






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

snake_case__   :						Optional[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      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase):
   for attribute in key.split('.'):
      UpperCamelCase_						=  getattr(__lowercase     ,   __lowercase)

   if weight_type is not None:
      UpperCamelCase_						=  getattr(__lowercase     ,   __lowercase).shape
   else:
      UpperCamelCase_						=  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_						=  value
   elif weight_type == "weight_g":
      UpperCamelCase_						=  value
   elif weight_type == "weight_v":
      UpperCamelCase_						=  value
   elif weight_type == "bias":
      UpperCamelCase_						=  value
   else:
      UpperCamelCase_						=  value

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






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   UpperCamelCase_						=  []
   UpperCamelCase_						=  fairseq_model.state_dict()

   UpperCamelCase_						=  hf_model.hubert.feature_extractor if is_finetuned else hf_model.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:
         for key, mapped_key in MAPPING.items():
            UpperCamelCase_						=  '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_						=  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 "weight" in name:
                  UpperCamelCase_						=  'weight'
               elif "bias" in name:
                  UpperCamelCase_						=  'bias'
               else:
                  UpperCamelCase_						=  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      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase):
   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:
         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_						=  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_						=  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_						=  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_						=  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      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase=None     ,   __lowercase=None     ,   __lowercase=True):
   if config_path is not None:
      UpperCamelCase_						=  HubertConfig.from_pretrained(__lowercase)
   else:
      UpperCamelCase_						=  HubertConfig()

   if 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)
         with open(__lowercase     ,   'w'     ,   encoding='utf-8') as vocab_handle:
            json.dump(target_dict.indices     ,   __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=16000     ,   padding_value=0     ,   do_normalize=__lowercase     ,   return_attention_mask=__lowercase     ,   )
         UpperCamelCase_						=  WavaVecaProcessor(feature_extractor=__lowercase     ,   tokenizer=__lowercase)
         processor.save_pretrained(__lowercase)

      UpperCamelCase_						=  HubertForCTC(__lowercase)
   else:
      UpperCamelCase_						=  HubertModel(__lowercase)

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

   UpperCamelCase_						=  model[0].eval()

   recursively_load_weights(__lowercase     ,   __lowercase     ,   __lowercase)

   hf_wavavec.save_pretrained(__lowercase)


if __name__ == "__main__":
 snake_case__   :						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"""
 )
 snake_case__   :						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
 )













from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


snake_case__   :						str													=   logging.get_logger(__name__)






def      _snake_case     (__lowercase):
   if isinstance(__lowercase     ,   (list, tuple)) and isinstance(videos[0]     ,   (list, tuple)) and is_valid_image(videos[0][0]):
      return videos

   elif isinstance(__lowercase     ,   (list, tuple)) and is_valid_image(videos[0]):
      return [videos]

   elif is_valid_image(__lowercase):
      return [[videos]]

   raise ValueError(f"""Could not make batched video from {videos}""")


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = ["""pixel_values"""]




  def __init__(  self		,		_UpperCAmelCase = True		,		_UpperCAmelCase = None		,		_UpperCAmelCase = PILImageResampling.BILINEAR		,		_UpperCAmelCase = True		,		_UpperCAmelCase = None		,		_UpperCAmelCase = True		,		_UpperCAmelCase = 1 / 255		,		_UpperCAmelCase = True		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)      ->			None:
     super().__init__(**_UpperCAmelCase			)
     UpperCamelCase_						=  size if size is not None else {'shortest_edge': 224}
     UpperCamelCase_						=  get_size_dict(_UpperCAmelCase		,		default_to_square=_UpperCAmelCase			)
     UpperCamelCase_						=  crop_size if crop_size is not None else {'height': 224, 'width': 224}
     UpperCamelCase_						=  get_size_dict(_UpperCAmelCase		,		param_name='crop_size'			)

     UpperCamelCase_						=  do_resize
     UpperCamelCase_						=  size
     UpperCamelCase_						=  do_center_crop
     UpperCamelCase_						=  crop_size
     UpperCamelCase_						=  resample
     UpperCamelCase_						=  do_rescale
     UpperCamelCase_						=  rescale_factor
     UpperCamelCase_						=  do_normalize
     UpperCamelCase_						=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     UpperCamelCase_						=  image_std if image_std is not None else IMAGENET_STANDARD_STD




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = PILImageResampling.BILINEAR		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)      ->			np.ndarray:
     UpperCamelCase_						=  get_size_dict(_UpperCAmelCase		,		default_to_square=_UpperCAmelCase			)
     if "shortest_edge" in size:
        UpperCamelCase_						=  get_resize_output_image_size(_UpperCAmelCase		,		size['shortest_edge']		,		default_to_square=_UpperCAmelCase			)
     elif "height" in size and "width" in size:
        UpperCamelCase_						=  (size['height'], size['width'])
     else:
        raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""			)
     return resize(_UpperCAmelCase		,		size=_UpperCAmelCase		,		resample=_UpperCAmelCase		,		data_format=_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)      ->			np.ndarray:
     UpperCamelCase_						=  get_size_dict(_UpperCAmelCase			)
     if "height" not in size or "width" not in size:
        raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""			)
     return center_crop(_UpperCAmelCase		,		size=(size['height'], size['width'])		,		data_format=_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)      ->			int:
     return rescale(_UpperCAmelCase		,		scale=_UpperCAmelCase		,		data_format=_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)      ->			np.ndarray:
     return normalize(_UpperCAmelCase		,		mean=_UpperCAmelCase		,		std=_UpperCAmelCase		,		data_format=_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = ChannelDimension.FIRST		,		)      ->			np.ndarray:
     if do_resize and size is None or resample is None:
        raise ValueError('Size and resample must be specified if do_resize is True.'			)

     if do_center_crop and crop_size is None:
        raise ValueError('Crop size must be specified if do_center_crop is True.'			)

     if do_rescale and rescale_factor is None:
        raise ValueError('Rescale factor must be specified if do_rescale is True.'			)

     if do_normalize and (image_mean is None or image_std is None):
        raise ValueError('Image mean and std must be specified if do_normalize is True.'			)

     # All transformations expect numpy arrays.
     UpperCamelCase_						=  to_numpy_array(_UpperCAmelCase			)

     if do_resize:
        UpperCamelCase_						=  self.resize(image=_UpperCAmelCase		,		size=_UpperCAmelCase		,		resample=_UpperCAmelCase			)

     if do_center_crop:
        UpperCamelCase_						=  self.center_crop(_UpperCAmelCase		,		size=_UpperCAmelCase			)

     if do_rescale:
        UpperCamelCase_						=  self.rescale(image=_UpperCAmelCase		,		scale=_UpperCAmelCase			)

     if do_normalize:
        UpperCamelCase_						=  self.normalize(image=_UpperCAmelCase		,		mean=_UpperCAmelCase		,		std=_UpperCAmelCase			)

     UpperCamelCase_						=  to_channel_dimension_format(_UpperCAmelCase		,		_UpperCAmelCase			)
     return image




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = ChannelDimension.FIRST		,		**_UpperCAmelCase		,		)      ->			PIL.Image.Image:
     UpperCamelCase_						=  do_resize if do_resize is not None else self.do_resize
     UpperCamelCase_						=  resample if resample is not None else self.resample
     UpperCamelCase_						=  do_center_crop if do_center_crop is not None else self.do_center_crop
     UpperCamelCase_						=  do_rescale if do_rescale is not None else self.do_rescale
     UpperCamelCase_						=  rescale_factor if rescale_factor is not None else self.rescale_factor
     UpperCamelCase_						=  do_normalize if do_normalize is not None else self.do_normalize
     UpperCamelCase_						=  image_mean if image_mean is not None else self.image_mean
     UpperCamelCase_						=  image_std if image_std is not None else self.image_std

     UpperCamelCase_						=  size if size is not None else self.size
     UpperCamelCase_						=  get_size_dict(_UpperCAmelCase		,		default_to_square=_UpperCAmelCase			)
     UpperCamelCase_						=  crop_size if crop_size is not None else self.crop_size
     UpperCamelCase_						=  get_size_dict(_UpperCAmelCase		,		param_name='crop_size'			)

     if not valid_images(_UpperCAmelCase			):
        raise ValueError(
            'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
            'torch.Tensor, tf.Tensor or jax.ndarray.'			)

     UpperCamelCase_						=  make_batched(_UpperCAmelCase			)

     UpperCamelCase_						=  [
         [
             self._preprocess_image(
                 image=_UpperCAmelCase		,		do_resize=_UpperCAmelCase		,		size=_UpperCAmelCase		,		resample=_UpperCAmelCase		,		do_center_crop=_UpperCAmelCase		,		crop_size=_UpperCAmelCase		,		do_rescale=_UpperCAmelCase		,		rescale_factor=_UpperCAmelCase		,		do_normalize=_UpperCAmelCase		,		image_mean=_UpperCAmelCase		,		image_std=_UpperCAmelCase		,		data_format=_UpperCAmelCase		,		)
             for img in video
         ]
         for video in videos
     ]

     UpperCamelCase_						=  {'pixel_values': videos}
     return BatchFeature(data=_UpperCAmelCase		,		tensor_type=_UpperCAmelCase			)





