
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 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Union[str, Any]:

		"""simple docstring"""






		with open(lowerCAmelCase ) as metadata_file:
				_UpperCamelCase						    =	json.load(lowerCAmelCase )
		_UpperCamelCase						    =	LukeConfig(use_entity_aware_attention=lowerCAmelCase						,  **metadata["""model_config"""] )

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

		# Load the entity vocab file
		_UpperCamelCase						    =	load_entity_vocab(lowerCAmelCase )

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

		# Add special tokens to the token vocabulary for downstream tasks
		_UpperCamelCase						    =	AddedToken("""<ent>"""						,  lstrip=lowerCAmelCase						,  rstrip=lowerCAmelCase )
		_UpperCamelCase						    =	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 )

		_UpperCamelCase						    =	LukeTokenizer.from_pretrained(lowerCAmelCase )

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

		_UpperCamelCase						    =	LukeModel(config=lowerCAmelCase ).eval()

		_UpperCamelCase    ,	_UpperCamelCase						    =	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
		_UpperCamelCase						    =	LukeTokenizer.from_pretrained(lowerCAmelCase						,  task="""entity_classification""" )

		_UpperCamelCase						    =	(
		    """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 ."""
		)
		_UpperCamelCase						    =	(3_9, 4_2)
		_UpperCamelCase						    =	tokenizer(lowerCAmelCase						,  entity_spans=[span]						,  add_prefix_space=lowerCAmelCase						,  return_tensors="""pt""" )

		_UpperCamelCase						    =	model(**lowerCAmelCase )

		# Verify word hidden states
		if model_size == "large":
				_UpperCamelCase						    =	torch.Size((1, 4_2, 1_0_2_4) )
				_UpperCamelCase						    =	torch.tensor(
				    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]] )
		else:  # base
				_UpperCamelCase						    =	torch.Size((1, 4_2, 7_6_8) )
				_UpperCamelCase						    =	torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]] )

		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":
				_UpperCamelCase						    =	torch.Size((1, 1, 1_0_2_4) )
				_UpperCamelCase						    =	torch.tensor([[0.0466, -0.0106, -0.0179]] )
		else:  # base
				_UpperCamelCase						    =	torch.Size((1, 1, 7_6_8) )
				_UpperCamelCase						    =	torch.tensor([[0.1457, 0.1044, 0.0174]] )

		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 							__A(lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		_UpperCamelCase						    =	{}
		with open(lowerCAmelCase						,  """r"""						,  encoding="""utf-8""" ) as f:
				for index, line in enumerate(lowerCAmelCase ):
						_UpperCamelCase    ,	_UpperCamelCase						    =	line.rstrip().split("""\t""" )
						_UpperCamelCase						    =	index

		return entity_vocab


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,
				)





lowerCamelCase__	=							[
    "DownloadConfig",
    "DownloadManager",
    "DownloadMode",
    "StreamingDownloadManager",
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager



