




import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

__a          =			{
    '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',
    'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
    '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',
    'label_embs_concat': 'label_embeddings_concat',
    'mask_emb': 'masked_spec_embed',
    'spk_proj': 'speaker_proj',
}
__a          =			[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     for attribute in key.split('''.'''				):
          UpperCAmelCase_      :							Optional[int]       =      getattr(_lowercase			,						_lowercase				)

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

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







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


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

     UpperCAmelCase_      :							List[str]       =      hf_model.unispeech_sat.feature_extractor

     for name, value in fairseq_dict.items():
          UpperCAmelCase_      :							Dict       =      False
          if "conv_layers" in name:
               load_conv_layer(
                   _lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						hf_model.config.feat_extract_norm == '''group'''			,						)
               UpperCAmelCase_      :							Any       =      True
          else:
               for key, mapped_key in MAPPING.items():
                    UpperCAmelCase_      :							List[str]       =      '''unispeech_sat.''' + 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]:
                         if "layer_norm_for_extract" in name and (".".join(name.split('''.'''				)[:-1]				) != key):
                              # special case since naming is very similar
                              continue
                         UpperCAmelCase_      :							str       =      True
                         if "*" in mapped_key:
                              UpperCAmelCase_      :							Dict       =      name.split(_lowercase				)[0].split('''.'''				)[-2]
                              UpperCAmelCase_      :							Dict       =      mapped_key.replace('''*'''			,						_lowercase				)
                         if "weight_g" in name:
                              UpperCAmelCase_      :							Any       =      '''weight_g'''
                         elif "weight_v" in name:
                              UpperCAmelCase_      :							int       =      '''weight_v'''
                         elif "bias" in name:
                              UpperCAmelCase_      :							Optional[int]       =      '''bias'''
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              UpperCAmelCase_      :							Dict       =      '''weight'''
                         else:
                              UpperCAmelCase_      :							Optional[Any]       =      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 lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							int       =      full_name.split('''conv_layers.'''				)[-1]
     UpperCAmelCase_      :							Tuple       =      name.split('''.'''				)
     UpperCAmelCase_      :							Dict       =      int(items[0]				)
     UpperCAmelCase_      :							Tuple       =      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_      :							Optional[Any]       =      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_      :							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:
               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[layer_id].layer_norm.bias.data.shape} was found.'''				)
               UpperCAmelCase_      :							Tuple       =      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[layer_id].layer_norm.weight.data.shape} was found.'''				)
               UpperCAmelCase_      :							Dict       =      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 lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase=None			,						_lowercase=None			,						_lowercase=True				):

     '''simple docstring'''


     if config_path is not None:
          UpperCAmelCase_      :							Any       =      UniSpeechSatConfig.from_pretrained(_lowercase				)
     else:
          UpperCAmelCase_      :							Union[str, Any]       =      UniSpeechSatConfig()

     UpperCAmelCase_      :							Optional[Any]       =      ''''''

     if is_finetuned:
          UpperCAmelCase_      :							str       =      UniSpeechSatForCTC(_lowercase				)
     else:
          UpperCAmelCase_      :							int       =      UniSpeechSatForPreTraining(_lowercase				)

     UpperCAmelCase_      :							str       =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path]			,						arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''				)[:-1]				)}				)
     UpperCAmelCase_      :							Optional[Any]       =      model[0].eval()

     recursively_load_weights(_lowercase			,						_lowercase				)

     hf_wavavec.save_pretrained(_lowercase				)


if __name__ == "__main__":
       __a          =			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'
       )
       __a          =			parser.parse_args()
       convert_unispeech_sat_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


__a          =			logging.get_logger(__name__)







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     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(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				['''pixel_values''']





    def __init__(				self       ,_SCREAMING_SNAKE_CASE = True       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR       ,_SCREAMING_SNAKE_CASE = True       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = True       ,_SCREAMING_SNAKE_CASE = 1 / 255       ,_SCREAMING_SNAKE_CASE = True       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,**_SCREAMING_SNAKE_CASE       ,)			->		None:
         super().__init__(**_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      size if size is not None else {'''shortest_edge''': 224}
         UpperCAmelCase_      :							Any       =      get_size_dict(_SCREAMING_SNAKE_CASE       ,default_to_square=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Any       =      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         UpperCAmelCase_      :							List[str]       =      get_size_dict(_SCREAMING_SNAKE_CASE       ,param_name='''crop_size'''  )

         UpperCAmelCase_      :							str       =      do_resize
         UpperCAmelCase_      :							Union[str, Any]       =      size
         UpperCAmelCase_      :							int       =      do_center_crop
         UpperCAmelCase_      :							List[str]       =      crop_size
         UpperCAmelCase_      :							Optional[int]       =      resample
         UpperCAmelCase_      :							List[Any]       =      do_rescale
         UpperCAmelCase_      :							Tuple       =      rescale_factor
         UpperCAmelCase_      :							Optional[Any]       =      do_normalize
         UpperCAmelCase_      :							int       =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         UpperCAmelCase_      :							Optional[int]       =      image_std if image_std is not None else IMAGENET_STANDARD_STD





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





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,**_SCREAMING_SNAKE_CASE       ,)			->		np.ndarray:
         UpperCAmelCase_      :							str       =      get_size_dict(_SCREAMING_SNAKE_CASE  )
         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(_SCREAMING_SNAKE_CASE       ,size=(size['''height'''], size['''width'''])       ,data_format=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,**_SCREAMING_SNAKE_CASE       ,)			->		Dict:
         return rescale(_SCREAMING_SNAKE_CASE       ,scale=_SCREAMING_SNAKE_CASE       ,data_format=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )





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





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = 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_      :							Any       =      to_numpy_array(_SCREAMING_SNAKE_CASE  )

         if do_resize:
              UpperCAmelCase_      :							Union[str, Any]       =      self.resize(image=_SCREAMING_SNAKE_CASE       ,size=_SCREAMING_SNAKE_CASE       ,resample=_SCREAMING_SNAKE_CASE  )

         if do_center_crop:
              UpperCAmelCase_      :							Optional[int]       =      self.center_crop(_SCREAMING_SNAKE_CASE       ,size=_SCREAMING_SNAKE_CASE  )

         if do_rescale:
              UpperCAmelCase_      :							str       =      self.rescale(image=_SCREAMING_SNAKE_CASE       ,scale=_SCREAMING_SNAKE_CASE  )

         if do_normalize:
              UpperCAmelCase_      :							List[Any]       =      self.normalize(image=_SCREAMING_SNAKE_CASE       ,mean=_SCREAMING_SNAKE_CASE       ,std=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Optional[int]       =      to_channel_dimension_format(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
         return image





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST       ,**_SCREAMING_SNAKE_CASE       ,)			->		PIL.Image.Image:
         UpperCAmelCase_      :							Dict       =      do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase_      :							int       =      resample if resample is not None else self.resample
         UpperCAmelCase_      :							List[str]       =      do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase_      :							Optional[Any]       =      do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase_      :							List[Any]       =      rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase_      :							Tuple       =      do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase_      :							Optional[int]       =      image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase_      :							Optional[int]       =      image_std if image_std is not None else self.image_std

         UpperCAmelCase_      :							List[str]       =      size if size is not None else self.size
         UpperCAmelCase_      :							Optional[int]       =      get_size_dict(_SCREAMING_SNAKE_CASE       ,default_to_square=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase_      :							Any       =      get_size_dict(_SCREAMING_SNAKE_CASE       ,param_name='''crop_size'''  )

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

         UpperCAmelCase_      :							List[Any]       =      make_batched(_SCREAMING_SNAKE_CASE  )

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

         UpperCAmelCase_      :							Any       =      {'''pixel_values''': videos}
         return BatchFeature(data=_SCREAMING_SNAKE_CASE       ,tensor_type=_SCREAMING_SNAKE_CASE  )