

"""simple docstring"""





import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_a							=							16
_a							=							32




def   _A  (  UpperCamelCase_					:	Accelerator,      UpperCamelCase_					:	int = 16,      UpperCamelCase_					:	str = "bert-base-cased")     ->						Tuple:
   '''simple docstring'''


   __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase_)
   __lowercase								=						load_dataset("glue",      "mrpc")

   def tokenize_function(UpperCamelCase_					:	Union[str, Any]):
      # max_length=None => use the model max length (it's actually the default)
      __lowercase								=						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
   __lowercase								=						datasets.map(
       UpperCAmelCase_,      batched=UpperCAmelCase_,      remove_columns=["idx", "sentence1", "sentence2"],      load_from_cache_file=UpperCAmelCase_)

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   __lowercase								=						tokenized_datasets.rename_column("label",      "labels")

   def collate_fn(UpperCamelCase_					:	List[str]):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      if accelerator.distributed_type == DistributedType.TPU:
         return tokenizer.pad(UpperCAmelCase_,      padding="max_length",      max_length=128,      return_tensors="pt")
      return tokenizer.pad(UpperCAmelCase_,      padding="longest",      return_tensors="pt")

   # Instantiate dataloaders.
   __lowercase								=						DataLoader(
       tokenized_datasets["train"],      shuffle=UpperCAmelCase_,      collate_fn=UpperCAmelCase_,      batch_size=UpperCAmelCase_)
   __lowercase								=						DataLoader(
       tokenized_datasets["validation"],      shuffle=UpperCAmelCase_,      collate_fn=UpperCAmelCase_,      batch_size=UpperCAmelCase_)

   return train_dataloader, eval_dataloader




def   _A  (  UpperCamelCase_					:	Union[str, Any],      UpperCamelCase_					:	Any,      UpperCamelCase_					:	Dict,      UpperCamelCase_					:	List[str])     ->						Union[str, Any]:
   '''simple docstring'''


   model.eval()
   __lowercase								=						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():
         __lowercase								=						model(**UpperCAmelCase_)
      __lowercase								=						outputs.logits.argmax(dim=-1)
      # It is slightly faster to call this once, than multiple times
      __lowercase		,__lowercase								=						accelerator.gather(
          (predictions, batch["labels"]))  # If we are in a multiprocess environment, the last batch has duplicates
      if accelerator.use_distributed:
         if step == len(UpperCAmelCase_) - 1:
            __lowercase								=						predictions[: len(eval_dataloader.dataset) - samples_seen]
            __lowercase								=						references[: len(eval_dataloader.dataset) - samples_seen]
         else:
            samples_seen += references.shape[0]
      metric.add_batch(
          predictions=UpperCAmelCase_,      references=UpperCAmelCase_,      )

   __lowercase								=						metric.compute()
   return eval_metric["accuracy"]




def   _A  (  UpperCamelCase_					:	str,      UpperCamelCase_					:	Any)     ->						Union[str, Any]:
   '''simple docstring'''


   __lowercase								=						Accelerator()

   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   __lowercase								=						config["lr"]
   __lowercase								=						int(config["num_epochs"])
   __lowercase								=						int(config["seed"])
   __lowercase								=						int(config["batch_size"])
   __lowercase								=						args.model_name_or_path

   set_seed(UpperCAmelCase_)
   __lowercase		,__lowercase								=						get_dataloaders(UpperCAmelCase_,      UpperCAmelCase_,      UpperCAmelCase_)

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   __lowercase								=						AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase_,      return_dict=UpperCAmelCase_)

   # Instantiate optimizer
   __lowercase								=						(
       AdamW
       if accelerator.state.deepspeed_plugin is None
       or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
       else DummyOptim
   )
   __lowercase								=						optimizer_cls(params=model.parameters(),      lr=UpperCAmelCase_)

   if accelerator.state.deepspeed_plugin is not None:
      __lowercase								=						accelerator.state.deepspeed_plugin.deepspeed_config[
          "gradient_accumulation_steps"
      ]
   else:
      __lowercase								=						1
   __lowercase								=						(len(UpperCAmelCase_) * num_epochs) // gradient_accumulation_steps

   # Instantiate scheduler
   if (
       accelerator.state.deepspeed_plugin is None
       or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
   ):
      __lowercase								=						get_linear_schedule_with_warmup(
          optimizer=UpperCAmelCase_,      num_warmup_steps=0,      num_training_steps=UpperCAmelCase_,      )
   else:
      __lowercase								=						DummyScheduler(UpperCAmelCase_,      total_num_steps=UpperCAmelCase_,      warmup_num_steps=0)

   # 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.
   __lowercase		,__lowercase		,__lowercase		,__lowercase		,__lowercase								=						accelerator.prepare(
       UpperCAmelCase_,      UpperCAmelCase_,      UpperCAmelCase_,      UpperCAmelCase_,      UpperCAmelCase_)

   # We need to keep track of how many total steps we have iterated over
   __lowercase								=						0
   # We also need to keep track of the stating epoch so files are named properly
   __lowercase								=						0
   __lowercase								=						evaluate.load("glue",      "mrpc")
   __lowercase								=						num_epochs

   if args.partial_train_epoch is not None:
      __lowercase								=						args.partial_train_epoch

   if args.resume_from_checkpoint:
      accelerator.load_state(args.resume_from_checkpoint)
      __lowercase								=						args.resume_from_checkpoint.split("epoch_")[1]
      __lowercase								=						""
      for char in epoch_string:
         if char.isdigit():
            state_epoch_num += char
         else:
            break
      __lowercase								=						int(UpperCAmelCase_) + 1
      __lowercase								=						evaluation_loop(UpperCAmelCase_,      UpperCAmelCase_,      UpperCAmelCase_,      UpperCAmelCase_)
      accelerator.print("resumed checkpoint performance:",      UpperCAmelCase_)
      accelerator.print("resumed checkpoint's scheduler's lr:",      lr_scheduler.get_lr()[0])
      accelerator.print("resumed optimizers's lr:",      optimizer.param_groups[0]["lr"])
      with open(os.path.join(args.output_dir,      F"""state_{starting_epoch-1}.json"""),      "r") as f:
         __lowercase								=						json.load(UpperCAmelCase_)
         assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
         assert (
             resumed_state["lr"] == lr_scheduler.get_lr()[0]
         ), "Scheduler learning rate mismatch, loading from checkpoint failed"
         assert (
             resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
         ), "Optimizer learning rate mismatch, loading from checkpoint failed"
         assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
         return

    # Now we train the model
   __lowercase								=						{}
   for epoch in range(UpperCAmelCase_,      UpperCAmelCase_):
      model.train()
      for step, batch in enumerate(UpperCAmelCase_):
         __lowercase								=						model(**UpperCAmelCase_)
         __lowercase								=						outputs.loss
         __lowercase								=						loss / gradient_accumulation_steps
         accelerator.backward(UpperCAmelCase_)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1
      __lowercase								=						F"""epoch_{epoch}"""
      __lowercase								=						os.path.join(args.output_dir,      UpperCAmelCase_)
      accelerator.save_state(UpperCAmelCase_)
      __lowercase								=						evaluation_loop(UpperCAmelCase_,      UpperCAmelCase_,      UpperCAmelCase_,      UpperCAmelCase_)
      __lowercase								=						accuracy
      __lowercase								=						lr_scheduler.get_lr()[0]
      __lowercase								=						optimizer.param_groups[0]["lr"]
      __lowercase								=						epoch
      __lowercase								=						overall_step
      accelerator.print(F"""epoch {epoch}:""",      UpperCAmelCase_)

      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
         with open(os.path.join(args.output_dir,      F"""state_{epoch}.json"""),      "w") as f:
            json.dump(UpperCAmelCase_,      UpperCAmelCase_)




def   _A  (  )     ->						str:
   '''simple docstring'''


   __lowercase								=						argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage.")
   parser.add_argument(
       "--model_name_or_path",      type=UpperCAmelCase_,      default="bert-base-cased",      help="Path to pretrained model or model identifier from huggingface.co/models.",      required=UpperCAmelCase_,      )
   parser.add_argument(
       "--output_dir",      type=UpperCAmelCase_,      default=".",      help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.",      )
   parser.add_argument(
       "--resume_from_checkpoint",      type=UpperCAmelCase_,      default=UpperCAmelCase_,      help="If the training should continue from a checkpoint folder.",      )
   parser.add_argument(
       "--partial_train_epoch",      type=UpperCAmelCase_,      default=UpperCAmelCase_,      help="If passed, the training will stop after this number of epochs.",      )
   parser.add_argument(
       "--num_epochs",      type=UpperCAmelCase_,      default=2,      help="Number of train epochs.",      )
   __lowercase								=						parser.parse_args()
   __lowercase								=						{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

   training_function(UpperCAmelCase_,      UpperCAmelCase_)


if __name__ == "__main__":
   main()







"""simple docstring"""





from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
   from .notes_encoder import SpectrogramNotesEncoder
   from .continous_encoder import SpectrogramContEncoder
   from .pipeline_spectrogram_diffusion import (
       SpectrogramContEncoder,
       SpectrogramDiffusionPipeline,
       TaFilmDecoder,
   )

try:
   if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
   from .midi_utils import MidiProcessor




