
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()
_SCREAMING_SNAKE_CASE    :      Any          =			logging.get_logger(__name__)

set_seed(7_7_0)


_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			{
    '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',
}


_SCREAMING_SNAKE_CASE    :      Optional[int]          =			{
    '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',
    },
}

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

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Any							,						__UpperCamelCase :						Union[str, Any]=False       ) ->       Optional[Any]:

				"""simple docstring"""







				A__  :	Optional[Any]         =							model_type
				if use_small:
								key += "_small"
				return os.path.join(__UpperCamelCase							,						REMOTE_MODEL_PATHS[key]['''file_name''']       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[Any]							,						__UpperCamelCase :						Dict       ) ->       Any:

				"""simple docstring"""







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

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[Any]							,						__UpperCamelCase :						Any							,						__UpperCamelCase :						int=False							,						__UpperCamelCase :						Optional[int]="text"       ) ->       Union[str, Any]:

				"""simple docstring"""







				if model_type == "text":
								A__  :	Optional[int]         =							BarkSemanticModel
								A__  :	str         =							BarkSemanticConfig
								A__  :	int         =							BarkSemanticGenerationConfig
				elif model_type == "coarse":
								A__  :	Optional[int]         =							BarkCoarseModel
								A__  :	Tuple         =							BarkCoarseConfig
								A__  :	Optional[int]         =							BarkCoarseGenerationConfig
				elif model_type == "fine":
								A__  :	str         =							BarkFineModel
								A__  :	List[Any]         =							BarkFineConfig
								A__  :	Optional[Any]         =							BarkFineGenerationConfig
				else:
								raise NotImplementedError()
				A__  :	Optional[Any]         =							F"{model_type}_small" if use_small else model_type
				A__  :	str         =							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__  :	int         =							torch.load(__UpperCamelCase							,						map_location=__UpperCamelCase       )
				# this is a hack
				A__  :	Union[str, Any]         =							checkpoint['''model_args''']
				if "input_vocab_size" not in model_args:
								A__  :	Any         =							model_args['''vocab_size''']
								A__  :	int         =							model_args['''vocab_size''']
								del model_args["vocab_size"]

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

				A__  :	Any         =							ConfigClass(**checkpoint['''model_args''']       )
				A__  :	Tuple         =							ModelClass(config=__UpperCamelCase       )
				A__  :	Any         =							GenerationConfigClass()

				A__  :	Optional[int]         =							model_generation_config
				A__  :	Union[str, Any]         =							checkpoint['''model''']
				# fixup checkpoint
				A__  :	List[str]         =							'''_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__  :	List[Any]         =							k[len(__UpperCamelCase       ) :]
												for old_layer_name in new_layer_name_dict:
																A__  :	List[Any]         =							new_k.replace(__UpperCamelCase							,						new_layer_name_dict[old_layer_name]       )

												A__  :	Optional[int]         =							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__  :	Optional[int]         =							set(model.state_dict().keys()       ) - set(state_dict.keys()       )
				A__  :	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__  :	Optional[Any]         =							model.num_parameters(exclude_embeddings=__UpperCamelCase       )
				A__  :	str         =							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 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[Any]							,						__UpperCamelCase :						Optional[Any]=False							,						__UpperCamelCase :						Tuple="text"       ) ->       int:

				"""simple docstring"""







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

				A__  :	str         =							'''cpu'''  # do conversion on cpu

				A__  :	int         =							_get_ckpt_path(__UpperCamelCase							,						use_small=__UpperCamelCase       )
				A__  :	Optional[int]         =							_load_model(__UpperCamelCase							,						__UpperCamelCase							,						model_type=__UpperCamelCase							,						use_small=__UpperCamelCase       )

				# load bark initial model
				A__  :	List[Any]         =							_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__  :	Dict         =							10

				if model_type in ["text", "coarse"]:
								A__  :	Tuple         =							torch.randint(2_56							,						(batch_size, sequence_length)							,						dtype=torch.int       )
								A__  :	Tuple         =							bark_model(__UpperCamelCase       )[0]

								A__  :	int         =							model(__UpperCamelCase       )

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

				else:
								A__  :	str         =							3
								A__  :	List[str]         =							8
								A__  :	List[Any]         =							torch.randint(2_56							,						(batch_size, sequence_length, n_codes_total)							,						dtype=torch.int       )

								A__  :	List[Any]         =							model(__UpperCamelCase							,						__UpperCamelCase       )
								A__  :	Union[str, Any]         =							bark_model(__UpperCamelCase							,						__UpperCamelCase       )

								A__  :	Optional[Any]         =							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 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Dict							,						__UpperCamelCase :						Optional[Any]							,						__UpperCamelCase :						Union[str, Any]							,						__UpperCamelCase :						Optional[Any]							,						__UpperCamelCase :						Dict							,						__UpperCamelCase :						Optional[int]							,						) ->       Dict:

				"""simple docstring"""







				A__  :	int         =							os.path.join(__UpperCamelCase							,						__UpperCamelCase       )

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

				A__  :	Dict         =							BarkSemanticModel.from_pretrained(__UpperCamelCase       )
				A__  :	str         =							BarkCoarseModel.from_pretrained(__UpperCamelCase       )
				A__  :	Optional[int]         =							BarkFineModel.from_pretrained(__UpperCamelCase       )
				A__  :	int         =							EncodecModel.from_pretrained('''facebook/encodec_24khz'''       )

				A__  :	str         =							BarkConfig.from_sub_model_configs(
				    __UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase       )

				A__  :	Any         =							BarkGenerationConfig.from_sub_model_configs(
				    semantic.generation_config							,						coarseAcoustic.generation_config							,						fineAcoustic.generation_config       )

				A__  :	List[Any]         =							BarkModel(__UpperCamelCase       )

				A__  :	List[Any]         =							semantic
				A__  :	Tuple         =							coarseAcoustic
				A__  :	Union[str, Any]         =							fineAcoustic
				A__  :	Any         =							codec

				A__  :	Union[str, Any]         =							bark_generation_config

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


if __name__ == "__main__":
					_SCREAMING_SNAKE_CASE    :      int          =			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.')

					_SCREAMING_SNAKE_CASE    :      Tuple          =			parser.parse_args()

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

import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_SCREAMING_SNAKE_CASE    :      str          =			1_6
_SCREAMING_SNAKE_CASE    :      Tuple          =			3_2

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Accelerator							,						__UpperCamelCase :						int = 16       ) ->       Optional[int]:

				"""simple docstring"""







				A__  :	List[str]         =							AutoTokenizer.from_pretrained('''bert-base-cased'''       )
				A__  :	Optional[int]         =							load_dataset('''glue'''							,						'''mrpc'''       )

				def tokenize_function(__UpperCamelCase :						Union[str, Any]       ):
								# max_length=None => use the model max length (it's actually the default)
								A__  :	int         =							tokenizer(examples['''sentence1''']							,						examples['''sentence2''']							,						truncation=__UpperCamelCase							,						max_length=__UpperCamelCase       )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								A__  :	Optional[int]         =							datasets.map(
								    __UpperCamelCase							,						batched=__UpperCamelCase							,						remove_columns=['''idx''', '''sentence1''', '''sentence2''']							,						)

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				A__  :	List[Any]         =							tokenized_datasets.rename_column('''label'''							,						'''labels'''       )

				def collate_fn(__UpperCamelCase :						Any       ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								A__  :	Optional[Any]         =							1_28 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												A__  :	int         =							16
								elif accelerator.mixed_precision != "no":
												A__  :	Any         =							8
								else:
												A__  :	Union[str, Any]         =							None

								return tokenizer.pad(
								    __UpperCamelCase							,						padding='''longest'''							,						max_length=__UpperCamelCase							,						pad_to_multiple_of=__UpperCamelCase							,						return_tensors='''pt'''							,						)

				# Instantiate dataloaders.
				A__  :	Optional[int]         =							DataLoader(
				    tokenized_datasets['''train''']							,						shuffle=__UpperCamelCase							,						collate_fn=__UpperCamelCase							,						batch_size=__UpperCamelCase       )
				A__  :	Tuple         =							DataLoader(
				    tokenized_datasets['''validation''']							,						shuffle=__UpperCamelCase							,						collate_fn=__UpperCamelCase							,						batch_size=__UpperCamelCase       )

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
					from accelerate.test_utils.training import mocked_dataloaders

					_SCREAMING_SNAKE_CASE    :      Dict          =			mocked_dataloaders  # noqa: F811

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int							,						__UpperCamelCase :						List[Any]       ) ->       Optional[Any]:

				"""simple docstring"""







				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''							,						__UpperCamelCase       ) == "1":
								A__  :	List[str]         =							2
				# Initialize accelerator
				A__  :	Optional[Any]         =							Accelerator(cpu=args.cpu							,						mixed_precision=args.mixed_precision       )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				A__  :	Tuple         =							config['''lr''']
				A__  :	Dict         =							int(config['''num_epochs''']       )
				A__  :	int         =							int(config['''seed''']       )
				A__  :	Optional[Any]         =							int(config['''batch_size''']       )

				A__  :	int         =							evaluate.load('''glue'''							,						'''mrpc'''       )

				# If the batch size is too big we use gradient accumulation
				A__  :	Union[str, Any]         =							1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								A__  :	List[Any]         =							batch_size // MAX_GPU_BATCH_SIZE
								A__  :	Dict         =							MAX_GPU_BATCH_SIZE

				set_seed(__UpperCamelCase       )
				A__       ,							A__  :	int         =							get_dataloaders(__UpperCamelCase							,						__UpperCamelCase       )
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				A__  :	Optional[int]         =							AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''							,						return_dict=__UpperCamelCase       )

				# 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__  :	Tuple         =							model.to(accelerator.device       )

				# Instantiate optimizer
				A__  :	Optional[int]         =							AdamW(params=model.parameters()							,						lr=__UpperCamelCase       )

				# Instantiate scheduler
				A__  :	Any         =							get_linear_schedule_with_warmup(
				    optimizer=__UpperCamelCase							,						num_warmup_steps=1_00							,						num_training_steps=(len(__UpperCamelCase       ) * num_epochs) // gradient_accumulation_steps							,						)

				# 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__  :	Dict         =							accelerator.prepare(
				    __UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase       )

				# Now we train the model
				for epoch in range(__UpperCamelCase       ):
								model.train()
								for step, batch in enumerate(__UpperCamelCase       ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device       )
												A__  :	Dict         =							model(**__UpperCamelCase       )
												A__  :	Dict         =							outputs.loss
												A__  :	List[str]         =							loss / gradient_accumulation_steps
												accelerator.backward(__UpperCamelCase       )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								A__  :	Optional[int]         =							0
								for step, batch in enumerate(__UpperCamelCase       ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device       )
												with torch.no_grad():
																A__  :	Union[str, Any]         =							model(**__UpperCamelCase       )
												A__  :	int         =							outputs.logits.argmax(dim=-1       )
												A__       ,							A__  :	Optional[Any]         =							accelerator.gather((predictions, batch['''labels'''])       )
												# New Code #
												# First we check if it's a distributed system
												if accelerator.use_distributed:
																# Then see if we're on the last batch of our eval dataloader
																if step == len(__UpperCamelCase       ) - 1:
																				# Last batch needs to be truncated on distributed systems as it contains additional samples
																				A__  :	Tuple         =							predictions[: len(eval_dataloader.dataset       ) - samples_seen]
																				A__  :	int         =							references[: len(eval_dataloader.dataset       ) - samples_seen]
																else:
																				# Otherwise we add the number of samples seen
																				samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
												metric.add_batch(
												    predictions=__UpperCamelCase							,						references=__UpperCamelCase							,						)

								A__  :	Union[str, Any]         =							metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"epoch {epoch}:"							,						__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      ) ->       Union[str, Any]:

				"""simple docstring"""







				A__  :	Tuple         =							argparse.ArgumentParser(description='''Simple example of training script.'''       )
				parser.add_argument(
				    '''--mixed_precision'''							,						type=__UpperCamelCase							,						default=__UpperCamelCase							,						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('''--cpu'''							,						action='''store_true'''							,						help='''If passed, will train on the CPU.'''       )
				A__  :	Dict         =							parser.parse_args()
				A__  :	Any         =							{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(__UpperCamelCase							,						__UpperCamelCase       )


if __name__ == "__main__":
					main()