

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_							:     Any							,       UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     List[str]							,       UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     Tuple	)						->				Optional[Any]:




	'''simple docstring'''




	with open(lowerCamelCase_	) as metadata_file:
		_UpperCamelCase   :		int			=						json.load(lowerCamelCase_	)
	_UpperCamelCase   :		str			=						LukeConfig(use_entity_aware_attention=lowerCamelCase_							,       **metadata['model_config']	)

	# Load in the weights from the checkpoint_path
	_UpperCamelCase   :		List[Any]			=						torch.load(lowerCamelCase_							,       map_location='cpu'	)

	# Load the entity vocab file
	_UpperCamelCase   :		Dict			=						load_entity_vocab(lowerCamelCase_	)

	_UpperCamelCase   :		Tuple			=						RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']	)

	# Add special tokens to the token vocabulary for downstream tasks
	_UpperCamelCase   :		int			=						AddedToken('<ent>'							,       lstrip=lowerCamelCase_							,       rstrip=lowerCamelCase_	)
	_UpperCamelCase   :		List[str]			=						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   :		Any			=						LukeTokenizer.from_pretrained(lowerCamelCase_	)

	# Initialize the embeddings of the special tokens
	_UpperCamelCase   :		Tuple			=						state_dict['embeddings.word_embeddings.weight']
	_UpperCamelCase   :		Optional[int]			=						word_emb[tokenizer.convert_tokens_to_ids(['@']	)[0]].unsqueeze(0	)
	_UpperCamelCase   :		Optional[Any]			=						word_emb[tokenizer.convert_tokens_to_ids(['#']	)[0]].unsqueeze(0	)
	_UpperCamelCase   :		Optional[Any]			=						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   :		Optional[int]			=						F'''encoder.layer.{layer_index}.attention.self.'''
			_UpperCamelCase   :		Tuple			=						state_dict[prefix + matrix_name]
			_UpperCamelCase   :		Optional[int]			=						state_dict[prefix + matrix_name]
			_UpperCamelCase   :		Any			=						state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
	_UpperCamelCase   :		Union[str, Any]			=						state_dict['entity_embeddings.entity_embeddings.weight']
	_UpperCamelCase   :		str			=						entity_emb[entity_vocab['[MASK]']]

	_UpperCamelCase   :		Any			=						LukeModel(config=lowerCamelCase_	).eval()

	_UpperCamelCase	,		_UpperCamelCase   :		int			=						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   :		int			=						LukeTokenizer.from_pretrained(lowerCamelCase_							,       task='entity_classification'	)

	_UpperCamelCase   :		Optional[int]			=						(
	    '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   :		List[str]			=						(3_9, 4_2)
	_UpperCamelCase   :		Tuple			=						tokenizer(lowerCamelCase_							,       entity_spans=[span]							,       add_prefix_space=lowerCamelCase_							,       return_tensors='pt'	)

	_UpperCamelCase   :		Dict			=						model(**lowerCamelCase_	)

	# Verify word hidden states
	if model_size == "large":
		_UpperCamelCase   :		Optional[Any]			=						torch.Size((1, 4_2, 1_0_2_4)	)
		_UpperCamelCase   :		Optional[Any]			=						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
		_UpperCamelCase   :		Tuple			=						torch.Size((1, 4_2, 7_6_8)	)
		_UpperCamelCase   :		List[str]			=						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]							,       lowerCamelCase_							,       atol=1e-4	):
		raise ValueError

	# Verify entity hidden states
	if model_size == "large":
		_UpperCamelCase   :		str			=						torch.Size((1, 1, 1_0_2_4)	)
		_UpperCamelCase   :		Union[str, Any]			=						torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]	)
	else:  # base
		_UpperCamelCase   :		Tuple			=						torch.Size((1, 1, 7_6_8)	)
		_UpperCamelCase   :		Tuple			=						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]							,       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 					lowerCamelCase_     (							UpperCAmelCase_							:     Any	)						->				Dict:




	'''simple docstring'''




	_UpperCamelCase   :		Dict			=						{}
	with open(lowerCamelCase_							,       'r'							,       encoding='utf-8'	) as f:
		for index, line in enumerate(lowerCamelCase_	):
			_UpperCamelCase	,		_UpperCamelCase   :		Optional[Any]			=						line.rstrip().split('\t'	)
			_UpperCamelCase   :		Optional[int]			=						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,
							)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__								=		logging.get_logger(__name__)

lowerCAmelCase__								=		{
    """facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json""",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "vit_mae"


		def __init__(				self	,       __snake_case=7_68	,       __snake_case=12	,       __snake_case=12	,       __snake_case=30_72	,       __snake_case="gelu"	,       __snake_case=0.0	,       __snake_case=0.0	,       __snake_case=0.0_2	,       __snake_case=1e-12	,       __snake_case=2_24	,       __snake_case=16	,       __snake_case=3	,       __snake_case=True	,       __snake_case=16	,       __snake_case=5_12	,       __snake_case=8	,       __snake_case=20_48	,       __snake_case=0.7_5	,       __snake_case=False	,       **__snake_case	,       ):
			super().__init__(**__snake_case)

			_UpperCamelCase   :		Optional[int]			=						hidden_size
			_UpperCamelCase   :		Optional[int]			=						num_hidden_layers
			_UpperCamelCase   :		Tuple			=						num_attention_heads
			_UpperCamelCase   :		List[str]			=						intermediate_size
			_UpperCamelCase   :		str			=						hidden_act
			_UpperCamelCase   :		List[str]			=						hidden_dropout_prob
			_UpperCamelCase   :		List[Any]			=						attention_probs_dropout_prob
			_UpperCamelCase   :		str			=						initializer_range
			_UpperCamelCase   :		Any			=						layer_norm_eps
			_UpperCamelCase   :		int			=						image_size
			_UpperCamelCase   :		Any			=						patch_size
			_UpperCamelCase   :		List[Any]			=						num_channels
			_UpperCamelCase   :		Union[str, Any]			=						qkv_bias
			_UpperCamelCase   :		str			=						decoder_num_attention_heads
			_UpperCamelCase   :		Union[str, Any]			=						decoder_hidden_size
			_UpperCamelCase   :		Union[str, Any]			=						decoder_num_hidden_layers
			_UpperCamelCase   :		Any			=						decoder_intermediate_size
			_UpperCamelCase   :		int			=						mask_ratio
			_UpperCamelCase   :		List[Any]			=						norm_pix_loss




