


'''simple docstring'''



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 				lowerCamelCase__				(	A       :      Dict				,       A       :      Union[str, Any]				,       A       :      Union[str, Any]				,       A       :      Any				,       A       :      int					):




						'''simple docstring'''






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

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

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

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

						UpperCAmelCase					    =     MLukeTokenizer.from_pretrained(A					)

						# 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=A					).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(A				,       strict=A					)

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

						UpperCAmelCase					    =     model(**A					)

						# Verify word hidden states
						if model_size == "large":
												raise NotImplementedError
						else:  # base
												UpperCAmelCase					    =     torch.Size((1, 33, 7_68)					)
												UpperCAmelCase					    =     torch.tensor([[0.0_892, 0.0_596, -0.2_819], [0.0_134, 0.1_199, 0.0_573], [-0.0_169, 0.0_927, 0.0_644]]					)

						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]				,       A				,       atol=1E-4					):
												raise ValueError

						# Verify entity hidden states
						if model_size == "large":
												raise NotImplementedError
						else:  # base
												UpperCAmelCase					    =     torch.Size((1, 1, 7_68)					)
												UpperCAmelCase					    =     torch.tensor([[-0.1_482, 0.0_609, 0.0_322]]					)

						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]				,       A				,       atol=1E-4					):
												raise ValueError

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

						UpperCAmelCase					    =     model(**A					)

						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(A					)

						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(A					)					)
						model.save_pretrained(A					)


def 				lowerCamelCase__				(	A       :      Tuple					):




						'''simple docstring'''






						UpperCAmelCase					    =     ['''[MASK]''', '''[PAD]''', '''[UNK]''']

						UpperCAmelCase					    =     [json.loads(A					) for line in open(A					)]

						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__":
	_lowercase      :      str            =					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      :      Dict            =					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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase      :      List[Any]            =					{
    """configuration_x_clip""": [
        """XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XCLIPConfig""",
        """XCLIPTextConfig""",
        """XCLIPVisionConfig""",
    ],
    """processing_x_clip""": ["""XCLIPProcessor"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      List[str]            =					[
	    """XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XCLIPModel""",
	    """XCLIPPreTrainedModel""",
	    """XCLIPTextModel""",
	    """XCLIPVisionModel""",
	]

if TYPE_CHECKING:
	from .configuration_x_clip import (
	    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    XCLIPConfig,
	    XCLIPTextConfig,
	    XCLIPVisionConfig,
	)
	from .processing_x_clip import XCLIPProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_x_clip import (
		    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XCLIPModel,
		    XCLIPPreTrainedModel,
		    XCLIPTextModel,
		    XCLIPVisionModel,
		)

else:
	import sys

	_lowercase      :      List[Any]            =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





