





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      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Optional[Any]:
				# Load configuration defined in the metadata file
				with open(_UpperCAmelCase				) as metadata_file:
								lowerCamelCase	 =json.load(_UpperCAmelCase				)
				lowerCamelCase	 =LukeConfig(use_entity_aware_attention=_UpperCAmelCase			,      **metadata["""model_config"""]				)

				# Load in the weights from the checkpoint_path
				lowerCamelCase	 =torch.load(_UpperCAmelCase			,      map_location="""cpu"""				)

				# Load the entity vocab file
				lowerCamelCase	 =load_entity_vocab(_UpperCAmelCase				)

				lowerCamelCase	 =RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]				)

				# Add special tokens to the token vocabulary for downstream tasks
				lowerCamelCase	 =AddedToken("""<ent>"""			,      lstrip=_UpperCAmelCase			,      rstrip=_UpperCAmelCase				)
				lowerCamelCase	 =AddedToken("""<ent2>"""			,      lstrip=_UpperCAmelCase			,      rstrip=_UpperCAmelCase				)
				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(_UpperCAmelCase				)
				with open(os.path.join(_UpperCAmelCase			,      LukeTokenizer.vocab_files_names["""entity_vocab_file"""]				)			,      """w"""				) as f:
								json.dump(_UpperCAmelCase			,      _UpperCAmelCase				)

				lowerCamelCase	 =LukeTokenizer.from_pretrained(_UpperCAmelCase				)

				# Initialize the embeddings of the special tokens
				lowerCamelCase	 =state_dict["""embeddings.word_embeddings.weight"""]
				lowerCamelCase	 =word_emb[tokenizer.convert_tokens_to_ids(["""@"""]				)[0]].unsqueeze(0				)
				lowerCamelCase	 =word_emb[tokenizer.convert_tokens_to_ids(["""#"""]				)[0]].unsqueeze(0				)
				lowerCamelCase	 =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"]:
												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]"""]]

				lowerCamelCase	 =LukeModel(config=_UpperCAmelCase				).eval()

				lowerCamelCase ,  lowerCamelCase	 =model.load_state_dict(_UpperCAmelCase			,      strict=_UpperCAmelCase				)
				if not (len(_UpperCAmelCase				) == 1 and missing_keys[0] == "embeddings.position_ids"):
								raise ValueError(F"""Missing keys {", ".join(_UpperCAmelCase				)}. 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
				lowerCamelCase	 =LukeTokenizer.from_pretrained(_UpperCAmelCase			,      task="""entity_classification"""				)

				lowerCamelCase	 =(
				    """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 ."""
				)
				lowerCamelCase	 =(39, 42)
				lowerCamelCase	 =tokenizer(_UpperCAmelCase			,      entity_spans=[span]			,      add_prefix_space=_UpperCAmelCase			,      return_tensors="""pt"""				)

				lowerCamelCase	 =model(**_UpperCAmelCase				)

				# Verify word hidden states
				if model_size == "large":
								lowerCamelCase	 =torch.Size((1, 42, 10_24)				)
								lowerCamelCase	 =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
								lowerCamelCase	 =torch.Size((1, 42, 7_68)				)
								lowerCamelCase	 =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]			,      _UpperCAmelCase			,      atol=1e-4				):
								raise ValueError

				# Verify entity hidden states
				if model_size == "large":
								lowerCamelCase	 =torch.Size((1, 1, 10_24)				)
								lowerCamelCase	 =torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]				)
				else:  # base
								lowerCamelCase	 =torch.Size((1, 1, 7_68)				)
								lowerCamelCase	 =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]			,      _UpperCAmelCase			,      atol=1e-4				):
								raise ValueError

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





def      _lowercase					(						_UpperCAmelCase				)						->  str:
				lowerCamelCase	 ={}
				with open(_UpperCAmelCase			,      """r"""			,      encoding="""utf-8"""				) as f:
								for index, line in enumerate(_UpperCAmelCase				):
												lowerCamelCase ,  lowerCamelCase	 =line.rstrip().split("""\t"""				)
												lowerCamelCase	 =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__						:			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,
							)












from typing import List

from .keymap import KEYMAP, get_character





def      _lowercase					(						_UpperCAmelCase				)						->  Tuple:

				def decorator(_UpperCAmelCase				):
								lowerCamelCase	 =getattr(_UpperCAmelCase			,      """handle_key"""			,      []				)
								handle += [key]
								setattr(_UpperCAmelCase			,      """handle_key"""			,      _UpperCAmelCase				)
								return func

				return decorator





def      _lowercase					(						*_UpperCAmelCase				)						->  Tuple:

				def decorator(_UpperCAmelCase				):
								lowerCamelCase	 =getattr(_UpperCAmelCase			,      """handle_key"""			,      []				)
								handle += keys
								setattr(_UpperCAmelCase			,      """handle_key"""			,      _UpperCAmelCase				)
								return func

				return decorator

class       __A     (						a    ):


				def __new__(		cls   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =super().__new__(cls   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							)
								if not hasattr(UpperCAmelCase_   , """key_handler"""							):
												setattr(UpperCAmelCase_   , """key_handler"""   , {}							)
								setattr(UpperCAmelCase_   , """handle_input"""   , KeyHandler.handle_input							)

								for value in attrs.values():
												lowerCamelCase	 =getattr(UpperCAmelCase_   , """handle_key"""   , []							)
												for key in handled_keys:
																lowerCamelCase	 =value
								return new_cls


				@staticmethod
				def _snake_case       (		cls							):
								lowerCamelCase	 =get_character()
								if char != KEYMAP["undefined"]:
												lowerCamelCase	 =ord(UpperCAmelCase_							)
								lowerCamelCase	 =cls.key_handler.get(UpperCAmelCase_							)
								if handler:
												lowerCamelCase	 =char
												return handler(cls							)
								else:
												return None





def      _lowercase					(						cls				)						->  List[str]:
				return KeyHandler(cls.__name__			,      cls.__bases__			,      cls.__dict__.copy()				)





