






import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

snake_case			 =						{
    """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""",
    """mask_emb""": """masked_spec_embed""",
}


def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  for attribute in key.split("." ):
    SCREAMING_SNAKE_CASE				:					str		  =			getattr(lowercase  ,   lowercase )

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

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


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					int		  =			[]
  SCREAMING_SNAKE_CASE				:					Tuple		  =			fairseq_model.state_dict()

  SCREAMING_SNAKE_CASE				:					List[str]		  =			hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    SCREAMING_SNAKE_CASE				:					Any		  =			False
    if "conv_layers" in name:
      load_conv_layer(
          lowercase  ,   lowercase  ,   lowercase  ,   lowercase  ,   hf_model.config.feat_extract_norm == "group"  ,   )
      SCREAMING_SNAKE_CASE				:					Optional[int]		  =			True
    else:
      for key, mapped_key in MAPPING.items():
        SCREAMING_SNAKE_CASE				:					Tuple		  =			mapped_key

        if key in name:
          SCREAMING_SNAKE_CASE				:					str		  =			True
          if "*" in mapped_key:
            SCREAMING_SNAKE_CASE				:					int		  =			name.split(lowercase )[0].split("." )[-2]
            SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			mapped_key.replace("*"  ,   lowercase )
          if "weight_g" in name:
            SCREAMING_SNAKE_CASE				:					Dict		  =			"weight_g"
          elif "weight_v" in name:
            SCREAMING_SNAKE_CASE				:					Dict		  =			"weight_v"
          elif "weight" in name:
            SCREAMING_SNAKE_CASE				:					Dict		  =			"weight"
          elif "bias" in name:
            SCREAMING_SNAKE_CASE				:					Dict		  =			"bias"
          else:
            SCREAMING_SNAKE_CASE				:					int		  =			None
          set_recursively(lowercase  ,   lowercase  ,   lowercase  ,   lowercase  ,   lowercase )
        continue
    if not is_used:
      unused_weights.append(lowercase )

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


def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					List[str]		  =			full_name.split("conv_layers." )[-1]
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			name.split("." )
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			int(items[0] )
  SCREAMING_SNAKE_CASE				:					Tuple		  =			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.'''
      )
      SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			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.'''
      )
      SCREAMING_SNAKE_CASE				:					List[str]		  =			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."
      )
      SCREAMING_SNAKE_CASE				:					Optional[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.'''
      )
      SCREAMING_SNAKE_CASE				:					Any		  =			value
      logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
  else:
    unused_weights.append(lowercase )


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			HubertConfig()
  SCREAMING_SNAKE_CASE				:					Any		  =			model.config

  SCREAMING_SNAKE_CASE				:					Any		  =			fs_config.activation_dropout
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			False
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			fs_config.attention_dropout
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			False
  SCREAMING_SNAKE_CASE				:					int		  =			eval(fs_config.extractor_conv_feature_layers )
  SCREAMING_SNAKE_CASE				:					List[str]		  =			[x[0] for x in conv_layers]
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			[x[1] for x in conv_layers]
  SCREAMING_SNAKE_CASE				:					int		  =			[x[2] for x in conv_layers]
  SCREAMING_SNAKE_CASE				:					Tuple		  =			"gelu"
  SCREAMING_SNAKE_CASE				:					int		  =			"layer" if fs_config.extractor_mode == "layer_norm" else "group"
  SCREAMING_SNAKE_CASE				:					List[str]		  =			False
  SCREAMING_SNAKE_CASE				:					Dict		  =			0.0
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			0.0
  SCREAMING_SNAKE_CASE				:					Any		  =			fs_config.activation_fn
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			fs_config.dropout
  SCREAMING_SNAKE_CASE				:					Any		  =			fs_config.encoder_embed_dim
  SCREAMING_SNAKE_CASE				:					Tuple		  =			0.02
  SCREAMING_SNAKE_CASE				:					Dict		  =			fs_config.encoder_ffn_embed_dim
  SCREAMING_SNAKE_CASE				:					Tuple		  =			1E-5
  SCREAMING_SNAKE_CASE				:					int		  =			0.0
  SCREAMING_SNAKE_CASE				:					Tuple		  =			fs_config.encoder_attention_heads
  SCREAMING_SNAKE_CASE				:					List[str]		  =			fs_config.conv_pos_groups
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			fs_config.conv_pos
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			len(lowercase )
  SCREAMING_SNAKE_CASE				:					Tuple		  =			fs_config.encoder_layers

  return config


@torch.no_grad()
def      lowerCamelCase__	(   lowercase  ,   lowercase=None ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			distilhubert().model.model

  if config_path is not None:
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			HubertConfig.from_pretrained(lowercase )
  else:
    SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			convert_config(lowercase )
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			model.eval()

  SCREAMING_SNAKE_CASE				:					List[Any]		  =			WavaVecaFeatureExtractor(
      feature_size=1  ,   sampling_rate=16000  ,   padding_value=0  ,   do_normalize=lowercase  ,   return_attention_mask=lowercase  ,   )
  SCREAMING_SNAKE_CASE				:					Dict		  =			HubertModel(lowercase )

  recursively_load_weights(lowercase  ,   lowercase )

  feature_extractor.save_pretrained(lowercase )
  hf_model.save_pretrained(lowercase )


if __name__ == "__main__":
   snake_case			 =						argparse.ArgumentParser()
   parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
   parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
   snake_case			 =						parser.parse_args()
   convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)














import math
from collections.abc import Generator


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Generator		  =			(i for i in range(1  ,   (max_n + 1) ))
  for i in (n for n in numbers if n > 1):
    for j in range(2  ,   lowercase ):
      if (i % j) == 0:
        break
    else:
      yield i


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Generator		  =			(i for i in range(1  ,   (max_n + 1) ))
  for i in (n for n in numbers if n > 1):
    # only need to check for factors up to sqrt(i)
    SCREAMING_SNAKE_CASE				:					str		  =			int(math.sqrt(lowercase ) ) + 1
    for j in range(2  ,   lowercase ):
      if (i % j) == 0:
        break
    else:
      yield i


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Generator		  =			(i for i in range(1  ,   (max_n + 1)  ,   2 ))
  # It's useless to test even numbers as they will not be prime
  if max_n > 2:
    yield 2  # Because 2 will not be tested, it's necessary to yield it now
  for i in (n for n in numbers if n > 1):
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			int(math.sqrt(lowercase ) ) + 1
    for j in range(3  ,   lowercase  ,   2 ):
      # As we removed the even numbers, we don't need them now
      if (i % j) == 0:
        break
    else:
      yield i


def      lowerCamelCase__	(   ):

  """simple docstring"""






  from timeit import timeit

  SCREAMING_SNAKE_CASE				:					Tuple		  =			"from __main__ import slow_primes, primes, fast_primes"
  print(timeit("slow_primes(1_000_000_000_000)"  ,   setup=lowercase  ,   number=1000000 ) )
  print(timeit("primes(1_000_000_000_000)"  ,   setup=lowercase  ,   number=1000000 ) )
  print(timeit("fast_primes(1_000_000_000_000)"  ,   setup=lowercase  ,   number=1000000 ) )


if __name__ == "__main__":
   snake_case			 =						int(input("""Calculate primes up to:\n>> """).strip())
   for ret in primes(number):
      print(ret)
   benchmark()






