import argparse

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
#
# 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 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		:     Union[str, Any]         =	16
SCREAMING_SNAKE_CASE		:     str         =	32






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Accelerator		, _SCREAMING_SNAKE_CASE     :				int = 16			):
						UpperCamelCase_		:							Tuple  =		AutoTokenizer.from_pretrained("""bert-base-cased"""			)
						UpperCamelCase_		:							Tuple  =		load_dataset("""glue"""		, """mrpc"""			)

						def tokenize_function(_SCREAMING_SNAKE_CASE     :				str			):
												# max_length=None => use the model max length (it's actually the default)
												UpperCamelCase_		:							Optional[int]  =		tokenizer(examples["""sentence1"""]		, examples["""sentence2"""]		, truncation=_SCREAMING_SNAKE_CASE		, max_length=_SCREAMING_SNAKE_CASE			)
												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():
												UpperCamelCase_		:							List[str]  =		datasets.map(
												    _SCREAMING_SNAKE_CASE		, batched=_SCREAMING_SNAKE_CASE		, 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
						UpperCamelCase_		:							Any  =		tokenized_datasets.rename_column("""label"""		, """labels"""			)

						def collate_fn(_SCREAMING_SNAKE_CASE     :				Dict			):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												UpperCamelCase_		:							List[str]  =		128 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		UpperCamelCase_		:							Dict  =		16
												elif accelerator.mixed_precision != "no":
																		UpperCamelCase_		:							Optional[Any]  =		8
												else:
																		UpperCamelCase_		:							List[str]  =		None

												return tokenizer.pad(
												    _SCREAMING_SNAKE_CASE		, padding="""longest"""		, max_length=_SCREAMING_SNAKE_CASE		, pad_to_multiple_of=_SCREAMING_SNAKE_CASE		, return_tensors="""pt"""		, )

						# Instantiate dataloaders.
						UpperCamelCase_		:							Optional[Any]  =		DataLoader(
						    tokenized_datasets["""train"""]		, shuffle=_SCREAMING_SNAKE_CASE		, collate_fn=_SCREAMING_SNAKE_CASE		, batch_size=_SCREAMING_SNAKE_CASE		, drop_last=_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							Union[str, Any]  =		DataLoader(
						    tokenized_datasets["""validation"""]		, shuffle=_SCREAMING_SNAKE_CASE		, collate_fn=_SCREAMING_SNAKE_CASE		, batch_size=_SCREAMING_SNAKE_CASE		, drop_last=(accelerator.mixed_precision == """fp8""")		, )

						return train_dataloader, eval_dataloader






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Union[str, Any]		, _SCREAMING_SNAKE_CASE     :				str			):
						# Initialize accelerator
						UpperCamelCase_		:							Union[str, Any]  =		Accelerator(cpu=args.cpu		, mixed_precision=args.mixed_precision			)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						UpperCamelCase_		:							Optional[Any]  =		config["""lr"""]
						UpperCamelCase_		:							List[str]  =		int(config["""num_epochs"""]			)
						UpperCamelCase_		:							int  =		int(config["""seed"""]			)
						UpperCamelCase_		:							Tuple  =		int(config["""batch_size"""]			)

						UpperCamelCase_		:							str  =		evaluate.load("""glue"""		, """mrpc"""			)

						# If the batch size is too big we use gradient accumulation
						UpperCamelCase_		:							int  =		1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												UpperCamelCase_		:							int  =		batch_size // MAX_GPU_BATCH_SIZE
												UpperCamelCase_		:							Optional[int]  =		MAX_GPU_BATCH_SIZE

						set_seed(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_,UpperCamelCase_		:							List[Any]  =		get_dataloaders(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						UpperCamelCase_		:							str  =		AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""		, return_dict=_SCREAMING_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).
						UpperCamelCase_		:							List[str]  =		model.to(accelerator.device			)
						# Instantiate optimizer
						UpperCamelCase_		:							Optional[int]  =		AdamW(params=model.parameters()		, lr=_SCREAMING_SNAKE_CASE			)

						# Instantiate scheduler
						UpperCamelCase_		:							Optional[int]  =		get_linear_schedule_with_warmup(
						    optimizer=_SCREAMING_SNAKE_CASE		, num_warmup_steps=100		, num_training_steps=(len(_SCREAMING_SNAKE_CASE			) * 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.

						UpperCamelCase_,UpperCamelCase_,UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							Union[str, Any]  =		accelerator.prepare(
						    _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)

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

												model.eval()
												for step, batch in enumerate(_SCREAMING_SNAKE_CASE			):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device			)
																		with torch.no_grad():
																								UpperCamelCase_		:							Optional[Any]  =		model(**_SCREAMING_SNAKE_CASE			)
																		UpperCamelCase_		:							Optional[int]  =		outputs.logits.argmax(dim=-1			)
																		UpperCamelCase_,UpperCamelCase_		:							Optional[Any]  =		accelerator.gather_for_metrics((predictions, batch["""labels"""])			)
																		metric.add_batch(
																		    predictions=_SCREAMING_SNAKE_CASE		, references=_SCREAMING_SNAKE_CASE		, )

												UpperCamelCase_		:							str  =		metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f'''epoch {epoch}:'''		, _SCREAMING_SNAKE_CASE			)






def     lowerCAmelCase_			(   ):
						UpperCamelCase_		:							Dict  =		argparse.ArgumentParser(description="""Simple example of training script."""			)
						parser.add_argument(
						    """--mixed_precision"""		, type=_SCREAMING_SNAKE_CASE		, default=_SCREAMING_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("""--cpu"""		, action="""store_true"""		, help="""If passed, will train on the CPU."""			)
						UpperCamelCase_		:							List[str]  =		parser.parse_args()
						UpperCamelCase_		:							Any  =		{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
						training_function(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
	main()







import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

SCREAMING_SNAKE_CASE		:     Tuple         =	{
    "facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json",
    "facebook/encodec_48khz": "https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json",
}

class        UpperCamelCase						(				__a				):
						a__		:int      =			'''encodec'''





						def __init__(self			,     __UpperCamelCase=[1.5, 3.0, 6.0, 12.0, 24.0]			,     __UpperCamelCase=24_000			,     __UpperCamelCase=1			,     __UpperCamelCase=False			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=128			,     __UpperCamelCase=32			,     __UpperCamelCase=1			,     __UpperCamelCase=[8, 5, 4, 2]			,     __UpperCamelCase="weight_norm"			,     __UpperCamelCase=7			,     __UpperCamelCase=7			,     __UpperCamelCase=3			,     __UpperCamelCase=2			,     __UpperCamelCase=True			,     __UpperCamelCase="reflect"			,     __UpperCamelCase=2			,     __UpperCamelCase=2			,     __UpperCamelCase=1.0			,     __UpperCamelCase=1_024			,     __UpperCamelCase=None			,     __UpperCamelCase=True			,     **__UpperCamelCase			,     )				->				Dict:
												UpperCamelCase_		:							str  =		target_bandwidths
												UpperCamelCase_		:							Tuple  =		sampling_rate
												UpperCamelCase_		:							str  =		audio_channels
												UpperCamelCase_		:							Any  =		normalize
												UpperCamelCase_		:							Union[str, Any]  =		chunk_length_s
												UpperCamelCase_		:							Optional[Any]  =		overlap
												UpperCamelCase_		:							Any  =		hidden_size
												UpperCamelCase_		:							Dict  =		num_filters
												UpperCamelCase_		:							List[str]  =		num_residual_layers
												UpperCamelCase_		:							Optional[int]  =		upsampling_ratios
												UpperCamelCase_		:							str  =		norm_type
												UpperCamelCase_		:							str  =		kernel_size
												UpperCamelCase_		:							Tuple  =		last_kernel_size
												UpperCamelCase_		:							int  =		residual_kernel_size
												UpperCamelCase_		:							str  =		dilation_growth_rate
												UpperCamelCase_		:							str  =		use_causal_conv
												UpperCamelCase_		:							str  =		pad_mode
												UpperCamelCase_		:							Union[str, Any]  =		compress
												UpperCamelCase_		:							Dict  =		num_lstm_layers
												UpperCamelCase_		:							Optional[int]  =		trim_right_ratio
												UpperCamelCase_		:							int  =		codebook_size
												UpperCamelCase_		:							str  =		codebook_dim if codebook_dim is not None else hidden_size
												UpperCamelCase_		:							Optional[int]  =		use_conv_shortcut

												if self.norm_type not in ["weight_norm", "time_group_norm"]:
																		raise ValueError(
																		    f'''self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'''						)

												super().__init__(**__UpperCamelCase						)





						@property
						def        A_							(self						)				->				Optional[int]:
												if self.chunk_length_s is None:
																		return None
												else:
																		return int(self.chunk_length_s * self.sampling_rate						)





						@property
						def        A_							(self						)				->				Optional[int]:
												if self.chunk_length_s is None or self.overlap is None:
																		return None
												else:
																		return max(1			,     int((1.0 - self.overlap) * self.chunk_length						)						)





						@property
						def        A_							(self						)				->				int:
												UpperCamelCase_		:							int  =		np.prod(self.upsampling_ratios						)
												return math.ceil(self.sampling_rate / hop_length						)





						@property
						def        A_							(self						)				->				int:
												return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)						)






