


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowercase_	   =     logging.get_logger(__name__)

lowercase_	   =     {
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 for attribute in key.split('.'						):
  __lowerCamelCase		:							Any		 =						getattr(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)

 if weight_type is not None:
  __lowerCamelCase		:							Tuple		 =						getattr(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						).shape
 else:
  __lowerCamelCase		:							List[Any]		 =						hf_pointer.shape

 assert hf_shape == value.shape, (
     f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
     f' {value.shape} for {full_name}'
 )

 if weight_type == "weight":
  __lowerCamelCase		:							Dict		 =						value
 elif weight_type == "weight_g":
  __lowerCamelCase		:							List[str]		 =						value
 elif weight_type == "weight_v":
  __lowerCamelCase		:							Tuple		 =						value
 elif weight_type == "bias":
  __lowerCamelCase		:							Dict		 =						value
 else:
  __lowerCamelCase		:							Tuple		 =						value

 logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Dict		 =						[]
 __lowerCamelCase		:							int		 =						fairseq_model.state_dict()

 __lowerCamelCase		:							Optional[Any]		 =						hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  __lowerCamelCase		:							List[str]		 =						False
  if "conv_layers" in name:
   load_conv_layer(
       SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	hf_model.config.feat_extract_norm == 'group'      ,	)
   __lowerCamelCase		:							List[str]		 =						True
  else:
   for key, mapped_key in MAPPING.items():
    __lowerCamelCase		:							Any		 =						'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

    if key in name or key.split('w2v_model.'						)[-1] == name.split('.'						)[0]:
     __lowerCamelCase		:							Dict		 =						True
     if "*" in mapped_key:
      __lowerCamelCase		:							Optional[Any]		 =						name.split(SCREAMING_SNAKE_CASE__						)[0].split('.'						)[-2]
      __lowerCamelCase		:							List[Any]		 =						mapped_key.replace('*'      ,	SCREAMING_SNAKE_CASE__						)
     if "weight_g" in name:
      __lowerCamelCase		:							Tuple		 =						'weight_g'
     elif "weight_v" in name:
      __lowerCamelCase		:							Dict		 =						'weight_v'
     elif "weight" in name:
      __lowerCamelCase		:							Optional[Any]		 =						'weight'
     elif "bias" in name:
      __lowerCamelCase		:							Optional[int]		 =						'bias'
     else:
      __lowerCamelCase		:							Tuple		 =						None
     set_recursively(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)
    continue
  if not is_used:
   unused_weights.append(SCREAMING_SNAKE_CASE__						)

 logger.warning(f'Unused weights: {unused_weights}'						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[int]		 =						full_name.split('conv_layers.'						)[-1]
 __lowerCamelCase		:							int		 =						name.split('.'						)
 __lowerCamelCase		:							Any		 =						int(items[0]						)
 __lowerCamelCase		:							Union[str, Any]		 =						int(items[1]						)

 if type_id == 0:
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
       f'{full_name} has size {value.shape}, but'
       f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
   )
   __lowerCamelCase		:							Optional[int]		 =						value
   logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'						)
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
       f'{full_name} has size {value.shape}, but'
       f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
   )
   __lowerCamelCase		:							Optional[Any]		 =						value
   logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'						)
 elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
       f'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
       " found."
   )
   __lowerCamelCase		:							List[Any]		 =						value
   logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'						)
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
       f'{full_name} has size {value.shape}, but'
       f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
   )
   __lowerCamelCase		:							Optional[int]		 =						value
   logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'						)
 else:
  unused_weights.append(SCREAMING_SNAKE_CASE__						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Tuple		 =						SEWConfig()
 if is_finetuned:
  __lowerCamelCase		:							Dict		 =						model.wav_encoder.wav_model.cfg
 else:
  __lowerCamelCase		:							str		 =						model.cfg

 __lowerCamelCase		:							Union[str, Any]		 =						fs_config.conv_bias
 __lowerCamelCase		:							int		 =						eval(fs_config.conv_feature_layers						)
 __lowerCamelCase		:							List[str]		 =						[x[0] for x in conv_layers]
 __lowerCamelCase		:							List[Any]		 =						[x[1] for x in conv_layers]
 __lowerCamelCase		:							Optional[int]		 =						[x[2] for x in conv_layers]
 __lowerCamelCase		:							Optional[int]		 =						'gelu'
 __lowerCamelCase		:							List[str]		 =						'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
 __lowerCamelCase		:							Any		 =						0.0
 __lowerCamelCase		:							Any		 =						fs_config.activation_fn.name
 __lowerCamelCase		:							Union[str, Any]		 =						fs_config.encoder_embed_dim
 __lowerCamelCase		:							Dict		 =						0.02
 __lowerCamelCase		:							Optional[int]		 =						fs_config.encoder_ffn_embed_dim
 __lowerCamelCase		:							Dict		 =						1e-5
 __lowerCamelCase		:							Optional[int]		 =						fs_config.encoder_layerdrop
 __lowerCamelCase		:							Dict		 =						fs_config.encoder_attention_heads
 __lowerCamelCase		:							Optional[int]		 =						fs_config.conv_pos_groups
 __lowerCamelCase		:							Optional[Any]		 =						fs_config.conv_pos
 __lowerCamelCase		:							Union[str, Any]		 =						len(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							Optional[int]		 =						fs_config.encoder_layers
 __lowerCamelCase		:							Dict		 =						fs_config.squeeze_factor

 # take care of any params that are overridden by the Wav2VecCtc model
 if is_finetuned:
  __lowerCamelCase		:							str		 =						model.cfg
  __lowerCamelCase		:							List[Any]		 =						fs_config.final_dropout
  __lowerCamelCase		:							List[str]		 =						fs_config.layerdrop
 __lowerCamelCase		:							Union[str, Any]		 =						fs_config.activation_dropout
 __lowerCamelCase		:							Tuple		 =						fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
 __lowerCamelCase		:							Any		 =						fs_config.attention_dropout
 __lowerCamelCase		:							List[Any]		 =						fs_config.dropout_input
 __lowerCamelCase		:							Any		 =						fs_config.dropout
 __lowerCamelCase		:							List[Any]		 =						fs_config.mask_channel_length
 __lowerCamelCase		:							Optional[int]		 =						fs_config.mask_channel_prob
 __lowerCamelCase		:							str		 =						fs_config.mask_length
 __lowerCamelCase		:							str		 =						fs_config.mask_prob

 __lowerCamelCase		:							str		 =						'Wav2Vec2FeatureExtractor'
 __lowerCamelCase		:							Union[str, Any]		 =						'Wav2Vec2CTCTokenizer'

 return config
@torch.no_grad()
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__=None      ,	SCREAMING_SNAKE_CASE__=None      ,	SCREAMING_SNAKE_CASE__=True						):

 if is_finetuned:
  __lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		:							str		 =						fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]      ,	arg_overrides={'data': '/'.join(dict_path.split('/'						)[:-1]						)}						)
 else:
  __lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		:							Any		 =						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						)

 if config_path is not None:
  __lowerCamelCase		:							List[Any]		 =						SEWConfig.from_pretrained(SCREAMING_SNAKE_CASE__						)
 else:
  __lowerCamelCase		:							Dict		 =						convert_config(model[0]      ,	SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							Dict		 =						model[0].eval()

 __lowerCamelCase		:							Optional[Any]		 =						True if config.feat_extract_norm == 'layer' else False
 __lowerCamelCase		:							str		 =						WavaVecaFeatureExtractor(
     feature_size=1      ,	sampling_rate=16_000      ,	padding_value=0      ,	do_normalize=SCREAMING_SNAKE_CASE__      ,	return_attention_mask=SCREAMING_SNAKE_CASE__      ,	)

 if is_finetuned:
  if dict_path:
   __lowerCamelCase		:							Optional[int]		 =						Dictionary.load(SCREAMING_SNAKE_CASE__						)

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   __lowerCamelCase		:							int		 =						target_dict.pad_index
   __lowerCamelCase		:							Dict		 =						target_dict.bos_index
   __lowerCamelCase		:							Optional[int]		 =						target_dict.pad_index
   __lowerCamelCase		:							Dict		 =						target_dict.bos_index
   __lowerCamelCase		:							List[str]		 =						target_dict.eos_index
   __lowerCamelCase		:							List[Any]		 =						len(target_dict.symbols						)
   __lowerCamelCase		:							Dict		 =						os.path.join(SCREAMING_SNAKE_CASE__      ,	'vocab.json'						)
   if not os.path.isdir(SCREAMING_SNAKE_CASE__						):
    logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(SCREAMING_SNAKE_CASE__						)						)
    return
   os.makedirs(SCREAMING_SNAKE_CASE__      ,	exist_ok=SCREAMING_SNAKE_CASE__						)
   with open(SCREAMING_SNAKE_CASE__      ,	'w'      ,	encoding='utf-8'						) as vocab_handle:
    json.dump(target_dict.indices      ,	SCREAMING_SNAKE_CASE__						)
   __lowerCamelCase		:							Tuple		 =						WavaVecaCTCTokenizer(
       SCREAMING_SNAKE_CASE__      ,	unk_token=target_dict.unk_word      ,	pad_token=target_dict.pad_word      ,	bos_token=target_dict.bos_word      ,	eos_token=target_dict.eos_word      ,	word_delimiter_token='|'      ,	do_lower_case=SCREAMING_SNAKE_CASE__      ,	)
   __lowerCamelCase		:							str		 =						WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__      ,	tokenizer=SCREAMING_SNAKE_CASE__						)
   processor.save_pretrained(SCREAMING_SNAKE_CASE__						)

  __lowerCamelCase		:							Optional[Any]		 =						SEWForCTC(SCREAMING_SNAKE_CASE__						)
 else:
  __lowerCamelCase		:							List[str]		 =						SEWModel(SCREAMING_SNAKE_CASE__						)
  feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__						)

 recursively_load_weights(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)

 hf_model.save_pretrained(SCREAMING_SNAKE_CASE__						)


if __name__ == "__main__":
       lowercase_	   =     argparse.ArgumentParser()
       parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
       parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
       parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
       parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
       parser.add_argument(
           '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
       )
       lowercase_	   =     parser.parse_args()
       convert_sew_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
       )





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 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 # Load configuration defined in the metadata file
 with open(SCREAMING_SNAKE_CASE__						) as metadata_file:
  __lowerCamelCase		:							List[str]		 =						json.load(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							int		 =						LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE__      ,	**metadata['model_config']						)

 # Load in the weights from the checkpoint_path
 __lowerCamelCase		:							Union[str, Any]		 =						torch.load(SCREAMING_SNAKE_CASE__      ,	map_location='cpu'						)

 # Load the entity vocab file
 __lowerCamelCase		:							List[Any]		 =						load_entity_vocab(SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							Optional[int]		 =						RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']						)

 # Add special tokens to the token vocabulary for downstream tasks
 __lowerCamelCase		:							str		 =						AddedToken('<ent>'      ,	lstrip=SCREAMING_SNAKE_CASE__      ,	rstrip=SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							Dict		 =						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__      ,	LukeTokenizer.vocab_files_names['entity_vocab_file']						)      ,	'w'						) as f:
  json.dump(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							str		 =						LukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__						)

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

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
 __lowerCamelCase		:							Optional[int]		 =						state_dict['entity_embeddings.entity_embeddings.weight']
 __lowerCamelCase		:							Union[str, Any]		 =						entity_emb[entity_vocab['[MASK]']]

 __lowerCamelCase		:							Optional[Any]		 =						LukeModel(config=SCREAMING_SNAKE_CASE__						).eval()

 __lowerCamelCase ,					__lowerCamelCase		:							List[Any]		 =						model.load_state_dict(SCREAMING_SNAKE_CASE__      ,	strict=SCREAMING_SNAKE_CASE__						)
 if not (len(SCREAMING_SNAKE_CASE__						) == 1 and missing_keys[0] == "embeddings.position_ids"):
  raise ValueError(f'Missing keys {", ".join(SCREAMING_SNAKE_CASE__						)}. 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
 __lowerCamelCase		:							Optional[Any]		 =						LukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__      ,	task='entity_classification'						)

 __lowerCamelCase		:							Dict		 =						(
     '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 .'
 )
 __lowerCamelCase		:							Union[str, Any]		 =						(39, 42)
 __lowerCamelCase		:							Optional[Any]		 =						tokenizer(SCREAMING_SNAKE_CASE__      ,	entity_spans=[span]      ,	add_prefix_space=SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'						)

 __lowerCamelCase		:							List[str]		 =						model(**SCREAMING_SNAKE_CASE__						)

 # Verify word hidden states
 if model_size == "large":
  __lowerCamelCase		:							Dict		 =						torch.Size((1, 42, 1_024)						)
  __lowerCamelCase		:							int		 =						torch.tensor(
      [[0.0_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]]						)
 else:  # base
  __lowerCamelCase		:							Union[str, Any]		 =						torch.Size((1, 42, 768)						)
  __lowerCamelCase		:							Tuple		 =						torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]]						)

 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":
  __lowerCamelCase		:							Union[str, Any]		 =						torch.Size((1, 1, 1_024)						)
  __lowerCamelCase		:							Dict		 =						torch.tensor([[0.0_466, -0.0_106, -0.0_179]]						)
 else:  # base
  __lowerCamelCase		:							int		 =						torch.Size((1, 1, 768)						)
  __lowerCamelCase		:							Dict		 =						torch.tensor([[0.1_457, 0.1_044, 0.0_174]]						)

 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

 # Finally, save our PyTorch model and tokenizer
 print('Saving PyTorch model to {}'.format(SCREAMING_SNAKE_CASE__						)						)
 model.save_pretrained(SCREAMING_SNAKE_CASE__						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Tuple		 =						{}
 with open(SCREAMING_SNAKE_CASE__      ,	'r'      ,	encoding='utf-8'						) as f:
  for index, line in enumerate(SCREAMING_SNAKE_CASE__						):
   __lowerCamelCase ,					__lowerCamelCase		:							List[Any]		 =						line.rstrip().split('\t'						)
   __lowerCamelCase		:							Any		 =						index

 return entity_vocab


if __name__ == "__main__":
       lowercase_	   =     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.'
       )
       lowercase_	   =     parser.parse_args()
       convert_luke_checkpoint(
           args.checkpoint_path,
           args.metadata_path,
           args.entity_vocab_path,
           args.pytorch_dump_folder_path,
           args.model_size,
       )

