
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_snake_case      :							List[Any]     	=    logging.get_logger(__name__)

_snake_case      :							str     	=    {
    "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",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}

def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


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

				if weight_type is not None:
								lowercase         =      getattr(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__						).shape
				else:
								lowercase         =      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":
								lowercase         =      value
				elif weight_type == "weight_g":
								lowercase         =      value
				elif weight_type == "weight_v":
								lowercase         =      value
				elif weight_type == "bias":
								lowercase         =      value
				else:
								lowercase         =      value

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

def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


				lowercase         =      []
				lowercase         =      fairseq_model.state_dict()

				lowercase         =      hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								lowercase         =      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"		,    )
												lowercase         =      True
								else:
												for key, mapped_key in MAPPING.items():
																lowercase         =      'hubert.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

																if key in name or (key.split("w2v_model."						)[-1] == name.split("."						)[0] and not is_finetuned):
																				lowercase         =      True
																				if "*" in mapped_key:
																								lowercase         =      name.split(SCREAMING_SNAKE_CASE__						)[0].split("."						)[-2]
																								lowercase         =      mapped_key.replace("*"		,    SCREAMING_SNAKE_CASE__						)
																				if "weight_g" in name:
																								lowercase         =      'weight_g'
																				elif "weight_v" in name:
																								lowercase         =      'weight_v'
																				elif "weight" in name:
																								lowercase         =      'weight'
																				elif "bias" in name:
																								lowercase         =      'bias'
																				else:
																								lowercase         =      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  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


				lowercase         =      full_name.split("conv_layers."						)[-1]
				lowercase         =      name.split("."						)
				lowercase         =      int(items[0]						)
				lowercase         =      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.'''
												)
												lowercase         =      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.'''
												)
												lowercase         =      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."
												)
												lowercase         =      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.'''
												)
												lowercase         =      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  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__=None		,    __magic_name__=None		,    __magic_name__=True						):







				'''simple docstring'''


				if config_path is not None:
								lowercase         =      HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE__						)
				else:
								lowercase         =      HubertConfig()

				if is_finetuned:
								if dict_path:
												lowercase         =      Dictionary.load(SCREAMING_SNAKE_CASE__						)

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												lowercase         =      target_dict.pad_index
												lowercase         =      target_dict.bos_index
												lowercase         =      target_dict.eos_index
												lowercase         =      len(target_dict.symbols						)
												lowercase         =      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__						)
												with open(SCREAMING_SNAKE_CASE__		,    "w"		,    encoding="utf-8"						) as vocab_handle:
																json.dump(target_dict.indices		,    SCREAMING_SNAKE_CASE__						)
												lowercase         =      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__		,    )
												lowercase         =      True if config.feat_extract_norm == 'layer' else False
												lowercase         =      WavaVecaFeatureExtractor(
												    feature_size=1		,    sampling_rate=16000		,    padding_value=0		,    do_normalize=SCREAMING_SNAKE_CASE__		,    return_attention_mask=SCREAMING_SNAKE_CASE__		,    )
												lowercase         =      WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__		,    tokenizer=SCREAMING_SNAKE_CASE__						)
												processor.save_pretrained(SCREAMING_SNAKE_CASE__						)

								lowercase         =      HubertForCTC(SCREAMING_SNAKE_CASE__						)
				else:
								lowercase         =      HubertModel(SCREAMING_SNAKE_CASE__						)

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

				lowercase         =      model[0].eval()

				recursively_load_weights(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__						)

				hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__						)


if __name__ == "__main__":
						_snake_case      :							Tuple     	=    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"
						)
						_snake_case      :							Union[str, Any]     	=    parser.parse_args()
						convert_hubert_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
						)




from math import isqrt




def       a_ (       SCREAMING_SNAKE_CASE__   : int	):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =[True] * max_number
       for i in range(2						,     isqrt(max_number - 1	) + 1	):
              if is_prime[i]:
                     for j in range(i**2						,     SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	):
                            _lowerCamelCase			:						str            =False

       return [i for i in range(2						,     SCREAMING_SNAKE_CASE__	) if is_prime[i]]




def       a_ (       SCREAMING_SNAKE_CASE__   : int = 10**8	):
       '''simple docstring'''





       _lowerCamelCase			:						Union[str, Any]            =calculate_prime_numbers(max_number // 2	)

       _lowerCamelCase			:						Dict            =0
       _lowerCamelCase			:						int            =0
       _lowerCamelCase			:						Optional[Any]            =len(SCREAMING_SNAKE_CASE__	) - 1
       while left <= right:
              while prime_numbers[left] * prime_numbers[right] >= max_number:
                     right -= 1
              semiprimes_count += right - left + 1
              left += 1

       return semiprimes_count


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


