





def 	lowerCamelCase_		(							_UpperCamelCase					)						->		bool:






				"""simple docstring"""


				return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6''')					)






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		bool:






				"""simple docstring"""


				snake_case_		:       Optional[int]           =    credit_card_number
				snake_case_		:       List[Any]           =    0
				snake_case_		:       int           =    len(_UpperCamelCase					) - 2
				for i in range(_UpperCamelCase					,							-1					,							-2					):
								#  double the value of every second digit
								snake_case_		:       Union[str, Any]           =    int(cc_number[i]					)
								digit *= 2
								# If doubling of a number results in a two digit number
								# i.e greater than 9(e.g., 6 × 2 = 12),
								# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
								# to get a single digit number.
								if digit > 9:
												digit %= 10
												digit += 1
								snake_case_		:       List[Any]           =    cc_number[:i] + str(_UpperCamelCase					) + cc_number[i + 1 :]
								total += digit

				# Sum up the remaining digits
				for i in range(len(_UpperCamelCase					) - 1					,							-1					,							-2					):
								total += int(cc_number[i]					)

				return total % 10 == 0






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		bool:






				"""simple docstring"""


				snake_case_		:       Optional[Any]           =    f'''{credit_card_number} is an invalid credit card number because'''
				if not credit_card_number.isdigit():
								print(f'''{error_message} it has nonnumerical characters.'''					)
								return False

				if not 13 <= len(_UpperCamelCase					) <= 16:
								print(f'''{error_message} of its length.'''					)
								return False

				if not validate_initial_digits(_UpperCamelCase					):
								print(f'''{error_message} of its first two digits.'''					)
								return False

				if not luhn_validation(_UpperCamelCase					):
								print(f'''{error_message} it fails the Luhn check.'''					)
								return False

				print(f'''{credit_card_number} is a valid credit card number.'''					)
				return True


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	validate_credit_card_number('''4111111111111111''')
	validate_credit_card_number('''32323''')




'''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()
UpperCAmelCase_			:  Union[str, Any]										=   logging.get_logger(__name__)

set_seed(7_7_0)


UpperCAmelCase_			:  List[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',
}


UpperCAmelCase_			:  List[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',
    },
}

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


def       _lowercase							(					UpperCamelCase__							:       Dict,					UpperCamelCase__							:       Tuple=False      ):
 __A	:		List[Any]	  =      model_type
 if use_small:
  key += "_small"
 return os.path.join(UpperCamelCase__,					REMOTE_MODEL_PATHS[key]['file_name']      )


def       _lowercase							(					UpperCamelCase__							:       Any,					UpperCamelCase__							:       Any      ):
 os.makedirs(UpperCamelCase__,					exist_ok=UpperCamelCase__      )
 hf_hub_download(repo_id=UpperCamelCase__,					filename=UpperCamelCase__,					local_dir=UpperCamelCase__      )


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

 # convert Bark model arguments to HF Bark model arguments
 __A	:		Union[str, Any]	  =      model_args.pop('n_head'      )
 __A	:		Optional[int]	  =      model_args.pop('n_embd'      )
 __A	:		str	  =      model_args.pop('n_layer'      )

 __A	:		Optional[Any]	  =      ConfigClass(**checkpoint['model_args']      )
 __A	:		Union[str, Any]	  =      ModelClass(config=UpperCamelCase__      )
 __A	:		Tuple	  =      GenerationConfigClass()

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

   __A	:		Optional[Any]	  =      state_dict.pop(UpperCamelCase__      )

 __A	:		List[str]	  =      set(state_dict.keys()      ) - set(model.state_dict().keys()      )
 __A	:		Optional[int]	  =      {k for k in extra_keys if not k.endswith('.attn.bias'      )}
 __A	:		Dict	  =      set(model.state_dict().keys()      ) - set(state_dict.keys()      )
 __A	:		Union[str, Any]	  =      {k for k in missing_keys if not k.endswith('.attn.bias'      )}
 if len(UpperCamelCase__      ) != 0:
  raise ValueError(f"""extra keys found: {extra_keys}"""      )
 if len(UpperCamelCase__      ) != 0:
  raise ValueError(f"""missing keys: {missing_keys}"""      )
 model.load_state_dict(UpperCamelCase__,					strict=UpperCamelCase__      )
 __A	:		str	  =      model.num_parameters(exclude_embeddings=UpperCamelCase__      )
 __A	:		int	  =      checkpoint['best_val_loss'].item()
 logger.info(f"""model loaded: {round(n_params/1E6,					1      )}M params, {round(UpperCamelCase__,					3      )} loss"""      )
 model.eval()
 model.to(UpperCamelCase__      )
 del checkpoint, state_dict

 return model


def       _lowercase							(					UpperCamelCase__							:       int,					UpperCamelCase__							:       Tuple=False,					UpperCamelCase__							:       Optional[Any]="text"      ):
 if model_type not in ("text", "coarse", "fine"):
  raise NotImplementedError()

 __A	:		Any	  =      'cpu'  # do conversion on cpu

 __A	:		int	  =      _get_ckpt_path(UpperCamelCase__,					use_small=UpperCamelCase__      )
 __A	:		Any	  =      _load_model(UpperCamelCase__,					UpperCamelCase__,					model_type=UpperCamelCase__,					use_small=UpperCamelCase__      )

 # load bark initial model
 __A	:		Optional[int]	  =      _bark_load_model(UpperCamelCase__,					'cpu',					model_type=UpperCamelCase__,					use_small=UpperCamelCase__      )

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

 if model.num_parameters(exclude_embeddings=UpperCamelCase__      ) != 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
 __A	:		Optional[Any]	  =      5
 __A	:		str	  =      10

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

  __A	:		Union[str, Any]	  =      model(UpperCamelCase__      )

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

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

  __A	:		List[str]	  =      model(UpperCamelCase__,					UpperCamelCase__      )
  __A	:		Optional[int]	  =      bark_model(UpperCamelCase__,					UpperCamelCase__      )

  __A	:		Dict	  =      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(UpperCamelCase__      ).mkdir(exist_ok=UpperCamelCase__      )
 model.save_pretrained(UpperCamelCase__      )


def       _lowercase							(					UpperCamelCase__							:       int,					UpperCamelCase__							:       int,					UpperCamelCase__							:       Optional[int],					UpperCamelCase__							:       Optional[Any],					UpperCamelCase__							:       Tuple,					UpperCamelCase__							:       str,					):
 __A	:		Optional[Any]	  =      os.path.join(UpperCamelCase__,					UpperCamelCase__      )

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

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

 __A	:		int	  =      BarkConfig.from_sub_model_configs(
     UpperCamelCase__,					UpperCamelCase__,					UpperCamelCase__,					UpperCamelCase__      )

 __A	:		Optional[Any]	  =      BarkGenerationConfig.from_sub_model_configs(
     semantic.generation_config,					coarseAcoustic.generation_config,					fineAcoustic.generation_config      )

 __A	:		int	  =      BarkModel(UpperCamelCase__      )

 __A	:		List[str]	  =      semantic
 __A	:		Any	  =      coarseAcoustic
 __A	:		Tuple	  =      fineAcoustic
 __A	:		Optional[Any]	  =      codec

 __A	:		Dict	  =      bark_generation_config

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


if __name__ == "__main__":
 UpperCAmelCase_			:  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.')

 UpperCAmelCase_			:  Any										=   parser.parse_args()

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






