




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

lowerCAmelCase_					    = {
    """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""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	int:
  for attribute in key.split('''.'''      ):
    lowerCAmelCase__				:  List[Any]		       =     getattr(_lowerCamelCase	,      _lowerCamelCase      )

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

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



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	Tuple:
  lowerCAmelCase__				:  List[Any]		       =     []
  lowerCAmelCase__				:  Optional[int]		       =     fairseq_model.state_dict()

  lowerCAmelCase__				:  Any		       =     hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	Optional[Any]:
  lowerCAmelCase__				:  str		       =     full_name.split('''conv_layers.'''      )[-1]
  lowerCAmelCase__				:  Union[str, Any]		       =     name.split('''.'''      )
  lowerCAmelCase__				:  List[Any]		       =     int(items[0]      )
  lowerCAmelCase__				:  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__				:  Any		       =     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__				:  Optional[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__				:  List[Any]		       =     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      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase=None	,      UpperCamelCase=None	,      UpperCamelCase=True      )     ->	Tuple:
  if config_path is not None:
    lowerCAmelCase__				:  List[Any]		       =     HubertConfig.from_pretrained(_lowerCamelCase      )
  else:
    lowerCAmelCase__				:  List[Any]		       =     HubertConfig()

  if is_finetuned:
    if dict_path:
      lowerCAmelCase__				:  Optional[int]		       =     Dictionary.load(_lowerCamelCase      )

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      lowerCAmelCase__				:  Dict		       =     target_dict.pad_index
      lowerCAmelCase__				:  Any		       =     target_dict.bos_index
      lowerCAmelCase__				:  Tuple		       =     target_dict.eos_index
      lowerCAmelCase__				:  int		       =     len(target_dict.symbols      )
      lowerCAmelCase__				:  Tuple		       =     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      )
      with open(_lowerCamelCase	,      '''w'''	,      encoding='''utf-8'''      ) as vocab_handle:
        json.dump(target_dict.indices	,      _lowerCamelCase      )
      lowerCAmelCase__				:  Optional[Any]		       =     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	,      )
      lowerCAmelCase__				:  Dict		       =     True if config.feat_extract_norm == "layer" else False
      lowerCAmelCase__				:  Tuple		       =     WavaVecaFeatureExtractor(
          feature_size=1	,      sampling_rate=16000	,      padding_value=0	,      do_normalize=_lowerCamelCase	,      return_attention_mask=_lowerCamelCase	,      )
      lowerCAmelCase__				:  int		       =     WavaVecaProcessor(feature_extractor=_lowerCamelCase	,      tokenizer=_lowerCamelCase      )
      processor.save_pretrained(_lowerCamelCase      )

    lowerCAmelCase__				:  str		       =     HubertForCTC(_lowerCamelCase      )
  else:
    lowerCAmelCase__				:  int		       =     HubertModel(_lowerCamelCase      )

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

  lowerCAmelCase__				:  Optional[Any]		       =     model[0].eval()

  recursively_load_weights(_lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase      )

  hf_wavavec.save_pretrained(_lowerCamelCase      )


if __name__ == "__main__":
     lowerCAmelCase_					    = 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"""
     )
     lowerCAmelCase_					    = parser.parse_args()
     convert_hubert_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )







import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


lowerCAmelCase_					    = logging.get_logger(__name__)






class  _lowerCAmelCase     (  _lowercase	):

      def __init__(   self      ,				*__UpperCAmelCase      ,				**__UpperCAmelCase		):
        warnings.warn(
            '''The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
            ''' use OwlViTImageProcessor instead.'''      ,				__UpperCAmelCase      ,				)
        super().__init__(*__UpperCAmelCase      ,				**__UpperCAmelCase		)

