

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,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

_a									=  1_6
_a									=  3_2







def _a					(			SCREAMING_SNAKE_CASE						:     Accelerator       ,   SCREAMING_SNAKE_CASE						:     int = 16						)       ->       Dict:

 """simple docstring"""






 __lowerCAmelCase:  Optional[Any]        = AutoTokenizer.from_pretrained('bert-base-cased'						)
 __lowerCAmelCase:  Optional[Any]        = load_dataset('glue'       ,   'mrpc'						)

 def tokenize_function(SCREAMING_SNAKE_CASE						:     List[str]						):
  # max_length=None => use the model max length (it's actually the default)
  __lowerCAmelCase:  Dict        = tokenizer(examples['sentence1']       ,   examples['sentence2']       ,   truncation=SCREAMING_SNAKE_CASE       ,   max_length=SCREAMING_SNAKE_CASE						)
  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():
  __lowerCAmelCase:  List[str]        = datasets.map(
      SCREAMING_SNAKE_CASE       ,   batched=SCREAMING_SNAKE_CASE       ,   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
 __lowerCAmelCase:  Any        = tokenized_datasets.rename_column('label'       ,   'labels'						)

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

  return tokenizer.pad(
      SCREAMING_SNAKE_CASE       ,   padding='longest'       ,   max_length=SCREAMING_SNAKE_CASE       ,   pad_to_multiple_of=SCREAMING_SNAKE_CASE       ,   return_tensors='pt'       ,   )

 # Instantiate dataloaders.
 __lowerCAmelCase:  str        = DataLoader(
     tokenized_datasets['train']       ,   shuffle=SCREAMING_SNAKE_CASE       ,   collate_fn=SCREAMING_SNAKE_CASE       ,   batch_size=SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Optional[Any]        = DataLoader(
     tokenized_datasets['validation']       ,   shuffle=SCREAMING_SNAKE_CASE       ,   collate_fn=SCREAMING_SNAKE_CASE       ,   batch_size=SCREAMING_SNAKE_CASE						)

 return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
  from accelerate.test_utils.training import mocked_dataloaders

  _a									=  mocked_dataloaders  # noqa: F811







def _a					(			SCREAMING_SNAKE_CASE						:     str       ,   SCREAMING_SNAKE_CASE						:     Dict						)       ->       Any:

 """simple docstring"""






 if os.environ.get('TESTING_MOCKED_DATALOADERS'       ,   SCREAMING_SNAKE_CASE						) == "1":
  __lowerCAmelCase:  Optional[int]        = 2
 # Initialize accelerator
 __lowerCAmelCase:  Tuple        = Accelerator(cpu=args.cpu       ,   mixed_precision=args.mixed_precision						)
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 __lowerCAmelCase:  Optional[Any]        = config['lr']
 __lowerCAmelCase:  Dict        = int(config['num_epochs']						)
 __lowerCAmelCase:  Union[str, Any]        = int(config['seed']						)
 __lowerCAmelCase:  Optional[int]        = int(config['batch_size']						)

 # New Code #
 # 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:  Optional[Any]        = args.checkpointing_steps
  elif args.checkpointing_steps.isdigit():
   __lowerCAmelCase:  int        = int(args.checkpointing_steps						)
  else:
   raise ValueError(
       f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''						)
 else:
  __lowerCAmelCase:  Tuple        = None

 set_seed(SCREAMING_SNAKE_CASE						)

 __lowerCAmelCase      ,				__lowerCAmelCase:  List[str]        = get_dataloaders(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Union[str, Any]        = evaluate.load('glue'       ,   'mrpc'						)

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

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 __lowerCAmelCase:  Optional[int]        = AutoModelForSequenceClassification.from_pretrained('bert-base-cased'       ,   return_dict=SCREAMING_SNAKE_CASE						)

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

 # Instantiate optimizer
 __lowerCAmelCase:  List[Any]        = AdamW(params=model.parameters()       ,   lr=SCREAMING_SNAKE_CASE						)

 # Instantiate scheduler
 __lowerCAmelCase:  List[Any]        = get_linear_schedule_with_warmup(
     optimizer=SCREAMING_SNAKE_CASE       ,   num_warmup_steps=1_00       ,   num_training_steps=(len(SCREAMING_SNAKE_CASE						) * 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.
 __lowerCAmelCase      ,				__lowerCAmelCase      ,				__lowerCAmelCase      ,				__lowerCAmelCase      ,				__lowerCAmelCase:  Union[str, Any]        = accelerator.prepare(
     SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

 # New Code #
 # We need to keep track of how many total steps we have iterated over
 __lowerCAmelCase:  List[Any]        = 0
 # We also need to keep track of the stating epoch so files are named properly
 __lowerCAmelCase:  Any        = 0

 # We need to load the checkpoint back in before training here with `load_state`
 # The total number of epochs is adjusted based on where the state is being loaded from,
 # as we assume continuation of the same training script
 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:  Any        = os.path.basename(args.resume_from_checkpoint						)
  else:
   # Get the most recent checkpoint
   __lowerCAmelCase:  List[Any]        = [f.name for f in os.scandir(os.getcwd()						) if f.is_dir()]
   dirs.sort(key=os.path.getctime						)
   __lowerCAmelCase:  int        = dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  # Extract `epoch_{i}` or `step_{i}`
  __lowerCAmelCase:  Dict        = os.path.splitext(SCREAMING_SNAKE_CASE						)[0]

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

    # Now we train the model
 for epoch in range(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						):
  model.train()
  # New Code #
  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:  List[str]        = accelerator.skip_first_batches(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
   overall_step += resume_step
  else:
   # After the first iteration though, we need to go back to the original dataloader
   __lowerCAmelCase:  Union[str, Any]        = train_dataloader
  for step, batch in enumerate(SCREAMING_SNAKE_CASE						):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device						)
   __lowerCAmelCase:  Union[str, Any]        = model(**SCREAMING_SNAKE_CASE						)
   __lowerCAmelCase:  Dict        = outputs.loss
   __lowerCAmelCase:  Union[str, Any]        = loss / gradient_accumulation_steps
   accelerator.backward(SCREAMING_SNAKE_CASE						)
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()
   # New Code #
   overall_step += 1

   # New Code #
   # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
   # These are saved to folders named `step_{overall_step}`
   # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
   # If mixed precision was used, will also save a "scalar.bin" file
   if isinstance(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						):
    __lowerCAmelCase:  str        = f'''step_{overall_step}'''
    if overall_step % checkpointing_steps == 0:
     if args.output_dir is not None:
      __lowerCAmelCase:  Optional[Any]        = os.path.join(args.output_dir       ,   SCREAMING_SNAKE_CASE						)
     accelerator.save_state(SCREAMING_SNAKE_CASE						)

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

  __lowerCAmelCase:  List[str]        = metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(f'''epoch {epoch}:'''       ,   SCREAMING_SNAKE_CASE						)

  # New Code #
  # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
  # These are saved to folders named `epoch_{epoch}`
  # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
  # If mixed precision was used, will also save a "scalar.bin" file
  if checkpointing_steps == "epoch":
   __lowerCAmelCase:  Tuple        = f'''epoch_{epoch}'''
   if args.output_dir is not None:
    __lowerCAmelCase:  Optional[int]        = os.path.join(args.output_dir       ,   SCREAMING_SNAKE_CASE						)
   accelerator.save_state(SCREAMING_SNAKE_CASE						)







def _a					(			)       ->       List[str]:

 """simple docstring"""






 __lowerCAmelCase:  List[str]        = argparse.ArgumentParser(description='Simple example of training script.'						)
 parser.add_argument(
     '--mixed_precision'       ,   type=SCREAMING_SNAKE_CASE       ,   default=SCREAMING_SNAKE_CASE       ,   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=SCREAMING_SNAKE_CASE       ,   default=SCREAMING_SNAKE_CASE       ,   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=SCREAMING_SNAKE_CASE       ,   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=SCREAMING_SNAKE_CASE       ,   default=SCREAMING_SNAKE_CASE       ,   help='If the training should continue from a checkpoint folder.'       ,   )
 __lowerCAmelCase:  Optional[Any]        = parser.parse_args()
 __lowerCAmelCase:  Tuple        = {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
 training_function(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
  main()








import json
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image
from torch import nn
from torchvision import transforms
from torchvision.transforms import functional

import transformers
from transformers import (
    AutoConfig,
    AutoImageProcessor,
    AutoModelForSemanticSegmentation,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_a									=  logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=2.0.0''', '''To fix: pip install -r examples/pytorch/semantic-segmentation/requirements.txt''')







def _a					(			SCREAMING_SNAKE_CASE						:     Dict       ,   SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     Tuple=0						)       ->       Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:  List[str]        = (size, size) if isinstance(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						) else size
 __lowerCAmelCase      ,				__lowerCAmelCase:  List[Any]        = img.size
 __lowerCAmelCase:  Union[str, Any]        = size[1] - original_height if original_height < size[1] else 0
 __lowerCAmelCase:  Tuple        = size[0] - original_width if original_width < size[0] else 0
 __lowerCAmelCase:  Optional[int]        = functional.pad(SCREAMING_SNAKE_CASE       ,   (0, 0, pad_width, pad_height)       ,   fill=SCREAMING_SNAKE_CASE						)
 return img






class 				A_					:


     def __init__(				self     :						List[Any]     ,    UpperCAmelCase     :						int   )				->      Any:
      __lowerCAmelCase:  str        = transforms


     def __call__(				self     :						Dict     ,    UpperCAmelCase     :						str     ,    UpperCAmelCase     :						Optional[int]   )				->      Dict:
      for t in self.transforms:
       __lowerCAmelCase      ,				__lowerCAmelCase:  Optional[Any]        = t(UpperCAmelCase     ,    UpperCAmelCase   )
      return image, target







class 				A_					:


     def __init__(				self     :						Optional[Any]   )				->      Dict:
      pass


     def __call__(				self     :						int     ,    UpperCAmelCase     :						Optional[Any]     ,    UpperCAmelCase     :						List[str]   )				->      Dict:
      return image, target







class 				A_					:


     def __init__(				self     :						int     ,    UpperCAmelCase     :						Any   )				->      Optional[int]:
      __lowerCAmelCase:  int        = size


     def __call__(				self     :						Optional[int]     ,    UpperCAmelCase     :						Dict     ,    UpperCAmelCase     :						Optional[Any]   )				->      Dict:
      __lowerCAmelCase:  Dict        = functional.resize(UpperCAmelCase     ,    self.size   )
      __lowerCAmelCase:  Optional[int]        = functional.resize(UpperCAmelCase     ,    self.size     ,    interpolation=transforms.InterpolationMode.NEAREST   )
      return image, target







class 				A_					:


     def __init__(				self     :						Optional[int]     ,    UpperCAmelCase     :						str     ,    UpperCAmelCase     :						str=None   )				->      Dict:
      __lowerCAmelCase:  Optional[int]        = min_size
      if max_size is None:
       __lowerCAmelCase:  str        = min_size
      __lowerCAmelCase:  str        = max_size


     def __call__(				self     :						Optional[Any]     ,    UpperCAmelCase     :						Union[str, Any]     ,    UpperCAmelCase     :						Tuple   )				->      Union[str, Any]:
      __lowerCAmelCase:  Optional[Any]        = random.randint(self.min_size     ,    self.max_size   )
      __lowerCAmelCase:  List[str]        = functional.resize(UpperCAmelCase     ,    UpperCAmelCase   )
      __lowerCAmelCase:  Optional[Any]        = functional.resize(UpperCAmelCase     ,    UpperCAmelCase     ,    interpolation=transforms.InterpolationMode.NEAREST   )
      return image, target







class 				A_					:


     def __init__(				self     :						Union[str, Any]     ,    UpperCAmelCase     :						List[Any]   )				->      int:
      __lowerCAmelCase:  List[Any]        = size if isinstance(UpperCAmelCase     ,    UpperCAmelCase   ) else (size, size)


     def __call__(				self     :						Dict     ,    UpperCAmelCase     :						List[Any]     ,    UpperCAmelCase     :						Dict   )				->      Union[str, Any]:
      __lowerCAmelCase:  Optional[Any]        = pad_if_smaller(UpperCAmelCase     ,    self.size   )
      __lowerCAmelCase:  str        = pad_if_smaller(UpperCAmelCase     ,    self.size     ,    fill=2_5_5   )
      __lowerCAmelCase:  List[Any]        = transforms.RandomCrop.get_params(UpperCAmelCase     ,    self.size   )
      __lowerCAmelCase:  Union[str, Any]        = functional.crop(UpperCAmelCase     ,    *UpperCAmelCase   )
      __lowerCAmelCase:  Optional[Any]        = functional.crop(UpperCAmelCase     ,    *UpperCAmelCase   )
      return image, target







class 				A_					:


     def __init__(				self     :						Optional[int]     ,    UpperCAmelCase     :						Optional[int]   )				->      Optional[Any]:
      __lowerCAmelCase:  Optional[Any]        = flip_prob


     def __call__(				self     :						List[Any]     ,    UpperCAmelCase     :						Any     ,    UpperCAmelCase     :						Union[str, Any]   )				->      Union[str, Any]:
      if random.random() < self.flip_prob:
       __lowerCAmelCase:  Optional[int]        = functional.hflip(UpperCAmelCase   )
       __lowerCAmelCase:  Dict        = functional.hflip(UpperCAmelCase   )
      return image, target







class 				A_					:


     def __call__(				self     :						Optional[Any]     ,    UpperCAmelCase     :						Any     ,    UpperCAmelCase     :						int   )				->      Any:
      __lowerCAmelCase:  List[Any]        = functional.pil_to_tensor(UpperCAmelCase   )
      __lowerCAmelCase:  Tuple        = torch.as_tensor(np.array(UpperCAmelCase   )     ,    dtype=torch.intaa   )
      return image, target







class 				A_					:


     def __init__(				self     :						str     ,    UpperCAmelCase     :						Tuple   )				->      Any:
      __lowerCAmelCase:  int        = dtype


     def __call__(				self     :						Optional[Any]     ,    UpperCAmelCase     :						str     ,    UpperCAmelCase     :						str   )				->      List[Any]:
      __lowerCAmelCase:  Optional[int]        = functional.convert_image_dtype(UpperCAmelCase     ,    self.dtype   )
      return image, target







class 				A_					:


     def __init__(				self     :						Optional[int]     ,    UpperCAmelCase     :						Tuple     ,    UpperCAmelCase     :						Optional[Any]   )				->      Tuple:
      __lowerCAmelCase:  Optional[int]        = mean
      __lowerCAmelCase:  Optional[Any]        = std


     def __call__(				self     :						Union[str, Any]     ,    UpperCAmelCase     :						str     ,    UpperCAmelCase     :						Optional[int]   )				->      str:
      __lowerCAmelCase:  Tuple        = functional.normalize(UpperCAmelCase     ,    mean=self.mean     ,    std=self.std   )
      return image, target







class 				A_					:


     def __call__(				self     :						List[Any]     ,    UpperCAmelCase     :						Dict     ,    UpperCAmelCase     :						List[str]   )				->      List[Any]:
      if not isinstance(UpperCAmelCase     ,    np.ndarray   ):
       __lowerCAmelCase:  Union[str, Any]        = np.array(UpperCAmelCase   ).astype(np.uinta   )
      # avoid using underflow conversion
      __lowerCAmelCase:  Dict        = 2_5_5
      __lowerCAmelCase:  List[Any]        = target - 1
      __lowerCAmelCase:  List[str]        = 2_5_5

      __lowerCAmelCase:  Tuple        = Image.fromarray(UpperCAmelCase   )
      return image, target







@dataclass
class 				A_					:
     _lowercase     :	Optional[str]        =      field(
         default='segments/sidewalk-semantic'				,      metadata={
             'help': 'Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'
         }				,      )
     _lowercase     :	Optional[str]        =      field(
         default=snake_case__				,      metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}   )
     _lowercase     :	Optional[float]        =      field(
         default=0.15				,      metadata={'help': 'Percent to split off of train for validation.'}   )
     _lowercase     :	Optional[int]        =      field(
         default=snake_case__				,      metadata={
             'help': (
                 'For debugging purposes or quicker training, truncate the number of training examples to this '
                 'value if set.'
             )
         }				,      )
     _lowercase     :	Optional[int]        =      field(
         default=snake_case__				,      metadata={
             'help': (
                 'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
                 'value if set.'
             )
         }				,      )
     _lowercase     :	Optional[bool]        =      field(
         default=snake_case__				,      metadata={'help': 'Whether or not to reduce all labels by 1 and replace background by 255.'}				,      )


     def     UpperCAmelCase    (				self     :						Union[str, Any]   )				->      Optional[Any]:
      if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
       raise ValueError(
           'You must specify either a dataset name from the hub or a train and/or validation directory.'   )







@dataclass
class 				A_					:
     _lowercase     :	str        =      field(
         default='nvidia/mit-b0'				,      metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}				,      )
     _lowercase     :	Optional[str]        =      field(
         default=snake_case__				,      metadata={'help': 'Pretrained config name or path if not the same as model_name'}   )
     _lowercase     :	Optional[str]        =      field(
         default=snake_case__				,      metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}   )
     _lowercase     :	str        =      field(
         default='main'				,      metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}				,      )
     _lowercase     :	str        =      field(default=snake_case__				,      metadata={'help': 'Name or path of preprocessor config.'}   )
     _lowercase     :	bool        =      field(
         default=snake_case__				,      metadata={
             'help': (
                 'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
                 'with private models).'
             )
         }				,      )







def _a					(			)       ->       Any:

 """simple docstring"""






 __lowerCAmelCase:  Any        = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
 if len(sys.argv						) == 2 and sys.argv[1].endswith('.json'						):
  # If we pass only one argument to the script and it's the path to a json file,
  # let's parse it to get our arguments.
  __lowerCAmelCase      ,				__lowerCAmelCase      ,				__lowerCAmelCase:  Tuple        = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
 else:
  __lowerCAmelCase      ,				__lowerCAmelCase      ,				__lowerCAmelCase:  Optional[Any]        = parser.parse_args_into_dataclasses()

 # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
 # information sent is the one passed as arguments along with your Python/PyTorch versions.
 send_example_telemetry('run_semantic_segmentation'       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

 # Setup logging
 logging.basicConfig(
     format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'       ,   datefmt='%m/%d/%Y %H:%M:%S'       ,   handlers=[logging.StreamHandler(sys.stdout						)]       ,   )

 if training_args.should_log:
  # The default of training_args.log_level is passive, so we set log level at info here to have that default.
  transformers.utils.logging.set_verbosity_info()

 __lowerCAmelCase:  Any        = training_args.get_process_log_level()
 logger.setLevel(SCREAMING_SNAKE_CASE						)
 transformers.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE						)
 transformers.utils.logging.enable_default_handler()
 transformers.utils.logging.enable_explicit_format()

 # Log on each process the small summary:
 logger.warning(
     f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
     + f'''distributed training: {bool(training_args.local_rank != -1						)}, 16-bits training: {training_args.fpaa}'''						)
 logger.info(f'''Training/evaluation parameters {training_args}'''						)

 # Detecting last checkpoint.
 __lowerCAmelCase:  Optional[Any]        = None
 if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
  __lowerCAmelCase:  Optional[int]        = get_last_checkpoint(training_args.output_dir						)
  if last_checkpoint is None and len(os.listdir(training_args.output_dir						)						) > 0:
   raise ValueError(
       f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
       'Use --overwrite_output_dir to overcome.'						)
  elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
   logger.info(
       f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
       'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'						)

    # Load dataset
    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
    # download the dataset.
    # TODO support datasets from local folders
 __lowerCAmelCase:  List[str]        = load_dataset(data_args.dataset_name       ,   cache_dir=model_args.cache_dir						)

 # Rename column names to standardized names (only "image" and "label" need to be present)
 if "pixel_values" in dataset["train"].column_names:
  __lowerCAmelCase:  Union[str, Any]        = dataset.rename_columns({'pixel_values': 'image'}						)
 if "annotation" in dataset["train"].column_names:
  __lowerCAmelCase:  Tuple        = dataset.rename_columns({'annotation': 'label'}						)

 # If we don't have a validation split, split off a percentage of train as validation.
 __lowerCAmelCase:  Dict        = None if 'validation' in dataset.keys() else data_args.train_val_split
 if isinstance(data_args.train_val_split       ,   SCREAMING_SNAKE_CASE						) and data_args.train_val_split > 0.0:
  __lowerCAmelCase:  List[Any]        = dataset['train'].train_test_split(data_args.train_val_split						)
  __lowerCAmelCase:  Tuple        = split['train']
  __lowerCAmelCase:  int        = split['test']

 # Prepare label mappings.
 # We'll include these in the model's config to get human readable labels in the Inference API.
 if data_args.dataset_name == "scene_parse_150":
  __lowerCAmelCase:  Union[str, Any]        = 'huggingface/label-files'
  __lowerCAmelCase:  Tuple        = 'ade20k-id2label.json'
 else:
  __lowerCAmelCase:  Any        = data_args.dataset_name
  __lowerCAmelCase:  Any        = 'id2label.json'
 __lowerCAmelCase:  Dict        = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   repo_type='dataset'						)       ,   'r'						)						)
 __lowerCAmelCase:  Optional[Any]        = {int(SCREAMING_SNAKE_CASE						): v for k, v in idalabel.items()}
 __lowerCAmelCase:  List[str]        = {v: str(SCREAMING_SNAKE_CASE						) for k, v in idalabel.items()}

 # Load the mean IoU metric from the datasets package
 __lowerCAmelCase:  List[str]        = evaluate.load('mean_iou'						)

 # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
 # predictions and label_ids field) and has to return a dictionary string to float.
 @torch.no_grad()
 def compute_metrics(SCREAMING_SNAKE_CASE						:     str						):
  __lowerCAmelCase      ,				__lowerCAmelCase:  List[Any]        = eval_pred
  __lowerCAmelCase:  Dict        = torch.from_numpy(SCREAMING_SNAKE_CASE						)
  # scale the logits to the size of the label
  __lowerCAmelCase:  Union[str, Any]        = nn.functional.interpolate(
      SCREAMING_SNAKE_CASE       ,   size=labels.shape[-2:]       ,   mode='bilinear'       ,   align_corners=SCREAMING_SNAKE_CASE       ,   ).argmax(dim=1						)

  __lowerCAmelCase:  Tuple        = logits_tensor.detach().cpu().numpy()
  __lowerCAmelCase:  Dict        = metric.compute(
      predictions=SCREAMING_SNAKE_CASE       ,   references=SCREAMING_SNAKE_CASE       ,   num_labels=len(SCREAMING_SNAKE_CASE						)       ,   ignore_index=0       ,   reduce_labels=image_processor.do_reduce_labels       ,   )
  # add per category metrics as individual key-value pairs
  __lowerCAmelCase:  List[str]        = metrics.pop('per_category_accuracy'						).tolist()
  __lowerCAmelCase:  Optional[int]        = metrics.pop('per_category_iou'						).tolist()

  metrics.update({f'''accuracy_{idalabel[i]}''': v for i, v in enumerate(SCREAMING_SNAKE_CASE						)}						)
  metrics.update({f'''iou_{idalabel[i]}''': v for i, v in enumerate(SCREAMING_SNAKE_CASE						)}						)

  return metrics

 __lowerCAmelCase:  Union[str, Any]        = AutoConfig.from_pretrained(
     model_args.config_name or model_args.model_name_or_path       ,   labelaid=SCREAMING_SNAKE_CASE       ,   idalabel=SCREAMING_SNAKE_CASE       ,   cache_dir=model_args.cache_dir       ,   revision=model_args.model_revision       ,   use_auth_token=True if model_args.use_auth_token else None       ,   )
 __lowerCAmelCase:  Union[str, Any]        = AutoModelForSemanticSegmentation.from_pretrained(
     model_args.model_name_or_path       ,   from_tf=bool('.ckpt' in model_args.model_name_or_path						)       ,   config=SCREAMING_SNAKE_CASE       ,   cache_dir=model_args.cache_dir       ,   revision=model_args.model_revision       ,   use_auth_token=True if model_args.use_auth_token else None       ,   )
 __lowerCAmelCase:  Optional[Any]        = AutoImageProcessor.from_pretrained(
     model_args.image_processor_name or model_args.model_name_or_path       ,   cache_dir=model_args.cache_dir       ,   revision=model_args.model_revision       ,   use_auth_token=True if model_args.use_auth_token else None       ,   )

 # Define torchvision transforms to be applied to each image + target.
 # Not that straightforward in torchvision: https://github.com/pytorch/vision/issues/9
 # Currently based on official torchvision references: https://github.com/pytorch/vision/blob/main/references/segmentation/transforms.py
 if "shortest_edge" in image_processor.size:
  # We instead set the target size as (shortest_edge, shortest_edge) to here to ensure all images are batchable.
  __lowerCAmelCase:  List[str]        = (image_processor.size['shortest_edge'], image_processor.size['shortest_edge'])
 else:
  __lowerCAmelCase:  str        = (image_processor.size['height'], image_processor.size['width'])
 __lowerCAmelCase:  List[Any]        = Compose(
     [
         ReduceLabels() if data_args.reduce_labels else Identity(),
         RandomCrop(size=SCREAMING_SNAKE_CASE						),
         RandomHorizontalFlip(flip_prob=0.5						),
         PILToTensor(),
         ConvertImageDtype(torch.float						),
         Normalize(mean=image_processor.image_mean       ,   std=image_processor.image_std						),
     ]						)
 # Define torchvision transform to be applied to each image.
 # jitter = ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1)
 __lowerCAmelCase:  Optional[Any]        = Compose(
     [
         ReduceLabels() if data_args.reduce_labels else Identity(),
         Resize(size=SCREAMING_SNAKE_CASE						),
         PILToTensor(),
         ConvertImageDtype(torch.float						),
         Normalize(mean=image_processor.image_mean       ,   std=image_processor.image_std						),
     ]						)

 def preprocess_train(SCREAMING_SNAKE_CASE						:     int						):
  __lowerCAmelCase:  List[Any]        = []
  __lowerCAmelCase:  Optional[int]        = []
  for image, target in zip(example_batch['image']       ,   example_batch['label']						):
   __lowerCAmelCase      ,				__lowerCAmelCase:  Tuple        = train_transforms(image.convert('RGB'						)       ,   SCREAMING_SNAKE_CASE						)
   pixel_values.append(SCREAMING_SNAKE_CASE						)
   labels.append(SCREAMING_SNAKE_CASE						)

  __lowerCAmelCase:  Any        = {}
  __lowerCAmelCase:  List[str]        = torch.stack(SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  Dict        = torch.stack(SCREAMING_SNAKE_CASE						)

  return encoding

 def preprocess_val(SCREAMING_SNAKE_CASE						:     List[str]						):
  __lowerCAmelCase:  Optional[Any]        = []
  __lowerCAmelCase:  Optional[int]        = []
  for image, target in zip(example_batch['image']       ,   example_batch['label']						):
   __lowerCAmelCase      ,				__lowerCAmelCase:  Optional[int]        = val_transforms(image.convert('RGB'						)       ,   SCREAMING_SNAKE_CASE						)
   pixel_values.append(SCREAMING_SNAKE_CASE						)
   labels.append(SCREAMING_SNAKE_CASE						)

  __lowerCAmelCase:  Any        = {}
  __lowerCAmelCase:  Any        = torch.stack(SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  Tuple        = torch.stack(SCREAMING_SNAKE_CASE						)

  return encoding

 if training_args.do_train:
  if "train" not in dataset:
   raise ValueError('--do_train requires a train dataset'						)
  if data_args.max_train_samples is not None:
   __lowerCAmelCase:  int        = (
       dataset['train'].shuffle(seed=training_args.seed						).select(range(data_args.max_train_samples						)						)
   )
  # Set the training transforms
  dataset["train"].set_transform(SCREAMING_SNAKE_CASE						)

 if training_args.do_eval:
  if "validation" not in dataset:
   raise ValueError('--do_eval requires a validation dataset'						)
  if data_args.max_eval_samples is not None:
   __lowerCAmelCase:  Optional[Any]        = (
       dataset['validation'].shuffle(seed=training_args.seed						).select(range(data_args.max_eval_samples						)						)
   )
  # Set the validation transforms
  dataset["validation"].set_transform(SCREAMING_SNAKE_CASE						)

 # Initalize our trainer
 __lowerCAmelCase:  int        = Trainer(
     model=SCREAMING_SNAKE_CASE       ,   args=SCREAMING_SNAKE_CASE       ,   train_dataset=dataset['train'] if training_args.do_train else None       ,   eval_dataset=dataset['validation'] if training_args.do_eval else None       ,   compute_metrics=SCREAMING_SNAKE_CASE       ,   tokenizer=SCREAMING_SNAKE_CASE       ,   data_collator=SCREAMING_SNAKE_CASE       ,   )

 # Training
 if training_args.do_train:
  __lowerCAmelCase:  Tuple        = None
  if training_args.resume_from_checkpoint is not None:
   __lowerCAmelCase:  Tuple        = training_args.resume_from_checkpoint
  elif last_checkpoint is not None:
   __lowerCAmelCase:  Optional[int]        = last_checkpoint
  __lowerCAmelCase:  List[str]        = trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE						)
  trainer.save_model()
  trainer.log_metrics('train'       ,   train_result.metrics						)
  trainer.save_metrics('train'       ,   train_result.metrics						)
  trainer.save_state()

 # Evaluation
 if training_args.do_eval:
  __lowerCAmelCase:  List[str]        = trainer.evaluate()
  trainer.log_metrics('eval'       ,   SCREAMING_SNAKE_CASE						)
  trainer.save_metrics('eval'       ,   SCREAMING_SNAKE_CASE						)

 # Write model card and (optionally) push to hub
 __lowerCAmelCase:  List[Any]        = {
     'finetuned_from': model_args.model_name_or_path,
     'dataset': data_args.dataset_name,
     'tags': ['image-segmentation', 'vision'],
 }
 if training_args.push_to_hub:
  trainer.push_to_hub(**SCREAMING_SNAKE_CASE						)
 else:
  trainer.create_model_card(**SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
  main()





