"""simple docstring"""



import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, 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.grep_linear": "encoder.layers.*.attention.gru_rel_pos_linear",
    "self_attn.relative_attention_bias": "encoder.layers.*.attention.rel_attn_embed",
    "self_attn.grep_a": "encoder.layers.*.attention.gru_rel_pos_const",
    "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  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							for attribute in key.split("""."""							):
														A__       :     Any								=			getattr(lowerCAmelCase			,  lowerCAmelCase							)

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

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

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     List[Any]								=			[]
							A__       :     Tuple								=			fairseq_model.state_dict()

							A__       :     Dict								=			hf_model.feature_extractor

							for name, value in fairseq_dict.items():
														A__       :     Optional[int]								=			False
														if "conv_layers" in name:
																					load_conv_layer(
																					    lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  hf_model.config.feat_extract_norm == """group"""			,  )
																					A__       :     Union[str, Any]								=			True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split("""w2v_model."""							)[-1] == name.split("""."""							)[0]:
																																			A__       :     List[str]								=			True
																																			if "*" in mapped_key:
																																										A__       :     Optional[Any]								=			name.split(lowerCAmelCase							)[0].split("""."""							)[-2]
																																										A__       :     str								=			mapped_key.replace("""*"""			,  lowerCAmelCase							)
																																			if "weight_g" in name:
																																										A__       :     Dict								=			"""weight_g"""
																																			elif "weight_v" in name:
																																										A__       :     Union[str, Any]								=			"""weight_v"""
																																			elif "bias" in name and "relative_attention_bias" not in name:
																																										A__       :     List[str]								=			"""bias"""
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										A__       :     int								=			"""weight"""
																																			else:
																																										A__       :     int								=			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			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Dict								=			full_name.split("""conv_layers."""							)[-1]
							A__       :     int								=			name.split("""."""							)
							A__       :     List[Any]								=			int(items[0]							)
							A__       :     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.'''
																					)
																					A__       :     Optional[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.'''
																					)
																					A__       :     int								=			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."
																					)
																					A__       :     Tuple								=			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.'''
																					)
																					A__       :     int								=			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  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase=None							):
							# load the pre-trained checkpoints
							A__       :     Union[str, Any]								=			torch.load(lowerCAmelCase							)
							A__       :     int								=			WavLMConfigOrig(checkpoint["""cfg"""]							)
							A__       :     int								=			WavLMOrig(lowerCAmelCase							)
							model.load_state_dict(checkpoint["""model"""]							)
							model.eval()

							if config_path is not None:
														A__       :     Tuple								=			WavLMConfig.from_pretrained(lowerCAmelCase							)
							else:
														A__       :     Tuple								=			WavLMConfig()

							A__       :     Any								=			WavLMModel(lowerCAmelCase							)

							recursively_load_weights(lowerCAmelCase			,  lowerCAmelCase							)

							hf_wavlm.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("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
				_UpperCamelCase								=		parser.parse_args()
				convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)






"""simple docstring"""



import math
from collections.abc import Iterator
from itertools import takewhile

def  _A(		lowerCAmelCase							):

							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(math.sqrt(lowerCAmelCase							) + 1							)			,  6							):
														if number % i == 0 or number % (i + 2) == 0:
																					return False
							return True

def  _A(		):
							A__       :     List[str]								=			2
							while True:
														if is_prime(lowerCAmelCase							):
																					yield num
														num += 1

def  _A(		lowerCAmelCase = 200_0000							):
							return sum(takewhile(lambda lowerCAmelCase							: x < n			,  prime_generator()							)							)


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





