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 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case						):
					# Load configuration defined in the metadata file
					with open(__snake_case						) as metadata_file:
										_UpperCamelCase			     =     json.load(__snake_case						)
					_UpperCamelCase			     =     LukeConfig(use_entity_aware_attention=__snake_case  ,     **metadata['''model_config''']						)

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

					# Load the entity vocab file
					_UpperCamelCase			     =     load_original_entity_vocab(__snake_case						)
					# add an entry for [MASK2]
					_UpperCamelCase			     =     max(entity_vocab.values()						) + 1
					config.entity_vocab_size += 1

					_UpperCamelCase			     =     XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']						)

					# Add special tokens to the token vocabulary for downstream tasks
					_UpperCamelCase			     =     AddedToken('''<ent>'''  ,     lstrip=__snake_case  ,     rstrip=__snake_case						)
					_UpperCamelCase			     =     AddedToken('''<ent2>'''  ,     lstrip=__snake_case  ,     rstrip=__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(__snake_case						)
					with open(os.path.join(__snake_case  ,     '''tokenizer_config.json'''						)  ,     '''r'''						) as f:
										_UpperCamelCase			     =     json.load(__snake_case						)
					_UpperCamelCase			     =     '''MLukeTokenizer'''
					with open(os.path.join(__snake_case  ,     '''tokenizer_config.json'''						)  ,     '''w'''						) as f:
										json.dump(__snake_case  ,     __snake_case						)

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

					_UpperCamelCase			     =     MLukeTokenizer.from_pretrained(__snake_case						)

					# Initialize the embeddings of the special tokens
					_UpperCamelCase			     =     tokenizer.convert_tokens_to_ids(['''@''']						)[0]
					_UpperCamelCase			     =     tokenizer.convert_tokens_to_ids(['''#''']						)[0]

					_UpperCamelCase			     =     state_dict['''embeddings.word_embeddings.weight''']
					_UpperCamelCase			     =     word_emb[ent_init_index].unsqueeze(0						)
					_UpperCamelCase			     =     word_emb[enta_init_index].unsqueeze(0						)
					_UpperCamelCase			     =     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"]:
										_UpperCamelCase			     =     state_dict[bias_name]
										_UpperCamelCase			     =     decoder_bias[ent_init_index].unsqueeze(0						)
										_UpperCamelCase			     =     decoder_bias[enta_init_index].unsqueeze(0						)
										_UpperCamelCase			     =     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"]:
															_UpperCamelCase			     =     f"""encoder.layer.{layer_index}.attention.self."""
															_UpperCamelCase			     =     state_dict[prefix + matrix_name]
															_UpperCamelCase			     =     state_dict[prefix + matrix_name]
															_UpperCamelCase			     =     state_dict[prefix + matrix_name]

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

					_UpperCamelCase			     =     LukeForMaskedLM(config=__snake_case						).eval()

					state_dict.pop('''entity_predictions.decoder.weight'''						)
					state_dict.pop('''lm_head.decoder.weight'''						)
					state_dict.pop('''lm_head.decoder.bias'''						)
					_UpperCamelCase			     =     OrderedDict()
					for key, value in state_dict.items():
										if not (key.startswith('''lm_head'''						) or key.startswith('''entity_predictions'''						)):
															_UpperCamelCase			     =     state_dict[key]
										else:
															_UpperCamelCase			     =     state_dict[key]

					_UpperCamelCase     ,    _UpperCamelCase			     =     model.load_state_dict(__snake_case  ,     strict=__snake_case						)

					if set(__snake_case						) != {"luke.embeddings.position_ids"}:
										raise ValueError(f"""Unexpected unexpected_keys: {unexpected_keys}"""						)
					if set(__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
					_UpperCamelCase			     =     MLukeTokenizer.from_pretrained(__snake_case  ,     task='''entity_classification'''						)

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

					_UpperCamelCase			     =     model(**__snake_case						)

					# Verify word hidden states
					if model_size == "large":
										raise NotImplementedError
					else:  # base
										_UpperCamelCase			     =     torch.Size((1, 33, 768)						)
										_UpperCamelCase			     =     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]  ,     __snake_case  ,     atol=1E-4						):
										raise ValueError

					# Verify entity hidden states
					if model_size == "large":
										raise NotImplementedError
					else:  # base
										_UpperCamelCase			     =     torch.Size((1, 1, 768)						)
										_UpperCamelCase			     =     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]  ,     __snake_case  ,     atol=1E-4						):
										raise ValueError

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

					_UpperCamelCase			     =     model(**__snake_case						)

					_UpperCamelCase			     =     encoding['''input_ids'''][0].tolist()
					_UpperCamelCase			     =     input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>'''						)						)
					_UpperCamelCase			     =     outputs.logits[0][mask_position_id].argmax(dim=-1						)
					assert "Japan" == tokenizer.decode(__snake_case						)

					_UpperCamelCase			     =     outputs.entity_logits[0][0].argmax().item()
					_UpperCamelCase			     =     [
					    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(__snake_case						)						)
					model.save_pretrained(__snake_case						)
def 					_snake_case	(  __snake_case						):
					_UpperCamelCase			     =     ['''[MASK]''', '''[PAD]''', '''[UNK]''']

					_UpperCamelCase			     =     [json.loads(__snake_case						) for line in open(__snake_case						)]

					_UpperCamelCase			     =     {}
					for entry in data:
										_UpperCamelCase			     =     entry['''id''']
										for entity_name, language in entry["entities"]:
															if entity_name in SPECIAL_TOKENS:
																				_UpperCamelCase			     =     entity_id
																				break
															_UpperCamelCase			     =     f"""{language}:{entity_name}"""
															_UpperCamelCase			     =     entity_id
					return new_mapping


if __name__ == "__main__":
					_lowerCAmelCase       =		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       =		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 inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version
def 					_snake_case	(  *__snake_case  ,     __snake_case = None  ,     __snake_case=True  ,     __snake_case=2						):
					from .. import __version__

					_UpperCamelCase			     =     take_from
					_UpperCamelCase			     =     ()
					if not isinstance(args[0]  ,     __snake_case						):
										_UpperCamelCase			     =     (args,)

					for attribute, version_name, message in args:
										if version.parse(version.parse(__snake_case						).base_version						) >= version.parse(__snake_case						):
															raise ValueError(
															    f"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
															    f""" version {__version__} is >= {version_name}"""						)

										_UpperCamelCase			     =     None
										if isinstance(__snake_case  ,     __snake_case						) and attribute in deprecated_kwargs:
															values += (deprecated_kwargs.pop(__snake_case						),)
															_UpperCamelCase			     =     f"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
										elif hasattr(__snake_case  ,     __snake_case						):
															values += (getattr(__snake_case  ,     __snake_case						),)
															_UpperCamelCase			     =     f"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
										elif deprecated_kwargs is None:
															_UpperCamelCase			     =     f"""`{attribute}` is deprecated and will be removed in version {version_name}."""

										if warning is not None:
															_UpperCamelCase			     =     warning + ''' ''' if standard_warn else ''''''
															warnings.warn(warning + message  ,     __snake_case  ,     stacklevel=__snake_case						)

					if isinstance(__snake_case  ,     __snake_case						) and len(__snake_case						) > 0:
										_UpperCamelCase			     =     inspect.getouterframes(inspect.currentframe()						)[1]
										_UpperCamelCase			     =     call_frame.filename
										_UpperCamelCase			     =     call_frame.lineno
										_UpperCamelCase			     =     call_frame.function
										_UpperCamelCase     ,    _UpperCamelCase			     =     next(iter(deprecated_kwargs.items()						)						)
										raise TypeError(f"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"""						)

					if len(__snake_case						) == 0:
										return
					elif len(__snake_case						) == 1:
										return values[0]
					return values
