
import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

_A  					= {
    "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 						lowercase_			(   A__ ,		A__ ,		A__ ,		A__ ,		A__		)							->							List[Any]:
    """simple docstring"""

    for attribute in key.split("."		):
        snake_case      =					getattr(A__ ,		A__		)

    if weight_type is not None:
        snake_case      =					getattr(A__ ,		A__		).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 						lowercase_			(   A__ ,		A__		)							->							Tuple:
    """simple docstring"""

    snake_case      =					[]
    snake_case      =					fairseq_model.state_dict()

    snake_case      =					hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        snake_case      =					False
        if "conv_layers" in name:
            load_conv_layer(
                A__ ,		A__ ,		A__ ,		A__ ,		hf_model.config.feat_extract_norm == "group" ,		)
            snake_case      =					True
        else:
            for key, mapped_key in MAPPING.items():
                snake_case      =					mapped_key

                if key in name:
                    snake_case      =					True
                    if "*" in mapped_key:
                        snake_case      =					name.split(A__		)[0].split("."		)[-2]
                        snake_case      =					mapped_key.replace("*" ,		A__		)
                    if "weight_g" in name:
                        snake_case      =					"weight_g"
                    elif "weight_v" in name:
                        snake_case      =					"weight_v"
                    elif "weight" in name:
                        snake_case      =					"weight"
                    elif "bias" in name:
                        snake_case      =					"bias"
                    else:
                        snake_case      =					None
                    set_recursively(A__ ,		A__ ,		A__ ,		A__ ,		A__		)
                continue
        if not is_used:
            unused_weights.append(A__		)

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



def 						lowercase_			(   A__ ,		A__ ,		A__ ,		A__ ,		A__		)							->							List[str]:
    """simple docstring"""

    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(A__		)



def 						lowercase_			(   A__		)							->							Optional[Any]:
    """simple docstring"""

    snake_case      =					HubertConfig()
    snake_case      =					model.config

    snake_case      =					fs_config.activation_dropout
    snake_case      =					False
    snake_case      =					fs_config.attention_dropout
    snake_case      =					False
    snake_case      =					eval(fs_config.extractor_conv_feature_layers		)
    snake_case      =					[x[0] for x in conv_layers]
    snake_case      =					[x[1] for x in conv_layers]
    snake_case      =					[x[2] for x in conv_layers]
    snake_case      =					"gelu"
    snake_case      =					"layer" if fs_config.extractor_mode == "layer_norm" else "group"
    snake_case      =					False
    snake_case      =					0.0
    snake_case      =					0.0
    snake_case      =					fs_config.activation_fn
    snake_case      =					fs_config.dropout
    snake_case      =					fs_config.encoder_embed_dim
    snake_case      =					0.02
    snake_case      =					fs_config.encoder_ffn_embed_dim
    snake_case      =					1e-5
    snake_case      =					0.0
    snake_case      =					fs_config.encoder_attention_heads
    snake_case      =					fs_config.conv_pos_groups
    snake_case      =					fs_config.conv_pos
    snake_case      =					len(A__		)
    snake_case      =					fs_config.encoder_layers

    return config



@torch.no_grad()
def 						lowercase_			(   A__ ,		A__=None		)							->							Optional[int]:
    """simple docstring"""

    snake_case      =					distilhubert().model.model

    if config_path is not None:
        snake_case      =					HubertConfig.from_pretrained(A__		)
    else:
        snake_case      =					convert_config(A__		)
    snake_case      =					model.eval()

    snake_case      =					WavaVecaFeatureExtractor(
        feature_size=1 ,		sampling_rate=1_6000 ,		padding_value=0 ,		do_normalize=A__ ,		return_attention_mask=A__ ,		)
    snake_case      =					HubertModel(A__		)

    recursively_load_weights(A__ ,		A__		)

    feature_extractor.save_pretrained(A__		)
    hf_model.save_pretrained(A__		)


if __name__ == "__main__":
   _A  					= 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")
   _A  					= 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()






