



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

_lowerCamelCase						    =  {
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
    """self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
    """self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
    """self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
    """self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
    """self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
    """self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
    """self_attn.rotary_emb""": """encoder.embed_positions""",
    """self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
    """conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
    """conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
    """conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
    """conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
    """conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
    """ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
    """ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
    """ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
    """ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
    """ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
    """ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
    """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""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
_lowerCamelCase						    =  [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]






def 	_lowerCAmelCase						(				__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     List[Any]		,	__lowerCamelCase   :     str		,	__lowerCamelCase   :     Optional[int]		,	__lowerCamelCase   :     int     ):



 """simple docstring"""





 for attribute in key.split("."     ):
  __SCREAMING_SNAKE_CASE       :    Dict       =	getattr(__SCREAMING_SNAKE_CASE		,	__SCREAMING_SNAKE_CASE     )

 if weight_type is not None:
  __SCREAMING_SNAKE_CASE       :    str       =	getattr(__SCREAMING_SNAKE_CASE		,	__SCREAMING_SNAKE_CASE     ).shape
 else:
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	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":
  __SCREAMING_SNAKE_CASE       :    Dict       =	value
 elif weight_type == "weight_g":
  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	value
 elif weight_type == "weight_v":
  __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	value
 elif weight_type == "bias":
  __SCREAMING_SNAKE_CASE       :    Dict       =	value
 elif weight_type == "running_mean":
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	value
 elif weight_type == "running_var":
  __SCREAMING_SNAKE_CASE       :    int       =	value
 elif weight_type == "num_batches_tracked":
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	value
 elif weight_type == "inv_freq":
  __SCREAMING_SNAKE_CASE       :    Any       =	value
 else:
  __SCREAMING_SNAKE_CASE       :    Dict       =	value

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






def 	_lowerCAmelCase						(				__lowerCamelCase   :     List[Any]		,	__lowerCamelCase   :     Any		,	__lowerCamelCase   :     Union[str, Any]     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    List[Any]       =	[]
 __SCREAMING_SNAKE_CASE       :    List[str]       =	fairseq_model.state_dict()

 __SCREAMING_SNAKE_CASE       :    List[Any]       =	hf_model.wavaveca_conformer.feature_extractor

 for name, value in fairseq_dict.items():
  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	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"		,	)
   __SCREAMING_SNAKE_CASE       :    List[Any]       =	True
  else:
   for key, mapped_key in MAPPING.items():
    __SCREAMING_SNAKE_CASE       :    Dict       =	"wav2vec2_conformer." + 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]:
     __SCREAMING_SNAKE_CASE       :    Tuple       =	True
     if "*" in mapped_key:
      __SCREAMING_SNAKE_CASE       :    str       =	name.split(__SCREAMING_SNAKE_CASE     )[0].split("."     )[-2]
      __SCREAMING_SNAKE_CASE       :    Any       =	mapped_key.replace("*"		,	__SCREAMING_SNAKE_CASE     )
     if "pos_bias_u" in name:
      __SCREAMING_SNAKE_CASE       :    int       =	None
     elif "pos_bias_v" in name:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	None
     elif "weight_g" in name:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	"weight_g"
     elif "weight_v" in name:
      __SCREAMING_SNAKE_CASE       :    int       =	"weight_v"
     elif "bias" in name:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	"bias"
     elif "weight" in name:
      # TODO: don't match quantizer.weight_proj
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	"weight"
     elif "running_mean" in name:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	"running_mean"
     elif "inv_freq" in name:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	"inv_freq"
     elif "running_var" in name:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	"running_var"
     elif "num_batches_tracked" in name:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	"num_batches_tracked"
     else:
      __SCREAMING_SNAKE_CASE       :    str       =	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						(				__lowerCamelCase   :     Optional[Any]		,	__lowerCamelCase   :     Tuple		,	__lowerCamelCase   :     Dict		,	__lowerCamelCase   :     List[Any]		,	__lowerCamelCase   :     Tuple     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Any       =	full_name.split("conv_layers."     )[-1]
 __SCREAMING_SNAKE_CASE       :    List[str]       =	name.split("."     )
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	int(items[0]     )
 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	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."""     )
   __SCREAMING_SNAKE_CASE       :    List[str]       =	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."""     )
   __SCREAMING_SNAKE_CASE       :    Any       =	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.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""     )
   __SCREAMING_SNAKE_CASE       :    Dict       =	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.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""     )
   __SCREAMING_SNAKE_CASE       :    Tuple       =	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						(				__lowerCamelCase   :     Any		,	__lowerCamelCase   :     Optional[Any]		,	__lowerCamelCase   :     List[str]=None		,	__lowerCamelCase   :     Any=None		,	__lowerCamelCase   :     Optional[Any]=True     ):



 """simple docstring"""





 if config_path is not None:
  __SCREAMING_SNAKE_CASE       :    Dict       =	WavaVecaConformerConfig.from_pretrained(__SCREAMING_SNAKE_CASE		,	hidden_act="swish"     )
 else:
  __SCREAMING_SNAKE_CASE       :    List[str]       =	WavaVecaConformerConfig()

 if "rope" in checkpoint_path:
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	"rotary"

 if is_finetuned:
  if dict_path:
   __SCREAMING_SNAKE_CASE       :    Dict       =	Dictionary.load(__SCREAMING_SNAKE_CASE     )

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   __SCREAMING_SNAKE_CASE       :    List[Any]       =	target_dict.pad_index
   __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	target_dict.bos_index
   __SCREAMING_SNAKE_CASE       :    str       =	target_dict.eos_index
   __SCREAMING_SNAKE_CASE       :    int       =	len(target_dict.symbols     )
   __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	os.path.join(__SCREAMING_SNAKE_CASE		,	"vocab.json"     )
   if not os.path.isdir(__SCREAMING_SNAKE_CASE     ):
    logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(__SCREAMING_SNAKE_CASE     )     )
    return
   os.makedirs(__SCREAMING_SNAKE_CASE		,	exist_ok=__SCREAMING_SNAKE_CASE     )
   __SCREAMING_SNAKE_CASE       :    Tuple       =	target_dict.indices

   # fairseq has the <pad> and <s> switched
   __SCREAMING_SNAKE_CASE       :    Optional[int]       =	0
   __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	1
   with open(__SCREAMING_SNAKE_CASE		,	"w"		,	encoding="utf-8"     ) as vocab_handle:
    json.dump(__SCREAMING_SNAKE_CASE		,	__SCREAMING_SNAKE_CASE     )
   __SCREAMING_SNAKE_CASE       :    str       =	WavaVecaCTCTokenizer(
       __SCREAMING_SNAKE_CASE		,	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=__SCREAMING_SNAKE_CASE		,	)
   __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	True if config.feat_extract_norm == "layer" else False
   __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	WavaVecaFeatureExtractor(
       feature_size=1		,	sampling_rate=16000		,	padding_value=0		,	do_normalize=__SCREAMING_SNAKE_CASE		,	return_attention_mask=__SCREAMING_SNAKE_CASE		,	)
   __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	WavaVecaProcessor(feature_extractor=__SCREAMING_SNAKE_CASE		,	tokenizer=__SCREAMING_SNAKE_CASE     )
   processor.save_pretrained(__SCREAMING_SNAKE_CASE     )

  __SCREAMING_SNAKE_CASE       :    Tuple       =	WavaVecaConformerForCTC(__SCREAMING_SNAKE_CASE     )
 else:
  __SCREAMING_SNAKE_CASE       :    Tuple       =	WavaVecaConformerForPreTraining(__SCREAMING_SNAKE_CASE     )

 if is_finetuned:
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]		,	arg_overrides={"data": "/".join(dict_path.split("/"     )[:-1]     )}     )
 else:
  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	argparse.Namespace(task="audio_pretraining"     )
  __SCREAMING_SNAKE_CASE       :    List[str]       =	fairseq.tasks.setup_task(__SCREAMING_SNAKE_CASE     )

  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		,	task=__SCREAMING_SNAKE_CASE     )

 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	model[0].eval()

 recursively_load_weights(__SCREAMING_SNAKE_CASE		,	__SCREAMING_SNAKE_CASE		,	not is_finetuned     )

 hf_wavavec.save_pretrained(__SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
      _lowerCamelCase						    =  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						    =  parser.parse_args()
      convert_wavaveca_conformer_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )











from math import sqrt






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int     ):



 """simple docstring"""





 if 1 < number < 4:
  # 2 and 3 are primes
  return True
 elif number < 2 or number % 2 == 0 or number % 3 == 0:
  # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
  return False

 # All primes number are in format of 6k +/- 1
 for i in range(5		,	int(sqrt(__lowerCamelCase     ) + 1     )		,	6     ):
  if number % i == 0 or number % (i + 2) == 0:
   return False
 return True






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int = 10001     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    List[Any]       =	0
 __SCREAMING_SNAKE_CASE       :    Dict       =	1
 while count != nth and number < 3:
  number += 1
  if is_prime(__lowerCamelCase     ):
   count += 1
 while count != nth:
  number += 2
  if is_prime(__lowerCamelCase     ):
   count += 1
 return number


if __name__ == "__main__":
      print(f'''{solution() = }''')






