





"""simple docstring"""





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  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Optional[int]:





  """simple docstring"""


  with open(_SCREAMING_SNAKE_CASE     ) as metadata_file:
    lowerCAmelCase__     :Union[str, Any]											=				json.load(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :List[str]											=				LukeConfig(use_entity_aware_attention=_SCREAMING_SNAKE_CASE	,  **metadata['model_config']     )

  # Load in the weights from the checkpoint_path
  lowerCAmelCase__     :Optional[int]											=				torch.load(_SCREAMING_SNAKE_CASE	,  map_location='cpu'     )['module']

  # Load the entity vocab file
  lowerCAmelCase__     :Any											=				load_original_entity_vocab(_SCREAMING_SNAKE_CASE     )
  # add an entry for [MASK2]
  lowerCAmelCase__     :Union[str, Any]											=				max(entity_vocab.values()     ) + 1
  config.entity_vocab_size += 1

  lowerCAmelCase__     :List[str]											=				XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']     )

  # Add special tokens to the token vocabulary for downstream tasks
  lowerCAmelCase__     :List[Any]											=				AddedToken('<ent>'	,  lstrip=_SCREAMING_SNAKE_CASE	,  rstrip=_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Optional[Any]											=				AddedToken('<ent2>'	,  lstrip=_SCREAMING_SNAKE_CASE	,  rstrip=_SCREAMING_SNAKE_CASE     )
  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(_SCREAMING_SNAKE_CASE     )
  with open(os.path.join(_SCREAMING_SNAKE_CASE	,  'tokenizer_config.json'     )	,  'r'     ) as f:
    lowerCAmelCase__     :Optional[Any]											=				json.load(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Tuple											=				'MLukeTokenizer'
  with open(os.path.join(_SCREAMING_SNAKE_CASE	,  'tokenizer_config.json'     )	,  'w'     ) as f:
    json.dump(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )

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

  lowerCAmelCase__     :Union[str, Any]											=				MLukeTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE     )

  # Initialize the embeddings of the special tokens
  lowerCAmelCase__     :str											=				tokenizer.convert_tokens_to_ids(['@']     )[0]
  lowerCAmelCase__     :int											=				tokenizer.convert_tokens_to_ids(['#']     )[0]

  lowerCAmelCase__     :Dict											=				state_dict['embeddings.word_embeddings.weight']
  lowerCAmelCase__     :Union[str, Any]											=				word_emb[ent_init_index].unsqueeze(0     )
  lowerCAmelCase__     :str											=				word_emb[enta_init_index].unsqueeze(0     )
  lowerCAmelCase__     :Any											=				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__     :str											=				state_dict[bias_name]
    lowerCAmelCase__     :List[str]											=				decoder_bias[ent_init_index].unsqueeze(0     )
    lowerCAmelCase__     :str											=				decoder_bias[enta_init_index].unsqueeze(0     )
    lowerCAmelCase__     :Union[str, Any]											=				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__     :Dict											=				F"encoder.layer.{layer_index}.attention.self."
      lowerCAmelCase__     :int											=				state_dict[prefix + matrix_name]
      lowerCAmelCase__     :str											=				state_dict[prefix + matrix_name]
      lowerCAmelCase__     :str											=				state_dict[prefix + matrix_name]

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

  lowerCAmelCase__     :int											=				LukeForMaskedLM(config=_SCREAMING_SNAKE_CASE     ).eval()

  state_dict.pop('entity_predictions.decoder.weight'     )
  state_dict.pop('lm_head.decoder.weight'     )
  state_dict.pop('lm_head.decoder.bias'     )
  lowerCAmelCase__     :Tuple											=				OrderedDict()
  for key, value in state_dict.items():
    if not (key.startswith('lm_head'     ) or key.startswith('entity_predictions'     )):
      lowerCAmelCase__     :List[str]											=				state_dict[key]
    else:
      lowerCAmelCase__     :Union[str, Any]											=				state_dict[key]

  lowerCAmelCase__				,							lowerCAmelCase__     :int											=				model.load_state_dict(_SCREAMING_SNAKE_CASE	,  strict=_SCREAMING_SNAKE_CASE     )

  if set(_SCREAMING_SNAKE_CASE     ) != {"luke.embeddings.position_ids"}:
    raise ValueError(F"Unexpected unexpected_keys: {unexpected_keys}"     )
  if set(_SCREAMING_SNAKE_CASE     ) != {
      "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__     :List[Any]											=				MLukeTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE	,  task='entity_classification'     )

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

  lowerCAmelCase__     :List[str]											=				model(**_SCREAMING_SNAKE_CASE     )

  # Verify word hidden states
  if model_size == "large":
    raise NotImplementedError
  else:  # base
    lowerCAmelCase__     :Union[str, Any]											=				torch.Size((1, 33, 768)     )
    lowerCAmelCase__     :Optional[int]											=				torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]]     )

  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]	,  _SCREAMING_SNAKE_CASE	,  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__     :Any											=				torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]]     )

  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]	,  _SCREAMING_SNAKE_CASE	,  atol=1e-4     ):
    raise ValueError

  # Verify masked word/entity prediction
  lowerCAmelCase__     :str											=				MLukeTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Tuple											=				'Tokyo is the capital of <mask>.'
  lowerCAmelCase__     :Optional[int]											=				(24, 30)
  lowerCAmelCase__     :Any											=				tokenizer(_SCREAMING_SNAKE_CASE	,  entity_spans=[span]	,  return_tensors='pt'     )

  lowerCAmelCase__     :int											=				model(**_SCREAMING_SNAKE_CASE     )

  lowerCAmelCase__     :Optional[int]											=				encoding['input_ids'][0].tolist()
  lowerCAmelCase__     :Optional[int]											=				input_ids.index(tokenizer.convert_tokens_to_ids('<mask>'     )     )
  lowerCAmelCase__     :str											=				outputs.logits[0][mask_position_id].argmax(dim=-1     )
  assert "Japan" == tokenizer.decode(_SCREAMING_SNAKE_CASE     )

  lowerCAmelCase__     :Optional[int]											=				outputs.entity_logits[0][0].argmax().item()
  lowerCAmelCase__     :Dict											=				[
      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(_SCREAMING_SNAKE_CASE     )     )
  model.save_pretrained(_SCREAMING_SNAKE_CASE     )
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->str:





  """simple docstring"""


  lowerCAmelCase__     :List[Any]											=				['[MASK]', '[PAD]', '[UNK]']

  lowerCAmelCase__     :Any											=				[json.loads(_SCREAMING_SNAKE_CASE     ) for line in open(_SCREAMING_SNAKE_CASE     )]

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


if __name__ == "__main__":
  __A											=						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											=						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 importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE			: str				   =					"scheduler_config.json"






class _lowerCamelCase(		_a  ):
       lowercase_					:      Any       					=     1
       lowercase_					:      Dict       					=     2
       lowercase_					:      Union[str, Any]       					=     3
       lowercase_					:      Tuple       					=     4
       lowercase_					:      Optional[Any]       					=     5






@dataclass
class _lowerCamelCase(		_a  ):
       lowercase_					:      jnp.ndarray






class _lowerCamelCase:
       lowercase_					:      Union[str, Any]       					=     SCHEDULER_CONFIG_NAME
       lowercase_					:      str       					=     ["""dtype"""]
       lowercase_					:      Dict       					=     []
       lowercase_					:      int       					=     True
       @classmethod
       def      UpperCamelCase	(   cls, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase=False, **lowerCamelCase, )			->  Optional[int]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Optional[int] 						=						cls.load_config(
               pretrained_model_name_or_path=lowerCamelCase, subfolder=lowerCamelCase, return_unused_kwargs=lowerCamelCase, **lowerCamelCase, )
           _lowercase						,							_lowercase			:				Tuple 						=						cls.from_config(lowerCamelCase, return_unused_kwargs=lowerCamelCase, **lowerCamelCase)

           if hasattr(lowerCamelCase, 'create_state') and getattr(lowerCamelCase, 'has_state', lowerCamelCase):
               _lowercase			:				List[Any] 						=						scheduler.create_state()

           if return_unused_kwargs:
               return scheduler, state, unused_kwargs

           return scheduler, state
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = False, **lowerCamelCase)			->  Any:




           """simple docstring"""

           self.save_config(save_directory=lowerCamelCase, push_to_hub=lowerCamelCase, **lowerCamelCase)
       @property
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           return self._get_compatibles()




       @classmethod
       def      UpperCamelCase	(   cls)			->  Any:




           """simple docstring"""

           _lowercase			:				Any 						=						list(set([cls.__name__] + cls._compatibles))
           _lowercase			:				Dict 						=						importlib.import_module(__name__.split('.')[0])
           _lowercase			:				Any 						=						[
               getattr(lowerCamelCase, lowerCamelCase) for c in compatible_classes_str if hasattr(lowerCamelCase, lowerCamelCase)
           ]
           return compatible_classes



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		jnp.ndarray:
    assert len(lowerCamelCase_					) >= x.ndim
    return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCamelCase_					) - x.ndim)					)				,					lowerCamelCase_					)



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=0.9_99				,					lowerCamelCase_=jnp.floataa					)       ->		jnp.ndarray:

    def alpha_bar(lowerCamelCase_					):
        return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2					) ** 2

    _lowercase			:				List[Any] 						=						[]
    for i in range(lowerCamelCase_					):
        _lowercase			:				Any 						=						i / num_diffusion_timesteps
        _lowercase			:				Any 						=						(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar(lowerCamelCase_					) / alpha_bar(lowerCamelCase_					)				,					lowerCamelCase_					)					)
    return jnp.array(lowerCamelCase_				,					dtype=lowerCamelCase_					)






@flax.struct.dataclass
class _lowerCamelCase:
       lowercase_					:      jnp.ndarray
       lowercase_					:      jnp.ndarray
       lowercase_					:      jnp.ndarray
       @classmethod
       def      UpperCamelCase	(   cls, lowerCamelCase)			->  str:




           """simple docstring"""

           _lowercase			:				int 						=						scheduler.config

           if config.trained_betas is not None:
               _lowercase			:				str 						=						jnp.asarray(config.trained_betas, dtype=scheduler.dtype)
           elif config.beta_schedule == "linear":
               _lowercase			:				List[Any] 						=						jnp.linspace(config.beta_start, config.beta_end, config.num_train_timesteps, dtype=scheduler.dtype)
           elif config.beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _lowercase			:				Dict 						=						(
                   jnp.linspace(
                       config.beta_start**0.5, config.beta_end**0.5, config.num_train_timesteps, dtype=scheduler.dtype)
                   ** 2
               )
           elif config.beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _lowercase			:				Optional[int] 						=						betas_for_alpha_bar(config.num_train_timesteps, dtype=scheduler.dtype)
           else:
               raise NotImplementedError(
                   F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''')

           _lowercase			:				List[str] 						=						1.0 - betas

           _lowercase			:				Union[str, Any] 						=						jnp.cumprod(lowerCamelCase, axis=0)

           return cls(
               alphas=lowerCamelCase, betas=lowerCamelCase, alphas_cumprod=lowerCamelCase, )



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		int:
    _lowercase			:				str 						=						state.alphas_cumprod

    _lowercase			:				str 						=						alphas_cumprod[timesteps] ** 0.5
    _lowercase			:				Optional[Any] 						=						sqrt_alpha_prod.flatten()
    _lowercase			:				Tuple 						=						broadcast_to_shape_from_left(lowerCamelCase_				,					original_samples.shape					)

    _lowercase			:				Union[str, Any] 						=						(1 - alphas_cumprod[timesteps]) ** 0.5
    _lowercase			:				Optional[Any] 						=						sqrt_one_minus_alpha_prod.flatten()
    _lowercase			:				int 						=						broadcast_to_shape_from_left(lowerCamelCase_				,					original_samples.shape					)

    return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		List[str]:
    _lowercase						,							_lowercase			:				Optional[int] 						=						get_sqrt_alpha_prod(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    _lowercase			:				str 						=						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
    return noisy_samples



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Tuple:
    _lowercase						,							_lowercase			:				Tuple 						=						get_sqrt_alpha_prod(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    _lowercase			:				List[Any] 						=						sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
    return velocity


