



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 (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


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

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

				# Load the entity vocab file
				lowercase__					:   List[Any]													=					load_entity_vocab(lowerCamelCase__						)

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

				# Add special tokens to the token vocabulary for downstream tasks
				lowercase__					:   int													=					AddedToken("<ent>"					,			lstrip=lowerCamelCase__					,			rstrip=lowerCamelCase__						)
				lowercase__					:   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__						)

				lowercase__					:   Optional[int]													=					LukeTokenizer.from_pretrained(lowerCamelCase__						)

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

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

				lowercase__					:   Any													=					LukeModel(config=lowerCamelCase__						).eval()

				lowercase__							,							lowercase__					:   Dict													=					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
				lowercase__					:   List[str]													=					LukeTokenizer.from_pretrained(lowerCamelCase__					,			task="entity_classification"						)

				lowercase__					:   Union[str, Any]													=					(
				    "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__					:   Tuple													=					(39, 42)
				lowercase__					:   str													=					tokenizer(lowerCamelCase__					,			entity_spans=[span]					,			add_prefix_space=lowerCamelCase__					,			return_tensors="pt"						)

				lowercase__					:   int													=					model(**lowerCamelCase__						)

				# Verify word hidden states
				if model_size == "large":
								lowercase__					:   Union[str, Any]													=					torch.Size((1, 42, 1_024)						)
								lowercase__					:   Dict													=					torch.tensor(
								    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]						)
				else:  # base
								lowercase__					:   Optional[int]													=					torch.Size((1, 42, 768)						)
								lowercase__					:   int													=					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":
								lowercase__					:   Optional[int]													=					torch.Size((1, 1, 1_024)						)
								lowercase__					:   Optional[Any]													=					torch.tensor([[0.0466, -0.0106, -0.0179]]						)
				else:  # base
								lowercase__					:   int													=					torch.Size((1, 1, 768)						)
								lowercase__					:   int													=					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  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


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









import copy
import os
from typing import Union

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


lowerCAmelCase__					  =  logging.get_logger(__name__)

lowerCAmelCase__					  =  {
    '''BridgeTower/bridgetower-base''': '''https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json''',
    '''BridgeTower/bridgetower-base-itm-mlm''': (
        '''https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'''
    ),
}

class 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""






					lowercase_								=		"""bridgetower_vision_model"""







					def __init__(							self :     str				,       SCREAMING_SNAKE_CASE :     Dict=768				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=12				,       SCREAMING_SNAKE_CASE :     Dict=3				,       SCREAMING_SNAKE_CASE :     Dict=16				,       SCREAMING_SNAKE_CASE :     List[Any]=288				,       SCREAMING_SNAKE_CASE :     List[Any]=1				,       SCREAMING_SNAKE_CASE :     List[str]=1E-0_5				,       SCREAMING_SNAKE_CASE :     str=False				,       SCREAMING_SNAKE_CASE :     Any=True				,       SCREAMING_SNAKE_CASE :     Tuple=False				,       **SCREAMING_SNAKE_CASE :     List[str]				,       ):
									super().__init__(**SCREAMING_SNAKE_CASE					)
									lowercase__					:   Union[str, Any]													=					hidden_size
									lowercase__					:   str													=					num_hidden_layers
									lowercase__					:   str													=					num_channels
									lowercase__					:   Optional[int]													=					patch_size
									lowercase__					:   Dict													=					image_size
									lowercase__					:   List[Any]													=					initializer_factor
									lowercase__					:   int													=					layer_norm_eps
									lowercase__					:   List[str]													=					stop_gradient
									lowercase__					:   Optional[int]													=					share_layernorm
									lowercase__					:   Optional[int]													=					remove_last_layer







					@classmethod
					def 			snake_case					(							cls :     Tuple				,       SCREAMING_SNAKE_CASE :     Union[str, os.PathLike]				,       **SCREAMING_SNAKE_CASE :     str					):
									lowercase__							,							lowercase__					:   Optional[Any]													=					cls.get_config_dict(SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)

									if config_dict.get("model_type"					) == "bridgetower":
													lowercase__					:   Optional[int]													=					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(SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)






class 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""






					lowercase_								=		"""bridgetower_text_model"""







					def __init__(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=50_265				,       SCREAMING_SNAKE_CASE :     Optional[int]=768				,       SCREAMING_SNAKE_CASE :     List[str]=12				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=12				,       SCREAMING_SNAKE_CASE :     List[Any]=1				,       SCREAMING_SNAKE_CASE :     Any=3_072				,       SCREAMING_SNAKE_CASE :     Union[str, Any]="gelu"				,       SCREAMING_SNAKE_CASE :     Optional[int]=0.1				,       SCREAMING_SNAKE_CASE :     List[str]=0.1				,       SCREAMING_SNAKE_CASE :     Tuple=514				,       SCREAMING_SNAKE_CASE :     List[str]=1				,       SCREAMING_SNAKE_CASE :     Dict=1E-0_5				,       SCREAMING_SNAKE_CASE :     List[str]=1				,       SCREAMING_SNAKE_CASE :     str=0				,       SCREAMING_SNAKE_CASE :     List[Any]=2				,       SCREAMING_SNAKE_CASE :     Optional[Any]="absolute"				,       SCREAMING_SNAKE_CASE :     int=True				,       **SCREAMING_SNAKE_CASE :     int				,       ):
									super().__init__(**SCREAMING_SNAKE_CASE					)

									lowercase__					:   Any													=					vocab_size
									lowercase__					:   Optional[Any]													=					hidden_size
									lowercase__					:   Optional[int]													=					num_hidden_layers
									lowercase__					:   Optional[int]													=					num_attention_heads
									lowercase__					:   int													=					hidden_act
									lowercase__					:   Optional[Any]													=					initializer_factor
									lowercase__					:   Dict													=					intermediate_size
									lowercase__					:   Tuple													=					hidden_dropout_prob
									lowercase__					:   List[Any]													=					attention_probs_dropout_prob
									lowercase__					:   Optional[int]													=					max_position_embeddings
									lowercase__					:   str													=					type_vocab_size
									lowercase__					:   str													=					layer_norm_eps
									lowercase__					:   Dict													=					position_embedding_type
									lowercase__					:   Optional[Any]													=					use_cache
									lowercase__					:   List[str]													=					pad_token_id
									lowercase__					:   Optional[Any]													=					bos_token_id
									lowercase__					:   Optional[Any]													=					eos_token_id







					@classmethod
					def 			snake_case					(							cls :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Union[str, os.PathLike]				,       **SCREAMING_SNAKE_CASE :     str					):
									lowercase__							,							lowercase__					:   Optional[Any]													=					cls.get_config_dict(SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)

									if config_dict.get("model_type"					) == "bridgetower":
													lowercase__					:   List[Any]													=					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(SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)






class 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""






					lowercase_								=		"""bridgetower"""







					def __init__(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     Optional[int]=True				,       SCREAMING_SNAKE_CASE :     Dict="gelu"				,       SCREAMING_SNAKE_CASE :     Any=768				,       SCREAMING_SNAKE_CASE :     Dict=1				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=1E-0_5				,       SCREAMING_SNAKE_CASE :     Tuple=False				,       SCREAMING_SNAKE_CASE :     List[Any]="add"				,       SCREAMING_SNAKE_CASE :     Any=12				,       SCREAMING_SNAKE_CASE :     Any=6				,       SCREAMING_SNAKE_CASE :     Any=False				,       SCREAMING_SNAKE_CASE :     List[Any]=False				,       SCREAMING_SNAKE_CASE :     str=None				,       SCREAMING_SNAKE_CASE :     Dict=None				,       **SCREAMING_SNAKE_CASE :     List[Any]				,       ):
									# TODO: remove this once the Hub files are updated.
									lowercase__					:   int													=					kwargs.pop("text_config_dict"				,       SCREAMING_SNAKE_CASE					)
									lowercase__					:   int													=					kwargs.pop("vision_config_dict"				,       SCREAMING_SNAKE_CASE					)

									super().__init__(**SCREAMING_SNAKE_CASE					)
									lowercase__					:   Dict													=					share_cross_modal_transformer_layers
									lowercase__					:   int													=					hidden_act
									lowercase__					:   int													=					hidden_size
									lowercase__					:   Optional[int]													=					initializer_factor
									lowercase__					:   Optional[int]													=					layer_norm_eps
									lowercase__					:   str													=					share_link_tower_layers
									lowercase__					:   Optional[int]													=					link_tower_type
									lowercase__					:   List[Any]													=					num_attention_heads
									lowercase__					:   Dict													=					num_hidden_layers
									lowercase__					:   Dict													=					tie_word_embeddings
									lowercase__					:   Optional[Any]													=					init_layernorm_from_vision_encoder

									if text_config is None:
													lowercase__					:   Optional[Any]													=					{}
													logger.info("`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."					)

									if vision_config is None:
													lowercase__					:   List[Any]													=					{}
													logger.info("`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values."					)

									lowercase__					:   Any													=					BridgeTowerTextConfig(**SCREAMING_SNAKE_CASE					)
									lowercase__					:   Dict													=					BridgeTowerVisionConfig(**SCREAMING_SNAKE_CASE					)







					@classmethod
					def 			snake_case					(							cls :     Optional[int]				,       SCREAMING_SNAKE_CASE :     BridgeTowerTextConfig				,       SCREAMING_SNAKE_CASE :     BridgeTowerVisionConfig				,       **SCREAMING_SNAKE_CASE :     List[str]					):
									return cls(text_config=text_config.to_dict()				,       vision_config=vision_config.to_dict()				,       **SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   List[str]													=					copy.deepcopy(self.__dict__					)
									lowercase__					:   Dict													=					self.text_config.to_dict()
									lowercase__					:   Tuple													=					self.vision_config.to_dict()
									lowercase__					:   Optional[Any]													=					self.__class__.model_type
									return output




