





'''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:					Optional[Any]							=				logging.get_logger(__name__)

_A:					Optional[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:					Tuple							=				[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     List[Any]:
 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 _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     Tuple:
 __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 _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     Union[str, Any]:
 __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 _lowerCAmelCase     (      _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					=   42
   __UpperCAmelCase					=   43
   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_60_00       ,    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:					Optional[int]							=				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:					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
    )












'''simple docstring'''




from __future__ import annotations

from collections.abc import Sequence
from typing import Literal







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str | Literal[False]:
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 __UpperCAmelCase					=   0
 for i in range(len(_lowerCAmelCase    )    ):
  if lista[i] != lista[i]:
   count += 1
   __UpperCAmelCase					=   '_'
 if count > 1:
  return False
 else:
  return "".join(_lowerCAmelCase    )







def _lowerCAmelCase     (      _lowerCAmelCase    )->     list[str]:
 __UpperCAmelCase					=   []
 while True:
  __UpperCAmelCase					=   ['$'] * len(_lowerCAmelCase    )
  __UpperCAmelCase					=   []
  for i in range(len(_lowerCAmelCase    )    ):
   for j in range(i + 1       ,    len(_lowerCAmelCase    )    ):
    __UpperCAmelCase					=   compare_string(binary[i]       ,    binary[j]    )
    if k is False:
     __UpperCAmelCase					=   '*'
     __UpperCAmelCase					=   '*'
     temp.append('X'    )
  for i in range(len(_lowerCAmelCase    )    ):
   if checka[i] == "$":
    pi.append(binary[i]    )
  if len(_lowerCAmelCase    ) == 0:
   return pi
  __UpperCAmelCase					=   list(set(_lowerCAmelCase    )    )







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     list[str]:
 __UpperCAmelCase					=   []
 for minterm in minterms:
  __UpperCAmelCase					=   ''
  for _ in range(_lowerCAmelCase    ):
   __UpperCAmelCase					=   str(minterm % 2    ) + string
   minterm //= 2
  temp.append(_lowerCAmelCase    )
 return temp







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     bool:
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 __UpperCAmelCase					=   0
 for i in range(len(_lowerCAmelCase    )    ):
  if lista[i] != lista[i]:
   count_n += 1
 return count_n == count







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     list[str]:
 __UpperCAmelCase					=   []
 __UpperCAmelCase					=   [0] * len(_lowerCAmelCase    )
 for i in range(len(chart[0]    )    ):
  __UpperCAmelCase					=   0
  __UpperCAmelCase					=   -1
  for j in range(len(_lowerCAmelCase    )    ):
   if chart[j][i] == 1:
    count += 1
    __UpperCAmelCase					=   j
  if count == 1:
   __UpperCAmelCase					=   1
 for i in range(len(_lowerCAmelCase    )    ):
  if select[i] == 1:
   for j in range(len(chart[0]    )    ):
    if chart[i][j] == 1:
     for k in range(len(_lowerCAmelCase    )    ):
      __UpperCAmelCase					=   0
   temp.append(prime_implicants[i]    )
 while True:
  __UpperCAmelCase					=   0
  __UpperCAmelCase					=   -1
  __UpperCAmelCase					=   0
  for i in range(len(_lowerCAmelCase    )    ):
   __UpperCAmelCase					=   chart[i].count(1    )
   if count_n > max_n:
    __UpperCAmelCase					=   count_n
    __UpperCAmelCase					=   i

  if max_n == 0:
   return temp

  temp.append(prime_implicants[rem]    )

  for i in range(len(chart[0]    )    ):
   if chart[rem][i] == 1:
    for j in range(len(_lowerCAmelCase    )    ):
     __UpperCAmelCase					=   0







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     list[list[int]]:
 __UpperCAmelCase					=   [[0 for x in range(len(_lowerCAmelCase    )    )] for x in range(len(_lowerCAmelCase    )    )]
 for i in range(len(_lowerCAmelCase    )    ):
  __UpperCAmelCase					=   prime_implicants[i].count('_'    )
  for j in range(len(_lowerCAmelCase    )    ):
   if is_for_table(prime_implicants[i]       ,    binary[j]       ,    _lowerCAmelCase    ):
    __UpperCAmelCase					=   1

 return chart







def _lowerCAmelCase     (      )->     None:
 __UpperCAmelCase					=   int(input('Enter the no. of variables\n'    )    )
 __UpperCAmelCase					=   [
     float(_lowerCAmelCase    )
     for x in input(
         'Enter the decimal representation of Minterms \'Spaces Separated\'\n'    ).split()
 ]
 __UpperCAmelCase					=   decimal_to_binary(_lowerCAmelCase       ,    _lowerCAmelCase    )

 __UpperCAmelCase					=   check(_lowerCAmelCase    )
 print('Prime Implicants are:'    )
 print(_lowerCAmelCase    )
 __UpperCAmelCase					=   prime_implicant_chart(_lowerCAmelCase       ,    _lowerCAmelCase    )

 __UpperCAmelCase					=   selection(_lowerCAmelCase       ,    _lowerCAmelCase    )
 print('Essential Prime Implicants are:'    )
 print(_lowerCAmelCase    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()





