
"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
A__      :	Dict          =    logging.get_logger(__name__)

A__      :	str          =    {
    """post_extract_proj""": """feature_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.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}



def 							a__ (				lowerCAmelCase	:       Tuple       ,    lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       List[str]       ,    lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       Tuple		):






					'''simple docstring'''
					for attribute in key.split("."		):
										UpperCAmelCase__							: int 							=						getattr(lowerCAmelCase       ,    lowerCAmelCase		)

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

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



def 							a__ (				lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       Optional[int]       ,    lowerCAmelCase	:       List[Any]		):






					'''simple docstring'''
					UpperCAmelCase__							: Dict 							=						[]
					UpperCAmelCase__							: Any 							=						fairseq_model.state_dict()

					UpperCAmelCase__							: List[str] 							=						hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

					for name, value in fairseq_dict.items():
										UpperCAmelCase__							: Union[str, Any] 							=						False
										if "conv_layers" in name:
															load_conv_layer(
															    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    hf_model.config.feat_extract_norm == "group"       ,    )
															UpperCAmelCase__							: List[str] 							=						True
										else:
															for key, mapped_key in MAPPING.items():
																				UpperCAmelCase__							: str 							=						"""sew.""" + 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]:
																									UpperCAmelCase__							: Union[str, Any] 							=						True
																									if "*" in mapped_key:
																														UpperCAmelCase__							: Optional[Any] 							=						name.split(lowerCAmelCase		)[0].split("."		)[-2]
																														UpperCAmelCase__							: int 							=						mapped_key.replace("*"       ,    lowerCAmelCase		)
																									if "weight_g" in name:
																														UpperCAmelCase__							: Optional[int] 							=						"""weight_g"""
																									elif "weight_v" in name:
																														UpperCAmelCase__							: List[Any] 							=						"""weight_v"""
																									elif "weight" in name:
																														UpperCAmelCase__							: List[str] 							=						"""weight"""
																									elif "bias" in name:
																														UpperCAmelCase__							: Optional[int] 							=						"""bias"""
																									else:
																														UpperCAmelCase__							: List[Any] 							=						None
																									set_recursively(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase		)
																				continue
										if not is_used:
															unused_weights.append(lowerCAmelCase		)

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



def 							a__ (				lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       str		):






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



def 							a__ (				lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       List[str]		):






					'''simple docstring'''
					UpperCAmelCase__							: Dict 							=						SEWConfig()
					if is_finetuned:
										UpperCAmelCase__							: Union[str, Any] 							=						model.wav_encoder.wav_model.cfg
					else:
										UpperCAmelCase__							: Tuple 							=						model.cfg

					UpperCAmelCase__							: List[str] 							=						fs_config.conv_bias
					UpperCAmelCase__							: List[Any] 							=						eval(fs_config.conv_feature_layers		)
					UpperCAmelCase__							: Optional[Any] 							=						[x[0] for x in conv_layers]
					UpperCAmelCase__							: Dict 							=						[x[1] for x in conv_layers]
					UpperCAmelCase__							: List[str] 							=						[x[2] for x in conv_layers]
					UpperCAmelCase__							: str 							=						"""gelu"""
					UpperCAmelCase__							: Tuple 							=						"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
					UpperCAmelCase__							: int 							=						0.0
					UpperCAmelCase__							: Union[str, Any] 							=						fs_config.activation_fn.name
					UpperCAmelCase__							: str 							=						fs_config.encoder_embed_dim
					UpperCAmelCase__							: Union[str, Any] 							=						0.02
					UpperCAmelCase__							: str 							=						fs_config.encoder_ffn_embed_dim
					UpperCAmelCase__							: List[str] 							=						1E-5
					UpperCAmelCase__							: List[Any] 							=						fs_config.encoder_layerdrop
					UpperCAmelCase__							: Union[str, Any] 							=						fs_config.encoder_attention_heads
					UpperCAmelCase__							: Any 							=						fs_config.conv_pos_groups
					UpperCAmelCase__							: Optional[Any] 							=						fs_config.conv_pos
					UpperCAmelCase__							: List[str] 							=						len(lowerCAmelCase		)
					UpperCAmelCase__							: int 							=						fs_config.encoder_layers
					UpperCAmelCase__							: List[Any] 							=						fs_config.squeeze_factor

					# take care of any params that are overridden by the Wav2VecCtc model
					if is_finetuned:
										UpperCAmelCase__							: int 							=						model.cfg
										UpperCAmelCase__							: Union[str, Any] 							=						fs_config.final_dropout
										UpperCAmelCase__							: Tuple 							=						fs_config.layerdrop
					UpperCAmelCase__							: Dict 							=						fs_config.activation_dropout
					UpperCAmelCase__							: Union[str, Any] 							=						fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
					UpperCAmelCase__							: str 							=						fs_config.attention_dropout
					UpperCAmelCase__							: Any 							=						fs_config.dropout_input
					UpperCAmelCase__							: Dict 							=						fs_config.dropout
					UpperCAmelCase__							: str 							=						fs_config.mask_channel_length
					UpperCAmelCase__							: Any 							=						fs_config.mask_channel_prob
					UpperCAmelCase__							: List[Any] 							=						fs_config.mask_length
					UpperCAmelCase__							: Dict 							=						fs_config.mask_prob

					UpperCAmelCase__							: List[str] 							=						"""Wav2Vec2FeatureExtractor"""
					UpperCAmelCase__							: Tuple 							=						"""Wav2Vec2CTCTokenizer"""

					return config



@torch.no_grad()
def 							a__ (				lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       Tuple=None       ,    lowerCAmelCase	:       List[str]=None       ,    lowerCAmelCase	:       Optional[Any]=True		):






					'''simple docstring'''

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

					if config_path is not None:
										UpperCAmelCase__							: int 							=						SEWConfig.from_pretrained(lowerCAmelCase		)
					else:
										UpperCAmelCase__							: Dict 							=						convert_config(model[0]       ,    lowerCAmelCase		)
					UpperCAmelCase__							: Any 							=						model[0].eval()

					UpperCAmelCase__							: Optional[int] 							=						True if config.feat_extract_norm == """layer""" else False
					UpperCAmelCase__							: Optional[int] 							=						WavaVecaFeatureExtractor(
					    feature_size=1       ,    sampling_rate=1_6000       ,    padding_value=0       ,    do_normalize=lowerCAmelCase       ,    return_attention_mask=lowerCAmelCase       ,    )

					if is_finetuned:
										if dict_path:
															UpperCAmelCase__							: int 							=						Dictionary.load(lowerCAmelCase		)

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															UpperCAmelCase__							: Optional[Any] 							=						target_dict.pad_index
															UpperCAmelCase__							: Any 							=						target_dict.bos_index
															UpperCAmelCase__							: List[str] 							=						target_dict.pad_index
															UpperCAmelCase__							: List[str] 							=						target_dict.bos_index
															UpperCAmelCase__							: str 							=						target_dict.eos_index
															UpperCAmelCase__							: Optional[Any] 							=						len(target_dict.symbols		)
															UpperCAmelCase__							: int 							=						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		)
															with open(lowerCAmelCase       ,    "w"       ,    encoding="utf-8"		) as vocab_handle:
																				json.dump(target_dict.indices       ,    lowerCAmelCase		)
															UpperCAmelCase__							: Tuple 							=						WavaVecaCTCTokenizer(
															    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       ,    )
															UpperCAmelCase__							: Any 							=						WavaVecaProcessor(feature_extractor=lowerCAmelCase       ,    tokenizer=lowerCAmelCase		)
															processor.save_pretrained(lowerCAmelCase		)

										UpperCAmelCase__							: Optional[int] 							=						SEWForCTC(lowerCAmelCase		)
					else:
										UpperCAmelCase__							: int 							=						SEWModel(lowerCAmelCase		)
										feature_extractor.save_pretrained(lowerCAmelCase		)

					recursively_load_weights(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase		)

					hf_model.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
				A__      :	int          =    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(
				    """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
				)
				A__      :	Optional[Any]          =    parser.parse_args()
				convert_sew_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
				)





"""simple docstring"""




from math import isqrt



def 							a__ (				lowerCAmelCase	:       int		):






     '''simple docstring'''
     UpperCAmelCase__							: Tuple 							=						[True] * max_number
     for i in range(2       ,    isqrt(max_number - 1		) + 1		):
          if is_prime[i]:
               for j in range(i**2       ,    lowerCAmelCase       ,    lowerCAmelCase		):
                    UpperCAmelCase__							: List[Any] 							=						False

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



def 							a__ (				lowerCAmelCase	:       int = 10**8		):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						calculate_prime_numbers(max_number // 2		)

     UpperCAmelCase__							: Optional[int] 							=						0
     UpperCAmelCase__							: Optional[int] 							=						0
     UpperCAmelCase__							: Tuple 							=						len(lowerCAmelCase		) - 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() = }""")



