



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 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Tuple     ,				_SCREAMING_SNAKE_CASE   :	Optional[int]     ,				_SCREAMING_SNAKE_CASE   :	Tuple     ,				_SCREAMING_SNAKE_CASE   :	Union[str, Any]     ,				_SCREAMING_SNAKE_CASE   :	Any				)->Optional[Any]:
      for attribute in key.split('''.'''				):
            _lowerCAmelCase 			=  getattr(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)

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

      logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''				)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Optional[int]     ,				_SCREAMING_SNAKE_CASE   :	Any				)->List[str]:
      _lowerCAmelCase 			=  []
      _lowerCAmelCase 			=  fairseq_model.state_dict()

      _lowerCAmelCase 			=  hf_model.unispeech_sat.feature_extractor

      for name, value in fairseq_dict.items():
            _lowerCAmelCase 			=  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'''     ,				)
                  _lowerCAmelCase 			=  True
            else:
                  for key, mapped_key in MAPPING.items():
                        _lowerCAmelCase 			=  '''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
                              _lowerCAmelCase 			=  True
                              if "*" in mapped_key:
                                    _lowerCAmelCase 			=  name.split(_SCREAMING_SNAKE_CASE				)[0].split('''.'''				)[-2]
                                    _lowerCAmelCase 			=  mapped_key.replace('''*'''     ,				_SCREAMING_SNAKE_CASE				)
                              if "weight_g" in name:
                                    _lowerCAmelCase 			=  '''weight_g'''
                              elif "weight_v" in name:
                                    _lowerCAmelCase 			=  '''weight_v'''
                              elif "bias" in name:
                                    _lowerCAmelCase 			=  '''bias'''
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    _lowerCAmelCase 			=  '''weight'''
                              else:
                                    _lowerCAmelCase 			=  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 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Tuple     ,				_SCREAMING_SNAKE_CASE   :	Dict     ,				_SCREAMING_SNAKE_CASE   :	Dict     ,				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	int				)->str:
      _lowerCAmelCase 			=  full_name.split('''conv_layers.'''				)[-1]
      _lowerCAmelCase 			=  name.split('''.'''				)
      _lowerCAmelCase 			=  int(items[0]				)
      _lowerCAmelCase 			=  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.'''				)
                  _lowerCAmelCase 			=  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.'''				)
                  _lowerCAmelCase 			=  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.'''				)
                  _lowerCAmelCase 			=  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.'''				)
                  _lowerCAmelCase 			=  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 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	List[str]     ,				_SCREAMING_SNAKE_CASE   :	Optional[int]     ,				_SCREAMING_SNAKE_CASE   :	str=None     ,				_SCREAMING_SNAKE_CASE   :	List[Any]=None     ,				_SCREAMING_SNAKE_CASE   :	List[Any]=True				)->Any:
      if config_path is not None:
            _lowerCAmelCase 			=  UniSpeechSatConfig.from_pretrained(_SCREAMING_SNAKE_CASE				)
      else:
            _lowerCAmelCase 			=  UniSpeechSatConfig()

      _lowerCAmelCase 			=  ''''''

      if is_finetuned:
            _lowerCAmelCase 			=  UniSpeechSatForCTC(_SCREAMING_SNAKE_CASE				)
      else:
            _lowerCAmelCase 			=  UniSpeechSatForPreTraining(_SCREAMING_SNAKE_CASE				)

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




import numpy





class 		UpperCAmelCase						:





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  input_array

         # Random initial weights are assigned where first argument is the
         # number of nodes in previous layer and second argument is the
         # number of nodes in the next layer.

         # Random initial weights are assigned.
         # self.input_array.shape[1] is used to represent number of nodes in input layer.
         # First hidden layer consists of 4 nodes.
         _lowerCAmelCase 			=  numpy.random.rand(
             self.input_array.shape[1]       ,     4			)

         # Random initial values for the first hidden layer.
         # First hidden layer has 4 nodes.
         # Second hidden layer has 3 nodes.
         _lowerCAmelCase 			=  numpy.random.rand(
             4       ,     3			)

         # Random initial values for the second hidden layer.
         # Second hidden layer has 3 nodes.
         # Output layer has 1 node.
         _lowerCAmelCase 			=  numpy.random.rand(3       ,     1			)

         # Real output values provided.
         _lowerCAmelCase 			=  output_array

         # Predicted output values by the neural network.
         # Predicted_output array initially consists of zeroes.
         _lowerCAmelCase 			=  numpy.zeros(output_array.shape			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  sigmoid(
             numpy.dot(self.input_array       ,     self.input_layer_and_first_hidden_layer_weights			)			)

         # layer_between_first_hidden_layer_and_second_hidden_layer is the layer
         # connecting the first hidden set of nodes with the second hidden set of nodes.
         _lowerCAmelCase 			=  sigmoid(
             numpy.dot(
                 self.layer_between_input_and_first_hidden_layer       ,     self.first_hidden_layer_and_second_hidden_layer_weights       ,     )			)

         # layer_between_second_hidden_layer_and_output is the layer connecting
         # second hidden layer with the output node.
         _lowerCAmelCase 			=  sigmoid(
             numpy.dot(
                 self.layer_between_first_hidden_layer_and_second_hidden_layer       ,     self.second_hidden_layer_and_output_layer_weights       ,     )			)

         return self.layer_between_second_hidden_layer_and_output





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  numpy.dot(
             self.layer_between_first_hidden_layer_and_second_hidden_layer.T       ,     2
             * (self.output_array - self.predicted_output)
             * sigmoid_derivative(self.predicted_output			)       ,     )
         _lowerCAmelCase 			=  numpy.dot(
             self.layer_between_input_and_first_hidden_layer.T       ,     numpy.dot(
                 2
                 * (self.output_array - self.predicted_output)
                 * sigmoid_derivative(self.predicted_output			)       ,     self.second_hidden_layer_and_output_layer_weights.T       ,     )
             * sigmoid_derivative(
                 self.layer_between_first_hidden_layer_and_second_hidden_layer			)       ,     )
         _lowerCAmelCase 			=  numpy.dot(
             self.input_array.T       ,     numpy.dot(
                 numpy.dot(
                     2
                     * (self.output_array - self.predicted_output)
                     * sigmoid_derivative(self.predicted_output			)       ,     self.second_hidden_layer_and_output_layer_weights.T       ,     )
                 * sigmoid_derivative(
                     self.layer_between_first_hidden_layer_and_second_hidden_layer			)       ,     self.first_hidden_layer_and_second_hidden_layer_weights.T       ,     )
             * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer			)       ,     )

         self.input_layer_and_first_hidden_layer_weights += (
             updated_input_layer_and_first_hidden_layer_weights
         )
         self.first_hidden_layer_and_second_hidden_layer_weights += (
             updated_first_hidden_layer_and_second_hidden_layer_weights
         )
         self.second_hidden_layer_and_output_layer_weights += (
             updated_second_hidden_layer_and_output_layer_weights
         )





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         for iteration in range(1       ,     iterations + 1			):
               _lowerCAmelCase 			=  self.feedforward()
               self.back_propagation()
               if give_loss:
                     _lowerCAmelCase 			=  numpy.mean(numpy.square(output - self.feedforward()			)			)
                     print(F'''Iteration {iteration} Loss: {loss}'''			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  input_arr

         _lowerCAmelCase 			=  sigmoid(
             numpy.dot(self.array       ,     self.input_layer_and_first_hidden_layer_weights			)			)

         _lowerCAmelCase 			=  sigmoid(
             numpy.dot(
                 self.layer_between_input_and_first_hidden_layer       ,     self.first_hidden_layer_and_second_hidden_layer_weights       ,     )			)

         _lowerCAmelCase 			=  sigmoid(
             numpy.dot(
                 self.layer_between_first_hidden_layer_and_second_hidden_layer       ,     self.second_hidden_layer_and_output_layer_weights       ,     )			)

         return int(self.layer_between_second_hidden_layer_and_output > 0.6			)






def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	numpy.ndarray				)->numpy.ndarray:
      return 1 / (1 + numpy.exp(-value				))
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	numpy.ndarray				)->numpy.ndarray:
      return (value) * (1 - (value))
def 							UpperCAmelCase__		(				)->int:
      _lowerCAmelCase 			=  numpy.array(
          (
              [0, 0, 0],
              [0, 0, 1],
              [0, 1, 0],
              [0, 1, 1],
              [1, 0, 0],
              [1, 0, 1],
              [1, 1, 0],
              [1, 1, 1],
          )     ,				dtype=numpy.floataa     ,				)

      # True output values for the given input values.
      _lowerCAmelCase 			=  numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])     ,				dtype=numpy.floataa				)

      # Calling neural network class.
      _lowerCAmelCase 			=  TwoHiddenLayerNeuralNetwork(
          input_array=_SCREAMING_SNAKE_CASE     ,				output_array=_SCREAMING_SNAKE_CASE				)

      # Calling training function.
      # Set give_loss to True if you want to see loss in every iteration.
      neural_network.train(output=_SCREAMING_SNAKE_CASE     ,				iterations=1_0     ,				give_loss=_SCREAMING_SNAKE_CASE				)

      return neural_network.predict(numpy.array(([1, 1, 1])     ,				dtype=numpy.floataa				)				)


if __name__ == "__main__":
    example()