


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     =			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""",
    """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     =			[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Tuple:
   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
            UpperCAmelCase							=     """lm_head"""

      UpperCAmelCase							=     getattr(lowerCAmelCase_  ,						lowerCAmelCase_   )

   if weight_type is not None:
      UpperCAmelCase							=     getattr(lowerCAmelCase_  ,						lowerCAmelCase_   ).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   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->List[str]:
   UpperCAmelCase							=     []
   UpperCAmelCase							=     fairseq_model.state_dict()

   UpperCAmelCase							=     hf_model.unispeech.feature_extractor

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

   logger.warning(F"""Unused weights: {unused_weights}"""   )
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Optional[int]:
   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(lowerCAmelCase_   )
@torch.no_grad()
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=True   )						->List[Any]:
   if config_path is not None:
      UpperCAmelCase							=     UniSpeechConfig.from_pretrained(lowerCAmelCase_   )
   else:
      UpperCAmelCase							=     UniSpeechConfig()

   if is_finetuned:
      if dict_path:
         UpperCAmelCase							=     Dictionary.load_from_json(lowerCAmelCase_   )

         # 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(lowerCAmelCase_  ,						"""vocab.json"""   )
         if not os.path.isdir(lowerCAmelCase_   ):
            logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowerCAmelCase_   )   )
            return
         os.makedirs(lowerCAmelCase_  ,						exist_ok=lowerCAmelCase_   )
         UpperCAmelCase							=     target_dict.indices

         # fairseq has the <pad> and <s> switched
         UpperCAmelCase							=     4_2
         UpperCAmelCase							=     4_3
         with open(lowerCAmelCase_  ,						"""w"""  ,						encoding="""utf-8"""   ) as vocab_handle:
            json.dump(lowerCAmelCase_  ,						lowerCAmelCase_   )
         UpperCAmelCase							=     WavaVecaPhonemeCTCTokenizer(
             lowerCAmelCase_  ,						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=lowerCAmelCase_  ,						)
         UpperCAmelCase							=     True if config.feat_extract_norm == """layer""" else False
         UpperCAmelCase							=     WavaVecaFeatureExtractor(
             feature_size=1  ,						sampling_rate=1_6_0_0_0  ,						padding_value=0  ,						do_normalize=lowerCAmelCase_  ,						return_attention_mask=lowerCAmelCase_  ,						)
         UpperCAmelCase							=     WavaVecaProcessor(feature_extractor=lowerCAmelCase_  ,						tokenizer=lowerCAmelCase_   )
         processor.save_pretrained(lowerCAmelCase_   )

      UpperCAmelCase							=     UniSpeechForCTC(lowerCAmelCase_   )
   else:
      UpperCAmelCase							=     UniSpeechForPreTraining(lowerCAmelCase_   )

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

   UpperCAmelCase							=     model[0].eval()

   recursively_load_weights(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )

   hf_unispeech.save_pretrained(lowerCAmelCase_   )


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_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )







import numpy






class   __lowercase				:





      def __init__(		self						:					Union[str, Any]					,       __lowerCamelCase						:					numpy.ndarray					,       __lowerCamelCase						:					numpy.ndarray       )    ->   None:






         """simple docstring"""







         UpperCAmelCase							=     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.
         UpperCAmelCase							=     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.
         UpperCAmelCase							=     numpy.random.rand(
             4					,       3       )

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

         # Real output values provided.
         UpperCAmelCase							=     output_array

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





      def        _lowercase				(		self						:					List[str]       )    ->   numpy.ndarray:






         """simple docstring"""







         UpperCAmelCase							=     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.
         UpperCAmelCase							=     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.
         UpperCAmelCase							=     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        _lowercase				(		self						:					Optional[Any]       )    ->   None:






         """simple docstring"""







         UpperCAmelCase							=     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       )					,       )
         UpperCAmelCase							=     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       )					,       )
         UpperCAmelCase							=     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        _lowercase				(		self						:					Any					,       __lowerCamelCase						:					numpy.ndarray					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					bool       )    ->   None:






         """simple docstring"""







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





      def        _lowercase				(		self						:					List[str]					,       __lowerCamelCase						:					numpy.ndarray       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     input_arr

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

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

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

   # Calling neural network class.
   UpperCAmelCase							=     TwoHiddenLayerNeuralNetwork(
       input_array=lowerCAmelCase_  ,						output_array=lowerCAmelCase_   )

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

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


if __name__ == "__main__":
 example()



