'''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
#
########################################################################


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



def  _lowerCAmelCase				(				__a			,      __a      )  ->      Optional[int]:
     '''simple docstring'''







     if args.with_tracking:
          _UpperCamelCase  :Tuple       =Accelerator(
              cpu=args.cpu			,      mixed_precision=args.mixed_precision			,      log_with="""all"""			,      project_dir=args.project_dir      )
     else:
          _UpperCamelCase  :Tuple       =Accelerator(cpu=args.cpu			,      mixed_precision=args.mixed_precision      )

     if hasattr(args.checkpointing_steps			,      """isdigit"""      ):
          if args.checkpointing_steps == "epoch":
               _UpperCamelCase  :Dict       =args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               _UpperCamelCase  :Optional[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  :Optional[Any]       =None
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     _UpperCamelCase  :Tuple       =config["""lr"""]
     _UpperCamelCase  :List[Any]       =int(config["""num_epochs"""]      )
     _UpperCamelCase  :int       =int(config["""seed"""]      )
     _UpperCamelCase  :Optional[int]       =int(config["""batch_size"""]      )

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

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

     def tokenize_function(__a      ):
          # max_length=None => use the model max length (it's actually the default)
          _UpperCamelCase  :int       =tokenizer(examples["""sentence1"""]			,      examples["""sentence2"""]			,      truncation=__a			,      max_length=__a      )
          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  :Dict       =datasets.map(
              __a			,      batched=__a			,      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  :Optional[Any]       =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[Any]       =batch_size // MAX_GPU_BATCH_SIZE
          _UpperCamelCase  :str       =MAX_GPU_BATCH_SIZE

     def collate_fn(__a      ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          _UpperCamelCase  :Optional[Any]       =1_28 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  :Tuple       =16
          elif accelerator.mixed_precision != "no":
               _UpperCamelCase  :Any       =8
          else:
               _UpperCamelCase  :Optional[Any]       =None

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

     # Instantiate dataloaders.
     _UpperCamelCase  :str       =DataLoader(
         tokenized_datasets["""train"""]			,      shuffle=__a			,      collate_fn=__a			,      batch_size=__a      )
     _UpperCamelCase  :Union[str, Any]       =DataLoader(
         tokenized_datasets["""validation"""]			,      shuffle=__a			,      collate_fn=__a			,      batch_size=__a      )

     set_seed(__a      )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     _UpperCamelCase  :Union[str, Any]       =AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""			,      return_dict=__a      )

     # 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  :Any       =model.to(accelerator.device      )

     # Instantiate optimizer
     _UpperCamelCase  :Optional[int]       =AdamW(params=model.parameters()			,      lr=__a      )

     # Instantiate scheduler
     _UpperCamelCase  :Any       =get_linear_schedule_with_warmup(
         optimizer=__a			,      num_warmup_steps=1_00			,      num_training_steps=(len(__a      ) * 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  :Optional[int]       =accelerator.prepare(
         __a			,      __a			,      __a			,      __a			,      __a      )

     # We need to keep track of how many total steps we have iterated over
     _UpperCamelCase  :int       =0
     # We also need to keep track of the stating epoch so files are named properly
     _UpperCamelCase  :int       =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  :Any       =os.path.basename(args.resume_from_checkpoint      )
          else:
               # Get the most recent checkpoint
               _UpperCamelCase  :str       =[f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
               dirs.sort(key=os.path.getctime      )
               _UpperCamelCase  :str       =dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          _UpperCamelCase  :Union[str, Any]       =os.path.splitext(__a      )[0]

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

    # Now we train the model
     for epoch in range(__a			,      __a      ):
          model.train()
          if args.with_tracking:
               _UpperCamelCase  :Any       =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  :Optional[Any]       =accelerator.skip_first_batches(__a			,      __a      )
               overall_step += resume_step
          else:
               # After the first iteration though, we need to go back to the original dataloader
               _UpperCamelCase  :Optional[int]       =train_dataloader
          for step, batch in enumerate(__a      ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device      )
               _UpperCamelCase  :int       =model(**__a      )
               _UpperCamelCase  :int       =outputs.loss
               _UpperCamelCase  :List[Any]       =loss / gradient_accumulation_steps
               # We keep track of the loss at each epoch
               if args.with_tracking:
                    total_loss += loss.detach().float()
               accelerator.backward(__a      )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

               if isinstance(__a			,      __a      ):
                    _UpperCamelCase  :Any       =F'''step_{overall_step}'''
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              _UpperCamelCase  :int       =os.path.join(args.output_dir			,      __a      )
                         accelerator.save_state(__a      )

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

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

          if checkpointing_steps == "epoch":
               _UpperCamelCase  :List[str]       =F'''epoch_{epoch}'''
               if args.output_dir is not None:
                    _UpperCamelCase  :List[str]       =os.path.join(args.output_dir			,      __a      )
               accelerator.save_state(__a      )

     if args.with_tracking:
          accelerator.end_training()



def  _lowerCAmelCase				(				)  ->      Tuple:
     '''simple docstring'''







     _UpperCamelCase  :Any       =argparse.ArgumentParser(description="""Simple example of training script."""      )
     parser.add_argument(
         """--mixed_precision"""			,      type=__a			,      default=__a			,      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=__a			,      default=__a			,      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=__a			,      default=__a			,      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=__a			,      default="""."""			,      help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""			,      )
     parser.add_argument(
         """--project_dir"""			,      type=__a			,      default="""logs"""			,      help="""Location on where to store experiment tracking logs` and relevent project information"""			,      )
     _UpperCamelCase  :Tuple       =parser.parse_args()
     _UpperCamelCase  :List[Any]       ={"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
     training_function(__a			,      __a      )


if __name__ == "__main__":
      main()
'''simple docstring'''

import subprocess
import sys
import warnings
from argparse import ArgumentParser
from pathlib import Path

from packaging import version

from .. import AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer
from ..utils import logging
from ..utils.import_utils import is_optimum_available
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import get_preprocessor


_lowerCamelCase :				List[Any]														= """1.5.0"""

_lowerCamelCase :				List[Any]														= ["""vision-encoder-decoder"""]



def  _lowerCAmelCase				(				__a      )  ->      Any:
     '''simple docstring'''







     if is_optimum_available():
          from optimum.version import __version__ as optimum_version

          _UpperCamelCase  :Dict       =version.parse(__a      )
          if parsed_optimum_version < version.parse(__a      ):
               raise RuntimeError(
                   F'''transformers.onnx requires optimum >= {MIN_OPTIMUM_VERSION} but {optimum_version} is installed. You '''
                   """can upgrade optimum by running: pip install -U optimum[exporters]"""      )
     else:
          raise RuntimeError(
              """transformers.onnx requires optimum to run, you can install the library by running: pip install """
              """optimum[exporters]"""      )
     _UpperCamelCase  :Tuple       =[
         sys.executable,
         """-m""",
         """optimum.exporters.onnx""",
         F'''--model {args.model}''',
         F'''--task {args.feature}''',
         F'''--framework {args.framework}''' if args.framework is not None else """""",
         F'''{args.output}''',
     ]
     _UpperCamelCase  :Union[str, Any]       =subprocess.Popen(""" """.join(__a      )			,      stdout=subprocess.PIPE			,      shell=__a      )
     proc.wait()

     logger.info(
         """The export was done by optimum.exporters.onnx. We recommend using to use this package directly in future, as """
         """transformers.onnx is deprecated, and will be removed in v5. You can find more information here: """
         """https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."""      )



def  _lowerCAmelCase				(				__a      )  ->      Optional[int]:
     '''simple docstring'''







     _UpperCamelCase  :Optional[int]       =args.output if args.output.is_file() else args.output.joinpath("""model.onnx"""      )
     if not args.output.parent.exists():
          args.output.parent.mkdir(parents=__a      )

     # Allocate the model
     _UpperCamelCase  :Dict       =FeaturesManager.get_model_from_feature(
         args.feature			,      args.model			,      framework=args.framework			,      cache_dir=args.cache_dir      )

     _UpperCamelCase       ,      _UpperCamelCase  :List[str]       =FeaturesManager.check_supported_model_or_raise(__a			,      feature=args.feature      )
     _UpperCamelCase  :int       =model_onnx_config(model.config      )

     if model_kind in ENCODER_DECODER_MODELS:
          _UpperCamelCase  :Tuple       =model.get_encoder()
          _UpperCamelCase  :Tuple       =model.get_decoder()

          _UpperCamelCase  :Dict       =onnx_config.get_encoder_config(encoder_model.config      )
          _UpperCamelCase  :str       =onnx_config.get_decoder_config(
              encoder_model.config			,      decoder_model.config			,      feature=args.feature      )

          if args.opset is None:
               _UpperCamelCase  :int       =max(encoder_onnx_config.default_onnx_opset			,      decoder_onnx_config.default_onnx_opset      )

          if args.opset < min(encoder_onnx_config.default_onnx_opset			,      decoder_onnx_config.default_onnx_opset      ):
               raise ValueError(
                   F'''Opset {args.opset} is not sufficient to export {model_kind}. At least '''
                   F''' {min(encoder_onnx_config.default_onnx_opset			,      decoder_onnx_config.default_onnx_opset      )} is required.'''      )

          _UpperCamelCase  :Optional[Any]       =AutoFeatureExtractor.from_pretrained(args.model      )

          _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =export(
              __a			,      __a			,      __a			,      args.opset			,      args.output.parent.joinpath("""encoder_model.onnx"""      )			,      )

          validate_model_outputs(
              __a			,      __a			,      __a			,      args.output.parent.joinpath("""encoder_model.onnx"""      )			,      __a			,      args.atol if args.atol else encoder_onnx_config.atol_for_validation			,      )

          _UpperCamelCase  :str       =AutoTokenizer.from_pretrained(args.model      )

          _UpperCamelCase       ,      _UpperCamelCase  :str       =export(
              __a			,      __a			,      __a			,      args.opset			,      args.output.parent.joinpath("""decoder_model.onnx"""      )			,      )

          validate_model_outputs(
              __a			,      __a			,      __a			,      args.output.parent.joinpath("""decoder_model.onnx"""      )			,      __a			,      args.atol if args.atol else decoder_onnx_config.atol_for_validation			,      )
          logger.info(
              F'''All good, model saved at: {args.output.parent.joinpath('encoder_model.onnx'      ).as_posix()},'''
              F''' {args.output.parent.joinpath('decoder_model.onnx'      ).as_posix()}'''      )

     else:
          # Instantiate the appropriate preprocessor
          if args.preprocessor == "auto":
               _UpperCamelCase  :Union[str, Any]       =get_preprocessor(args.model      )
          elif args.preprocessor == "tokenizer":
               _UpperCamelCase  :Dict       =AutoTokenizer.from_pretrained(args.model      )
          elif args.preprocessor == "image_processor":
               _UpperCamelCase  :Union[str, Any]       =AutoImageProcessor.from_pretrained(args.model      )
          elif args.preprocessor == "feature_extractor":
               _UpperCamelCase  :int       =AutoFeatureExtractor.from_pretrained(args.model      )
          elif args.preprocessor == "processor":
               _UpperCamelCase  :Dict       =AutoProcessor.from_pretrained(args.model      )
          else:
               raise ValueError(F'''Unknown preprocessor type \'{args.preprocessor}\''''      )

          # Ensure the requested opset is sufficient
          if args.opset is None:
               _UpperCamelCase  :Optional[int]       =onnx_config.default_onnx_opset

          if args.opset < onnx_config.default_onnx_opset:
               raise ValueError(
                   F'''Opset {args.opset} is not sufficient to export {model_kind}. '''
                   F'''At least  {onnx_config.default_onnx_opset} is required.'''      )

          _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =export(
              __a			,      __a			,      __a			,      args.opset			,      args.output			,      )

          if args.atol is None:
               _UpperCamelCase  :List[Any]       =onnx_config.atol_for_validation

          validate_model_outputs(__a			,      __a			,      __a			,      args.output			,      __a			,      args.atol      )
          logger.info(F'''All good, model saved at: {args.output.as_posix()}'''      )
          warnings.warn(
              """The export was done by transformers.onnx which is deprecated and will be removed in v5. We recommend"""
              """ using optimum.exporters.onnx in future. You can find more information here:"""
              """ https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."""			,      __a			,      )



def  _lowerCAmelCase				(				)  ->      List[str]:
     '''simple docstring'''







     _UpperCamelCase  :Union[str, Any]       =ArgumentParser("""Hugging Face Transformers ONNX exporter"""      )
     parser.add_argument(
         """-m"""			,      """--model"""			,      type=__a			,      required=__a			,      help="""Model ID on huggingface.co or path on disk to load model from."""      )
     parser.add_argument(
         """--feature"""			,      default="""default"""			,      help="""The type of features to export the model with."""			,      )
     parser.add_argument("""--opset"""			,      type=__a			,      default=__a			,      help="""ONNX opset version to export the model with."""      )
     parser.add_argument(
         """--atol"""			,      type=__a			,      default=__a			,      help="""Absolute difference tolerance when validating the model."""      )
     parser.add_argument(
         """--framework"""			,      type=__a			,      choices=["""pt""", """tf"""]			,      default=__a			,      help=(
             """The framework to use for the ONNX export."""
             """ If not provided, will attempt to use the local checkpoint's original framework"""
             """ or what is available in the environment."""
         )			,      )
     parser.add_argument("""output"""			,      type=__a			,      help="""Path indicating where to store generated ONNX model."""      )
     parser.add_argument("""--cache_dir"""			,      type=__a			,      default=__a			,      help="""Path indicating where to store cache."""      )
     parser.add_argument(
         """--preprocessor"""			,      type=__a			,      choices=["""auto""", """tokenizer""", """feature_extractor""", """image_processor""", """processor"""]			,      default="""auto"""			,      help="""Which type of preprocessor to use. 'auto' tries to automatically detect it."""			,      )
     parser.add_argument(
         """--export_with_transformers"""			,      action="""store_true"""			,      help=(
             """Whether to use transformers.onnx instead of optimum.exporters.onnx to perform the ONNX export. It can be """
             """useful when exporting a model supported in transformers but not in optimum, otherwise it is not """
             """recommended."""
         )			,      )

     _UpperCamelCase  :str       =parser.parse_args()
     if args.export_with_transformers or not is_optimum_available():
          export_with_transformers(__a      )
     else:
          export_with_optimum(__a      )


if __name__ == "__main__":
      _lowerCamelCase :				Optional[int]														= logging.get_logger("""transformers.onnx""")  # pylint: disable=invalid-name
      logger.setLevel(logging.INFO)
      main()