




"""simple docstring"""




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
#
########################################################################


_lowerCamelCase     :		List[str]								=       16
_lowerCamelCase     :		Any								=       32







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_ = 16						)       ->	Optional[Any]:



       """simple docstring"""

       A__							=   AutoTokenizer.from_pretrained('''bert-base-cased'''						)
       A__							=   load_dataset('''glue'''			,    '''mrpc'''						)

       def tokenize_function(lowercase_						):
              # max_length=None => use the model max length (it's actually the default)
              A__							=   tokenizer(examples['''sentence1''']			,    examples['''sentence2''']			,    truncation=lowercase_			,    max_length=lowercase_						)
              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__							=   datasets.map(
                  lowercase_			,    batched=lowercase_			,    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__							=   tokenized_datasets.rename_column('''label'''			,    '''labels'''						)

       def collate_fn(lowercase_						):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              A__							=   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":
                     A__							=   16
              elif accelerator.mixed_precision != "no":
                     A__							=   8
              else:
                     A__							=   None

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

       # Instantiate dataloaders.
       A__							=   DataLoader(
           tokenized_datasets['''train''']			,    shuffle=lowercase_			,    collate_fn=lowercase_			,    batch_size=lowercase_			,    drop_last=lowercase_						)
       A__							=   DataLoader(
           tokenized_datasets['''validation''']			,    shuffle=lowercase_			,    collate_fn=lowercase_			,    batch_size=lowercase_			,    drop_last=(accelerator.mixed_precision == '''fp8''')			,    )

       return train_dataloader, eval_dataloader







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	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__							=   evaluate.load('''glue'''			,    '''mrpc'''						)

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

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

       # 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						)
       # Instantiate optimizer
       A__							=   AdamW(params=model.parameters()			,    lr=lowercase_						)

       # Instantiate scheduler
       A__							=   get_linear_schedule_with_warmup(
           optimizer=lowercase_			,    num_warmup_steps=100			,    num_training_steps=(len(lowercase_						) * 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__							=   accelerator.prepare(
           lowercase_			,    lowercase_			,    lowercase_			,    lowercase_			,    lowercase_						)

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

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

              A__							=   metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f"""epoch {epoch}:"""			,    lowercase_						)







def SCREAMING_SNAKE_CASE    (	)       ->	Dict:



       """simple docstring"""

       A__							=   argparse.ArgumentParser(description='''Simple example of training script.'''						)
       parser.add_argument(
           '''--mixed_precision'''			,    type=lowercase_			,    default=lowercase_			,    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__							=   parser.parse_args()
       A__							=   {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(lowercase_			,    lowercase_						)


if __name__ == "__main__":
  main()








from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     float)						->float:





           '''simple docstring'''

           return 0.0







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	tuple[int | float, int | float]:



       """simple docstring"""

       A__							=   min([-20, np.min(fft_results[1 : samplerate // 2 - 1]						)]						)
       A__							=   max([20, np.max(fft_results[1 : samplerate // 2 - 1]						)]						)
       return lowest, highest







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	None:



       """simple docstring"""

       A__							=   512
       A__							=   [1] + [0] * (size - 1)
       A__							=   [filter_type.process(lowercase_						) for item in inputs]

       A__							=   [0] * (samplerate - size)  # zero-padding
       outputs += filler
       A__							=   np.abs(np.fft.fft(lowercase_						)						)
       A__							=   20 * np.logaa(lowercase_						)

       # Frequencies on log scale from 24 to nyquist frequency
       plt.xlim(24			,    samplerate / 2 - 1						)
       plt.xlabel('''Frequency (Hz)'''						)
       plt.xscale('''log'''						)

       # Display within reasonable bounds
       A__							=   get_bounds(lowercase_			,    lowercase_						)
       plt.ylim(max([-80, bounds[0]]						)			,    min([80, bounds[1]]						)						)
       plt.ylabel('''Gain (dB)'''						)

       plt.plot(lowercase_						)
       plt.show()







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	None:



       """simple docstring"""

       A__							=   512
       A__							=   [1] + [0] * (size - 1)
       A__							=   [filter_type.process(lowercase_						) for item in inputs]

       A__							=   [0] * (samplerate - size)  # zero-padding
       outputs += filler
       A__							=   np.angle(np.fft.fft(lowercase_						)						)

       # Frequencies on log scale from 24 to nyquist frequency
       plt.xlim(24			,    samplerate / 2 - 1						)
       plt.xlabel('''Frequency (Hz)'''						)
       plt.xscale('''log'''						)

       plt.ylim(-2 * pi			,    2 * pi						)
       plt.ylabel('''Phase shift (Radians)'''						)
       plt.plot(np.unwrap(lowercase_			,    -2 * pi						)						)
       plt.show()


