





import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator

def   a_   (			lowerCAmelCase_				:      List[str]     ):
   __lowerCAmelCase							     =     fname.split(os.path.sep     )[-1]
   return re.search(R'^(.*)_\d+\.jpg$',     _a     ).groups()[0]



class     _UpperCAmelCase   (   SCREAMING_SNAKE_CASE__   ):

  """simple docstring"""


  def __init__(    self			:		Tuple ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		Union[str, Any]=None ,  lowerCAmelCase_			:		List[str]=None					)							->						str:
     __lowerCAmelCase							     =     file_names
     __lowerCAmelCase							     =     image_transform
     __lowerCAmelCase							     =     label_to_id






  def __len__(    self			:		Optional[int]					)							->						Tuple:
     return len(self.file_names					)






  def __getitem__(    self			:		int ,  lowerCAmelCase_			:		Optional[Any]					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     self.file_names[idx]
     __lowerCAmelCase							     =     PIL.Image.open(_SCREAMING_SNAKE_CASE					)
     __lowerCAmelCase							     =     raw_image.convert('RGB'					)
     if self.image_transform is not None:
        __lowerCAmelCase							     =     self.image_transform(_SCREAMING_SNAKE_CASE					)
     __lowerCAmelCase							     =     extract_label(_SCREAMING_SNAKE_CASE					)
     if self.label_to_id is not None:
        __lowerCAmelCase							     =     self.label_to_id[label]
     return {"image": image, "label": label}





def   a_   (			lowerCAmelCase_				:      Tuple,     lowerCAmelCase_				:      str     ):
   # Initialize accelerator
   if args.with_tracking:
      __lowerCAmelCase							     =     Accelerator(
          cpu=args.cpu,     mixed_precision=args.mixed_precision,     log_with='all',     project_dir=args.project_dir     )
   else:
      __lowerCAmelCase							     =     Accelerator(cpu=args.cpu,     mixed_precision=args.mixed_precision     )

   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   __lowerCAmelCase							     =     config["lr"]
   __lowerCAmelCase							     =     int(config['num_epochs']     )
   __lowerCAmelCase							     =     int(config['seed']     )
   __lowerCAmelCase							     =     int(config['batch_size']     )
   __lowerCAmelCase							     =     config["image_size"]
   if not isinstance(_a,     (list, tuple)     ):
      __lowerCAmelCase							     =     (image_size, image_size)

   # Parse out whether we are saving every epoch or after a certain number of batches
   if hasattr(args.checkpointing_steps,     'isdigit'     ):
      if args.checkpointing_steps == "epoch":
         __lowerCAmelCase							     =     args.checkpointing_steps
      elif args.checkpointing_steps.isdigit():
         __lowerCAmelCase							     =     int(args.checkpointing_steps     )
      else:
         raise ValueError(
             F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""     )
   else:
      __lowerCAmelCase							     =     None

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

   # Grab all the image filenames
   __lowerCAmelCase							     =     [os.path.join(args.data_dir,     _a     ) for fname in os.listdir(args.data_dir     ) if fname.endswith('.jpg'     )]

   # Build the label correspondences
   __lowerCAmelCase							     =     [extract_label(_a     ) for fname in file_names]
   __lowerCAmelCase							     =     list(set(_a     )     )
   id_to_label.sort()
   __lowerCAmelCase							     =     {lbl: i for i, lbl in enumerate(_a     )}

   # Set the seed before splitting the data.
   np.random.seed(_a     )
   torch.manual_seed(_a     )
   torch.cuda.manual_seed_all(_a     )

   # Split our filenames between train and validation
   __lowerCAmelCase							     =     np.random.permutation(len(_a     )     )
   __lowerCAmelCase							     =     int(0.8 * len(_a     )     )
   __lowerCAmelCase							     =     random_perm[:cut]
   __lowerCAmelCase							     =     random_perm[cut:]

   # For training we use a simple RandomResizedCrop
   __lowerCAmelCase							     =     Compose([RandomResizedCrop(_a,     scale=(0.5, 1.0)     ), ToTensor()]     )
   __lowerCAmelCase							     =     PetsDataset(
       [file_names[i] for i in train_split],     image_transform=_a,     label_to_id=_a     )

   # For evaluation, we use a deterministic Resize
   __lowerCAmelCase							     =     Compose([Resize(_a     ), ToTensor()]     )
   __lowerCAmelCase							     =     PetsDataset([file_names[i] for i in eval_split],     image_transform=_a,     label_to_id=_a     )

   # Instantiate dataloaders.
   __lowerCAmelCase							     =     DataLoader(_a,     shuffle=_a,     batch_size=_a,     num_workers=4     )
   __lowerCAmelCase							     =     DataLoader(_a,     shuffle=_a,     batch_size=_a,     num_workers=4     )

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   __lowerCAmelCase							     =     create_model('resnet50d',     pretrained=_a,     num_classes=len(_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).
   __lowerCAmelCase							     =     model.to(accelerator.device     )

   # Freezing the base model
   for param in model.parameters():
      __lowerCAmelCase							     =     False
   for param in model.get_classifier().parameters():
      __lowerCAmelCase							     =     True

   # We normalize the batches of images to be a bit faster.
   __lowerCAmelCase							     =     torch.tensor(model.default_cfg['mean']     )[None, :, None, None].to(accelerator.device     )
   __lowerCAmelCase							     =     torch.tensor(model.default_cfg['std']     )[None, :, None, None].to(accelerator.device     )

   # Instantiate optimizer
   __lowerCAmelCase							     =     torch.optim.Adam(params=model.parameters(),     lr=lr / 25     )

   # Instantiate learning rate scheduler
   __lowerCAmelCase							     =     OneCycleLR(optimizer=_a,     max_lr=_a,     epochs=_a,     steps_per_epoch=len(_a     )     )

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

      if "epoch" in training_difference:
         __lowerCAmelCase							     =     int(training_difference.replace('epoch_',     ''     )     ) + 1
         __lowerCAmelCase							     =     None
      else:
         __lowerCAmelCase							     =     int(training_difference.replace('step_',     ''     )     )
         __lowerCAmelCase							     =     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:
         __lowerCAmelCase							     =     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
         __lowerCAmelCase							     =     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
         __lowerCAmelCase							     =     train_dataloader
      for batch in active_dataloader:
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         __lowerCAmelCase							     =     {k: v.to(accelerator.device     ) for k, v in batch.items()}
         __lowerCAmelCase							     =     (batch["image"] - mean) / std
         __lowerCAmelCase							     =     model(_a     )
         __lowerCAmelCase							     =     torch.nn.functional.cross_entropy(_a,     batch['label']     )
         # We keep track of the loss at each epoch
         if args.with_tracking:
            total_loss += loss.detach().float()
         accelerator.backward(_a     )
         optimizer.step()
         lr_scheduler.step()
         optimizer.zero_grad()
         overall_step += 1
         if isinstance(_a,     _a     ):
            __lowerCAmelCase							     =     F"""step_{overall_step}"""
            if overall_step % checkpointing_steps == 0:
               if args.output_dir is not None:
                  __lowerCAmelCase							     =     os.path.join(args.output_dir,     _a     )
               accelerator.save_state(_a     )
      model.eval()
      __lowerCAmelCase							     =     0
      __lowerCAmelCase							     =     0
      for step, batch in enumerate(_a     ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         __lowerCAmelCase							     =     {k: v.to(accelerator.device     ) for k, v in batch.items()}
         __lowerCAmelCase							     =     (batch["image"] - mean) / std
         with torch.no_grad():
            __lowerCAmelCase							     =     model(_a     )
         __lowerCAmelCase							     =     outputs.argmax(dim=-1     )
         __lowerCAmelCase							     =     accelerator.gather_for_metrics((predictions, batch['label'])     )
         __lowerCAmelCase							     =     predictions == references
         num_elems += accurate_preds.shape[0]
         accurate += accurate_preds.long().sum()

      __lowerCAmelCase							     =     accurate.item() / num_elems
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"""epoch {epoch}: {100 * eval_metric:.2f}"""     )
      if args.with_tracking:
         accelerator.log(
             {
                 'accuracy': 100 * eval_metric,
                 'train_loss': total_loss.item() / len(_a     ),
                 'epoch': epoch,
             },     step=_a,     )
      if checkpointing_steps == "epoch":
         __lowerCAmelCase							     =     F"""epoch_{epoch}"""
         if args.output_dir is not None:
            __lowerCAmelCase							     =     os.path.join(args.output_dir,     _a     )
         accelerator.save_state(_a     )

   if args.with_tracking:
      accelerator.end_training()

def   a_   (			):
   __lowerCAmelCase							     =     argparse.ArgumentParser(description='Simple example of training script.'     )
   parser.add_argument('--data_dir',     required=_a,     help='The data folder on disk.'     )
   parser.add_argument('--fp16',     action='store_true',     help='If passed, will use FP16 training.'     )
   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(
       '--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(
       '--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(
       '--project_dir',     type=_a,     default='logs',     help='Location on where to store experiment tracking logs` and relevent project information',     )
   __lowerCAmelCase							     =     parser.parse_args()
   __lowerCAmelCase							     =     {"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
   training_function(_a,     _a     )


if __name__ == "__main__":
     main()






from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional

from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


try:
     from fastapi import Body, FastAPI, HTTPException
     from fastapi.routing import APIRoute
     from pydantic import BaseModel
     from starlette.responses import JSONResponse
     from uvicorn import run

     _snake_case  :   int						   =  True
except (ImportError, AttributeError):
     _snake_case  :   int						   =  object

     def   a_   (			*lowerCAmelCase_				:      List[str],     **lowerCAmelCase_				:      Optional[Any]     ):
        pass




     _snake_case  :   Union[str, Any]						   =  False


_snake_case  :   int						   =  logging.get_logger('transformers-cli/serving')

def   a_   (			lowerCAmelCase_				:      Namespace     ):
   __lowerCAmelCase							     =     pipeline(
       task=args.task,     model=args.model if args.model else None,     config=args.config,     tokenizer=args.tokenizer,     device=args.device,     )
   return ServeCommand(lowerCAmelCase_,     args.host,     args.port,     args.workers     )



class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			42





class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			42
  a_      =			42





class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			42





class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			42





class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  @staticmethod
  def     lowercase		(    lowerCAmelCase_			:		ArgumentParser					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     parser.add_parser(
         'serve' ,  help='CLI tool to run inference requests through REST and GraphQL endpoints.'					)
     serve_parser.add_argument(
         '--task' ,  type=lowerCAmelCase_ ,  choices=get_supported_tasks() ,  help='The task to run the pipeline on' ,  )
     serve_parser.add_argument('--host' ,  type=lowerCAmelCase_ ,  default='localhost' ,  help='Interface the server will listen on.'					)
     serve_parser.add_argument('--port' ,  type=lowerCAmelCase_ ,  default=8_8_8_8 ,  help='Port the serving will listen to.'					)
     serve_parser.add_argument('--workers' ,  type=lowerCAmelCase_ ,  default=1 ,  help='Number of http workers'					)
     serve_parser.add_argument('--model' ,  type=lowerCAmelCase_ ,  help='Model\'s name or path to stored model.'					)
     serve_parser.add_argument('--config' ,  type=lowerCAmelCase_ ,  help='Model\'s config name or path to stored model.'					)
     serve_parser.add_argument('--tokenizer' ,  type=lowerCAmelCase_ ,  help='Tokenizer name to use.'					)
     serve_parser.add_argument(
         '--device' ,  type=lowerCAmelCase_ ,  default=-1 ,  help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)' ,  )
     serve_parser.set_defaults(func=lowerCAmelCase_					)






  def __init__(    self			:		List[str] ,  lowerCAmelCase_			:		Pipeline ,  lowerCAmelCase_			:		str ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		int					)							->						str:
     __lowerCAmelCase							     =     pipeline

     __lowerCAmelCase							     =     host
     __lowerCAmelCase							     =     port
     __lowerCAmelCase							     =     workers

     if not _serve_dependencies_installed:
        raise RuntimeError(
            'Using serve command requires FastAPI and uvicorn. '
            'Please install transformers with [serving]: pip install "transformers[serving]".'
            'Or install FastAPI and uvicorn separately.'					)
     else:
        logger.info(f"""Serving model over {host}:{port}"""					)
        __lowerCAmelCase							     =     FastAPI(
            routes=[
                APIRoute(
                    '/' ,  self.model_info ,  response_model=lowerCAmelCase_ ,  response_class=lowerCAmelCase_ ,  methods=['GET'] ,  ),
                APIRoute(
                    '/tokenize' ,  self.tokenize ,  response_model=lowerCAmelCase_ ,  response_class=lowerCAmelCase_ ,  methods=['POST'] ,  ),
                APIRoute(
                    '/detokenize' ,  self.detokenize ,  response_model=lowerCAmelCase_ ,  response_class=lowerCAmelCase_ ,  methods=['POST'] ,  ),
                APIRoute(
                    '/forward' ,  self.forward ,  response_model=lowerCAmelCase_ ,  response_class=lowerCAmelCase_ ,  methods=['POST'] ,  ),
            ] ,  timeout=6_0_0 ,  )






  def     lowercase		(    self			:		Tuple					)							->						str:
     run(self._app ,  host=self.host ,  port=self.port ,  workers=self.workers					)






  def     lowercase		(    self			:		Any					)							->						List[str]:
     return ServeModelInfoResult(infos=vars(self._pipeline.model.config					)					)






  def     lowercase		(    self			:		int ,  lowerCAmelCase_			:		str = Body(lowerCAmelCase_ ,  embed=lowerCAmelCase_					) ,  lowerCAmelCase_			:		bool = Body(lowerCAmelCase_ ,  embed=lowerCAmelCase_					)					)							->						Dict:
     try:
        __lowerCAmelCase							     =     self._pipeline.tokenizer.tokenize(lowerCAmelCase_					)

        if return_ids:
           __lowerCAmelCase							     =     self._pipeline.tokenizer.convert_tokens_to_ids(lowerCAmelCase_					)
           return ServeTokenizeResult(tokens=lowerCAmelCase_ ,  tokens_ids=lowerCAmelCase_					)
        else:
           return ServeTokenizeResult(tokens=lowerCAmelCase_					)

     except Exception as e:
        raise HTTPException(status_code=5_0_0 ,  detail={'model': '', 'error': str(lowerCAmelCase_					)}					)






  def     lowercase		(    self			:		int ,  lowerCAmelCase_			:		List[int] = Body(lowerCAmelCase_ ,  embed=lowerCAmelCase_					) ,  lowerCAmelCase_			:		bool = Body(lowerCAmelCase_ ,  embed=lowerCAmelCase_					) ,  lowerCAmelCase_			:		bool = Body(lowerCAmelCase_ ,  embed=lowerCAmelCase_					) ,  )							->						Union[str, Any]:
     try:
        __lowerCAmelCase							     =     self._pipeline.tokenizer.decode(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)
        return ServeDeTokenizeResult(model='' ,  text=lowerCAmelCase_					)
     except Exception as e:
        raise HTTPException(status_code=5_0_0 ,  detail={'model': '', 'error': str(lowerCAmelCase_					)}					)






  async def     lowercase		(    self			:		List[Any] ,  lowerCAmelCase_			:		Union[str, Any]=Body(lowerCAmelCase_ ,  embed=lowerCAmelCase_					)					)							->						int:

     # Check we don't have empty string
     if len(lowerCAmelCase_					) == 0:
        return ServeForwardResult(output=[] ,  attention=[]					)

     try:
        # Forward through the model
        __lowerCAmelCase							     =     self._pipeline(lowerCAmelCase_					)
        return ServeForwardResult(output=lowerCAmelCase_					)
     except Exception as e:
        raise HTTPException(5_0_0 ,  {'error': str(lowerCAmelCase_					)}					)

