



"""simple docstring"""







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

					# Load in the weights from the checkpoint_path
					a__          =  torch.load(lowerCamelCase_ ,     map_location='''cpu''')

					# Load the entity vocab file
					a__          =  load_entity_vocab(lowerCamelCase_)

					a__          =  RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''])

					# Add special tokens to the token vocabulary for downstream tasks
					a__          =  AddedToken('''<ent>''' ,     lstrip=lowerCamelCase_ ,     rstrip=lowerCamelCase_)
					a__          =  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_)

					a__          =  LukeTokenizer.from_pretrained(lowerCamelCase_)

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

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

					a__          =  LukeModel(config=lowerCamelCase_).eval()

					a__       ,a__          =  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
					a__          =  LukeTokenizer.from_pretrained(lowerCamelCase_ ,     task='''entity_classification''')

					a__          =  (
					    '''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 .'''
					)
					a__          =  (39, 42)
					a__          =  tokenizer(lowerCamelCase_ ,     entity_spans=[span] ,     add_prefix_space=lowerCamelCase_ ,     return_tensors='''pt''')

					a__          =  model(**lowerCamelCase_)

					# Verify word hidden states
					if model_size == "large":
										a__          =  torch.Size((1, 42, 1024))
										a__          =  torch.tensor(
										    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]])
					else:  # base
										a__          =  torch.Size((1, 42, 768))
										a__          =  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":
										a__          =  torch.Size((1, 1, 1024))
										a__          =  torch.tensor([[0.0466, -0.0106, -0.0179]])
					else:  # base
										a__          =  torch.Size((1, 1, 768))
										a__          =  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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					a__          =  {}
					with open(lowerCamelCase_ ,     '''r''' ,     encoding='''utf-8''') as f:
										for index, line in enumerate(lowerCamelCase_):
															a__       ,a__          =  line.rstrip().split('''\t''')
															a__          =  index

					return entity_vocab


if __name__ == "__main__":
	__a       :	Dict             = 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       :	List[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,
	)










"""simple docstring"""







import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
	pass

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


__a       :	List[Any]             = logging.get_logger(__name__)

__a       :	Tuple             = {
    'kakaobrain/align-base': 'https://huggingface.co/kakaobrain/align-base/resolve/main/config.json',
}







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='align_text_model'




							def __init__(   self:     Tuple				,		__A:     Optional[int]=30522				,		__A:     Tuple=768				,		__A:     Any=12				,		__A:     Any=12				,		__A:     Dict=3072				,		__A:     Tuple="gelu"				,		__A:     Union[str, Any]=0.1				,		__A:     List[str]=0.1				,		__A:     Optional[int]=512				,		__A:     Tuple=2				,		__A:     str=0.0_2				,		__A:     int=1e-12				,		__A:     Optional[int]=0				,		__A:     Optional[int]="absolute"				,		__A:     List[Any]=True				,		**__A:     Tuple				,		):

												'''simple docstring'''

												super().__init__(**__A    )

												a__          =  vocab_size
												a__          =  hidden_size
												a__          =  num_hidden_layers
												a__          =  num_attention_heads
												a__          =  hidden_act
												a__          =  intermediate_size
												a__          =  hidden_dropout_prob
												a__          =  attention_probs_dropout_prob
												a__          =  max_position_embeddings
												a__          =  type_vocab_size
												a__          =  initializer_range
												a__          =  layer_norm_eps
												a__          =  position_embedding_type
												a__          =  use_cache
												a__          =  pad_token_id





							@classmethod
							def  lowercase		(   cls:     Any				,		__A:     Union[str, os.PathLike]				,		**__A:     Tuple    ):

												'''simple docstring'''

												cls._set_token_in_kwargs(__A    )

												a__       ,a__          =  cls.get_config_dict(__A				,		**__A    )

												# get the text config dict if we are loading from AlignConfig
												if config_dict.get('''model_type'''    ) == "align":
																	a__          =  config_dict['''text_config''']

												if "model_type" in config_dict and hasattr(cls				,		'''model_type'''    ) and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
																	    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'    )

												return cls.from_dict(__A				,		**__A    )







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='align_vision_model'




							def __init__(   self:     Dict				,		__A:     int = 3				,		__A:     int = 600				,		__A:     float = 2.0				,		__A:     float = 3.1				,		__A:     int = 8				,		__A:     List[int] = [3, 3, 5, 3, 5, 5, 3]				,		__A:     List[int] = [32, 16, 24, 40, 80, 112, 192]				,		__A:     List[int] = [16, 24, 40, 80, 112, 192, 320]				,		__A:     List[int] = []				,		__A:     List[int] = [1, 2, 2, 2, 1, 2, 1]				,		__A:     List[int] = [1, 2, 2, 3, 3, 4, 1]				,		__A:     List[int] = [1, 6, 6, 6, 6, 6, 6]				,		__A:     float = 0.2_5				,		__A:     str = "swish"				,		__A:     int = 2560				,		__A:     str = "mean"				,		__A:     float = 0.0_2				,		__A:     float = 0.0_0_1				,		__A:     float = 0.9_9				,		__A:     float = 0.2				,		**__A:     List[Any]				,		):

												'''simple docstring'''

												super().__init__(**__A    )

												a__          =  num_channels
												a__          =  image_size
												a__          =  width_coefficient
												a__          =  depth_coefficient
												a__          =  depth_divisor
												a__          =  kernel_sizes
												a__          =  in_channels
												a__          =  out_channels
												a__          =  depthwise_padding
												a__          =  strides
												a__          =  num_block_repeats
												a__          =  expand_ratios
												a__          =  squeeze_expansion_ratio
												a__          =  hidden_act
												a__          =  hidden_dim
												a__          =  pooling_type
												a__          =  initializer_range
												a__          =  batch_norm_eps
												a__          =  batch_norm_momentum
												a__          =  drop_connect_rate
												a__          =  sum(__A    ) * 4





							@classmethod
							def  lowercase		(   cls:     Dict				,		__A:     Union[str, os.PathLike]				,		**__A:     List[Any]    ):

												'''simple docstring'''

												cls._set_token_in_kwargs(__A    )

												a__       ,a__          =  cls.get_config_dict(__A				,		**__A    )

												# get the vision config dict if we are loading from AlignConfig
												if config_dict.get('''model_type'''    ) == "align":
																	a__          =  config_dict['''vision_config''']

												if "model_type" in config_dict and hasattr(cls				,		'''model_type'''    ) and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
																	    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'    )

												return cls.from_dict(__A				,		**__A    )







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='align'
							_SCREAMING_SNAKE_CASE    =True




							def __init__(   self:     Optional[int]				,		__A:     Optional[int]=None				,		__A:     Dict=None				,		__A:     List[str]=640				,		__A:     Optional[int]=1.0				,		__A:     str=0.0_2				,		**__A:     List[str]				,		):

												'''simple docstring'''

												super().__init__(**__A    )

												if text_config is None:
																	a__          =  {}
																	logger.info('''text_config is None. Initializing the AlignTextConfig with default values.'''    )

												if vision_config is None:
																	a__          =  {}
																	logger.info('''vision_config is None. Initializing the AlignVisionConfig with default values.'''    )

												a__          =  AlignTextConfig(**__A    )
												a__          =  AlignVisionConfig(**__A    )

												a__          =  projection_dim
												a__          =  temperature_init_value
												a__          =  initializer_range




							@classmethod
							def  lowercase		(   cls:     Dict				,		__A:     AlignTextConfig				,		__A:     AlignVisionConfig				,		**__A:     str    ):

												'''simple docstring'''

												return cls(text_config=text_config.to_dict()				,		vision_config=vision_config.to_dict()				,		**__A    )





							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  copy.deepcopy(self.__dict__    )
												a__          =  self.text_config.to_dict()
												a__          =  self.vision_config.to_dict()
												a__          =  self.__class__.model_type
												return output


