

import copy
import os
from typing import Dict, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings, logging
from ..auto import AutoConfig


_lowerCAmelCase      :    List[str]						       =    logging.get_logger(__name__)


_lowerCAmelCase      :    Optional[Any]						       =    {
    "suno/bark-small": "https://huggingface.co/suno/bark-small/resolve/main/config.json",
    "suno/bark": "https://huggingface.co/suno/bark/resolve/main/config.json",
}

_lowerCAmelCase      :    List[Any]						       =    "\n    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model\n    according to the specified arguments, defining the model architecture. Instantiating a configuration with the\n    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)\n    architecture.\n\n    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the\n    documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        block_size (`int`, *optional*, defaults to 1024):\n            The maximum sequence length that this model might ever be used with. Typically set this to something large\n            just in case (e.g., 512 or 1024 or 2048).\n        input_vocab_size (`int`, *optional*, defaults to 10_048):\n            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the\n            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with\n            regards to the chosen sub-model.\n        output_vocab_size (`int`, *optional*, defaults to 10_048):\n            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented\n            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought\n            with regards to the chosen sub-model.\n        num_layers (`int`, *optional*, defaults to 12):\n            Number of hidden layers in the given sub-model.\n        num_heads (`int`, *optional*, defaults to 12):\n            Number of attention heads for each attention layer in the Transformer architecture.\n        hidden_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the \"intermediate\" (often named feed-forward) layer in the architecture.\n        dropout (`float`, *optional*, defaults to 0.0):\n            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.\n        bias (`bool`, *optional*, defaults to `True`):\n            Whether or not to use bias in the linear layers and layer norm layers.\n        initializer_range (`float`, *optional*, defaults to 0.02):\n            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n"






class 			__magic_name__					(    lowerCAmelCase_   ):
			SCREAMING_SNAKE_CASE							     =		'bark_module'
			SCREAMING_SNAKE_CASE							     =		['past_key_values']

			SCREAMING_SNAKE_CASE							     =		{
			    'num_attention_heads': 'num_heads',
			    'num_hidden_layers': 'num_layers',
			    'vocab_size': 'input_vocab_size',
			    'window_size': 'block_size',
			}





			def __init__(	self      ,						__snake_case=1024      ,						__snake_case=1_0048      ,						__snake_case=1_0048      ,						__snake_case=12      ,						__snake_case=12      ,						__snake_case=768      ,						__snake_case=0.0      ,						__snake_case=True      ,						__snake_case=0.02      ,						__snake_case=True      ,						**__snake_case      ,						)     ->				Tuple:




							'''simple docstring'''
							__a						   =block_size
							__a						   =input_vocab_size
							__a						   =output_vocab_size
							__a						   =num_layers
							__a						   =num_heads
							__a						   =hidden_size
							__a						   =dropout
							__a						   =bias
							__a						   =use_cache
							__a						   =initializer_range

							super().__init__(**__snake_case      )





			@classmethod
			def 	__magic_name__					(	cls      ,						__snake_case      ,						__snake_case = None      ,						__snake_case = False      ,						__snake_case = False      ,						__snake_case = None      ,						__snake_case = "main"      ,						**__snake_case      ,						)     ->				"PretrainedConfig":




							'''simple docstring'''
							__a						   =cache_dir
							__a						   =force_download
							__a						   =local_files_only
							__a						   =revision

							cls._set_token_in_kwargs(__snake_case      ,						__snake_case      )

							__a   ,					__a						   =cls.get_config_dict(__snake_case      ,						**__snake_case      )

							# get the config dict if we are loading from Bark
							if config_dict.get('model_type'      ) == "bark":
											__a						   =config_dict[f'{cls.model_type}_config']

							if "model_type" in config_dict and hasattr(cls      ,						'model_type'      ) and config_dict["model_type"] != cls.model_type:
											logger.warning(
											    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
											    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'      )

							return cls.from_dict(__snake_case      ,						**__snake_case      )






@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='BarkSemanticConfig'						,					model='BarkSemanticModel'   )						,					'\n    Example:\n\n    ```python\n    >>> from transformers import BarkSemanticConfig, BarkSemanticModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkSemanticConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkSemanticModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```'						,					)
class 			__magic_name__					(    lowerCAmelCase_   ):
			SCREAMING_SNAKE_CASE							     =		'semantic'






@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='BarkCoarseConfig'						,					model='BarkCoarseModel'   )						,					'\n    Example:\n\n    ```python\n    >>> from transformers import BarkCoarseConfig, BarkCoarseModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkCoarseConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkCoarseModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```'						,					)
class 			__magic_name__					(    lowerCAmelCase_   ):
			SCREAMING_SNAKE_CASE							     =		'coarse_acoustics'






@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='BarkFineConfig'						,					model='BarkFineModel'   )						,					'\n        n_codes_total (`int`, *optional*, defaults to 8):\n            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.\n        n_codes_given (`int`, *optional*, defaults to 1):\n            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics\n            sub-models.\n    Example:\n\n    ```python\n    >>> from transformers import BarkFineConfig, BarkFineModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkFineConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkFineModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```'						,					)
class 			__magic_name__					(    lowerCAmelCase_   ):
			SCREAMING_SNAKE_CASE							     =		'fine_acoustics'





			def __init__(	self      ,						__snake_case=True      ,						__snake_case=8      ,						__snake_case=1      ,						**__snake_case      )     ->				List[str]:




							'''simple docstring'''
							__a						   =n_codes_total
							__a						   =n_codes_given

							super().__init__(tie_word_embeddings=__snake_case      ,						**__snake_case      )






class 			__magic_name__					(    lowerCAmelCase_   ):
			SCREAMING_SNAKE_CASE							     =		'bark'
			SCREAMING_SNAKE_CASE							     =		True





			def __init__(	self      ,						__snake_case = None      ,						__snake_case = None      ,						__snake_case = None      ,						__snake_case = None      ,						__snake_case=0.02      ,						**__snake_case      ,						)     ->				Tuple:




							'''simple docstring'''
							if semantic_config is None:
											__a						   ={}
											logger.info('semantic_config is None. initializing the semantic model with default values.'      )

							if coarse_acoustics_config is None:
											__a						   ={}
											logger.info('coarse_acoustics_config is None. initializing the coarse model with default values.'      )

							if fine_acoustics_config is None:
											__a						   ={}
											logger.info('fine_acoustics_config is None. initializing the fine model with default values.'      )

							if codec_config is None:
											__a						   ={}
											logger.info('codec_config is None. initializing the codec model with default values.'      )

							__a						   =BarkSemanticConfig(**__snake_case      )
							__a						   =BarkCoarseConfig(**__snake_case      )
							__a						   =BarkFineConfig(**__snake_case      )
							__a						   =AutoConfig.for_model(**__snake_case      )

							__a						   =initializer_range

							super().__init__(**__snake_case      )





			@classmethod
			def 	__magic_name__					(	cls      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						**__snake_case      ,						)     ->				Union[str, Any]:




							'''simple docstring'''
							return cls(
							    semantic_config=semantic_config.to_dict()      ,						coarse_acoustics_config=coarse_acoustics_config.to_dict()      ,						fine_acoustics_config=fine_acoustics_config.to_dict()      ,						codec_config=codec_config.to_dict()      ,						**__snake_case      ,						)





			def 	__magic_name__					(	self      )     ->				List[Any]:




							'''simple docstring'''
							__a						   =copy.deepcopy(self.__dict__      )

							__a						   =self.semantic_config.to_dict()
							__a						   =self.coarse_acoustics_config.to_dict()
							__a						   =self.fine_acoustics_config.to_dict()
							__a						   =self.codec_config.to_dict()

							__a						   =self.__class__.model_type
							return output









import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator






def  UpperCamelCase_(							_snake_case     :						Union[str, Any]			):




			"""simple docstring"""






			__a						   =fname.split(os.path.sep			)[-1]
			return re.search(r'^(.*)_\d+\.jpg$' ,  _snake_case			).groups()[0]






class 			__magic_name__					(    lowerCAmelCase_   ):





			def __init__(	self      ,						__snake_case      ,						__snake_case=None      ,						__snake_case=None      )     ->				Tuple:




						'''simple docstring'''
						__a						   =file_names
						__a						   =image_transform
						__a						   =label_to_id





			def __len__(	self      )     ->				int:




						'''simple docstring'''
						return len(self.file_names      )





			def __getitem__(	self      ,						__snake_case      )     ->				List[str]:




						'''simple docstring'''
						__a						   =self.file_names[idx]
						__a						   =PIL.Image.open(__snake_case      )
						__a						   =raw_image.convert('RGB'      )
						if self.image_transform is not None:
									__a						   =self.image_transform(__snake_case      )
						__a						   =extract_label(__snake_case      )
						if self.label_to_id is not None:
									__a						   =self.label_to_id[label]
						return {"image": image, "label": label}






def  UpperCamelCase_(							_snake_case     :						Union[str, Any] ,  _snake_case     :						Optional[int]			):




			"""simple docstring"""






			__a						   =Accelerator(cpu=args.cpu ,  mixed_precision=args.mixed_precision			)

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			__a						   =config['lr']
			__a						   =int(config['num_epochs']			)
			__a						   =int(config['seed']			)
			__a						   =int(config['batch_size']			)
			__a						   =config['image_size']
			if not isinstance(_snake_case ,  (list, tuple)			):
						__a						   =(image_size, image_size)

			# Grab all the image filenames
			__a						   =[os.path.join(args.data_dir ,  _snake_case			) for fname in os.listdir(args.data_dir			) if fname.endswith('.jpg'			)]

			# Build the label correspondences
			__a						   =[extract_label(_snake_case			) for fname in file_names]
			__a						   =list(set(_snake_case			)			)
			id_to_label.sort()
			__a						   ={lbl: i for i, lbl in enumerate(_snake_case			)}

			# Set the seed before splitting the data.
			np.random.seed(_snake_case			)
			torch.manual_seed(_snake_case			)
			torch.cuda.manual_seed_all(_snake_case			)

			# Split our filenames between train and validation
			__a						   =np.random.permutation(len(_snake_case			)			)
			__a						   =int(0.8 * len(_snake_case			)			)
			__a						   =random_perm[:cut]
			__a						   =random_perm[cut:]

			# For training we use a simple RandomResizedCrop
			__a						   =Compose([RandomResizedCrop(_snake_case ,  scale=(0.5, 1.0)			), ToTensor()]			)
			__a						   =PetsDataset(
			    [file_names[i] for i in train_split] ,  image_transform=_snake_case ,  label_to_id=_snake_case			)

			# For evaluation, we use a deterministic Resize
			__a						   =Compose([Resize(_snake_case			), ToTensor()]			)
			__a						   =PetsDataset([file_names[i] for i in eval_split] ,  image_transform=_snake_case ,  label_to_id=_snake_case			)

			# Instantiate dataloaders.
			__a						   =DataLoader(_snake_case ,  shuffle=_snake_case ,  batch_size=_snake_case ,  num_workers=4			)
			__a						   =DataLoader(_snake_case ,  shuffle=_snake_case ,  batch_size=_snake_case ,  num_workers=4			)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			__a						   =create_model('resnet50d' ,  pretrained=_snake_case ,  num_classes=len(_snake_case			)			)

			# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
			# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
			# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
			__a						   =model.to(accelerator.device			)

			# Freezing the base model
			for param in model.parameters():
						__a						   =False
			for param in model.get_classifier().parameters():
						__a						   =True

			# We normalize the batches of images to be a bit faster.
			__a						   =torch.tensor(model.default_cfg['mean']			)[None, :, None, None].to(accelerator.device			)
			__a						   =torch.tensor(model.default_cfg['std']			)[None, :, None, None].to(accelerator.device			)

			# Instantiate optimizer
			__a						   =torch.optim.Adam(params=model.parameters() ,  lr=lr / 25			)

			# Instantiate learning rate scheduler
			__a						   =OneCycleLR(optimizer=_snake_case ,  max_lr=_snake_case ,  epochs=_snake_case ,  steps_per_epoch=len(_snake_case			)			)

			# Prepare everything
			# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
			# prepare method.
			__a   ,					__a   ,					__a   ,					__a   ,					__a						   =accelerator.prepare(
			    _snake_case ,  _snake_case ,  _snake_case ,  _snake_case ,  _snake_case			)

			# Now we train the model
			for epoch in range(_snake_case			):
						model.train()
						for step, batch in enumerate(_snake_case			):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									__a						   ={k: v.to(accelerator.device			) for k, v in batch.items()}
									__a						   =(batch['image'] - mean) / std
									__a						   =model(_snake_case			)
									__a						   =torch.nn.functional.cross_entropy(_snake_case ,  batch['label']			)
									accelerator.backward(_snake_case			)
									optimizer.step()
									lr_scheduler.step()
									optimizer.zero_grad()

						model.eval()
						__a						   =0
						__a						   =0
						for _, batch in enumerate(_snake_case			):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									__a						   ={k: v.to(accelerator.device			) for k, v in batch.items()}
									__a						   =(batch['image'] - mean) / std
									with torch.no_grad():
												__a						   =model(_snake_case			)
									__a						   =outputs.argmax(dim=-1			)
									__a   ,					__a						   =accelerator.gather_for_metrics((predictions, batch['label'])			)
									__a						   =predictions == references
									num_elems += accurate_preds.shape[0]
									accurate += accurate_preds.long().sum()

						__a						   =accurate.item() / num_elems
						# Use accelerator.print to print only on the main process.
						accelerator.print(F'epoch {epoch}: {100 * eval_metric:.2f}'			)






def  UpperCamelCase_(							):




			"""simple docstring"""






			__a						   =argparse.ArgumentParser(description='Simple example of training script.'			)
			parser.add_argument('--data_dir' ,  required=_snake_case ,  help='The data folder on disk.'			)
			parser.add_argument(
			    '--mixed_precision' ,  type=_snake_case ,  default=_snake_case ,  choices=['no', 'fp16', 'bf16', 'fp8'] ,  help='Whether to use mixed precision. Choose'
			    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
			    'and an Nvidia Ampere GPU.' ,  )
			parser.add_argument(
			    '--checkpointing_steps' ,  type=_snake_case ,  default=_snake_case ,  help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.' ,  )
			parser.add_argument('--cpu' ,  action='store_true' ,  help='If passed, will train on the CPU.'			)
			__a						   =parser.parse_args()
			__a						   ={'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
			training_function(_snake_case ,  _snake_case			)


if __name__ == "__main__":
						main()






