





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()
_lowercase		:  Optional[int]			       =logging.get_logger(__name__)

_lowercase		:  List[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.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""",
}
_lowercase		:  Dict			       =[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		for attribute in key.split('.'						):
				lowerCamelCase_	:     str 						=      getattr(lowerCAmelCase__							,lowerCAmelCase__						)

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

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


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		lowerCamelCase_	:     str 						=      []
		lowerCamelCase_	:     Optional[Any] 						=      fairseq_model.state_dict()

		lowerCamelCase_	:     Optional[int] 						=      hf_model.feature_extractor

		for name, value in fairseq_dict.items():
				lowerCamelCase_	:     Union[str, Any] 						=      False
				if "conv_layers" in name:
						load_conv_layer(
						    lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,hf_model.config.feat_extract_norm == 'group'							,)
						lowerCamelCase_	:     Tuple 						=      True
				else:
						for key, mapped_key in MAPPING.items():
								if key in name or key.split('w2v_model.'						)[-1] == name.split('.'						)[0]:
										lowerCamelCase_	:     List[Any] 						=      True
										if "*" in mapped_key:
												lowerCamelCase_	:     List[Any] 						=      name.split(lowerCAmelCase__						)[0].split('.'						)[-2]
												lowerCamelCase_	:     Tuple 						=      mapped_key.replace('*'							,lowerCAmelCase__						)
										if "weight_g" in name:
												lowerCamelCase_	:     List[str] 						=      'weight_g'
										elif "weight_v" in name:
												lowerCamelCase_	:     Optional[Any] 						=      'weight_v'
										elif "bias" in name and "relative_attention_bias" not in name:
												lowerCamelCase_	:     List[str] 						=      'bias'
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												lowerCamelCase_	:     List[str] 						=      'weight'
										else:
												lowerCamelCase_	:     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 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		lowerCamelCase_	:     int 						=      full_name.split('conv_layers.'						)[-1]
		lowerCamelCase_	:     List[str] 						=      name.split('.'						)
		lowerCamelCase_	:     Dict 						=      int(items[0]						)
		lowerCamelCase_	:     Optional[Any] 						=      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."
						)
						lowerCamelCase_	:     Union[str, 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."
						)
						lowerCamelCase_	:     Optional[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."
						)
						lowerCamelCase_	:     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."
						)
						lowerCamelCase_	:     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__						)


@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__=None						):
		# load the pre-trained checkpoints
		lowerCamelCase_	:     Any 						=      torch.load(lowerCAmelCase__						)
		lowerCamelCase_	:     str 						=      WavLMConfigOrig(checkpoint['cfg']						)
		lowerCamelCase_	:     List[Any] 						=      WavLMOrig(lowerCAmelCase__						)
		model.load_state_dict(checkpoint['model']						)
		model.eval()

		if config_path is not None:
				lowerCamelCase_	:     int 						=      WavLMConfig.from_pretrained(lowerCAmelCase__						)
		else:
				lowerCamelCase_	:     str 						=      WavLMConfig()

		lowerCamelCase_	:     str 						=      WavLMModel(lowerCAmelCase__						)

		recursively_load_weights(lowerCAmelCase__							,lowerCAmelCase__						)

		hf_wavlm.save_pretrained(lowerCAmelCase__						)


if __name__ == "__main__":
		_lowercase		:  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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
		_lowercase		:  Tuple			       =parser.parse_args()
		convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)







from __future__ import annotations

import math


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):

		if num <= 0:
				lowerCamelCase_	:     Optional[int] 						=      F"{num}: Invalid input, please enter a positive integer."
				raise ValueError(lowerCAmelCase__						)

		lowerCamelCase_	:     str 						=      [True] * (num + 1)
		lowerCamelCase_	:     List[str] 						=      []
		lowerCamelCase_	:     Optional[int] 						=      2
		lowerCamelCase_	:     List[str] 						=      int(math.sqrt(lowerCAmelCase__						)						)

		while start <= end:
				# If start is a prime
				if sieve[start] is True:
						prime.append(lowerCAmelCase__						)

						# Set multiples of start be False
						for i in range(start * start							,num + 1							,lowerCAmelCase__						):
								if sieve[i] is True:
										lowerCamelCase_	:     Tuple 						=      False

				start += 1

		for j in range(end + 1							,num + 1						):
				if sieve[j] is True:
						prime.append(lowerCAmelCase__						)

		return prime


if __name__ == "__main__":
		print(prime_sieve(int(input("""Enter a positive integer: """).strip())))
