


'''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


_lowercase    :    Optional[int]			     =  16
_lowercase    :    List[Any]			     =  32


def    lowerCamelCase__   ( A     :    Accelerator   ,     A     :    int = 16   ,     A     :    str = "bert-base-cased"  ):




      '''simple docstring'''






      UpperCAmelCase         =			AutoTokenizer.from_pretrained(__snake_case  )
      UpperCAmelCase         =			load_dataset('''glue'''   ,     '''mrpc'''  )

      def tokenize_function(A     :    Tuple  ):
            # max_length=None => use the model max length (it's actually the default)
            UpperCAmelCase         =			tokenizer(examples['''sentence1''']   ,     examples['''sentence2''']   ,     truncation=__snake_case   ,     max_length=__snake_case  )
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      UpperCAmelCase         =			datasets.map(
          __snake_case   ,     batched=__snake_case   ,     remove_columns=['''idx''', '''sentence1''', '''sentence2''']   ,     load_from_cache_file=__snake_case  )

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

      def collate_fn(A     :    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(__snake_case   ,     padding='''max_length'''   ,     max_length=1_28   ,     return_tensors='''pt'''  )
            return tokenizer.pad(__snake_case   ,     padding='''longest'''   ,     return_tensors='''pt'''  )

      # Instantiate dataloaders.
      UpperCAmelCase         =			DataLoader(
          tokenized_datasets['''train''']   ,     shuffle=__snake_case   ,     collate_fn=__snake_case   ,     batch_size=__snake_case  )
      UpperCAmelCase         =			DataLoader(
          tokenized_datasets['''validation''']   ,     shuffle=__snake_case   ,     collate_fn=__snake_case   ,     batch_size=__snake_case  )

      return train_dataloader, eval_dataloader


def    lowerCamelCase__   ( A     :    Optional[Any]   ,     A     :    Any  ):




      '''simple docstring'''






      UpperCAmelCase         =			Accelerator()

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

      set_seed(__snake_case  )
      UpperCAmelCase         =			get_dataloaders(__snake_case   ,     __snake_case   ,     __snake_case  )

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

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

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

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

      # We need to keep track of how many total steps we have iterated over
      UpperCAmelCase         =			0
      # We also need to keep track of the stating epoch so files are named properly
      UpperCAmelCase         =			0

      # Now we train the model
      UpperCAmelCase         =			evaluate.load('''glue'''   ,     '''mrpc'''  )
      UpperCAmelCase         =			0
      UpperCAmelCase         =			{}
      for epoch in range(__snake_case   ,     __snake_case  ):
            model.train()
            for step, batch in enumerate(__snake_case  ):
                  UpperCAmelCase         =			model(**__snake_case  )
                  UpperCAmelCase         =			outputs.loss
                  UpperCAmelCase         =			loss / gradient_accumulation_steps
                  accelerator.backward(__snake_case  )
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

                  overall_step += 1

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

            UpperCAmelCase         =			metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"""epoch {epoch}:"""   ,     __snake_case  )
            UpperCAmelCase         =			eval_metric["accuracy"]

            if best_performance < eval_metric["accuracy"]:
                  UpperCAmelCase         =			eval_metric["accuracy"]

      if args.performance_lower_bound is not None:
            assert (
                args.performance_lower_bound <= best_performance
            ), f"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
            with open(os.path.join(args.output_dir   ,     '''all_results.json'''  )   ,     '''w'''  ) as f:
                  json.dump(__snake_case   ,     __snake_case  )


def    lowerCamelCase__   ( ):




      '''simple docstring'''






      UpperCAmelCase         =			argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''  )
      parser.add_argument(
          '''--model_name_or_path'''   ,     type=__snake_case   ,     default='''bert-base-cased'''   ,     help='''Path to pretrained model or model identifier from huggingface.co/models.'''   ,     required=__snake_case   ,     )
      parser.add_argument(
          '''--output_dir'''   ,     type=__snake_case   ,     default='''.'''   ,     help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''   ,     )
      parser.add_argument(
          '''--performance_lower_bound'''   ,     type=__snake_case   ,     default=__snake_case   ,     help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'''   ,     )
      parser.add_argument(
          '''--num_epochs'''   ,     type=__snake_case   ,     default=3   ,     help='''Number of train epochs.'''   ,     )
      UpperCAmelCase         =			parser.parse_args()
      UpperCAmelCase         =			{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
      training_function(__snake_case   ,     __snake_case  )


if __name__ == "__main__":
 main()









'''simple docstring'''



import math
from typing import Optional

import numpy as np

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


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

_lowercase    :    List[str]			     =  {
    """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__(     lowerCAmelCase ):
				__magic_name__       :   List[str]							= "encodec"




				def __init__(	self    :				List[str]   ,					lowerCAmelCase    :				int=[1.5, 3.0, 6.0, 12.0, 24.0]   ,					lowerCAmelCase    :				Tuple=24000   ,					lowerCAmelCase    :				List[Any]=1   ,					lowerCAmelCase    :				Union[str, Any]=False   ,					lowerCAmelCase    :				str=None   ,					lowerCAmelCase    :				Dict=None   ,					lowerCAmelCase    :				str=128   ,					lowerCAmelCase    :				Any=32   ,					lowerCAmelCase    :				Any=1   ,					lowerCAmelCase    :				List[Any]=[8, 5, 4, 2]   ,					lowerCAmelCase    :				Union[str, Any]="weight_norm"   ,					lowerCAmelCase    :				str=7   ,					lowerCAmelCase    :				Optional[int]=7   ,					lowerCAmelCase    :				Any=3   ,					lowerCAmelCase    :				Tuple=2   ,					lowerCAmelCase    :				Optional[Any]=True   ,					lowerCAmelCase    :				List[str]="reflect"   ,					lowerCAmelCase    :				Optional[int]=2   ,					lowerCAmelCase    :				List[Any]=2   ,					lowerCAmelCase    :				Union[str, Any]=1.0   ,					lowerCAmelCase    :				Optional[Any]=1024   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				str=True   ,					**lowerCAmelCase    :				str   ,					)->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			target_bandwidths
										UpperCAmelCase         =			sampling_rate
										UpperCAmelCase         =			audio_channels
										UpperCAmelCase         =			normalize
										UpperCAmelCase         =			chunk_length_s
										UpperCAmelCase         =			overlap
										UpperCAmelCase         =			hidden_size
										UpperCAmelCase         =			num_filters
										UpperCAmelCase         =			num_residual_layers
										UpperCAmelCase         =			upsampling_ratios
										UpperCAmelCase         =			norm_type
										UpperCAmelCase         =			kernel_size
										UpperCAmelCase         =			last_kernel_size
										UpperCAmelCase         =			residual_kernel_size
										UpperCAmelCase         =			dilation_growth_rate
										UpperCAmelCase         =			use_causal_conv
										UpperCAmelCase         =			pad_mode
										UpperCAmelCase         =			compress
										UpperCAmelCase         =			num_lstm_layers
										UpperCAmelCase         =			trim_right_ratio
										UpperCAmelCase         =			codebook_size
										UpperCAmelCase         =			codebook_dim if codebook_dim is not None else hidden_size
										UpperCAmelCase         =			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__(**lowerCAmelCase   )




				@property
				def   a__(	self    :				str   )->  Optional[int]:

										"""simple docstring"""




										if self.chunk_length_s is None:
																return None
										else:
																return int(self.chunk_length_s * self.sampling_rate   )




				@property
				def   a__(	self    :				List[str]   )->  Optional[int]:

										"""simple docstring"""




										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    :				List[Any]   )->  int:

										"""simple docstring"""




										UpperCAmelCase         =			np.prod(self.upsampling_ratios   )
										return math.ceil(self.sampling_rate / hop_length   )





				@property
				def   a__(	self    :				int   )->  int:

										"""simple docstring"""




										return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)   )





