


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


UpperCAmelCase    =					logging.get_logger(__name__)







class lowerCAmelCase_		(				__A ):

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   **_UpperCAmelCase      ):
       snake_case_ =   feature_size
       snake_case_ =   sampling_rate
       snake_case_ =   padding_value

       snake_case_ =   kwargs.pop('''padding_side'''	,   '''right'''      )
       snake_case_ =   kwargs.pop('''return_attention_mask'''	,   _UpperCAmelCase      )

       super().__init__(**_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase = True	,   _UpperCAmelCase = None	,   _UpperCAmelCase = False	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   ):





       if isinstance(_UpperCAmelCase	,   (list, tuple)      ) and isinstance(processed_features[0]	,   (dict, BatchFeature)      ):
             snake_case_ =   {
                 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()      )}'''      )

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

       if len(_UpperCAmelCase      ) == 0:
             if return_attention_mask:
                   snake_case_ =   []
             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

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

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

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

        # Convert padding_strategy in PaddingStrategy
       snake_case_ =   self._get_padding_strategies(padding=_UpperCAmelCase	,   max_length=_UpperCAmelCase      )

       snake_case_ =   processed_features[self.model_input_names[0]]

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

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

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

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

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

       return BatchFeature(_UpperCAmelCase	,   tensor_type=_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase = None	,   _UpperCAmelCase = PaddingStrategy.DO_NOT_PAD	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   ):
       snake_case_ =   processed_features[self.model_input_names[0]]

       if padding_strategy == PaddingStrategy.LONGEST:
             snake_case_ =   len(_UpperCAmelCase      )

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

       snake_case_ =   padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_UpperCAmelCase      ) < max_length

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

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

       return processed_features



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   ):





       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.'''      )

       snake_case_ =   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):
             snake_case_ =   ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

       snake_case_ =   len(_UpperCAmelCase      ) > max_length

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

       return processed_features





 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase=False	,   _UpperCAmelCase=None      ):





       if padding is not False:
             if padding is True:
                   snake_case_ =   PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
             elif not isinstance(_UpperCAmelCase	,   _UpperCAmelCase      ):
                   snake_case_ =   PaddingStrategy(_UpperCAmelCase      )
             elif isinstance(_UpperCAmelCase	,   _UpperCAmelCase      ):
                   snake_case_ =   padding
       else:
             snake_case_ =   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



import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

UpperCAmelCase    =					{
    """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""",
}
UpperCAmelCase    =					[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """label_embeddings_concat""",
    """speaker_proj""",
    """layer_norm_for_extract""",
]






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

      for attribute in key.split('''.'''      ):
            snake_case_ =   getattr(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )

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

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Any:




      """simple docstring"""

      snake_case_ =   []
      snake_case_ =   fairseq_model.state_dict()

      snake_case_ =   hf_model.unispeech_sat.feature_extractor

      for name, value in fairseq_dict.items():
            snake_case_ =   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'''      ,      )
                  snake_case_ =   True
            else:
                  for key, mapped_key in MAPPING.items():
                        snake_case_ =   '''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
                              snake_case_ =   True
                              if "*" in mapped_key:
                                    snake_case_ =   name.split(SCREAMING_SNAKE_CASE      )[0].split('''.'''      )[-2]
                                    snake_case_ =   mapped_key.replace('''*'''      ,      SCREAMING_SNAKE_CASE      )
                              if "weight_g" in name:
                                    snake_case_ =   '''weight_g'''
                              elif "weight_v" in name:
                                    snake_case_ =   '''weight_v'''
                              elif "bias" in name:
                                    snake_case_ =   '''bias'''
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    snake_case_ =   '''weight'''
                              else:
                                    snake_case_ =   None
                              set_recursively(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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

      snake_case_ =   full_name.split('''conv_layers.'''      )[-1]
      snake_case_ =   name.split('''.'''      )
      snake_case_ =   int(items[0]      )
      snake_case_ =   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.'''      )
                  snake_case_ =   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.'''      )
                  snake_case_ =   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.'''      )
                  snake_case_ =   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.'''      )
                  snake_case_ =   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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=None      ,      SCREAMING_SNAKE_CASE=None      ,      SCREAMING_SNAKE_CASE=True      )->						Dict:




      """simple docstring"""

      if config_path is not None:
            snake_case_ =   UniSpeechSatConfig.from_pretrained(SCREAMING_SNAKE_CASE      )
      else:
            snake_case_ =   UniSpeechSatConfig()

      snake_case_ =   ''''''

      if is_finetuned:
            snake_case_ =   UniSpeechSatForCTC(SCREAMING_SNAKE_CASE      )
      else:
            snake_case_ =   UniSpeechSatForPreTraining(SCREAMING_SNAKE_CASE      )

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

      recursively_load_weights(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )

      hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
   UpperCAmelCase    =					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    =					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
   )