5], [0.3_0_9_3, -0.2_5_7_6, -0.7_4_1_8], [-0.1_7_2_0, -0.2_1_1_7, -0.2_8_6_9]]   )
				else:  # base
								__magic_name__	:	Dict								=torch.Size((1, 42, 768)   )
								__magic_name__	:	List[Any]								=torch.tensor([[0.0_0_3_7, 0.1_3_6_8, -0.0_0_9_1], [0.1_0_9_9, 0.3_3_2_9, -0.1_0_9_5], [0.0_7_6_5, 0.5_3_3_5, 0.1_1_7_9]]   )

				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":
								__magic_name__	:	Optional[Any]								=torch.Size((1, 1, 1024)   )
								__magic_name__	:	Tuple								=torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]   )
				else:  # base
								__magic_name__	:	List[Any]								=torch.Size((1, 1, 768)   )
								__magic_name__	:	Dict								=torch.tensor([[0.1_4_5_7, 0.1_0_4_4, 0.0_1_7_4]]   )

				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

				# Finally, save our PyTorch model and tokenizer
				print("""Saving PyTorch model to {}""".format(lowerCamelCase   )   )
				model.save_pretrained(lowerCamelCase   )




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Tuple								={}
				with open(lowerCamelCase	,     """r"""	,     encoding="""utf-8"""   ) as f:
								for index, line in enumerate(lowerCamelCase   ):
												__magic_name__				, __magic_name__	:	Optional[Any]								=line.rstrip().split("""\t"""   )
												__magic_name__	:	Union[str, Any]								=index

				return entity_vocab


if __name__ == "__main__":
							UpperCAmelCase_		:				Optional[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."
							)
							UpperCAmelCase_		:				int									=     parser.parse_args()
							convert_luke_checkpoint(
							    args.checkpoint_path,
							    args.metadata_path,
							    args.entity_vocab_path,
							    args.pytorch_dump_folder_path,
							    args.model_size,
							)




