






'''simple docstring'''




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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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 :  List[Any]  =						16
__SCREAMING_SNAKE_CASE :  List[Any]  =						32

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Dict    ,			_UpperCAmelCase						:   Optional[int]					)					->					Union[str, Any]:


       """simple docstring"""







       if args.with_tracking:
              _UpperCAmelCase  :						str							   =					Accelerator(
                  cpu=args.cpu    ,			mixed_precision=args.mixed_precision    ,			log_with="all"    ,			project_dir=args.project_dir					)
       else:
              _UpperCAmelCase  :						Any							   =					Accelerator(cpu=args.cpu    ,			mixed_precision=args.mixed_precision					)

       if hasattr(args.checkpointing_steps    ,			"isdigit"					):
              if args.checkpointing_steps == "epoch":
                     _UpperCAmelCase  :						Optional[Any]							   =					args.checkpointing_steps
              elif args.checkpointing_steps.isdigit():
                     _UpperCAmelCase  :						int							   =					int(args.checkpointing_steps					)
              else:
                     raise ValueError(
                         F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""					)
       else:
              _UpperCAmelCase  :						List[Any]							   =					None
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       _UpperCAmelCase  :						Any							   =					config["lr"]
       _UpperCAmelCase  :						Union[str, Any]							   =					int(config["num_epochs"]					)
       _UpperCAmelCase  :						Optional[Any]							   =					int(config["seed"]					)
       _UpperCAmelCase  :						Dict							   =					int(config["batch_size"]					)

       # We need to initialize the trackers we use, and also store our configuration
       if args.with_tracking:
              _UpperCAmelCase  :						int							   =					os.path.split(_UpperCAmelCase					)[-1].split("."					)[0]
              accelerator.init_trackers(_UpperCAmelCase    ,			_UpperCAmelCase					)

       _UpperCAmelCase  :						Optional[Any]							   =					AutoTokenizer.from_pretrained("bert-base-cased"					)
       _UpperCAmelCase  :						Dict							   =					load_dataset("glue"    ,			"mrpc"					)
       _UpperCAmelCase  :						List[Any]							   =					evaluate.load("glue"    ,			"mrpc"					)

       def tokenize_function(_UpperCAmelCase						:   List[Any]					):
              # max_length=None => use the model max length (it's actually the default)
              _UpperCAmelCase  :						str							   =					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():
              _UpperCAmelCase  :						Optional[Any]							   =					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
       _UpperCAmelCase  :						List[str]							   =					tokenized_datasets.rename_column("label"    ,			"labels"					)

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

       def collate_fn(_UpperCAmelCase						:   Union[str, Any]					):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              _UpperCAmelCase  :						Union[str, Any]							   =					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  :						int							   =					16
              elif accelerator.mixed_precision != "no":
                     _UpperCAmelCase  :						List[str]							   =					8
              else:
                     _UpperCAmelCase  :						Any							   =					None

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

       # Instantiate dataloaders.
       _UpperCAmelCase  :						Dict							   =					DataLoader(
           tokenized_datasets["train"]    ,			shuffle=_UpperCAmelCase    ,			collate_fn=_UpperCAmelCase    ,			batch_size=_UpperCAmelCase					)
       _UpperCAmelCase  :						Optional[int]							   =					DataLoader(
           tokenized_datasets["validation"]    ,			shuffle=_UpperCAmelCase    ,			collate_fn=_UpperCAmelCase    ,			batch_size=_UpperCAmelCase					)

       set_seed(_UpperCAmelCase					)

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       _UpperCAmelCase  :						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).
       _UpperCAmelCase  :						Union[str, Any]							   =					model.to(accelerator.device					)

       # Instantiate optimizer
       _UpperCAmelCase  :						Any							   =					AdamW(params=model.parameters()    ,			lr=_UpperCAmelCase					)

       # Instantiate scheduler
       _UpperCAmelCase  :						Dict							   =					get_linear_schedule_with_warmup(
           optimizer=_UpperCAmelCase    ,			num_warmup_steps=100    ,			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.
       _UpperCAmelCase  :						Optional[Any]							   =					accelerator.prepare(
           _UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase					)

       # We need to keep track of how many total steps we have iterated over
       _UpperCAmelCase  :						Union[str, Any]							   =					0
       # We also need to keep track of the stating epoch so files are named properly
       _UpperCAmelCase  :						Tuple							   =					0

       # Potentially load in the weights and states from a previous save
       if args.resume_from_checkpoint:
              if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
                     accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""					)
                     accelerator.load_state(args.resume_from_checkpoint					)
                     _UpperCAmelCase  :						Dict							   =					os.path.basename(args.resume_from_checkpoint					)
              else:
                     # Get the most recent checkpoint
                     _UpperCAmelCase  :						Union[str, Any]							   =					[f.name for f in os.scandir(os.getcwd()					) if f.is_dir()]
                     dirs.sort(key=os.path.getctime					)
                     _UpperCAmelCase  :						Optional[Any]							   =					dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
              # Extract `epoch_{i}` or `step_{i}`
              _UpperCAmelCase  :						Dict							   =					os.path.splitext(_UpperCAmelCase					)[0]

              if "epoch" in training_difference:
                     _UpperCAmelCase  :						Optional[Any]							   =					int(training_difference.replace("epoch_"    ,			""					)					) + 1
                     _UpperCAmelCase  :						List[str]							   =					None
              else:
                     _UpperCAmelCase  :						Optional[Any]							   =					int(training_difference.replace("step_"    ,			""					)					)
                     _UpperCAmelCase  :						int							   =					resume_step // len(_UpperCAmelCase					)
                     resume_step -= starting_epoch * len(_UpperCAmelCase					)

    # Now we train the model
       for epoch in range(_UpperCAmelCase    ,			_UpperCAmelCase					):
              model.train()
              if args.with_tracking:
                     _UpperCAmelCase  :						Optional[int]							   =					0
              if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
                     # We need to skip steps until we reach the resumed step
                     _UpperCAmelCase  :						List[Any]							   =					accelerator.skip_first_batches(_UpperCAmelCase    ,			_UpperCAmelCase					)
                     overall_step += resume_step
              else:
                     # After the first iteration though, we need to go back to the original dataloader
                     _UpperCAmelCase  :						Dict							   =					train_dataloader
              for step, batch in enumerate(_UpperCAmelCase					):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device					)
                     _UpperCAmelCase  :						Union[str, Any]							   =					model(**_UpperCAmelCase					)
                     _UpperCAmelCase  :						Tuple							   =					outputs.loss
                     _UpperCAmelCase  :						int							   =					loss / gradient_accumulation_steps
                     # We keep track of the loss at each epoch
                     if args.with_tracking:
                            total_loss += loss.detach().float()
                     accelerator.backward(_UpperCAmelCase					)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1

                     if isinstance(_UpperCAmelCase    ,			_UpperCAmelCase					):
                            _UpperCAmelCase  :						Optional[Any]							   =					F"""step_{overall_step}"""
                            if overall_step % checkpointing_steps == 0:
                                   if args.output_dir is not None:
                                          _UpperCAmelCase  :						Optional[int]							   =					os.path.join(args.output_dir    ,			_UpperCAmelCase					)
                                   accelerator.save_state(_UpperCAmelCase					)

              model.eval()
              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():
                            _UpperCAmelCase  :						Dict							   =					model(**_UpperCAmelCase					)
                     _UpperCAmelCase  :						Dict							   =					outputs.logits.argmax(dim=-1					)
                     _UpperCAmelCase  :						str							   =					accelerator.gather_for_metrics((predictions, batch["labels"])					)
                     metric.add_batch(
                         predictions=_UpperCAmelCase    ,			references=_UpperCAmelCase    ,			)

              _UpperCAmelCase  :						Union[str, Any]							   =					metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F"""epoch {epoch}:"""    ,			_UpperCAmelCase					)
              if args.with_tracking:
                     accelerator.log(
                         {
                             "accuracy": eval_metric["accuracy"],
                             "f1": eval_metric["f1"],
                             "train_loss": total_loss.item() / len(_UpperCAmelCase					),
                             "epoch": epoch,
                         }    ,			step=_UpperCAmelCase    ,			)

              if checkpointing_steps == "epoch":
                     _UpperCAmelCase  :						Optional[Any]							   =					F"""epoch_{epoch}"""
                     if args.output_dir is not None:
                            _UpperCAmelCase  :						Union[str, Any]							   =					os.path.join(args.output_dir    ,			_UpperCAmelCase					)
                     accelerator.save_state(_UpperCAmelCase					)

       if args.with_tracking:
              accelerator.end_training()

def 			UpperCamelCase_							(      )					->					List[Any]:


       """simple docstring"""







       _UpperCAmelCase  :						List[str]							   =					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."					)
       parser.add_argument(
           "--checkpointing_steps"    ,			type=_UpperCAmelCase    ,			default=_UpperCAmelCase    ,			help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."    ,			)
       parser.add_argument(
           "--resume_from_checkpoint"    ,			type=_UpperCAmelCase    ,			default=_UpperCAmelCase    ,			help="If the training should continue from a checkpoint folder."    ,			)
       parser.add_argument(
           "--with_tracking"    ,			action="store_true"    ,			help="Whether to load in all available experiment trackers from the environment and use them for logging."    ,			)
       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(
           "--project_dir"    ,			type=_UpperCAmelCase    ,			default="logs"    ,			help="Location on where to store experiment tracking logs` and relevent project information"    ,			)
       _UpperCAmelCase  :						Dict							   =					parser.parse_args()
       _UpperCAmelCase  :						List[str]							   =					{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
       training_function(_UpperCAmelCase    ,			_UpperCAmelCase					)


if __name__ == "__main__":
  main()


'''simple docstring'''




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


__SCREAMING_SNAKE_CASE :  Optional[int]  =						logging.get_logger(__name__)

class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      __UpperCamelCase:						List[Any]      			=		"speech-encoder-decoder"
      __UpperCamelCase:						Optional[int]      			=		True




      def __init__( self		:       Optional[int]		,      **A		:       Tuple ):
             super().__init__(**A )
             if "encoder" not in kwargs or "decoder" not in kwargs:
                    raise ValueError(
                        F"""A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and"""
                        F""" `decoder` sub-configurations are passed, but only {kwargs}""" )

             _UpperCAmelCase  :						Dict							   =					kwargs.pop("encoder" )
             _UpperCAmelCase  :						Any							   =					encoder_config.pop("model_type" )
             _UpperCAmelCase  :						str							   =					kwargs.pop("decoder" )
             _UpperCAmelCase  :						int							   =					decoder_config.pop("model_type" )

             _UpperCAmelCase  :						List[Any]							   =					AutoConfig.for_model(A		,      **A )
             _UpperCAmelCase  :						str							   =					AutoConfig.for_model(A		,      **A )
             _UpperCAmelCase  :						Optional[int]							   =					True




      @classmethod
      def   _A			( cls		:       int		,      A		:       PretrainedConfig		,      A		:       PretrainedConfig		,      **A		:       Dict ):
             logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config" )
             _UpperCAmelCase  :						Any							   =					True
             _UpperCAmelCase  :						Union[str, Any]							   =					True

             return cls(encoder=encoder_config.to_dict()		,      decoder=decoder_config.to_dict()		,      **A )





      def   _A			( self		:       Tuple ):
             _UpperCAmelCase  :						List[str]							   =					copy.deepcopy(self.__dict__ )
             _UpperCAmelCase  :						Any							   =					self.encoder.to_dict()
             _UpperCAmelCase  :						Union[str, Any]							   =					self.decoder.to_dict()
             _UpperCAmelCase  :						Union[str, Any]							   =					self.__class__.model_type
             return output

