





"""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,
  )







"""simple docstring"""





from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:torch.FloatTensor






class    _lowerCAmelCase  (	a    ,  a			):
       """simple docstring"""




       @register_to_config
       def __init__(       self       ,					__UpperCAmelCase = 1_6       ,					__UpperCAmelCase = 8_8       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = 1       ,					__UpperCAmelCase = 0.0       ,					__UpperCAmelCase = 3_2       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = False       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = "geglu"       ,					__UpperCAmelCase = True       ,					__UpperCAmelCase = True       ,					):



         '''simple docstring'''

         super().__init__()
         lowerCAmelCase__     :Dict											=				num_attention_heads
         lowerCAmelCase__     :Any											=				attention_head_dim
         lowerCAmelCase__     :Optional[int]											=				num_attention_heads * attention_head_dim

         lowerCAmelCase__     :Any											=				in_channels

         lowerCAmelCase__     :str											=				torch.nn.GroupNorm(num_groups=__UpperCAmelCase       ,					num_channels=__UpperCAmelCase       ,					eps=1E-6       ,					affine=__UpperCAmelCase )
         lowerCAmelCase__     :int											=				nn.Linear(__UpperCAmelCase       ,					__UpperCAmelCase )

         # 3. Define transformers blocks
         lowerCAmelCase__     :List[Any]											=				nn.ModuleList(
             [
                 BasicTransformerBlock(
                     __UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					dropout=__UpperCAmelCase       ,					cross_attention_dim=__UpperCAmelCase       ,					activation_fn=__UpperCAmelCase       ,					attention_bias=__UpperCAmelCase       ,					double_self_attention=__UpperCAmelCase       ,					norm_elementwise_affine=__UpperCAmelCase       ,					)
                 for d in range(__UpperCAmelCase )
             ] )

         lowerCAmelCase__     :List[Any]											=				nn.Linear(__UpperCAmelCase       ,					__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=1       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase = True       ,					):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__     :int											=				hidden_states.shape
         lowerCAmelCase__     :Tuple											=				batch_frames // num_frames

         lowerCAmelCase__     :str											=				hidden_states

         lowerCAmelCase__     :Union[str, Any]											=				hidden_states[None, :].reshape(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )
         lowerCAmelCase__     :str											=				hidden_states.permute(0       ,					2       ,					1       ,					3       ,					4 )

         lowerCAmelCase__     :Optional[int]											=				self.norm(__UpperCAmelCase )
         lowerCAmelCase__     :Optional[Any]											=				hidden_states.permute(0       ,					3       ,					4       ,					2       ,					1 ).reshape(batch_size * height * width       ,					__UpperCAmelCase       ,					__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				self.proj_in(__UpperCAmelCase )

         # 2. Blocks
         for block in self.transformer_blocks:
           lowerCAmelCase__     :Optional[int]											=				block(
               __UpperCAmelCase       ,					encoder_hidden_states=__UpperCAmelCase       ,					timestep=__UpperCAmelCase       ,					cross_attention_kwargs=__UpperCAmelCase       ,					class_labels=__UpperCAmelCase       ,					)

         # 3. Output
         lowerCAmelCase__     :Any											=				self.proj_out(__UpperCAmelCase )
         lowerCAmelCase__     :Dict											=				(
             hidden_states[None, None, :]
             .reshape(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )
             .permute(0       ,					3       ,					4       ,					1       ,					2 )
             .contiguous()
         )
         lowerCAmelCase__     :Optional[Any]											=				hidden_states.reshape(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				hidden_states + residual

         if not return_dict:
           return (output,)

         return TransformerTemporalModelOutput(sample=__UpperCAmelCase )
