


"""simple docstring"""







import itertools
import math




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					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      UpperCAmelCase__ ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       2
					while True:
										if is_prime(lowerCAmelCase_     ):
															yield num
										num += 1




def      UpperCAmelCase__ (lowerCAmelCase_ = 1_0001     ):




					'''simple docstring'''


					return next(itertools.islice(prime_generator()						,		nth - 1						,		lowerCAmelCase_     )     )


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




"""simple docstring"""







import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
a__       :							Union[str, Any]        =  logging.get_logger('''transformers.models.encodec''')

a__       :							Optional[Any]        =  {
    '''quantizer.vq.layers.*._codebook.inited''': '''quantizer.layers.*.codebook.inited''',
    '''quantizer.vq.layers.*._codebook.cluster_size''': '''quantizer.layers.*.codebook.cluster_size''',
    '''quantizer.vq.layers.*._codebook.embed''': '''quantizer.layers.*.codebook.embed''',
    '''quantizer.vq.layers.*._codebook.embed_avg''': '''quantizer.layers.*.codebook.embed_avg''',
}
a__       :							int        =  {
    '''encoder.model.0.conv.conv''': '''encoder.layers.0.conv''',
    '''encoder.model.1.block.1.conv.conv''': '''encoder.layers.1.block.1.conv''',
    '''encoder.model.1.block.3.conv.conv''': '''encoder.layers.1.block.3.conv''',
    '''encoder.model.1.shortcut.conv.conv''': '''encoder.layers.1.shortcut.conv''',
    '''encoder.model.3.conv.conv''': '''encoder.layers.3.conv''',
    '''encoder.model.4.block.1.conv.conv''': '''encoder.layers.4.block.1.conv''',
    '''encoder.model.4.block.3.conv.conv''': '''encoder.layers.4.block.3.conv''',
    '''encoder.model.4.shortcut.conv.conv''': '''encoder.layers.4.shortcut.conv''',
    '''encoder.model.6.conv.conv''': '''encoder.layers.6.conv''',
    '''encoder.model.7.block.1.conv.conv''': '''encoder.layers.7.block.1.conv''',
    '''encoder.model.7.block.3.conv.conv''': '''encoder.layers.7.block.3.conv''',
    '''encoder.model.7.shortcut.conv.conv''': '''encoder.layers.7.shortcut.conv''',
    '''encoder.model.9.conv.conv''': '''encoder.layers.9.conv''',
    '''encoder.model.10.block.1.conv.conv''': '''encoder.layers.10.block.1.conv''',
    '''encoder.model.10.block.3.conv.conv''': '''encoder.layers.10.block.3.conv''',
    '''encoder.model.10.shortcut.conv.conv''': '''encoder.layers.10.shortcut.conv''',
    '''encoder.model.12.conv.conv''': '''encoder.layers.12.conv''',
    '''encoder.model.13.lstm''': '''encoder.layers.13.lstm''',
    '''encoder.model.15.conv.conv''': '''encoder.layers.15.conv''',
}
a__       :							List[Any]        =  {
    '''encoder.model.0.conv.norm''': '''encoder.layers.0.norm''',
    '''encoder.model.1.block.1.conv.norm''': '''encoder.layers.1.block.1.norm''',
    '''encoder.model.1.block.3.conv.norm''': '''encoder.layers.1.block.3.norm''',
    '''encoder.model.1.shortcut.conv.norm''': '''encoder.layers.1.shortcut.norm''',
    '''encoder.model.3.conv.norm''': '''encoder.layers.3.norm''',
    '''encoder.model.4.block.1.conv.norm''': '''encoder.layers.4.block.1.norm''',
    '''encoder.model.4.block.3.conv.norm''': '''encoder.layers.4.block.3.norm''',
    '''encoder.model.4.shortcut.conv.norm''': '''encoder.layers.4.shortcut.norm''',
    '''encoder.model.6.conv.norm''': '''encoder.layers.6.norm''',
    '''encoder.model.7.block.1.conv.norm''': '''encoder.layers.7.block.1.norm''',
    '''encoder.model.7.block.3.conv.norm''': '''encoder.layers.7.block.3.norm''',
    '''encoder.model.7.shortcut.conv.norm''': '''encoder.layers.7.shortcut.norm''',
    '''encoder.model.9.conv.norm''': '''encoder.layers.9.norm''',
    '''encoder.model.10.block.1.conv.norm''': '''encoder.layers.10.block.1.norm''',
    '''encoder.model.10.block.3.conv.norm''': '''encoder.layers.10.block.3.norm''',
    '''encoder.model.10.shortcut.conv.norm''': '''encoder.layers.10.shortcut.norm''',
    '''encoder.model.12.conv.norm''': '''encoder.layers.12.norm''',
    '''encoder.model.15.conv.norm''': '''encoder.layers.15.norm''',
}
a__       :							List[Any]        =  {
    '''decoder.model.0.conv.conv''': '''decoder.layers.0.conv''',
    '''decoder.model.1.lstm''': '''decoder.layers.1.lstm''',
    '''decoder.model.3.convtr.convtr''': '''decoder.layers.3.conv''',
    '''decoder.model.4.block.1.conv.conv''': '''decoder.layers.4.block.1.conv''',
    '''decoder.model.4.block.3.conv.conv''': '''decoder.layers.4.block.3.conv''',
    '''decoder.model.4.shortcut.conv.conv''': '''decoder.layers.4.shortcut.conv''',
    '''decoder.model.6.convtr.convtr''': '''decoder.layers.6.conv''',
    '''decoder.model.7.block.1.conv.conv''': '''decoder.layers.7.block.1.conv''',
    '''decoder.model.7.block.3.conv.conv''': '''decoder.layers.7.block.3.conv''',
    '''decoder.model.7.shortcut.conv.conv''': '''decoder.layers.7.shortcut.conv''',
    '''decoder.model.9.convtr.convtr''': '''decoder.layers.9.conv''',
    '''decoder.model.10.block.1.conv.conv''': '''decoder.layers.10.block.1.conv''',
    '''decoder.model.10.block.3.conv.conv''': '''decoder.layers.10.block.3.conv''',
    '''decoder.model.10.shortcut.conv.conv''': '''decoder.layers.10.shortcut.conv''',
    '''decoder.model.12.convtr.convtr''': '''decoder.layers.12.conv''',
    '''decoder.model.13.block.1.conv.conv''': '''decoder.layers.13.block.1.conv''',
    '''decoder.model.13.block.3.conv.conv''': '''decoder.layers.13.block.3.conv''',
    '''decoder.model.13.shortcut.conv.conv''': '''decoder.layers.13.shortcut.conv''',
    '''decoder.model.15.conv.conv''': '''decoder.layers.15.conv''',
}
a__       :							Union[str, Any]        =  {
    '''decoder.model.0.conv.norm''': '''decoder.layers.0.norm''',
    '''decoder.model.3.convtr.norm''': '''decoder.layers.3.norm''',
    '''decoder.model.4.block.1.conv.norm''': '''decoder.layers.4.block.1.norm''',
    '''decoder.model.4.block.3.conv.norm''': '''decoder.layers.4.block.3.norm''',
    '''decoder.model.4.shortcut.conv.norm''': '''decoder.layers.4.shortcut.norm''',
    '''decoder.model.6.convtr.norm''': '''decoder.layers.6.norm''',
    '''decoder.model.7.block.1.conv.norm''': '''decoder.layers.7.block.1.norm''',
    '''decoder.model.7.block.3.conv.norm''': '''decoder.layers.7.block.3.norm''',
    '''decoder.model.7.shortcut.conv.norm''': '''decoder.layers.7.shortcut.norm''',
    '''decoder.model.9.convtr.norm''': '''decoder.layers.9.norm''',
    '''decoder.model.10.block.1.conv.norm''': '''decoder.layers.10.block.1.norm''',
    '''decoder.model.10.block.3.conv.norm''': '''decoder.layers.10.block.3.norm''',
    '''decoder.model.10.shortcut.conv.norm''': '''decoder.layers.10.shortcut.norm''',
    '''decoder.model.12.convtr.norm''': '''decoder.layers.12.norm''',
    '''decoder.model.13.block.1.conv.norm''': '''decoder.layers.13.block.1.norm''',
    '''decoder.model.13.block.3.conv.norm''': '''decoder.layers.13.block.3.norm''',
    '''decoder.model.13.shortcut.conv.norm''': '''decoder.layers.13.shortcut.norm''',
    '''decoder.model.15.conv.norm''': '''decoder.layers.15.norm''',
}
a__       :							List[Any]        =  {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
a__       :							Any        =  {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
a__       :							List[Any]        =  []
a__       :							str        =  []




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					for attribute in key.split("."     ):
										__SCREAMING_SNAKE_CASE		     =       getattr(lowerCAmelCase_						,		lowerCAmelCase_     )

					if weight_type is not None:
										__SCREAMING_SNAKE_CASE		     =       getattr(lowerCAmelCase_						,		lowerCAmelCase_     ).shape
					else:
										__SCREAMING_SNAKE_CASE		     =       hf_pointer.shape

					if hf_shape != value.shape:
										raise ValueError(
										    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":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "weight_g":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "weight_v":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "bias":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "running_mean":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "running_var":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "num_batches_tracked":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "weight_ih_l0":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "weight_hh_l0":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "bias_ih_l0":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "bias_hh_l0":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "weight_ih_l1":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "weight_hh_l1":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "bias_ih_l1":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "bias_hh_l1":
										__SCREAMING_SNAKE_CASE		     =       value
					else:
										__SCREAMING_SNAKE_CASE		     =       value

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




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					for key in ignore_keys:
										if key.endswith(".*"     ):
															if name.startswith(key[:-1]     ):
																				return True
										elif ".*." in key:
															__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       key.split(".*."     )
															if prefix in name and suffix in name:
																				return True
										elif key in name:
															return True
					return False




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       []

					if model_name == "encodec_24khz" or "encodec_32khz":
										__SCREAMING_SNAKE_CASE		     =       MAPPING_24K
					elif model_name == "encodec_48khz":
										__SCREAMING_SNAKE_CASE		     =       MAPPING_48K
					else:
										raise ValueError(f"""Unsupported model: {model_name}"""     )

					for name, value in orig_dict.items():
										if should_ignore(lowerCAmelCase_						,		lowerCAmelCase_     ):
															logger.info(f"""{name} was ignored"""     )
															continue

										__SCREAMING_SNAKE_CASE		     =       False
										for key, mapped_key in MAPPING.items():
															if "*" in key:
																				__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       key.split(".*."     )
																				if prefix in name and suffix in name:
																									__SCREAMING_SNAKE_CASE		     =       suffix

															if key in name:
																				# HACK otherwise .embed gets initialized with .embed_avg too
																				if key.endswith("embed"     ) and name.endswith("embed_avg"     ):
																									continue

																				__SCREAMING_SNAKE_CASE		     =       True
																				if "*" in mapped_key:
																									__SCREAMING_SNAKE_CASE		     =       name.split(lowerCAmelCase_     )[0].split("."     )[-2]
																									__SCREAMING_SNAKE_CASE		     =       mapped_key.replace("*"						,		lowerCAmelCase_     )
																				if "weight_g" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight_g"
																				elif "weight_v" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight_v"
																				elif "weight_ih_l0" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight_ih_l0"
																				elif "weight_hh_l0" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight_hh_l0"
																				elif "bias_ih_l0" in name:
																									__SCREAMING_SNAKE_CASE		     =       "bias_ih_l0"
																				elif "bias_hh_l0" in name:
																									__SCREAMING_SNAKE_CASE		     =       "bias_hh_l0"
																				elif "weight_ih_l1" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight_ih_l1"
																				elif "weight_hh_l1" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight_hh_l1"
																				elif "bias_ih_l1" in name:
																									__SCREAMING_SNAKE_CASE		     =       "bias_ih_l1"
																				elif "bias_hh_l1" in name:
																									__SCREAMING_SNAKE_CASE		     =       "bias_hh_l1"
																				elif "bias" in name:
																									__SCREAMING_SNAKE_CASE		     =       "bias"
																				elif "weight" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight"
																				elif "running_mean" in name:
																									__SCREAMING_SNAKE_CASE		     =       "running_mean"
																				elif "running_var" in name:
																									__SCREAMING_SNAKE_CASE		     =       "running_var"
																				elif "num_batches_tracked" in name:
																									__SCREAMING_SNAKE_CASE		     =       "num_batches_tracked"
																				else:
																									__SCREAMING_SNAKE_CASE		     =       None
																				set_recursively(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     )
															continue
										if not is_used:
															unused_weights.append(lowerCAmelCase_     )

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




@torch.no_grad()
def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_=None						,		lowerCAmelCase_=None						,		):




					'''simple docstring'''


					if config_path is not None:
										__SCREAMING_SNAKE_CASE		     =       EncodecConfig.from_pretrained(lowerCAmelCase_     )
					else:
										__SCREAMING_SNAKE_CASE		     =       EncodecConfig()

					if model_name == "encodec_24khz":
										pass  # config is already correct
					elif model_name == "encodec_32khz":
										__SCREAMING_SNAKE_CASE		     =       [8, 5, 4, 4]
										__SCREAMING_SNAKE_CASE		     =       [2.2]
										__SCREAMING_SNAKE_CASE		     =       64
										__SCREAMING_SNAKE_CASE		     =       3_2000
										__SCREAMING_SNAKE_CASE		     =       2048
										__SCREAMING_SNAKE_CASE		     =       False
										__SCREAMING_SNAKE_CASE		     =       False
										__SCREAMING_SNAKE_CASE		     =       False
					elif model_name == "encodec_48khz":
										__SCREAMING_SNAKE_CASE		     =       [8, 5, 4, 2]
										__SCREAMING_SNAKE_CASE		     =       [3.0, 6.0, 12.0, 24.0]
										__SCREAMING_SNAKE_CASE		     =       4_8000
										__SCREAMING_SNAKE_CASE		     =       2
										__SCREAMING_SNAKE_CASE		     =       False
										__SCREAMING_SNAKE_CASE		     =       "time_group_norm"
										__SCREAMING_SNAKE_CASE		     =       True
										__SCREAMING_SNAKE_CASE		     =       1.0
										__SCREAMING_SNAKE_CASE		     =       0.01
					else:
										raise ValueError(f"""Unknown model name: {model_name}"""     )

					__SCREAMING_SNAKE_CASE		     =       EncodecModel(lowerCAmelCase_     )

					__SCREAMING_SNAKE_CASE		     =       EncodecFeatureExtractor(
					    feature_size=config.audio_channels						,		sampling_rate=config.sampling_rate						,		chunk_length_s=config.chunk_length_s						,		overlap=config.overlap						,		)
					feature_extractor.save_pretrained(lowerCAmelCase_     )

					__SCREAMING_SNAKE_CASE		     =       torch.load(lowerCAmelCase_     )
					if "best_state" in original_checkpoint:
										# we might have a training state saved, in which case discard the yaml results and just retain the weights
										__SCREAMING_SNAKE_CASE		     =       original_checkpoint["best_state"]
					recursively_load_weights(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     )
					model.save_pretrained(lowerCAmelCase_     )

					if repo_id:
										print("Pushing to the hub..."     )
										feature_extractor.push_to_hub(lowerCAmelCase_     )
										model.push_to_hub(lowerCAmelCase_     )


if __name__ == "__main__":
		a__       :							List[str]        =  argparse.ArgumentParser()
		parser.add_argument(
		    '''--model''',
		    default='''encodec_24khz''',
		    type=str,
		    help='''The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.''',
		)
		parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to original checkpoint''')
		parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
		)
		parser.add_argument(
		    '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
		)

		a__       :							Optional[int]        =  parser.parse_args()
		convert_checkpoint(
		    args.model,
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.push_to_hub,
		)
