
'''simple docstring'''





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						(							UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      List[Any]			,				UpperCAmelCase__				:      List[str]			,				UpperCAmelCase__				:      int			,				UpperCAmelCase__				:      List[Any]       )  ->    Union[str, Any]:

							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_							  =					(3_9, 4_2)
							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, 4_2, 1_0_2_4)       )
														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, 4_2, 7_6_8)       )
														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, 1_0_2_4)       )
														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_6_8)       )
														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      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[int]       )  ->    Union[str, Any]:
							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__":
		lowercase        =  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        =  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 ..utils import DummyObject, requires_backends





class       __A(				metaclass=UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    ['''torch''', '''scipy''']

					def __init__(	self					:     Any   ,					*__UpperCamelCase					:     str   ,					**__UpperCamelCase					:     Any   ):
												requires_backends(self   ,					["""torch""", """scipy"""]   )

					@classmethod
					def    lowercase__      (	cls					:     Dict   ,					*__UpperCamelCase					:     str   ,					**__UpperCamelCase					:     Dict   ):
												requires_backends(cls   ,					["""torch""", """scipy"""]   )



					@classmethod
					def    lowercase__      (	cls					:     str   ,					*__UpperCamelCase					:     Optional[int]   ,					**__UpperCamelCase					:     Optional[int]   ):
												requires_backends(cls   ,					["""torch""", """scipy"""]   )





