



import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken






@torch.no_grad()
def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							with open(_lowerCamelCase  ) as metadata_file:
														_lowerCAmelCase     :							str    =    json.load(_lowerCamelCase  )
							_lowerCAmelCase     :							Tuple    =    LukeConfig(use_entity_aware_attention=_lowerCamelCase				, **metadata["model_config"]  )

							# Load in the weights from the checkpoint_path
							_lowerCAmelCase     :							Dict    =    torch.load(_lowerCamelCase				, map_location="cpu"  )["module"]

							# Load the entity vocab file
							_lowerCAmelCase     :							List[Any]    =    load_original_entity_vocab(_lowerCamelCase  )
							# add an entry for [MASK2]
							_lowerCAmelCase     :							Dict    =    max(entity_vocab.values()  ) + 1
							config.entity_vocab_size += 1

							_lowerCAmelCase     :							Any    =    XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]  )

							# Add special tokens to the token vocabulary for downstream tasks
							_lowerCAmelCase     :							Union[str, Any]    =    AddedToken("<ent>"				, lstrip=_lowerCamelCase				, rstrip=_lowerCamelCase  )
							_lowerCAmelCase     :							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				, "tokenizer_config.json"  )				, "r"  ) as f:
														_lowerCAmelCase     :							str    =    json.load(_lowerCamelCase  )
							_lowerCAmelCase     :							Union[str, Any]    =    "MLukeTokenizer"
							with open(os.path.join(_lowerCamelCase				, "tokenizer_config.json"  )				, "w"  ) as f:
														json.dump(_lowerCamelCase				, _lowerCamelCase  )

							with open(os.path.join(_lowerCamelCase				, MLukeTokenizer.vocab_files_names["entity_vocab_file"]  )				, "w"  ) as f:
														json.dump(_lowerCamelCase				, _lowerCamelCase  )

							_lowerCAmelCase     :							int    =    MLukeTokenizer.from_pretrained(_lowerCamelCase  )

							# Initialize the embeddings of the special tokens
							_lowerCAmelCase     :							Any    =    tokenizer.convert_tokens_to_ids(["@"]  )[0]
							_lowerCAmelCase     :							Union[str, Any]    =    tokenizer.convert_tokens_to_ids(["#"]  )[0]

							_lowerCAmelCase     :							Optional[Any]    =    state_dict["embeddings.word_embeddings.weight"]
							_lowerCAmelCase     :							str    =    word_emb[ent_init_index].unsqueeze(0  )
							_lowerCAmelCase     :							Optional[int]    =    word_emb[enta_init_index].unsqueeze(0  )
							_lowerCAmelCase     :							Optional[int]    =    torch.cat([word_emb, ent_emb, enta_emb]  )
							# add special tokens for 'entity_predictions.bias'
							for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
														_lowerCAmelCase     :							Optional[Any]    =    state_dict[bias_name]
														_lowerCAmelCase     :							Any    =    decoder_bias[ent_init_index].unsqueeze(0  )
														_lowerCAmelCase     :							Optional[Any]    =    decoder_bias[enta_init_index].unsqueeze(0  )
														_lowerCAmelCase     :							str    =    torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]  )

							# 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"]:
																					_lowerCAmelCase     :							Optional[Any]    =    F"encoder.layer.{layer_index}.attention.self."
																					_lowerCAmelCase     :							Tuple    =    state_dict[prefix + matrix_name]
																					_lowerCAmelCase     :							int    =    state_dict[prefix + matrix_name]
																					_lowerCAmelCase     :							List[str]    =    state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
							_lowerCAmelCase     :							Dict    =    state_dict["entity_embeddings.entity_embeddings.weight"]
							_lowerCAmelCase     :							List[str]    =    entity_emb[entity_vocab["[MASK]"]].unsqueeze(0  )
							_lowerCAmelCase     :							Dict    =    torch.cat([entity_emb, entity_mask_emb]  )
							# add [MASK2] for 'entity_predictions.bias'
							_lowerCAmelCase     :							str    =    state_dict["entity_predictions.bias"]
							_lowerCAmelCase     :							str    =    entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0  )
							_lowerCAmelCase     :							List[Any]    =    torch.cat([entity_prediction_bias, entity_mask_bias]  )

							_lowerCAmelCase     :							Optional[Any]    =    LukeForMaskedLM(config=_lowerCamelCase  ).eval()

							state_dict.pop("entity_predictions.decoder.weight"  )
							state_dict.pop("lm_head.decoder.weight"  )
							state_dict.pop("lm_head.decoder.bias"  )
							_lowerCAmelCase     :							Any    =    OrderedDict()
							for key, value in state_dict.items():
														if not (key.startswith("lm_head"  ) or key.startswith("entity_predictions"  )):
																					_lowerCAmelCase     :							Optional[Any]    =    state_dict[key]
														else:
																					_lowerCAmelCase     :							str    =    state_dict[key]

							_lowerCAmelCase   ,    _lowerCAmelCase     :							Tuple    =    model.load_state_dict(_lowerCamelCase				, strict=_lowerCamelCase  )

							if set(_lowerCamelCase  ) != {"luke.embeddings.position_ids"}:
														raise ValueError(F"Unexpected unexpected_keys: {unexpected_keys}"  )
							if set(_lowerCamelCase  ) != {
							    "lm_head.decoder.weight",
							    "lm_head.decoder.bias",
							    "entity_predictions.decoder.weight",
							}:
														raise ValueError(F"Unexpected missing_keys: {missing_keys}"  )

							model.tie_weights()
							assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
							assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

							# Check outputs
							_lowerCAmelCase     :							str    =    MLukeTokenizer.from_pretrained(_lowerCamelCase				, task="entity_classification"  )

							_lowerCAmelCase     :							str    =    "ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
							_lowerCAmelCase     :							Dict    =    (0, 9)
							_lowerCAmelCase     :							Union[str, Any]    =    tokenizer(_lowerCamelCase				, entity_spans=[span]				, return_tensors="pt"  )

							_lowerCAmelCase     :							Dict    =    model(**_lowerCamelCase  )

							# Verify word hidden states
							if model_size == "large":
														raise NotImplementedError
							else:  # base
														_lowerCAmelCase     :							Tuple    =    torch.Size((1, 33, 768)  )
														_lowerCAmelCase     :							str    =    torch.tensor([[0.08_92, 0.05_96, -0.28_19], [0.01_34, 0.11_99, 0.05_73], [-0.01_69, 0.09_27, 0.06_44]]  )

							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":
														raise NotImplementedError
							else:  # base
														_lowerCAmelCase     :							Optional[Any]    =    torch.Size((1, 1, 768)  )
														_lowerCAmelCase     :							int    =    torch.tensor([[-0.14_82, 0.06_09, 0.03_22]]  )

							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

							# Verify masked word/entity prediction
							_lowerCAmelCase     :							Union[str, Any]    =    MLukeTokenizer.from_pretrained(_lowerCamelCase  )
							_lowerCAmelCase     :							List[str]    =    "Tokyo is the capital of <mask>."
							_lowerCAmelCase     :							Any    =    (24, 30)
							_lowerCAmelCase     :							str    =    tokenizer(_lowerCamelCase				, entity_spans=[span]				, return_tensors="pt"  )

							_lowerCAmelCase     :							Any    =    model(**_lowerCamelCase  )

							_lowerCAmelCase     :							List[Any]    =    encoding["input_ids"][0].tolist()
							_lowerCAmelCase     :							List[Any]    =    input_ids.index(tokenizer.convert_tokens_to_ids("<mask>"  )  )
							_lowerCAmelCase     :							Any    =    outputs.logits[0][mask_position_id].argmax(dim=-1  )
							assert "Japan" == tokenizer.decode(_lowerCamelCase  )

							_lowerCAmelCase     :							Tuple    =    outputs.entity_logits[0][0].argmax().item()
							_lowerCAmelCase     :							int    =    [
							    entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
							]
							assert [e for e in multilingual_predicted_entities if e.startswith("en:"  )][0] == "en:Japan"

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






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							int    =    ["[MASK]", "[PAD]", "[UNK]"]

							_lowerCAmelCase     :							Dict    =    [json.loads(_lowerCamelCase  ) for line in open(_lowerCamelCase  )]

							_lowerCAmelCase     :							List[Any]    =    {}
							for entry in data:
														_lowerCAmelCase     :							int    =    entry["id"]
														for entity_name, language in entry["entities"]:
																					if entity_name in SPECIAL_TOKENS:
																												_lowerCAmelCase     :							Optional[Any]    =    entity_id
																												break
																					_lowerCAmelCase     :							int    =    F"{language}:{entity_name}"
																					_lowerCAmelCase     :							Optional[int]    =    entity_id
							return new_mapping


if __name__ == "__main__":
						_snake_case		=  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."
						)
						_snake_case		=  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'''


from typing import Optional, Tuple

import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__=1e-1_2    )       ->	Dict:






   """simple docstring"""
   UpperCamelCase					=  jnp.divide(emb_a.T    ,						jnp.clip(jnp.linalg.norm(A__    ,						axis=1    )    ,						a_min=A__    )    ).T
   UpperCamelCase					=  jnp.divide(emb_a.T    ,						jnp.clip(jnp.linalg.norm(A__    ,						axis=1    )    ,						a_min=A__    )    ).T
   return jnp.matmul(A__    ,						norm_emb_a.T    )

class  SCREAMING_SNAKE_CASE (  nn.Module		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  42
    _SCREAMING_SNAKE_CASE	  =  jnp.floataa






    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase					=  FlaxCLIPVisionModule(self.config.vision_config )
       UpperCamelCase					=  nn.Dense(self.config.projection_dim  ,						use_bias=UpperCamelCase__  ,						dtype=self.dtype )

       UpperCamelCase					=  self.param('concept_embeds'  ,						jax.nn.initializers.ones  ,						(1_7, self.config.projection_dim) )
       UpperCamelCase					=  self.param(
           'special_care_embeds'  ,						jax.nn.initializers.ones  ,						(3, self.config.projection_dim) )

       UpperCamelCase					=  self.param('concept_embeds_weights'  ,						jax.nn.initializers.ones  ,						(1_7,) )
       UpperCamelCase					=  self.param('special_care_embeds_weights'  ,						jax.nn.initializers.ones  ,						(3,) )






    def __call__(  self      :					str  ,						UpperCamelCase__      :					List[str] ):


       """simple docstring"""







       UpperCamelCase					=  self.vision_model(UpperCamelCase__ )[1]
       UpperCamelCase					=  self.visual_projection(UpperCamelCase__ )

       UpperCamelCase					=  jax_cosine_distance(UpperCamelCase__  ,						self.special_care_embeds )
       UpperCamelCase					=  jax_cosine_distance(UpperCamelCase__  ,						self.concept_embeds )

       # increase this value to create a stronger `nfsw` filter
       # at the cost of increasing the possibility of filtering benign image inputs
       UpperCamelCase					=  0.0

       UpperCamelCase					=  special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
       UpperCamelCase					=  jnp.round(UpperCamelCase__  ,						3 )
       UpperCamelCase					=  jnp.any(special_scores > 0  ,						axis=1  ,						keepdims=UpperCamelCase__ )
       # Use a lower threshold if an image has any special care concept
       UpperCamelCase					=  is_special_care * 0.0_1

       UpperCamelCase					=  cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
       UpperCamelCase					=  jnp.round(UpperCamelCase__  ,						3 )
       UpperCamelCase					=  jnp.any(concept_scores > 0  ,						axis=1 )

       return has_nsfw_concepts

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  CLIPConfig
    _SCREAMING_SNAKE_CASE	  =  """clip_input"""
    _SCREAMING_SNAKE_CASE	  =  FlaxStableDiffusionSafetyCheckerModule






    def __init__(  self      :					Union[str, Any]  ,						UpperCamelCase__      :					CLIPConfig  ,						UpperCamelCase__      :					Optional[Tuple] = None  ,						UpperCamelCase__      :					int = 0  ,						UpperCamelCase__      :					jnp.dtype = jnp.floataa  ,						UpperCamelCase__      :					bool = True  ,						**UpperCamelCase__      :					List[str]  ,						):


       """simple docstring"""







       if input_shape is None:
          UpperCamelCase					=  (1, 2_2_4, 2_2_4, 3)
       UpperCamelCase					=  self.module_class(config=UpperCamelCase__  ,						dtype=UpperCamelCase__  ,						**UpperCamelCase__ )
       super().__init__(UpperCamelCase__  ,						UpperCamelCase__  ,						input_shape=UpperCamelCase__  ,						seed=UpperCamelCase__  ,						dtype=UpperCamelCase__  ,						_do_init=_do_init )






    def  A       (  self      :					int  ,						UpperCamelCase__      :					jax.random.KeyArray  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					FrozenDict = None ):


       """simple docstring"""







       UpperCamelCase					=  jax.random.normal(UpperCamelCase__  ,						UpperCamelCase__ )

       UpperCamelCase				,      UpperCamelCase					=  jax.random.split(UpperCamelCase__ )
       UpperCamelCase					=  {'params': params_rng, 'dropout': dropout_rng}

       UpperCamelCase					=  self.module.init(UpperCamelCase__  ,						UpperCamelCase__ )['params']

       return random_params






    def __call__(  self      :					List[Any]  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					dict = None  ,						):


       """simple docstring"""







       UpperCamelCase					=  jnp.transpose(UpperCamelCase__  ,						(0, 2, 3, 1) )

       return self.module.apply(
           {'params': params or self.params}  ,						jnp.array(UpperCamelCase__  ,						dtype=jnp.floataa )  ,						rngs={}  ,						)

