




"""simple docstring"""







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()
_UpperCamelCase					      =  logging.get_logger(__name__)

_UpperCamelCase					      =  {
    """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""",
}
_UpperCamelCase					      =  [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]

def _a			(		_snake_case							):







      """simple docstring"""






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

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      for attribute in key.split("""."""							):
            UpperCAmelCase          =	getattr(__lowerCamelCase				,	__lowerCamelCase							)

      UpperCAmelCase          =	None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(__lowerCamelCase							):
                  UpperCAmelCase          =	PARAM_MAPPING[full_name.split("""."""							)[-1]]
                  UpperCAmelCase          =	"param"

      if weight_type is not None and weight_type != "param":
            UpperCAmelCase          =	getattr(__lowerCamelCase				,	__lowerCamelCase							).shape
      elif weight_type is not None and weight_type == "param":
            UpperCAmelCase          =	hf_pointer
            for attribute in hf_param_name.split("""."""							):
                  UpperCAmelCase          =	getattr(__lowerCamelCase				,	__lowerCamelCase							)
            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(__lowerCamelCase				,	__lowerCamelCase							)
            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			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(__lowerCamelCase							):
                  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]


_UpperCamelCase					      =  {
    """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			(		_snake_case				,	_snake_case				,	_snake_case=None				,	_snake_case=None							):







      """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(__lowerCamelCase							)[0].split("""."""							)[-2]
                        UpperCAmelCase          =	mapped_key.replace("""*"""				,	__lowerCamelCase							)
                  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(__lowerCamelCase				,	__lowerCamelCase				,	__lowerCamelCase				,	__lowerCamelCase				,	__lowerCamelCase							)
                  else:
                        set_recursively(__lowerCamelCase				,	__lowerCamelCase				,	__lowerCamelCase				,	__lowerCamelCase				,	__lowerCamelCase							)
                  return is_used
      return is_used

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """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(
                      __lowerCamelCase				,	__lowerCamelCase				,	__lowerCamelCase				,	__lowerCamelCase				,	hf_model.config.feat_extract_norm == """group"""				,	)
                  UpperCAmelCase          =	True
            else:
                  UpperCAmelCase          =	load_wavaveca_layer(__lowerCamelCase				,	__lowerCamelCase				,	__lowerCamelCase							)
            if not is_used:
                  unused_weights.append(__lowerCamelCase							)

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

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """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(__lowerCamelCase							)

@torch.no_grad()
def _a			(		_snake_case				,	_snake_case				,	_snake_case=None				,	_snake_case=None				,	_snake_case=True				,	_snake_case=False							):







      """simple docstring"""






      if config_path is not None:
            UpperCAmelCase          =	WavaVecaConfig.from_pretrained(__lowerCamelCase							)
      else:
            UpperCAmelCase          =	WavaVecaConfig()

      if is_seq_class:
            UpperCAmelCase          =	read_txt_into_dict(__lowerCamelCase							)
            UpperCAmelCase          =	idalabel
            UpperCAmelCase          =	WavaVecaForSequenceClassification(__lowerCamelCase							)
            UpperCAmelCase          =	WavaVecaFeatureExtractor(
                feature_size=1				,	sampling_rate=1_6000				,	padding_value=0				,	do_normalize=__lowerCamelCase				,	return_attention_mask=__lowerCamelCase				,	)
            feature_extractor.save_pretrained(__lowerCamelCase							)

      elif is_finetuned:
            if dict_path:
                  UpperCAmelCase          =	Dictionary.load(__lowerCamelCase							)

                  # 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(__lowerCamelCase				,	"""vocab.json"""							)
                  if not os.path.isdir(__lowerCamelCase							):
                        logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(__lowerCamelCase							)							)
                        return
                  os.makedirs(__lowerCamelCase				,	exist_ok=__lowerCamelCase							)
                  UpperCAmelCase          =	target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  UpperCAmelCase          =	0
                  UpperCAmelCase          =	1
                  with open(__lowerCamelCase				,	"""w"""				,	encoding="""utf-8"""							) as vocab_handle:
                        json.dump(__lowerCamelCase				,	__lowerCamelCase							)
                  UpperCAmelCase          =	WavaVecaCTCTokenizer(
                      __lowerCamelCase				,	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=__lowerCamelCase				,	)
                  UpperCAmelCase          =	True if config.feat_extract_norm == "layer" else False
                  UpperCAmelCase          =	WavaVecaFeatureExtractor(
                      feature_size=1				,	sampling_rate=1_6000				,	padding_value=0				,	do_normalize=__lowerCamelCase				,	return_attention_mask=__lowerCamelCase				,	)
                  UpperCAmelCase          =	WavaVecaProcessor(feature_extractor=__lowerCamelCase				,	tokenizer=__lowerCamelCase							)
                  processor.save_pretrained(__lowerCamelCase							)

            UpperCAmelCase          =	WavaVecaForCTC(__lowerCamelCase							)
      else:
            UpperCAmelCase          =	WavaVecaForPreTraining(__lowerCamelCase							)

      if is_finetuned or is_seq_class:
            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(__lowerCamelCase							)

            UpperCAmelCase          =	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				,	task=__lowerCamelCase							)

      UpperCAmelCase          =	model[0].eval()

      recursively_load_weights(__lowerCamelCase				,	__lowerCamelCase				,	not is_finetuned							)

      hf_wavavec.save_pretrained(__lowerCamelCase							)


if __name__ == "__main__":
  _UpperCamelCase					      =  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""",
  )
  _UpperCamelCase					      =  parser.parse_args()

  _UpperCamelCase					      =  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 re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_UpperCamelCase					      =  {
    """text_branch""": """text_model""",
    """audio_branch""": """audio_model.audio_encoder""",
    """attn""": """attention.self""",
    """self.proj""": """output.dense""",
    """attention.self_mask""": """attn_mask""",
    """mlp.fc1""": """intermediate.dense""",
    """mlp.fc2""": """output.dense""",
    """norm1""": """layernorm_before""",
    """norm2""": """layernorm_after""",
    """bn0""": """batch_norm""",
}

_UpperCamelCase					      =  AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")

def _a			(		_snake_case				,	_snake_case=False							):







      """simple docstring"""






      UpperCAmelCase      , UpperCAmelCase          =	create_model(
          """HTSAT-tiny"""				,	"""roberta"""				,	_snake_case				,	precision="""fp32"""				,	device="""cuda:0""" if torch.cuda.is_available() else """cpu"""				,	enable_fusion=_snake_case				,	fusion_type="""aff_2d""" if enable_fusion else None				,	)
      return model, model_cfg

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	{}

      UpperCAmelCase          =	R""".*sequential.(\d+).*"""
      UpperCAmelCase          =	R""".*_projection.(\d+).*"""

      for key, value in state_dict.items():
            # check if any key needs to be modified
            for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
                  if key_to_modify in key:
                        UpperCAmelCase          =	key.replace(_snake_case				,	_snake_case							)

            if re.match(_snake_case				,	_snake_case							):
                  # replace sequential layers with list
                  UpperCAmelCase          =	re.match(_snake_case				,	_snake_case							).group(1							)

                  UpperCAmelCase          =	key.replace(F'''sequential.{sequential_layer}.'''				,	F'''layers.{int(_snake_case							)//3}.linear.'''							)
            elif re.match(_snake_case				,	_snake_case							):
                  UpperCAmelCase          =	int(re.match(_snake_case				,	_snake_case							).group(1							)							)

                  # Because in CLAP they use `nn.Sequential`...
                  UpperCAmelCase          =	1 if projecton_layer == 0 else 2

                  UpperCAmelCase          =	key.replace(F'''_projection.{projecton_layer}.'''				,	F'''_projection.linear{transformers_projection_layer}.'''							)

            if "audio" and "qkv" in key:
                  # split qkv into query key and value
                  UpperCAmelCase          =	value
                  UpperCAmelCase          =	mixed_qkv.size(0							) // 3

                  UpperCAmelCase          =	mixed_qkv[:qkv_dim]
                  UpperCAmelCase          =	mixed_qkv[qkv_dim : qkv_dim * 2]
                  UpperCAmelCase          =	mixed_qkv[qkv_dim * 2 :]

                  UpperCAmelCase          =	query_layer
                  UpperCAmelCase          =	key_layer
                  UpperCAmelCase          =	value_layer
            else:
                  UpperCAmelCase          =	value

      return model_state_dict

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case=False							):







      """simple docstring"""






      UpperCAmelCase      , UpperCAmelCase          =	init_clap(_snake_case				,	enable_fusion=_snake_case							)

      clap_model.eval()
      UpperCAmelCase          =	clap_model.state_dict()
      UpperCAmelCase          =	rename_state_dict(_snake_case							)

      UpperCAmelCase          =	ClapConfig()
      UpperCAmelCase          =	enable_fusion
      UpperCAmelCase          =	ClapModel(_snake_case							)

      # ignore the spectrogram embedding layer
      model.load_state_dict(_snake_case				,	strict=_snake_case							)

      model.save_pretrained(_snake_case							)
      transformers_config.save_pretrained(_snake_case							)


if __name__ == "__main__":
  _UpperCamelCase					      =  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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
  parser.add_argument("""--enable_fusion""", action="""store_true""", help="""Whether to enable fusion or not""")
  _UpperCamelCase					      =  parser.parse_args()

  convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)



