

"""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()
_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''',
    '''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''',
}
_UpperCamelCase		     =    [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__					,					lowercase__					,					lowercase__					,					lowercase__   )			->						Dict:
 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
    lowerCAmelCase__   :				Optional[Any] 			=				"lm_head"

  lowerCAmelCase__   :				Dict 			=				getattr(_lowerCAmelCase					,					_lowerCAmelCase   )

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

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



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Optional[int]:
 lowerCAmelCase__   :				List[Any] 			=				[]
 lowerCAmelCase__   :				str 			=				fairseq_model.state_dict()

 lowerCAmelCase__   :				int 			=				hf_model.unispeech.feature_extractor

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

 if is_finetuned:
  if dict_path:
   lowerCAmelCase__   :				str 			=				Dictionary.load_from_json(_lowerCAmelCase   )

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   lowerCAmelCase__   :				str 			=				target_dict.pad_index
   lowerCAmelCase__   :				Dict 			=				target_dict.bos_index
   lowerCAmelCase__   :				List[Any] 			=				target_dict.eos_index
   lowerCAmelCase__   :				Dict 			=				len(target_dict.symbols   )
   lowerCAmelCase__   :				Tuple 			=				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   )
   lowerCAmelCase__   :				Any 			=				target_dict.indices

   # fairseq has the <pad> and <s> switched
   lowerCAmelCase__   :				Tuple 			=				4_2
   lowerCAmelCase__   :				Dict 			=				4_3
   with open(_lowerCAmelCase					,					"w"					,					encoding="utf-8"   ) as vocab_handle:
    json.dump(_lowerCAmelCase					,					_lowerCAmelCase   )
   lowerCAmelCase__   :				Union[str, Any] 			=				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					,					)
   lowerCAmelCase__   :				Dict 			=				True if config.feat_extract_norm == "layer" else False
   lowerCAmelCase__   :				Optional[int] 			=				WavaVecaFeatureExtractor(
       feature_size=1					,					sampling_rate=1_6_0_0_0					,					padding_value=0					,					do_normalize=_lowerCAmelCase					,					return_attention_mask=_lowerCAmelCase					,					)
   lowerCAmelCase__   :				Tuple 			=				WavaVecaProcessor(feature_extractor=_lowerCAmelCase					,					tokenizer=_lowerCAmelCase   )
   processor.save_pretrained(_lowerCAmelCase   )

  lowerCAmelCase__   :				int 			=				UniSpeechForCTC(_lowerCAmelCase   )
 else:
  lowerCAmelCase__   :				Any 			=				UniSpeechForPreTraining(_lowerCAmelCase   )

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

 lowerCAmelCase__   :				List[str] 			=				model[0].eval()

 recursively_load_weights(_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase   )

 hf_unispeech.save_pretrained(_lowerCAmelCase   )


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













from math import sqrt

def   __snake_case    (       _lowerCAmelCase			:			int						)			->    bool:

   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   __snake_case    (       _lowerCAmelCase			:			int = 10001						)			->    int:
   A_       :					Any        =      0
   A_       :					Tuple        =      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() = }''')
