


'''simple docstring'''






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a_   :      Dict				    =      logging.get_logger(__name__)

a_   :      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",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
a_   :      Optional[Any]				    =      [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]






def 		_A			(lowerCAmelCase__					:Any			,  lowerCAmelCase__					:Dict			,  lowerCAmelCase__					:Union[str, Any]			,  lowerCAmelCase__					:List[str]			,  lowerCAmelCase__					:Union[str, Any]			,  lowerCAmelCase__					:Optional[int]						)				->					Dict:




     '''simple docstring'''



     for attribute in key.split('.'						):
          if is_finetuned:
               if attribute in ["quantizer", "project_q", "project_hid"]:
                    # those layers are only relevant for pretraining and should be dropped
                    return

               if attribute == "ctc_proj":
                    # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
                    _a      =							'lm_head'

          _a      =							getattr(SCREAMING_SNAKE_CASE__			,  SCREAMING_SNAKE_CASE__						)

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

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






def 		_A			(lowerCAmelCase__					:int			,  lowerCAmelCase__					:Optional[int]			,  lowerCAmelCase__					:Any						)				->					Tuple:




     '''simple docstring'''



     _a      =							[]
     _a      =							fairseq_model.state_dict()

     _a      =							hf_model.unispeech.feature_extractor

     for name, value in fairseq_dict.items():
          _a      =							False
          if "conv_layers" in name:
               load_conv_layer(
                   SCREAMING_SNAKE_CASE__			,  SCREAMING_SNAKE_CASE__			,  SCREAMING_SNAKE_CASE__			,  SCREAMING_SNAKE_CASE__			,  hf_model.config.feat_extract_norm == 'group'			,  )
               _a      =							True
          else:
               for key, mapped_key in MAPPING.items():
                    _a      =							'unispeech.' + 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]:
                         _a      =							True
                         if "*" in mapped_key:
                              _a      =							name.split(SCREAMING_SNAKE_CASE__						)[0].split('.'						)[-2]
                              _a      =							mapped_key.replace('*'			,  SCREAMING_SNAKE_CASE__						)
                         if "weight_g" in name:
                              _a      =							'weight_g'
                         elif "weight_v" in name:
                              _a      =							'weight_v'
                         elif "bias" in name:
                              _a      =							'bias'
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              _a      =							'weight'
                         else:
                              _a      =							None
                         set_recursively(SCREAMING_SNAKE_CASE__			,  SCREAMING_SNAKE_CASE__			,  SCREAMING_SNAKE_CASE__			,  SCREAMING_SNAKE_CASE__			,  SCREAMING_SNAKE_CASE__			,  SCREAMING_SNAKE_CASE__						)
                    continue
          if not is_used:
               unused_weights.append(SCREAMING_SNAKE_CASE__						)

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






def 		_A			(lowerCAmelCase__					:Optional[Any]			,  lowerCAmelCase__					:int			,  lowerCAmelCase__					:Any			,  lowerCAmelCase__					:int			,  lowerCAmelCase__					:Dict						)				->					int:




     '''simple docstring'''



     _a      =							full_name.split('conv_layers.'						)[-1]
     _a      =							name.split('.'						)
     _a      =							int(items[0]						)
     _a      =							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.'
               )
               _a      =							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.'
               )
               _a      =							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."
               )
               _a      =							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.'
               )
               _a      =							value
               logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'						)
     else:
          unused_weights.append(SCREAMING_SNAKE_CASE__						)






@torch.no_grad()
def 		_A			(lowerCAmelCase__					:int			,  lowerCAmelCase__					:Union[str, Any]			,  lowerCAmelCase__					:Any=None			,  lowerCAmelCase__					:List[Any]=None			,  lowerCAmelCase__					:Union[str, Any]=True						)				->					List[Any]:




     '''simple docstring'''



     if config_path is not None:
          _a      =							UniSpeechConfig.from_pretrained(SCREAMING_SNAKE_CASE__						)
     else:
          _a      =							UniSpeechConfig()

     if is_finetuned:
          if dict_path:
               _a      =							Dictionary.load_from_json(SCREAMING_SNAKE_CASE__						)

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               _a      =							target_dict.pad_index
               _a      =							target_dict.bos_index
               _a      =							target_dict.eos_index
               _a      =							len(target_dict.symbols						)
               _a      =							os.path.join(SCREAMING_SNAKE_CASE__			,  'vocab.json'						)
               if not os.path.isdir(SCREAMING_SNAKE_CASE__						):
                    logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(SCREAMING_SNAKE_CASE__						)						)
                    return
               os.makedirs(SCREAMING_SNAKE_CASE__			,  exist_ok=SCREAMING_SNAKE_CASE__						)
               _a      =							target_dict.indices

               # fairseq has the <pad> and <s> switched
               _a      =							42
               _a      =							43
               with open(SCREAMING_SNAKE_CASE__			,  'w'			,  encoding='utf-8'						) as vocab_handle:
                    json.dump(SCREAMING_SNAKE_CASE__			,  SCREAMING_SNAKE_CASE__						)
               _a      =							WavaVecaPhonemeCTCTokenizer(
                   SCREAMING_SNAKE_CASE__			,  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=SCREAMING_SNAKE_CASE__			,  )
               _a      =							True if config.feat_extract_norm == 'layer' else False
               _a      =							WavaVecaFeatureExtractor(
                   feature_size=1			,  sampling_rate=1_60_00			,  padding_value=0			,  do_normalize=SCREAMING_SNAKE_CASE__			,  return_attention_mask=SCREAMING_SNAKE_CASE__			,  )
               _a      =							WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__			,  tokenizer=SCREAMING_SNAKE_CASE__						)
               processor.save_pretrained(SCREAMING_SNAKE_CASE__						)

          _a      =							UniSpeechForCTC(SCREAMING_SNAKE_CASE__						)
     else:
          _a      =							UniSpeechForPreTraining(SCREAMING_SNAKE_CASE__						)

     if is_finetuned:
          _a ,				_a ,				_a      =							fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]			,  arg_overrides={'data': '/'.join(dict_path.split('/'						)[:-1]						), 'w2v_path': checkpoint_path}						)
     else:
          _a ,				_a ,				_a      =							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						)

     _a      =							model[0].eval()

     recursively_load_weights(SCREAMING_SNAKE_CASE__			,  SCREAMING_SNAKE_CASE__			,  SCREAMING_SNAKE_CASE__						)

     hf_unispeech.save_pretrained(SCREAMING_SNAKE_CASE__						)


if __name__ == "__main__":
 a_   :      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"
 )
 a_   :      Optional[Any]				    =      parser.parse_args()
 convert_unispeech_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )





'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


a_   :      List[str]				    =      logging.get_logger(__name__)




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 def __init__(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			**__magic_name__  )   ->							Any:
      _a      =							feature_size
      _a      =							sampling_rate
      _a      =							padding_value

      _a      =							kwargs.pop('padding_side' ,			'right'  )
      _a      =							kwargs.pop('return_attention_mask' ,			__magic_name__  )

      super().__init__(**__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = True ,			__magic_name__ = None ,			__magic_name__ = False ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			)   ->							BatchFeature:
      # If we have a list of dicts, let's convert it in a dict of lists
      # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
      if isinstance(__magic_name__ ,			(list, tuple)  ) and isinstance(processed_features[0] ,			(dict, BatchFeature)  ):
           _a      =							{
               key: [example[key] for example in processed_features] for key in processed_features[0].keys()
           }

      # The model's main input name, usually `input_values`, has be passed for padding
      if self.model_input_names[0] not in processed_features:
           raise ValueError(
               'You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'
               f' to this method that includes {self.model_input_names[0]}, but you provided'
               f' {list(processed_features.keys()  )}'  )

      _a      =							processed_features[self.model_input_names[0]]
      _a      =							(
          return_attention_mask if return_attention_mask is not None else self.return_attention_mask
      )

      if len(__magic_name__  ) == 0:
           if return_attention_mask:
                _a      =							[]
           return processed_features

      # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
      # and rebuild them afterwards if no return_tensors is specified
      # Note that we lose the specific device the tensor may be on for PyTorch

      _a      =							required_input[0]
      if isinstance(__magic_name__ ,			(list, tuple)  ):
           # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
           _a      =							0
           while len(required_input[index]  ) == 0:
                index += 1
           if index < len(__magic_name__  ):
                _a      =							required_input[index][0]

      if return_tensors is None:
           if is_tf_tensor(__magic_name__  ):
                _a      =							'tf'
           elif is_torch_tensor(__magic_name__  ):
                _a      =							'pt'
           elif isinstance(__magic_name__ ,			(int, float, list, tuple, np.ndarray)  ):
                _a      =							'np'
           else:
                raise ValueError(
                    f'type of {first_element} unknown: {type(__magic_name__  )}. '
                    'Should be one of a python, numpy, pytorch or tensorflow object.'  )

      for key, value in processed_features.items():
           if isinstance(value[0] ,			(int, float)  ):
                _a      =							to_numpy(__magic_name__  )
           else:
                _a      =							[to_numpy(__magic_name__  ) for v in value]

        # Convert padding_strategy in PaddingStrategy
      _a      =							self._get_padding_strategies(padding=__magic_name__ ,			max_length=__magic_name__  )

      _a      =							processed_features[self.model_input_names[0]]

      _a      =							len(__magic_name__  )
      if not all(len(__magic_name__  ) == batch_size for v in processed_features.values()  ):
           raise ValueError('Some items in the output dictionary have a different batch size than others.'  )

      _a      =							[]
      for i in range(__magic_name__  ):
           _a      =							{k: v[i] for k, v in processed_features.items()}
           # truncation
           _a      =							self._truncate(
               __magic_name__ ,			max_length=__magic_name__ ,			pad_to_multiple_of=__magic_name__ ,			truncation=__magic_name__ ,			)
           truncated_inputs.append(__magic_name__  )

      if padding_strategy == PaddingStrategy.LONGEST:
           # make sure that `max_length` cannot be longer than the longest truncated length
           _a      =							max(len(input_slice[self.model_input_names[0]]  ) for input_slice in truncated_inputs  )
           _a      =							PaddingStrategy.MAX_LENGTH

      _a      =							{}
      for i in range(__magic_name__  ):
           # padding
           _a      =							self._pad(
               truncated_inputs[i] ,			max_length=__magic_name__ ,			padding_strategy=__magic_name__ ,			pad_to_multiple_of=__magic_name__ ,			return_attention_mask=__magic_name__ ,			)

           for key, value in outputs.items():
                if key not in batch_outputs:
                     _a      =							[]
                if value.dtype is np.dtype(np.floataa  ):
                     _a      =							value.astype(np.floataa  )
                batch_outputs[key].append(__magic_name__  )

      return BatchFeature(__magic_name__ ,			tensor_type=__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None ,			__magic_name__ = PaddingStrategy.DO_NOT_PAD ,			__magic_name__ = None ,			__magic_name__ = None ,			)   ->							dict:
      _a      =							processed_features[self.model_input_names[0]]

      if padding_strategy == PaddingStrategy.LONGEST:
           _a      =							len(__magic_name__  )

      if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
           _a      =							((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      _a      =							padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__magic_name__  ) < max_length

      if return_attention_mask and "attention_mask" not in processed_features:
           _a      =							np.ones(len(__magic_name__  ) ,			dtype=np.intaa  )

      if needs_to_be_padded:
           _a      =							max_length - len(__magic_name__  )
           if self.padding_side == "right":
                if return_attention_mask:
                     _a      =							np.pad(
                         processed_features['attention_mask'] ,			(0, difference)  )
                _a      =							((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
                _a      =							np.pad(
                    __magic_name__ ,			__magic_name__ ,			'constant' ,			constant_values=self.padding_value  )
           elif self.padding_side == "left":
                if return_attention_mask:
                     _a      =							np.pad(
                         processed_features['attention_mask'] ,			(difference, 0)  )
                _a      =							((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
                _a      =							np.pad(
                    __magic_name__ ,			__magic_name__ ,			'constant' ,			constant_values=self.padding_value  )
           else:
                raise ValueError('Invalid padding strategy:' + str(self.padding_side  )  )

      return processed_features
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			)   ->							List[str]:
      if not truncation:
           return processed_features
      elif truncation and max_length is None:
           raise ValueError('When setting ``truncation=True``, make sure that ``max_length`` is defined.'  )

      _a      =							processed_features[self.model_input_names[0]]

      # find `max_length` that fits `pad_to_multiple_of`
      if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
           _a      =							((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      _a      =							len(__magic_name__  ) > max_length

      if needs_to_be_truncated:
           _a      =							processed_features[self.model_input_names[0]][:max_length]
           if "attention_mask" in processed_features:
                _a      =							processed_features['attention_mask'][:max_length]

      return processed_features




 def 							__UpperCAmelCase						(	self ,			__magic_name__=False ,			__magic_name__=None  )   ->							Dict:

      # Get padding strategy
      if padding is not False:
           if padding is True:
                _a      =							PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
           elif not isinstance(__magic_name__ ,			__magic_name__  ):
                _a      =							PaddingStrategy(__magic_name__  )
           elif isinstance(__magic_name__ ,			__magic_name__  ):
                _a      =							padding
      else:
           _a      =							PaddingStrategy.DO_NOT_PAD

      # Set max length if needed
      if max_length is None:
           if padding_strategy == PaddingStrategy.MAX_LENGTH:
                raise ValueError(
                    f'When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'  )

        # Test if we have a padding value
      if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
           raise ValueError(
               'Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'
               ' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'  )

      return padding_strategy

