


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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

__snake_case   							=						{
    '''post_extract_proj''': '''feature_projection.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.layer_norm''': '''encoder.layer_norm''',
    '''adapter_layer''': '''encoder.layers.*.adapter_layer''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
    '''pooling_layer.linear''': '''projector''',
    '''pooling_layer.projection''': '''classifier''',
}
__snake_case   							=						[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]






def 		_A		(		_lowercase )					-> Optional[Any]:


     """simple docstring"""


     __UpperCamelCase =			{}
     with open(_lowercase					,  'r' ) as file:
          for line_number, line in enumerate(_lowercase ):
               __UpperCamelCase =			line.strip()
               if line:
                    __UpperCamelCase =			line.split()
                    __UpperCamelCase =			line_number
                    __UpperCamelCase =			words[0]
                    __UpperCamelCase =			value
     return result






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase )					-> List[str]:


     """simple docstring"""


     for attribute in key.split('.' ):
          __UpperCamelCase =			getattr(_lowercase					,  _lowercase )

     __UpperCamelCase =			None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(_lowercase ):
               __UpperCamelCase =			PARAM_MAPPING[full_name.split('.' )[-1]]
               __UpperCamelCase =			'param'

     if weight_type is not None and weight_type != "param":
          __UpperCamelCase =			getattr(_lowercase					,  _lowercase ).shape
     elif weight_type is not None and weight_type == "param":
          __UpperCamelCase =			hf_pointer
          for attribute in hf_param_name.split('.' ):
               __UpperCamelCase =			getattr(_lowercase					,  _lowercase )
          __UpperCamelCase =			shape_pointer.shape

          # let's reduce dimension
          __UpperCamelCase =			value[0]
     else:
          __UpperCamelCase =			hf_pointer.shape

     if hf_shape != value.shape:
          raise ValueError(
              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":
          __UpperCamelCase =			value
     elif weight_type == "weight_g":
          __UpperCamelCase =			value
     elif weight_type == "weight_v":
          __UpperCamelCase =			value
     elif weight_type == "bias":
          __UpperCamelCase =			value
     elif weight_type == "param":
          for attribute in hf_param_name.split('.' ):
               __UpperCamelCase =			getattr(_lowercase					,  _lowercase )
          __UpperCamelCase =			value
     else:
          __UpperCamelCase =			value

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






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase )					-> List[Any]:


     """simple docstring"""


     __UpperCamelCase =			None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(_lowercase ):
               __UpperCamelCase =			PARAM_MAPPING[full_name.split('.' )[-1]]
               __UpperCamelCase =			'param'

     if weight_type is not None and weight_type != "param":
          __UpperCamelCase =			'.'.join([key, weight_type] )
     elif weight_type is not None and weight_type == "param":
          __UpperCamelCase =			'.'.join([key, hf_param_name] )
     else:
          __UpperCamelCase =			key

     __UpperCamelCase =			value if 'lm_head' in full_key else value[0]


__snake_case   							=						{
    '''W_a''': '''linear_1.weight''',
    '''W_b''': '''linear_2.weight''',
    '''b_a''': '''linear_1.bias''',
    '''b_b''': '''linear_2.bias''',
    '''ln_W''': '''norm.weight''',
    '''ln_b''': '''norm.bias''',
}






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase=None					,  _lowercase=None )					-> List[Any]:


     """simple docstring"""


     __UpperCamelCase =			False
     for key, mapped_key in MAPPING.items():
          __UpperCamelCase =			'wav2vec2.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
          if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
               __UpperCamelCase =			True
               if "*" in mapped_key:
                    __UpperCamelCase =			name.split(_lowercase )[0].split('.' )[-2]
                    __UpperCamelCase =			mapped_key.replace('*'					,  _lowercase )
               if "weight_g" in name:
                    __UpperCamelCase =			'weight_g'
               elif "weight_v" in name:
                    __UpperCamelCase =			'weight_v'
               elif "bias" in name:
                    __UpperCamelCase =			'bias'
               elif "weight" in name:
                    # TODO: don't match quantizer.weight_proj
                    __UpperCamelCase =			'weight'
               else:
                    __UpperCamelCase =			None
               if hf_dict is not None:
                    rename_dict(_lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase )
               else:
                    set_recursively(_lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase )
               return is_used
     return is_used






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase )					-> Dict:


     """simple docstring"""


     __UpperCamelCase =			[]
     __UpperCamelCase =			fairseq_model.state_dict()

     __UpperCamelCase =			hf_model.wavaveca.feature_extractor

     for name, value in fairseq_dict.items():
          __UpperCamelCase =			False
          if "conv_layers" in name:
               load_conv_layer(
                   _lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  hf_model.config.feat_extract_norm == 'group'					,  )
               __UpperCamelCase =			True
          else:
               __UpperCamelCase =			load_wavaveca_layer(_lowercase					,  _lowercase					,  _lowercase )
          if not is_used:
               unused_weights.append(_lowercase )

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






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase )					-> Any:


     """simple docstring"""


     __UpperCamelCase =			full_name.split('conv_layers.' )[-1]
     __UpperCamelCase =			name.split('.' )
     __UpperCamelCase =			int(items[0] )
     __UpperCamelCase =			int(items[1] )

     if type_id == 0:
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                    raise ValueError(
                        f'''{full_name} has size {value.shape}, but'''
                        f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' )
               __UpperCamelCase =			value
               logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                    raise ValueError(
                        f'''{full_name} has size {value.shape}, but'''
                        f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' )
               __UpperCamelCase =			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:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                    raise ValueError(
                        f'''{full_name} has size {value.shape}, but'''
                        f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''' )
               __UpperCamelCase =			value
               logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                    raise ValueError(
                        f'''{full_name} has size {value.shape}, but'''
                        f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.''' )
               __UpperCamelCase =			value
               logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
     else:
          unused_weights.append(_lowercase )






@torch.no_grad()
def 		_A		(		_lowercase					,  _lowercase					,  _lowercase=None					,  _lowercase=None					,  _lowercase=True					,  _lowercase=False )					-> Dict:


     """simple docstring"""


     if config_path is not None:
          __UpperCamelCase =			WavaVecaConfig.from_pretrained(_lowercase )
     else:
          __UpperCamelCase =			WavaVecaConfig()

     if is_seq_class:
          __UpperCamelCase =			read_txt_into_dict(_lowercase )
          __UpperCamelCase =			idalabel
          __UpperCamelCase =			WavaVecaForSequenceClassification(_lowercase )
          __UpperCamelCase =			WavaVecaFeatureExtractor(
              feature_size=1					,  sampling_rate=1_60_00					,  padding_value=0					,  do_normalize=_lowercase					,  return_attention_mask=_lowercase					,  )
          feature_extractor.save_pretrained(_lowercase )

     elif is_finetuned:
          if dict_path:
               __UpperCamelCase =			Dictionary.load(_lowercase )

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               __UpperCamelCase =			target_dict.pad_index
               __UpperCamelCase =			target_dict.bos_index
               __UpperCamelCase =			target_dict.eos_index
               __UpperCamelCase =			len(target_dict.symbols )
               __UpperCamelCase =			os.path.join(_lowercase					,  'vocab.json' )
               if not os.path.isdir(_lowercase ):
                    logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(_lowercase ) )
                    return
               os.makedirs(_lowercase					,  exist_ok=_lowercase )
               __UpperCamelCase =			target_dict.indices

               # fairseq has the <pad> and <s> switched
               __UpperCamelCase =			0
               __UpperCamelCase =			1
               with open(_lowercase					,  'w'					,  encoding='utf-8' ) as vocab_handle:
                    json.dump(_lowercase					,  _lowercase )
               __UpperCamelCase =			WavaVecaCTCTokenizer(
                   _lowercase					,  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=_lowercase					,  )
               __UpperCamelCase =			True if config.feat_extract_norm == 'layer' else False
               __UpperCamelCase =			WavaVecaFeatureExtractor(
                   feature_size=1					,  sampling_rate=1_60_00					,  padding_value=0					,  do_normalize=_lowercase					,  return_attention_mask=_lowercase					,  )
               __UpperCamelCase =			WavaVecaProcessor(feature_extractor=_lowercase					,  tokenizer=_lowercase )
               processor.save_pretrained(_lowercase )

          __UpperCamelCase =			WavaVecaForCTC(_lowercase )
     else:
          __UpperCamelCase =			WavaVecaForPreTraining(_lowercase )

     if is_finetuned or is_seq_class:
          __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]					,  arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
     else:
          __UpperCamelCase =			argparse.Namespace(task='audio_pretraining' )
          __UpperCamelCase =			fairseq.tasks.setup_task(_lowercase )

          __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					,  task=_lowercase )

     __UpperCamelCase =			model[0].eval()

     recursively_load_weights(_lowercase					,  _lowercase					,  not is_finetuned )

     hf_wavavec.save_pretrained(_lowercase )


if __name__ == "__main__":
    __snake_case   							=						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(
        '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
    )
    parser.add_argument(
        '''--is_seq_class''',
        action='''store_true''',
        help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
    )
    __snake_case   							=						parser.parse_args()

    __snake_case   							=						not args.not_finetuned and not args.is_seq_class
    convert_wavaveca_checkpoint(
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.dict_path,
        is_finetuned,
        args.is_seq_class,
    )





"""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 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> Tuple:
      # Load configuration defined in the metadata file
      with open(lowerCAmelCase    ) as metadata_file:
            UpperCAmelCase__			:							Optional[int]							=    json.load(lowerCAmelCase    )
      UpperCAmelCase__			:							Optional[int]							=    LukeConfig(use_entity_aware_attention=lowerCAmelCase			,	**metadata["""model_config"""]    )

      # Load in the weights from the checkpoint_path
      UpperCAmelCase__			:							Dict							=    torch.load(lowerCAmelCase			,	map_location="""cpu"""    )

      # Load the entity vocab file
      UpperCAmelCase__			:							List[str]							=    load_entity_vocab(lowerCAmelCase    )

      UpperCAmelCase__			:							Union[str, Any]							=    RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]    )

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

      UpperCAmelCase__			:							int							=    LukeTokenizer.from_pretrained(lowerCAmelCase    )

      # Initialize the embeddings of the special tokens
      UpperCAmelCase__			:							Any							=    state_dict["""embeddings.word_embeddings.weight"""]
      UpperCAmelCase__			:							Any							=    word_emb[tokenizer.convert_tokens_to_ids(["""@"""]    )[0]].unsqueeze(0    )
      UpperCAmelCase__			:							List[Any]							=    word_emb[tokenizer.convert_tokens_to_ids(["""#"""]    )[0]].unsqueeze(0    )
      UpperCAmelCase__			:							List[Any]							=    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__			:							Any							=    F"""encoder.layer.{layer_index}.attention.self."""
                  UpperCAmelCase__			:							List[Any]							=    state_dict[prefix + matrix_name]
                  UpperCAmelCase__			:							Dict							=    state_dict[prefix + matrix_name]
                  UpperCAmelCase__			:							Optional[int]							=    state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
      UpperCAmelCase__			:							List[str]							=    state_dict["""entity_embeddings.entity_embeddings.weight"""]
      UpperCAmelCase__			:							int							=    entity_emb[entity_vocab["""[MASK]"""]]

      UpperCAmelCase__			:							Tuple							=    LukeModel(config=lowerCAmelCase    ).eval()

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

      UpperCAmelCase__			:							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 ."""
      )
      UpperCAmelCase__			:							List[Any]							=    (39, 42)
      UpperCAmelCase__			:							str							=    tokenizer(lowerCAmelCase			,	entity_spans=[span]			,	add_prefix_space=lowerCAmelCase			,	return_tensors="""pt"""    )

      UpperCAmelCase__			:							List[str]							=    model(**lowerCAmelCase    )

      # Verify word hidden states
      if model_size == "large":
            UpperCAmelCase__			:							Optional[Any]							=    torch.Size((1, 42, 10_24)    )
            UpperCAmelCase__			:							Union[str, Any]							=    torch.tensor(
                [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]    )
      else:  # base
            UpperCAmelCase__			:							Optional[int]							=    torch.Size((1, 42, 7_68)    )
            UpperCAmelCase__			:							Optional[Any]							=    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]			,	lowerCAmelCase			,	atol=1E-4    ):
            raise ValueError

      # Verify entity hidden states
      if model_size == "large":
            UpperCAmelCase__			:							Union[str, Any]							=    torch.Size((1, 1, 10_24)    )
            UpperCAmelCase__			:							str							=    torch.tensor([[0.0466, -0.0106, -0.0179]]    )
      else:  # base
            UpperCAmelCase__			:							Tuple							=    torch.Size((1, 1, 7_68)    )
            UpperCAmelCase__			:							List[str]							=    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]			,	lowerCAmelCase			,	atol=1E-4    ):
            raise ValueError

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




def 					a__						(						lowerCAmelCase    )	-> Union[str, Any]:
      UpperCAmelCase__			:							List[str]							=    {}
      with open(lowerCAmelCase			,	"""r"""			,	encoding="""utf-8"""    ) as f:
            for index, line in enumerate(lowerCAmelCase    ):
                  UpperCAmelCase__					,		UpperCAmelCase__			:							Tuple							=    line.rstrip().split("""\t"""    )
                  UpperCAmelCase__			:							int							=    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,
    )

