


'''simple docstring'''






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()
__A					:  int											=      logging.get_logger(__name__)

__A					:  Tuple											=      {
    "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_			(    A__					:   Union[str, Any]							,    A__					:   Any							,    A__					:   int							,    A__					:   Any							,    A__					:   List[str]					):
  '''simple docstring'''





  for attribute in key.split("""."""					):
    lowerCAmelCase_			:      str			=   getattr(__snake_case							,    __snake_case					)

  if weight_type is not None:
    lowerCAmelCase_			:      Dict			=   getattr(__snake_case							,    __snake_case					).shape
  else:
    lowerCAmelCase_			:      Dict			=   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_			:      Any			=   value
  elif weight_type == "weight_g":
    lowerCAmelCase_			:      Dict			=   value
  elif weight_type == "weight_v":
    lowerCAmelCase_			:      Optional[int]			=   value
  elif weight_type == "bias":
    lowerCAmelCase_			:      str			=   value
  else:
    lowerCAmelCase_			:      List[str]			=   value

  logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'					)







def 				UpperCamelCase_			(    A__					:   int							,    A__					:   Union[str, Any]							,    A__					:   Dict					):
  '''simple docstring'''





  lowerCAmelCase_			:      str			=   []
  lowerCAmelCase_			:      str			=   fairseq_model.state_dict()

  lowerCAmelCase_			:      Union[str, Any]			=   hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    lowerCAmelCase_			:      Tuple			=   False
    if "conv_layers" in name:
      load_conv_layer(
          __snake_case							,    __snake_case							,    __snake_case							,    __snake_case							,    hf_model.config.feat_extract_norm == """group"""							,    )
      lowerCAmelCase_			:      Optional[Any]			=   True
    else:
      for key, mapped_key in MAPPING.items():
        lowerCAmelCase_			:      List[str]			=   """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_			:      int			=   True
          if "*" in mapped_key:
            lowerCAmelCase_			:      str			=   name.split(__snake_case					)[0].split("""."""					)[-2]
            lowerCAmelCase_			:      str			=   mapped_key.replace("""*"""							,    __snake_case					)
          if "weight_g" in name:
            lowerCAmelCase_			:      Any			=   """weight_g"""
          elif "weight_v" in name:
            lowerCAmelCase_			:      Optional[int]			=   """weight_v"""
          elif "weight" in name:
            lowerCAmelCase_			:      Any			=   """weight"""
          elif "bias" in name:
            lowerCAmelCase_			:      Any			=   """bias"""
          else:
            lowerCAmelCase_			:      List[str]			=   None
          set_recursively(__snake_case							,    __snake_case							,    __snake_case							,    __snake_case							,    __snake_case					)
        continue
    if not is_used:
      unused_weights.append(__snake_case					)

  logger.warning(f'Unused weights: {unused_weights}'					)







def 				UpperCamelCase_			(    A__					:   Any							,    A__					:   Any							,    A__					:   int							,    A__					:   Dict							,    A__					:   int					):
  '''simple docstring'''





  lowerCAmelCase_			:      Union[str, Any]			=   full_name.split("""conv_layers."""					)[-1]
  lowerCAmelCase_			:      int			=   name.split("""."""					)
  lowerCAmelCase_			:      List[str]			=   int(items[0]					)
  lowerCAmelCase_			:      List[str]			=   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_			:      Tuple			=   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_			:      Dict			=   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_			:      int			=   value
      logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
  else:
    unused_weights.append(__snake_case					)







def 				UpperCamelCase_			(    A__					:   Optional[int]							,    A__					:   List[str]					):
  '''simple docstring'''





  lowerCAmelCase_			:      Tuple			=   SEWConfig()
  if is_finetuned:
    lowerCAmelCase_			:      Optional[Any]			=   model.wav_encoder.wav_model.cfg
  else:
    lowerCAmelCase_			:      str			=   model.cfg

  lowerCAmelCase_			:      Optional[Any]			=   fs_config.conv_bias
  lowerCAmelCase_			:      Any			=   eval(fs_config.conv_feature_layers					)
  lowerCAmelCase_			:      Dict			=   [x[0] for x in conv_layers]
  lowerCAmelCase_			:      Tuple			=   [x[1] for x in conv_layers]
  lowerCAmelCase_			:      Union[str, Any]			=   [x[2] for x in conv_layers]
  lowerCAmelCase_			:      int			=   """gelu"""
  lowerCAmelCase_			:      str			=   """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
  lowerCAmelCase_			:      Optional[Any]			=   0.0
  lowerCAmelCase_			:      List[Any]			=   fs_config.activation_fn.name
  lowerCAmelCase_			:      Dict			=   fs_config.encoder_embed_dim
  lowerCAmelCase_			:      Dict			=   0.02
  lowerCAmelCase_			:      Union[str, Any]			=   fs_config.encoder_ffn_embed_dim
  lowerCAmelCase_			:      Optional[int]			=   1E-5
  lowerCAmelCase_			:      List[str]			=   fs_config.encoder_layerdrop
  lowerCAmelCase_			:      Any			=   fs_config.encoder_attention_heads
  lowerCAmelCase_			:      Optional[int]			=   fs_config.conv_pos_groups
  lowerCAmelCase_			:      List[str]			=   fs_config.conv_pos
  lowerCAmelCase_			:      Optional[Any]			=   len(__snake_case					)
  lowerCAmelCase_			:      List[Any]			=   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_			:      Tuple			=   model.cfg
    lowerCAmelCase_			:      str			=   fs_config.final_dropout
    lowerCAmelCase_			:      List[Any]			=   fs_config.layerdrop
  lowerCAmelCase_			:      Any			=   fs_config.activation_dropout
  lowerCAmelCase_			:      Dict			=   fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
  lowerCAmelCase_			:      Dict			=   fs_config.attention_dropout
  lowerCAmelCase_			:      Any			=   fs_config.dropout_input
  lowerCAmelCase_			:      Optional[Any]			=   fs_config.dropout
  lowerCAmelCase_			:      Any			=   fs_config.mask_channel_length
  lowerCAmelCase_			:      int			=   fs_config.mask_channel_prob
  lowerCAmelCase_			:      int			=   fs_config.mask_length
  lowerCAmelCase_			:      List[str]			=   fs_config.mask_prob

  lowerCAmelCase_			:      Union[str, Any]			=   """Wav2Vec2FeatureExtractor"""
  lowerCAmelCase_			:      List[Any]			=   """Wav2Vec2CTCTokenizer"""

  return config







@torch.no_grad()
def 				UpperCamelCase_			(    A__					:   Optional[Any]							,    A__					:   Optional[Any]							,    A__					:   List[str]=None							,    A__					:   Dict=None							,    A__					:   Union[str, Any]=True					):
  '''simple docstring'''





  if is_finetuned:
    lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_			:      int			=   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(__snake_case					)
  else:
    lowerCAmelCase_			:      Optional[Any]			=   convert_config(model[0]							,    __snake_case					)
  lowerCAmelCase_			:      int			=   model[0].eval()

  lowerCAmelCase_			:      Tuple			=   True if config.feat_extract_norm == """layer""" else False
  lowerCAmelCase_			:      int			=   WavaVecaFeatureExtractor(
      feature_size=1							,    sampling_rate=1_60_00							,    padding_value=0							,    do_normalize=__snake_case							,    return_attention_mask=__snake_case							,    )

  if is_finetuned:
    if dict_path:
      lowerCAmelCase_			:      Dict			=   Dictionary.load(__snake_case					)

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

    lowerCAmelCase_			:      str			=   SEWForCTC(__snake_case					)
  else:
    lowerCAmelCase_			:      Union[str, Any]			=   SEWModel(__snake_case					)
    feature_extractor.save_pretrained(__snake_case					)

  recursively_load_weights(__snake_case							,    __snake_case							,    __snake_case					)

  hf_model.save_pretrained(__snake_case					)


if __name__ == "__main__":
  __A					:  Optional[Any]											=      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"
  )
  __A					:  int											=      parser.parse_args()
  convert_sew_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
  )





"""simple docstring"""





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       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case	)							->		str:


					"""simple docstring"""





					with open(__snake_case	) as metadata_file:
										_UpperCamelCase							 =  json.load(__snake_case	)
					_UpperCamelCase							 =  LukeConfig(use_entity_aware_attention=__snake_case,     **metadata['''model_config''']	)

					# Load in the weights from the checkpoint_path
					_UpperCamelCase							 =  torch.load(__snake_case,     map_location='''cpu'''	)

					# Load the entity vocab file
					_UpperCamelCase							 =  load_entity_vocab(__snake_case	)

					_UpperCamelCase							 =  RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']	)

					# Add special tokens to the token vocabulary for downstream tasks
					_UpperCamelCase							 =  AddedToken('''<ent>''',     lstrip=__snake_case,     rstrip=__snake_case	)
					_UpperCamelCase							 =  AddedToken('''<ent2>''',     lstrip=__snake_case,     rstrip=__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(__snake_case	)
					with open(os.path.join(__snake_case,     LukeTokenizer.vocab_files_names['''entity_vocab_file''']	),     '''w'''	) as f:
										json.dump(__snake_case,     __snake_case	)

					_UpperCamelCase							 =  LukeTokenizer.from_pretrained(__snake_case	)

					# Initialize the embeddings of the special tokens
					_UpperCamelCase							 =  state_dict['''embeddings.word_embeddings.weight''']
					_UpperCamelCase							 =  word_emb[tokenizer.convert_tokens_to_ids(['''@''']	)[0]].unsqueeze(0	)
					_UpperCamelCase							 =  word_emb[tokenizer.convert_tokens_to_ids(['''#''']	)[0]].unsqueeze(0	)
					_UpperCamelCase							 =  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"]:
															_UpperCamelCase							 =  F'''encoder.layer.{layer_index}.attention.self.'''
															_UpperCamelCase							 =  state_dict[prefix + matrix_name]
															_UpperCamelCase							 =  state_dict[prefix + matrix_name]
															_UpperCamelCase							 =  state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
					_UpperCamelCase							 =  state_dict['''entity_embeddings.entity_embeddings.weight''']
					_UpperCamelCase							 =  entity_emb[entity_vocab['''[MASK]''']]

					_UpperCamelCase							 =  LukeModel(config=__snake_case	).eval()

					_UpperCamelCase ,  _UpperCamelCase							 =  model.load_state_dict(__snake_case,     strict=__snake_case	)
					if not (len(__snake_case	) == 1 and missing_keys[0] == "embeddings.position_ids"):
										raise ValueError(F'''Missing keys {", ".join(__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
					_UpperCamelCase							 =  LukeTokenizer.from_pretrained(__snake_case,     task='''entity_classification'''	)

					_UpperCamelCase							 =  (
					    '''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 .'''
					)
					_UpperCamelCase							 =  (39, 42)
					_UpperCamelCase							 =  tokenizer(__snake_case,     entity_spans=[span],     add_prefix_space=__snake_case,     return_tensors='''pt'''	)

					_UpperCamelCase							 =  model(**__snake_case	)

					# Verify word hidden states
					if model_size == "large":
										_UpperCamelCase							 =  torch.Size((1, 42, 10_24)	)
										_UpperCamelCase							 =  torch.tensor(
										    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]	)
					else:  # base
										_UpperCamelCase							 =  torch.Size((1, 42, 7_68)	)
										_UpperCamelCase							 =  torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]	)

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

					# Verify entity hidden states
					if model_size == "large":
										_UpperCamelCase							 =  torch.Size((1, 1, 10_24)	)
										_UpperCamelCase							 =  torch.tensor([[0.0466, -0.0106, -0.0179]]	)
					else:  # base
										_UpperCamelCase							 =  torch.Size((1, 1, 7_68)	)
										_UpperCamelCase							 =  torch.tensor([[0.1457, 0.1044, 0.0174]]	)

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

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



def       lowerCamelCase__		(			__snake_case	)							->		Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase							 =  {}
					with open(__snake_case,     '''r''',     encoding='''utf-8'''	) as f:
										for index, line in enumerate(__snake_case	):
															_UpperCamelCase ,  _UpperCamelCase							 =  line.rstrip().split('''\t'''	)
															_UpperCamelCase							 =  index

					return entity_vocab


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,
				)






