






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 			UpperCamelCase (     __magic_name__					:							Union[str, Any]   ,   __magic_name__					:							Tuple   ,   __magic_name__					:							List[str]   ,   __magic_name__					:							str   ,   __magic_name__					:							int					)       ->  Dict:
		"""simple docstring"""




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

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

		# Load the entity vocab file
		lowercase__					      =		load_entity_vocab(__magic_name__					)

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

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

		lowercase__					      =		LukeTokenizer.from_pretrained(__magic_name__					)

		# Initialize the embeddings of the special tokens
		lowercase__					      =		state_dict["""embeddings.word_embeddings.weight"""]
		lowercase__					      =		word_emb[tokenizer.convert_tokens_to_ids(["""@"""]					)[0]].unsqueeze(0					)
		lowercase__					      =		word_emb[tokenizer.convert_tokens_to_ids(["""#"""]					)[0]].unsqueeze(0					)
		lowercase__					      =		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"]:
						lowercase__					      =		f'''encoder.layer.{layer_index}.attention.self.'''
						lowercase__					      =		state_dict[prefix + matrix_name]
						lowercase__					      =		state_dict[prefix + matrix_name]
						lowercase__					      =		state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
		lowercase__					      =		state_dict["""entity_embeddings.entity_embeddings.weight"""]
		lowercase__					      =		entity_emb[entity_vocab["""[MASK]"""]]

		lowercase__					      =		LukeModel(config=__magic_name__					).eval()

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

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

		lowercase__					      =		model(**__magic_name__					)

		# Verify word hidden states
		if model_size == "large":
				lowercase__					      =		torch.Size((1, 42, 1024)					)
				lowercase__					      =		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
				lowercase__					      =		torch.Size((1, 42, 768)					)
				lowercase__					      =		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]   ,   __magic_name__   ,   atol=1E-4					):
				raise ValueError

		# Verify entity hidden states
		if model_size == "large":
				lowercase__					      =		torch.Size((1, 1, 1024)					)
				lowercase__					      =		torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]					)
		else:  # base
				lowercase__					      =		torch.Size((1, 1, 768)					)
				lowercase__					      =		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]   ,   __magic_name__   ,   atol=1E-4					):
				raise ValueError

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






def 			UpperCamelCase (     __magic_name__					:							Optional[Any]					)       ->  Optional[int]:
		"""simple docstring"""




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

		return entity_vocab


if __name__ == "__main__":
	A	:		Optional[int]    =       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.'
	)
	A	:		Any    =       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 __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
	import numpy as np
	import tensorflow as tf

	from transformers import TFXLMRobertaModel



@require_tf
@require_sentencepiece
@require_tokenizers
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					@slow
					def   lowerCamelCase__					(self   :						Any		)						->       Union[str, Any]:




							"""simple docstring"""


							lowercase__					      =		TFXLMRobertaModel.from_pretrained("""jplu/tf-xlm-roberta-base"""		)

							lowercase__					      =		{
							    """input_ids""": tf.convert_to_tensor([[0, 2646, 1_0269, 83, 9_9942, 2]]				, dtype=tf.intaa		),  # "My dog is cute"
							    """attention_mask""": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]				, dtype=tf.intaa		),
							}

							lowercase__					      =		model(_UpperCAmelCase		)["""last_hidden_state"""]
							lowercase__					      =		tf.TensorShape((1, 6, 768)		)
							self.assertEqual(output.shape				, _UpperCAmelCase		)
							# compare the actual values for a slice.
							lowercase__					      =		tf.convert_to_tensor(
							    [
							        [
							            [0.0_681_762, 0.10_894_451, 0.06_772_504],
							            [-0.06_423_668, 0.02_366_615, 0.04_329_344],
							            [-0.06_057_295, 0.09_974_135, -0.00_070_584],
							        ]
							    ]				, dtype=tf.floataa				, )

							self.assertTrue(np.allclose(output[:, :3, :3].numpy()				, expected_slice.numpy()				, atol=1E-4		)		)

