


'''simple docstring'''

from math import sqrt





def      lowerCamelCase      (      lowerCAmelCase    :     int = 100_0000		):





	"""simple docstring"""







	__magic_name__	:   int					     =					0
	__magic_name__	:   Optional[Any]					     =					0
	__magic_name__	:   List[Any]					     =					42

	while num_cuboids <= limit:
		max_cuboid_size += 1
		for sum_shortest_sides in range(2		,		2 * max_cuboid_size + 1		):
			if sqrt(sum_shortest_sides**2 + max_cuboid_size**2		).is_integer():
				num_cuboids += (
				    min(__lowerCamelCase		,		sum_shortest_sides // 2		)
				    - max(1		,		sum_shortest_sides - max_cuboid_size		)
				    + 1
				)

	return max_cuboid_size


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



'''simple docstring'''

import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


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

set_seed(7_7_0)


lowerCAmelCase				:str        =      {
    '''c_attn''': '''att_proj''',
    '''c_proj''': '''out_proj''',
    '''c_fc''': '''in_proj''',
    '''transformer.''': '''''',
    '''h.''': '''layers.''',
    '''ln_1''': '''layernorm_1''',
    '''ln_2''': '''layernorm_2''',
    '''ln_f''': '''layernorm_final''',
    '''wpe''': '''position_embeds_layer''',
    '''wte''': '''input_embeds_layer''',
}


lowerCAmelCase				:Any        =      {
    '''text_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text.pt''',
    },
    '''coarse_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse.pt''',
    },
    '''fine_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine.pt''',
    },
    '''text''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text_2.pt''',
    },
    '''coarse''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse_2.pt''',
    },
    '''fine''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine_2.pt''',
    },
}

lowerCAmelCase				:List[Any]        =      os.path.dirname(os.path.abspath(__file__))
lowerCAmelCase				:List[Any]        =      os.path.join(os.path.expanduser('''~'''), '''.cache''')
lowerCAmelCase				:List[str]        =      os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')





def lowerCamelCase      (      lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     List[Any]=False		):





	"""simple docstring"""







	__magic_name__	:   str					     =					model_type
	if use_small:
		key += "_small"
	return os.path.join(lowerCAmelCase		,		REMOTE_MODEL_PATHS[key]['file_name']		)





def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     Optional[Any]		):





	"""simple docstring"""







	os.makedirs(lowerCAmelCase		,		exist_ok=lowerCAmelCase		)
	hf_hub_download(repo_id=lowerCAmelCase		,		filename=lowerCAmelCase		,		local_dir=lowerCAmelCase		)





def lowerCamelCase      (      lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     Any		,		lowerCAmelCase    :     Optional[Any]=False		,		lowerCAmelCase    :     str="text"		):





	"""simple docstring"""







	if model_type == "text":
		__magic_name__	:   Tuple					     =					BarkSemanticModel
		__magic_name__	:   Optional[int]					     =					BarkSemanticConfig
		__magic_name__	:   List[Any]					     =					BarkSemanticGenerationConfig
	elif model_type == "coarse":
		__magic_name__	:   List[str]					     =					BarkCoarseModel
		__magic_name__	:   Dict					     =					BarkCoarseConfig
		__magic_name__	:   Tuple					     =					BarkCoarseGenerationConfig
	elif model_type == "fine":
		__magic_name__	:   Optional[Any]					     =					BarkFineModel
		__magic_name__	:   Dict					     =					BarkFineConfig
		__magic_name__	:   Tuple					     =					BarkFineGenerationConfig
	else:
		raise NotImplementedError()
	__magic_name__	:   int					     =					f'{model_type}_small' if use_small else model_type
	__magic_name__	:   List[str]					     =					REMOTE_MODEL_PATHS[model_key]
	if not os.path.exists(lowerCAmelCase		):
		logger.info(f'{model_type} model not found, downloading into `{CACHE_DIR}`.'		)
		_download(model_info['repo_id']		,		model_info['file_name']		)
	__magic_name__	:   Optional[Any]					     =					torch.load(lowerCAmelCase		,		map_location=lowerCAmelCase		)
	# this is a hack
	__magic_name__	:   Optional[Any]					     =					checkpoint['model_args']
	if "input_vocab_size" not in model_args:
		__magic_name__	:   Dict					     =					model_args['vocab_size']
		__magic_name__	:   Optional[int]					     =					model_args['vocab_size']
		del model_args["vocab_size"]

	# convert Bark model arguments to HF Bark model arguments
	__magic_name__	:   Optional[Any]					     =					model_args.pop('n_head'		)
	__magic_name__	:   List[str]					     =					model_args.pop('n_embd'		)
	__magic_name__	:   List[Any]					     =					model_args.pop('n_layer'		)

	__magic_name__	:   Optional[Any]					     =					ConfigClass(**checkpoint['model_args']		)
	__magic_name__	:   Any					     =					ModelClass(config=lowerCAmelCase		)
	__magic_name__	:   List[str]					     =					GenerationConfigClass()

	__magic_name__	:   List[Any]					     =					model_generation_config
	__magic_name__	:   str					     =					checkpoint['model']
	# fixup checkpoint
	__magic_name__	:   str					     =					'_orig_mod.'
	for k, v in list(state_dict.items()		):
		if k.startswith(lowerCAmelCase		):
			# replace part of the key with corresponding layer name in HF implementation
			__magic_name__	:   Tuple					     =					k[len(lowerCAmelCase		) :]
			for old_layer_name in new_layer_name_dict:
				__magic_name__	:   int					     =					new_k.replace(lowerCAmelCase		,		new_layer_name_dict[old_layer_name]		)

			__magic_name__	:   Union[str, Any]					     =					state_dict.pop(lowerCAmelCase		)

	__magic_name__	:   Optional[Any]					     =					set(state_dict.keys()		) - set(model.state_dict().keys()		)
	__magic_name__	:   Any					     =					{k for k in extra_keys if not k.endswith('.attn.bias'		)}
	__magic_name__	:   Any					     =					set(model.state_dict().keys()		) - set(state_dict.keys()		)
	__magic_name__	:   Dict					     =					{k for k in missing_keys if not k.endswith('.attn.bias'		)}
	if len(lowerCAmelCase		) != 0:
		raise ValueError(f'extra keys found: {extra_keys}'		)
	if len(lowerCAmelCase		) != 0:
		raise ValueError(f'missing keys: {missing_keys}'		)
	model.load_state_dict(lowerCAmelCase		,		strict=lowerCAmelCase		)
	__magic_name__	:   Union[str, Any]					     =					model.num_parameters(exclude_embeddings=lowerCAmelCase		)
	__magic_name__	:   Optional[Any]					     =					checkpoint['best_val_loss'].item()
	logger.info(f'model loaded: {round(n_params/1e6		,		1		)}M params, {round(lowerCAmelCase		,		3		)} loss'		)
	model.eval()
	model.to(lowerCAmelCase		)
	del checkpoint, state_dict

	return model





def lowerCamelCase      (      lowerCAmelCase    :     Any		,		lowerCAmelCase    :     Optional[Any]=False		,		lowerCAmelCase    :     Tuple="text"		):





	"""simple docstring"""







	if model_type not in ("text", "coarse", "fine"):
		raise NotImplementedError()

	__magic_name__	:   List[str]					     =					'cpu'  # do conversion on cpu

	__magic_name__	:   int					     =					_get_ckpt_path(lowerCAmelCase		,		use_small=lowerCAmelCase		)
	__magic_name__	:   Any					     =					_load_model(lowerCAmelCase		,		lowerCAmelCase		,		model_type=lowerCAmelCase		,		use_small=lowerCAmelCase		)

	# load bark initial model
	__magic_name__	:   List[str]					     =					_bark_load_model(lowerCAmelCase		,		'cpu'		,		model_type=lowerCAmelCase		,		use_small=lowerCAmelCase		)

	if model_type == "text":
		__magic_name__	:   int					     =					bark_model['model']

	if model.num_parameters(exclude_embeddings=lowerCAmelCase		) != bark_model.get_num_params():
		raise ValueError('initial and new models don\'t have the same number of parameters'		)

	# check if same output as the bark model
	__magic_name__	:   Union[str, Any]					     =					5
	__magic_name__	:   Optional[int]					     =					10

	if model_type in ["text", "coarse"]:
		__magic_name__	:   Optional[Any]					     =					torch.randint(256		,		(batch_size, sequence_length)		,		dtype=torch.int		)
		__magic_name__	:   List[str]					     =					bark_model(lowerCAmelCase		)[0]

		__magic_name__	:   Optional[int]					     =					model(lowerCAmelCase		)

		# take last logits
		__magic_name__	:   int					     =					output_new_model_total.logits[:, [-1], :]

	else:
		__magic_name__	:   Tuple					     =					3
		__magic_name__	:   List[str]					     =					8
		__magic_name__	:   List[str]					     =					torch.randint(256		,		(batch_size, sequence_length, n_codes_total)		,		dtype=torch.int		)

		__magic_name__	:   str					     =					model(lowerCAmelCase		,		lowerCAmelCase		)
		__magic_name__	:   Tuple					     =					bark_model(lowerCAmelCase		,		lowerCAmelCase		)

		__magic_name__	:   Tuple					     =					output_new_model_total.logits

	# output difference should come from the difference of self-attention implementation design
	if output_new_model.shape != output_old_model.shape:
		raise ValueError('initial and new outputs don\'t have the same shape'		)
	if (output_new_model - output_old_model).abs().max().item() > 1e-3:
		raise ValueError('initial and new outputs are not equal'		)

	Path(lowerCAmelCase		).mkdir(exist_ok=lowerCAmelCase		)
	model.save_pretrained(lowerCAmelCase		)





def lowerCamelCase      (      lowerCAmelCase    :     int		,		lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     str		,		):





	"""simple docstring"""







	__magic_name__	:   Union[str, Any]					     =					os.path.join(lowerCAmelCase		,		lowerCAmelCase		)

	__magic_name__	:   Dict					     =					BarkSemanticConfig.from_pretrained(os.path.join(lowerCAmelCase		,		'config.json'		)		)
	__magic_name__	:   str					     =					BarkCoarseConfig.from_pretrained(os.path.join(lowerCAmelCase		,		'config.json'		)		)
	__magic_name__	:   int					     =					BarkFineConfig.from_pretrained(os.path.join(lowerCAmelCase		,		'config.json'		)		)
	__magic_name__	:   List[Any]					     =					EncodecConfig.from_pretrained('facebook/encodec_24khz'		)

	__magic_name__	:   Optional[int]					     =					BarkSemanticModel.from_pretrained(lowerCAmelCase		)
	__magic_name__	:   Dict					     =					BarkCoarseModel.from_pretrained(lowerCAmelCase		)
	__magic_name__	:   List[str]					     =					BarkFineModel.from_pretrained(lowerCAmelCase		)
	__magic_name__	:   Optional[Any]					     =					EncodecModel.from_pretrained('facebook/encodec_24khz'		)

	__magic_name__	:   Dict					     =					BarkConfig.from_sub_model_configs(
	    lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		)

	__magic_name__	:   List[Any]					     =					BarkGenerationConfig.from_sub_model_configs(
	    semantic.generation_config		,		coarseAcoustic.generation_config		,		fineAcoustic.generation_config		)

	__magic_name__	:   int					     =					BarkModel(lowerCAmelCase		)

	__magic_name__	:   List[str]					     =					semantic
	__magic_name__	:   Optional[int]					     =					coarseAcoustic
	__magic_name__	:   List[str]					     =					fineAcoustic
	__magic_name__	:   int					     =					codec

	__magic_name__	:   Union[str, Any]					     =					bark_generation_config

	Path(lowerCAmelCase		).mkdir(exist_ok=lowerCAmelCase		)
	bark.save_pretrained(lowerCAmelCase		,		repo_id=lowerCAmelCase		,		push_to_hub=lowerCAmelCase		)


if __name__ == "__main__":
							lowerCAmelCase				:Optional[Any]        =      argparse.ArgumentParser()
							# Required parameters

							parser.add_argument('''model_type''', type=str, help='''text, coarse or fine.''')
							parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
							parser.add_argument('''--is_small''', action='''store_true''', help='''convert the small version instead of the large.''')

							lowerCAmelCase				:Union[str, Any]        =      parser.parse_args()

							load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)