
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 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						int:


  '''simple docstring'''






  with open(lowercase_ ) as metadata_file:
    lowercase__	: Any     		=   json.load(lowercase_ )
  lowercase__	: Tuple     		=   LukeConfig(use_entity_aware_attention=lowercase_			,			**metadata["""model_config"""] )

  # Load in the weights from the checkpoint_path
  lowercase__	: List[str]     		=   torch.load(lowercase_			,			map_location="""cpu""" )["""module"""]

  # Load the entity vocab file
  lowercase__	: Any     		=   load_original_entity_vocab(lowercase_ )
  # add an entry for [MASK2]
  lowercase__	: int     		=   max(entity_vocab.values() ) + 1
  config.entity_vocab_size += 1

  lowercase__	: str     		=   XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""] )

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

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

  lowercase__	: str     		=   MLukeTokenizer.from_pretrained(lowercase_ )

  # Initialize the embeddings of the special tokens
  lowercase__	: int     		=   tokenizer.convert_tokens_to_ids(["""@"""] )[0]
  lowercase__	: int     		=   tokenizer.convert_tokens_to_ids(["""#"""] )[0]

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

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

  lowercase__	: Dict     		=   LukeForMaskedLM(config=lowercase_ ).eval()

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

  lowercase__		,     lowercase__	: int     		=   model.load_state_dict(lowercase_			,			strict=lowercase_ )

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

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

  lowercase__	: Dict     		=   model(**lowercase_ )

  # Verify word hidden states
  if model_size == "large":
    raise NotImplementedError
  else:  # base
    lowercase__	: Tuple     		=   torch.Size((1, 33, 7_68) )
    lowercase__	: Any     		=   torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]] )

  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]			,			lowercase_			,			atol=1E-4 ):
    raise ValueError

  # Verify entity hidden states
  if model_size == "large":
    raise NotImplementedError
  else:  # base
    lowercase__	: List[str]     		=   torch.Size((1, 1, 7_68) )
    lowercase__	: List[str]     		=   torch.tensor([[-0.1482, 0.0609, 0.0322]] )

  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]			,			lowercase_			,			atol=1E-4 ):
    raise ValueError

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

  lowercase__	: str     		=   model(**lowercase_ )

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

  lowercase__	: str     		=   outputs.entity_logits[0][0].argmax().item()
  lowercase__	: List[str]     		=   [
      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(lowercase_ ) )
  model.save_pretrained(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						Any:


  '''simple docstring'''






  lowercase__	: Optional[Any]     		=   ["""[MASK]""", """[PAD]""", """[UNK]"""]

  lowercase__	: Optional[int]     		=   [json.loads(lowercase_ ) for line in open(lowercase_ )]

  lowercase__	: str     		=   {}
  for entry in data:
    lowercase__	: Tuple     		=   entry["""id"""]
    for entity_name, language in entry["entities"]:
      if entity_name in SPECIAL_TOKENS:
        lowercase__	: str     		=   entity_id
        break
      lowercase__	: Dict     		=   F'{language}:{entity_name}'
      lowercase__	: Dict     		=   entity_id
  return new_mapping


if __name__ == "__main__":
     lowerCamelCase__     :		int        = 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__     :		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,
     )








from typing import List

import datasets
from datasets.tasks import AudioClassification

from ..folder_based_builder import folder_based_builder


lowerCamelCase__     :		Any        = datasets.utils.logging.get_logger(__name__)



class    _snake_case (						folder_based_builder.FolderBasedBuilderConfig				):
 __lowerCAmelCase	:  bool	   =      None
 __lowerCAmelCase	:  bool	   =      None



class    _snake_case (						folder_based_builder.FolderBasedBuilder				):
 __lowerCAmelCase	:  Optional[Any]	   =      datasets.Audio()
 __lowerCAmelCase	:  Union[str, Any]	   =      'audio'
 __lowerCAmelCase	:  str	   =      AudioFolderConfig
 __lowerCAmelCase	:  List[str]  # definition at the bottom of the script
 __lowerCAmelCase	:  Optional[int]	   =      AudioClassification(audio_column='audio'	,    label_column='label'				)

lowerCamelCase__     :		int        = [
    """.aiff""",
    """.au""",
    """.avr""",
    """.caf""",
    """.flac""",
    """.htk""",
    """.svx""",
    """.mat4""",
    """.mat5""",
    """.mpc2k""",
    """.ogg""",
    """.paf""",
    """.pvf""",
    """.raw""",
    """.rf64""",
    """.sd2""",
    """.sds""",
    """.ircam""",
    """.voc""",
    """.w64""",
    """.wav""",
    """.nist""",
    """.wavex""",
    """.wve""",
    """.xi""",
    """.mp3""",
    """.opus""",
]
lowerCamelCase__     :		int        = AUDIO_EXTENSIONS






