


'''simple docstring'''



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()
__UpperCAmelCase    :Optional[int]				       =     logging.get_logger(__name__)

__UpperCAmelCase    :int				       =     {
    "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 			_a			(		_lowercase      :			str    ,      _lowercase      :			List[str]    ,      _lowercase      :			Tuple    ,      _lowercase      :			Any    ,      _lowercase      :			Optional[int]						):





     '''simple docstring'''






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

     if weight_type is not None:
          __UpperCAmelCase			:				Any													=    getattr(_lowercase    ,      _lowercase						).shape
     else:
          __UpperCAmelCase			:				str													=    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":
          __UpperCAmelCase			:				List[Any]													=    value
     elif weight_type == "weight_g":
          __UpperCAmelCase			:				Tuple													=    value
     elif weight_type == "weight_v":
          __UpperCAmelCase			:				Optional[Any]													=    value
     elif weight_type == "bias":
          __UpperCAmelCase			:				str													=    value
     else:
          __UpperCAmelCase			:				str													=    value

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




def 			_a			(		_lowercase      :			List[str]    ,      _lowercase      :			Union[str, Any]    ,      _lowercase      :			Optional[int]						):





     '''simple docstring'''






     __UpperCAmelCase			:				Optional[int]													=    []
     __UpperCAmelCase			:				Optional[Any]													=    fairseq_model.state_dict()

     __UpperCAmelCase			:				Optional[Any]													=    hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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




def 			_a			(		_lowercase      :			int    ,      _lowercase      :			List[str]    ,      _lowercase      :			Any    ,      _lowercase      :			List[Any]    ,      _lowercase      :			int						):





     '''simple docstring'''






     __UpperCAmelCase			:				List[str]													=    full_name.split('''conv_layers.'''						)[-1]
     __UpperCAmelCase			:				Dict													=    name.split('''.'''						)
     __UpperCAmelCase			:				Union[str, Any]													=    int(items[0]						)
     __UpperCAmelCase			:				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.'
               )
               __UpperCAmelCase			:				Optional[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.'
               )
               __UpperCAmelCase			:				int													=    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."
               )
               __UpperCAmelCase			:				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.'
               )
               __UpperCAmelCase			:				int													=    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      :			List[str]    ,      _lowercase      :			str    ,      _lowercase      :			Optional[int]=None    ,      _lowercase      :			Optional[Any]=None    ,      _lowercase      :			Union[str, Any]=True						):





     '''simple docstring'''






     if config_path is not None:
          __UpperCAmelCase			:				Optional[Any]													=    HubertConfig.from_pretrained(_lowercase						)
     else:
          __UpperCAmelCase			:				Tuple													=    HubertConfig()

     if is_finetuned:
          if dict_path:
               __UpperCAmelCase			:				Optional[int]													=    Dictionary.load(_lowercase						)

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

          __UpperCAmelCase			:				Dict													=    HubertForCTC(_lowercase						)
     else:
          __UpperCAmelCase			:				Optional[Any]													=    HubertModel(_lowercase						)

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

     __UpperCAmelCase			:				Dict													=    model[0].eval()

     recursively_load_weights(_lowercase    ,      _lowercase    ,      _lowercase						)

     hf_wavavec.save_pretrained(_lowercase						)


if __name__ == "__main__":
      __UpperCAmelCase    :str				       =     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"
      )
      __UpperCAmelCase    :Optional[int]				       =     parser.parse_args()
      convert_hubert_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )








'''simple docstring'''



import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey



class 			a       :


     """simple docstring"""






     def __init__(       self  :	Union[str, Any]							,				snake_case  :	List[Any]							,				snake_case  :	int							,				snake_case  :	int    )		->							List[Any]:
          if dst_width < 0 or dst_height < 0:
               raise ValueError('''Destination width/height should be > 0'''    )

          __UpperCAmelCase			:				str													=    img
          __UpperCAmelCase			:				List[Any]													=    img.shape[1]
          __UpperCAmelCase			:				Optional[Any]													=    img.shape[0]
          __UpperCAmelCase			:				Dict													=    dst_width
          __UpperCAmelCase			:				List[str]													=    dst_height

          __UpperCAmelCase			:				Union[str, Any]													=    self.src_w / self.dst_w
          __UpperCAmelCase			:				List[str]													=    self.src_h / self.dst_h

          __UpperCAmelCase			:				Optional[int]													=    (
              np.ones((self.dst_h, self.dst_w, 3)							,				np.uinta    ) * 255
          )

     def 				lowerCamelCase__				(       self  :	Any    )		->							str:
          for i in range(self.dst_h    ):
               for j in range(self.dst_w    ):
                    __UpperCAmelCase			:				Any													=    self.img[self.get_y(snake_case    )][self.get_x(snake_case    )]

     def 				lowerCamelCase__				(       self  :	int							,				snake_case  :	int    )		->							int:
          return int(self.ratio_x * x    )






     def 				lowerCamelCase__				(       self  :	Optional[Any]							,				snake_case  :	int    )		->							int:
          return int(self.ratio_y * y    )


if __name__ == "__main__":
      __UpperCAmelCase       ,					__UpperCAmelCase    :int				       =     8_0_0, 6_0_0
      __UpperCAmelCase    :Dict				       =     imread("image_data/lena.jpg", 1)
      __UpperCAmelCase    :int				       =     NearestNeighbour(im, dst_w, dst_h)
      n.process()

      imshow(
          f"""Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}""", n.output
      )
      waitKey(0)
      destroyAllWindows()