


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()
lowerCamelCase__							:						List[Any]							     =   logging.get_logger(__name__)

lowerCamelCase__							:						Optional[Any]							     =   {
    """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""",
}
lowerCamelCase__							:						Optional[Any]							     =   [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				str:
  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
        snake_case__						= '''lm_head'''

    snake_case__						= getattr(__lowerCAmelCase							,   __lowerCAmelCase				)

  if weight_type is not None:
    snake_case__						= getattr(__lowerCAmelCase							,   __lowerCAmelCase				).shape
  else:
    snake_case__						= 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":
    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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				Tuple:
  snake_case__						= []
  snake_case__						= fairseq_model.state_dict()

  snake_case__						= hf_model.unispeech.feature_extractor

  for name, value in fairseq_dict.items():
    snake_case__						= False
    if "conv_layers" in name:
      load_conv_layer(
          __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   hf_model.config.feat_extract_norm == '''group'''							,   )
      snake_case__						= True
    else:
      for key, mapped_key in MAPPING.items():
        snake_case__						= '''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]:
          snake_case__						= True
          if "*" in mapped_key:
            snake_case__						= name.split(__lowerCAmelCase				)[0].split('''.'''				)[-2]
            snake_case__						= mapped_key.replace('''*'''							,   __lowerCAmelCase				)
          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(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)
        continue
    if not is_used:
      unused_weights.append(__lowerCAmelCase				)

  logger.warning(F"""Unused weights: {unused_weights}"""				)







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				Optional[Any]:
  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:
      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."""
      )
      snake_case__						= 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."""
      )
      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:
      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."
      )
      snake_case__						= 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."""
      )
      snake_case__						= 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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase=None							,   __lowerCAmelCase=None							,   __lowerCAmelCase=True				)    ->				Optional[Any]:
  if config_path is not None:
    snake_case__						= UniSpeechConfig.from_pretrained(__lowerCAmelCase				)
  else:
    snake_case__						= UniSpeechConfig()

  if is_finetuned:
    if dict_path:
      snake_case__						= Dictionary.load_from_json(__lowerCAmelCase				)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      snake_case__						= target_dict.pad_index
      snake_case__						= target_dict.bos_index
      snake_case__						= target_dict.eos_index
      snake_case__						= len(target_dict.symbols				)
      snake_case__						= 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				)
      snake_case__						= target_dict.indices

      # fairseq has the <pad> and <s> switched
      snake_case__						= 42
      snake_case__						= 43
      with open(__lowerCAmelCase							,   '''w'''							,   encoding='''utf-8'''				) as vocab_handle:
        json.dump(__lowerCAmelCase							,   __lowerCAmelCase				)
      snake_case__						= 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							,   )
      snake_case__						= True if config.feat_extract_norm == '''layer''' else False
      snake_case__						= WavaVecaFeatureExtractor(
          feature_size=1							,   sampling_rate=1_6000							,   padding_value=0							,   do_normalize=__lowerCAmelCase							,   return_attention_mask=__lowerCAmelCase							,   )
      snake_case__						= WavaVecaProcessor(feature_extractor=__lowerCAmelCase							,   tokenizer=__lowerCAmelCase				)
      processor.save_pretrained(__lowerCAmelCase				)

    snake_case__						= UniSpeechForCTC(__lowerCAmelCase				)
  else:
    snake_case__						= UniSpeechForPreTraining(__lowerCAmelCase				)

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

  snake_case__						= model[0].eval()

  recursively_load_weights(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)

  hf_unispeech.save_pretrained(__lowerCAmelCase				)


if __name__ == "__main__":
   lowerCamelCase__							:						Dict							     =   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"""
   )
   lowerCamelCase__							:						List[str]							     =   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 as np







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				np.ndarray:
  return 1 / (1 + np.exp(-vector				))







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				np.ndarray:
  return vector * sigmoid(__lowerCAmelCase				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

