

"""simple docstring"""




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler







class        __lowercase     (      unittest.TestCase   ):
  # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
  snake_case_			    =    (ScoreSdeVeScheduler,)
  snake_case_			    =    ()

  @property
  def 				__lowercase    (					self						:     List[Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					List[Any]   				=   4
   UpperCAmelCase__   :					Union[str, Any]   				=   3
   UpperCAmelCase__   :					Union[str, Any]   				=   8
   UpperCAmelCase__   :					List[Any]   				=   8

   UpperCAmelCase__   :					Any   				=   torch.rand((batch_size, num_channels, height, width)      )

   return sample

  @property
  def 				__lowercase    (					self						:     Tuple      ):






   '''simple docstring'''
   UpperCAmelCase__   :					List[Any]   				=   4
   UpperCAmelCase__   :					Optional[Any]   				=   3
   UpperCAmelCase__   :					Dict   				=   8
   UpperCAmelCase__   :					Union[str, Any]   				=   8

   UpperCAmelCase__   :					Tuple   				=   batch_size * num_channels * height * width
   UpperCAmelCase__   :					Tuple   				=   torch.arange(A      )
   UpperCAmelCase__   :					Tuple   				=   sample.reshape(A   ,A   ,A   ,A      )
   UpperCAmelCase__   :					List[str]   				=   sample / num_elems
   UpperCAmelCase__   :					Dict   				=   sample.permute(3   ,0   ,1   ,2      )

   return sample

  def 				__lowercase    (					self						:     Tuple      ):






   '''simple docstring'''
   def model(A						:     Dict   ,A						:     str   ,*A						:     Union[str, Any]      ):
    return sample * t / (t + 1)

   return model

  def 				__lowercase    (					self						:     int   ,**A						:     List[str]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					int   				=   {
       """num_train_timesteps""": 2_000,
       """snr""": 0.1_5,
       """sigma_min""": 0.0_1,
       """sigma_max""": 1_348,
       """sampling_eps""": 1e-5,
   }

   config.update(**A      )
   return config

  def 				__lowercase    (					self						:     List[Any]   ,A						:     Any=0   ,**A						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[Any]   				=   dict(self.forward_default_kwargs      )

   for scheduler_class in self.scheduler_classes:
    UpperCAmelCase__   :					int   				=   self.dummy_sample
    UpperCAmelCase__   :					Optional[int]   				=   0.1 * sample

    UpperCAmelCase__   :					Dict   				=   self.get_scheduler_config(**A      )
    UpperCAmelCase__   :					List[Any]   				=   scheduler_class(**A      )

    with tempfile.TemporaryDirectory() as tmpdirname:
     scheduler.save_config(A      )
     UpperCAmelCase__   :					Any   				=   scheduler_class.from_pretrained(A      )

    UpperCAmelCase__   :					List[str]   				=   scheduler.step_pred(
        A   ,A   ,A   ,generator=torch.manual_seed(0      )   ,**A      ).prev_sample
    UpperCAmelCase__   :					List[str]   				=   new_scheduler.step_pred(
        A   ,A   ,A   ,generator=torch.manual_seed(0      )   ,**A      ).prev_sample

    assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"

    UpperCAmelCase__   :					Optional[int]   				=   scheduler.step_correct(A   ,A   ,generator=torch.manual_seed(0      )   ,**A      ).prev_sample
    UpperCAmelCase__   :					List[Any]   				=   new_scheduler.step_correct(
        A   ,A   ,generator=torch.manual_seed(0      )   ,**A      ).prev_sample

    assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler correction are not identical"

  def 				__lowercase    (					self						:     List[str]   ,A						:     str=0   ,**A						:     Optional[int]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					int   				=   dict(self.forward_default_kwargs      )
   kwargs.update(A      )

   for scheduler_class in self.scheduler_classes:
    UpperCAmelCase__   :					List[str]   				=   self.dummy_sample
    UpperCAmelCase__   :					List[str]   				=   0.1 * sample

    UpperCAmelCase__   :					Dict   				=   self.get_scheduler_config()
    UpperCAmelCase__   :					Optional[Any]   				=   scheduler_class(**A      )

    with tempfile.TemporaryDirectory() as tmpdirname:
     scheduler.save_config(A      )
     UpperCAmelCase__   :					int   				=   scheduler_class.from_pretrained(A      )

    UpperCAmelCase__   :					Tuple   				=   scheduler.step_pred(
        A   ,A   ,A   ,generator=torch.manual_seed(0      )   ,**A      ).prev_sample
    UpperCAmelCase__   :					Tuple   				=   new_scheduler.step_pred(
        A   ,A   ,A   ,generator=torch.manual_seed(0      )   ,**A      ).prev_sample

    assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"

    UpperCAmelCase__   :					List[str]   				=   scheduler.step_correct(A   ,A   ,generator=torch.manual_seed(0      )   ,**A      ).prev_sample
    UpperCAmelCase__   :					Tuple   				=   new_scheduler.step_correct(
        A   ,A   ,generator=torch.manual_seed(0      )   ,**A      ).prev_sample

    assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler correction are not identical"

  def 				__lowercase    (					self						:     List[Any]      ):






   '''simple docstring'''
   for timesteps in [10, 100, 1_000]:
    self.check_over_configs(num_train_timesteps=A      )

  def 				__lowercase    (					self						:     List[Any]      ):






   '''simple docstring'''
   for sigma_min, sigma_max in zip([0.0_0_0_1, 0.0_0_1, 0.0_1]   ,[1, 100, 1_000]      ):
    self.check_over_configs(sigma_min=A   ,sigma_max=A      )

  def 				__lowercase    (					self						:     int      ):






   '''simple docstring'''
   for t in [0.1, 0.5, 0.7_5]:
    self.check_over_forward(time_step=A      )

  def 				__lowercase    (					self						:     int      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Any   				=   dict(self.forward_default_kwargs      )

   UpperCAmelCase__   :					List[str]   				=   self.scheduler_classes[0]
   UpperCAmelCase__   :					List[str]   				=   self.get_scheduler_config()
   UpperCAmelCase__   :					Dict   				=   scheduler_class(**A      )

   UpperCAmelCase__   :					Optional[Any]   				=   3

   UpperCAmelCase__   :					Union[str, Any]   				=   self.dummy_model()
   UpperCAmelCase__   :					Optional[Any]   				=   self.dummy_sample_deter

   scheduler.set_sigmas(A      )
   scheduler.set_timesteps(A      )
   UpperCAmelCase__   :					str   				=   torch.manual_seed(0      )

   for i, t in enumerate(scheduler.timesteps      ):
    UpperCAmelCase__   :					Tuple   				=   scheduler.sigmas[i]

    for _ in range(scheduler.config.correct_steps      ):
     with torch.no_grad():
      UpperCAmelCase__   :					List[str]   				=   model(A   ,A      )
     UpperCAmelCase__   :					str   				=   scheduler.step_correct(A   ,A   ,generator=A   ,**A      ).prev_sample

    with torch.no_grad():
     UpperCAmelCase__   :					int   				=   model(A   ,A      )

    UpperCAmelCase__   :					Tuple   				=   scheduler.step_pred(A   ,A   ,A   ,generator=A   ,**A      )
    UpperCAmelCase__				, UpperCAmelCase__   :					Union[str, Any]   				=   output.prev_sample, output.prev_sample_mean

   UpperCAmelCase__   :					List[Any]   				=   torch.sum(torch.abs(A      )      )
   UpperCAmelCase__   :					str   				=   torch.mean(torch.abs(A      )      )

   assert np.isclose(result_sum.item()   ,1_4_3_7_2_7_5_8_5_2_8.0      )
   assert np.isclose(result_mean.item()   ,1_8_7_1_4_5_3_0.0      )




  def 				__lowercase    (					self						:     Union[str, Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Any   				=   dict(self.forward_default_kwargs      )

   UpperCAmelCase__   :					List[str]   				=   kwargs.pop("""num_inference_steps"""   ,A      )

   for scheduler_class in self.scheduler_classes:
    UpperCAmelCase__   :					Any   				=   self.get_scheduler_config()
    UpperCAmelCase__   :					List[str]   				=   scheduler_class(**A      )

    UpperCAmelCase__   :					Any   				=   self.dummy_sample
    UpperCAmelCase__   :					int   				=   0.1 * sample

    if num_inference_steps is not None and hasattr(A   ,"""set_timesteps"""      ):
     scheduler.set_timesteps(A      )
    elif num_inference_steps is not None and not hasattr(A   ,"""set_timesteps"""      ):
     UpperCAmelCase__   :					Dict   				=   num_inference_steps

    UpperCAmelCase__   :					Any   				=   scheduler.step_pred(A   ,0   ,A   ,generator=torch.manual_seed(0      )   ,**A      ).prev_sample
    UpperCAmelCase__   :					Tuple   				=   scheduler.step_pred(A   ,1   ,A   ,generator=torch.manual_seed(0      )   ,**A      ).prev_sample

    self.assertEqual(output_a.shape   ,sample.shape      )
    self.assertEqual(output_a.shape   ,output_a.shape      )








"""simple docstring"""




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
__UpperCAmelCase	=			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 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase=0 ):
 '''simple docstring'''





 UpperCAmelCase__   :					Optional[Any]   				=   (size, size) if isinstance(__UpperCamelCase							,      __UpperCamelCase ) else size
 UpperCAmelCase__				, UpperCAmelCase__   :					Optional[int]   				=   img.size
 UpperCAmelCase__   :					Tuple   				=   size[1] - original_height if original_height < size[1] else 0
 UpperCAmelCase__   :					Dict   				=   size[0] - original_width if original_width < size[0] else 0
 UpperCAmelCase__   :					Dict   				=   functional.pad(__UpperCamelCase							,      (0, 0, pad_width, pad_height)							,      fill=__UpperCamelCase )
 return img







class        __lowercase     :

  def __init__(					self						:     Tuple   ,A						:     Optional[Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Tuple   				=   transforms




  def __call__(					self						:     Union[str, Any]   ,A						:     Dict   ,A						:     Optional[int]      ):






   '''simple docstring'''
   for t in self.transforms:
    UpperCAmelCase__				, UpperCAmelCase__   :					Tuple   				=   t(A   ,A      )
   return image, target







class        __lowercase     :

  def __init__(					self						:     List[str]      ):






   '''simple docstring'''
   pass




  def __call__(					self						:     List[str]   ,A						:     int   ,A						:     int      ):






   '''simple docstring'''
   return image, target







class        __lowercase     :

  def __init__(					self						:     Optional[int]   ,A						:     Optional[Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[int]   				=   size




  def __call__(					self						:     Optional[Any]   ,A						:     Dict   ,A						:     Tuple      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Any   				=   functional.resize(A   ,self.size      )
   UpperCAmelCase__   :					str   				=   functional.resize(A   ,self.size   ,interpolation=transforms.InterpolationMode.NEAREST      )
   return image, target







class        __lowercase     :

  def __init__(					self						:     List[Any]   ,A						:     Union[str, Any]   ,A						:     Any=None      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Dict   				=   min_size
   if max_size is None:
    UpperCAmelCase__   :					List[Any]   				=   min_size
   UpperCAmelCase__   :					List[Any]   				=   max_size




  def __call__(					self						:     Dict   ,A						:     Optional[int]   ,A						:     Any      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Dict   				=   random.randint(self.min_size   ,self.max_size      )
   UpperCAmelCase__   :					int   				=   functional.resize(A   ,A      )
   UpperCAmelCase__   :					Any   				=   functional.resize(A   ,A   ,interpolation=transforms.InterpolationMode.NEAREST      )
   return image, target







class        __lowercase     :

  def __init__(					self						:     Optional[Any]   ,A						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Union[str, Any]   				=   size if isinstance(A   ,A      ) else (size, size)




  def __call__(					self						:     List[Any]   ,A						:     Any   ,A						:     Dict      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Tuple   				=   pad_if_smaller(A   ,self.size      )
   UpperCAmelCase__   :					str   				=   pad_if_smaller(A   ,self.size   ,fill=255      )
   UpperCAmelCase__   :					List[str]   				=   transforms.RandomCrop.get_params(A   ,self.size      )
   UpperCAmelCase__   :					Any   				=   functional.crop(A   ,*A      )
   UpperCAmelCase__   :					str   				=   functional.crop(A   ,*A      )
   return image, target







class        __lowercase     :

  def __init__(					self						:     str   ,A						:     Union[str, Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Any   				=   flip_prob




  def __call__(					self						:     Dict   ,A						:     Tuple   ,A						:     List[Any]      ):






   '''simple docstring'''
   if random.random() < self.flip_prob:
    UpperCAmelCase__   :					Dict   				=   functional.hflip(A      )
    UpperCAmelCase__   :					List[Any]   				=   functional.hflip(A      )
   return image, target







class        __lowercase     :

  def __call__(					self						:     Dict   ,A						:     int   ,A						:     List[Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Tuple   				=   functional.pil_to_tensor(A      )
   UpperCAmelCase__   :					Any   				=   torch.as_tensor(np.array(A      )   ,dtype=torch.intaa      )
   return image, target







class        __lowercase     :

  def __init__(					self						:     Any   ,A						:     Dict      ):






   '''simple docstring'''
   UpperCAmelCase__   :					str   				=   dtype




  def __call__(					self						:     Dict   ,A						:     Dict   ,A						:     Dict      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Dict   				=   functional.convert_image_dtype(A   ,self.dtype      )
   return image, target







class        __lowercase     :

  def __init__(					self						:     Any   ,A						:     Optional[int]   ,A						:     Union[str, Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[int]   				=   mean
   UpperCAmelCase__   :					str   				=   std




  def __call__(					self						:     Optional[Any]   ,A						:     str   ,A						:     Any      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[int]   				=   functional.normalize(A   ,mean=self.mean   ,std=self.std      )
   return image, target







class        __lowercase     :

  def __call__(					self						:     Tuple   ,A						:     List[Any]   ,A						:     Dict      ):






   '''simple docstring'''
   if not isinstance(A   ,np.ndarray      ):
    UpperCAmelCase__   :					Union[str, Any]   				=   np.array(A      ).astype(np.uinta      )
   # avoid using underflow conversion
   UpperCAmelCase__   :					Tuple   				=   255
   UpperCAmelCase__   :					Optional[int]   				=   target - 1
   UpperCAmelCase__   :					Dict   				=   255

   UpperCAmelCase__   :					Tuple   				=   Image.fromarray(A      )
   return image, target







@dataclass
class        __lowercase     :
  snake_case_			    =    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)."""
      }		,     )
  snake_case_			    =    field(
      default=__lowerCamelCase		,     metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}   )
  snake_case_			    =    field(
      default=0.15		,     metadata={"""help""": """Percent to split off of train for validation."""}   )
  snake_case_			    =    field(
      default=__lowerCamelCase		,     metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of training examples to this """
              """value if set."""
          )
      }		,     )
  snake_case_			    =    field(
      default=__lowerCamelCase		,     metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
              """value if set."""
          )
      }		,     )
  snake_case_			    =    field(
      default=__lowerCamelCase		,     metadata={"""help""": """Whether or not to reduce all labels by 1 and replace background by 255."""}		,     )

  def 				__lowercase    (					self						:     List[str]      ):






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







def 					lowerCAmelCase  (   ):
 '''simple docstring'''





 UpperCAmelCase__   :					int   				=   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.
  UpperCAmelCase__				, UpperCAmelCase__				, UpperCAmelCase__   :					List[str]   				=   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
 else:
  UpperCAmelCase__				, UpperCAmelCase__				, UpperCAmelCase__   :					str   				=   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"""							,      __UpperCamelCase							,      __UpperCamelCase )

 # 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()

 UpperCAmelCase__   :					List[str]   				=   training_args.get_process_log_level()
 logger.setLevel(__UpperCamelCase )
 transformers.utils.logging.set_verbosity(__UpperCamelCase )
 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.
 UpperCAmelCase__   :					Optional[int]   				=   None
 if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
  UpperCAmelCase__   :					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
 UpperCAmelCase__   :					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:
  UpperCAmelCase__   :					int   				=   dataset.rename_columns({"""pixel_values""": """image"""} )
 if "annotation" in dataset["train"].column_names:
  UpperCAmelCase__   :					List[Any]   				=   dataset.rename_columns({"""annotation""": """label"""} )

 # If we don't have a validation split, split off a percentage of train as validation.
 UpperCAmelCase__   :					Optional[int]   				=   None if """validation""" in dataset.keys() else data_args.train_val_split
 if isinstance(data_args.train_val_split							,      __UpperCamelCase ) and data_args.train_val_split > 0.0:
  UpperCAmelCase__   :					List[Any]   				=   dataset["""train"""].train_test_split(data_args.train_val_split )
  UpperCAmelCase__   :					Any   				=   split["""train"""]
  UpperCAmelCase__   :					Any   				=   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":
  UpperCAmelCase__   :					Union[str, Any]   				=   """huggingface/label-files"""
  UpperCAmelCase__   :					List[Any]   				=   """ade20k-id2label.json"""
 else:
  UpperCAmelCase__   :					Dict   				=   data_args.dataset_name
  UpperCAmelCase__   :					List[str]   				=   """id2label.json"""
 UpperCAmelCase__   :					Tuple   				=   json.load(open(hf_hub_download(__UpperCamelCase							,      __UpperCamelCase							,      repo_type="""dataset""" )							,      """r""" ) )
 UpperCAmelCase__   :					Dict   				=   {int(__UpperCamelCase ): v for k, v in idalabel.items()}
 UpperCAmelCase__   :					int   				=   {v: str(__UpperCamelCase ) for k, v in idalabel.items()}

 # Load the mean IoU metric from the datasets package
 UpperCAmelCase__   :					Tuple   				=   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(__UpperCamelCase ):
  UpperCAmelCase__				, UpperCAmelCase__   :					List[str]   				=   eval_pred
  UpperCAmelCase__   :					Dict   				=   torch.from_numpy(__UpperCamelCase )
  # scale the logits to the size of the label
  UpperCAmelCase__   :					Dict   				=   nn.functional.interpolate(
      __UpperCamelCase							,      size=labels.shape[-2:]							,      mode="""bilinear"""							,      align_corners=__UpperCamelCase							,      ).argmax(dim=1 )

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

  metrics.update({F"accuracy_{idalabel[i]}": v for i, v in enumerate(__UpperCamelCase )} )
  metrics.update({F"iou_{idalabel[i]}": v for i, v in enumerate(__UpperCamelCase )} )

  return metrics

 UpperCAmelCase__   :					Optional[int]   				=   AutoConfig.from_pretrained(
     model_args.config_name or model_args.model_name_or_path							,      labelaid=__UpperCamelCase							,      idalabel=__UpperCamelCase							,      cache_dir=model_args.cache_dir							,      revision=model_args.model_revision							,      use_auth_token=True if model_args.use_auth_token else None							,      )
 UpperCAmelCase__   :					Union[str, Any]   				=   AutoModelForSemanticSegmentation.from_pretrained(
     model_args.model_name_or_path							,      from_tf=bool(""".ckpt""" in model_args.model_name_or_path )							,      config=__UpperCamelCase							,      cache_dir=model_args.cache_dir							,      revision=model_args.model_revision							,      use_auth_token=True if model_args.use_auth_token else None							,      )
 UpperCAmelCase__   :					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.
  UpperCAmelCase__   :					Any   				=   (image_processor.size["""shortest_edge"""], image_processor.size["""shortest_edge"""])
 else:
  UpperCAmelCase__   :					Optional[int]   				=   (image_processor.size["""height"""], image_processor.size["""width"""])
 UpperCAmelCase__   :					Any   				=   Compose(
     [
         ReduceLabels() if data_args.reduce_labels else Identity(),
         RandomCrop(size=__UpperCamelCase ),
         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)
 UpperCAmelCase__   :					str   				=   Compose(
     [
         ReduceLabels() if data_args.reduce_labels else Identity(),
         Resize(size=__UpperCamelCase ),
         PILToTensor(),
         ConvertImageDtype(torch.float ),
         Normalize(mean=image_processor.image_mean							,      std=image_processor.image_std ),
     ] )

 def preprocess_train(__UpperCamelCase ):
  UpperCAmelCase__   :					List[Any]   				=   []
  UpperCAmelCase__   :					str   				=   []
  for image, target in zip(example_batch["""image"""]							,      example_batch["""label"""] ):
   UpperCAmelCase__				, UpperCAmelCase__   :					Dict   				=   train_transforms(image.convert("""RGB""" )							,      __UpperCamelCase )
   pixel_values.append(__UpperCamelCase )
   labels.append(__UpperCamelCase )

  UpperCAmelCase__   :					Any   				=   {}
  UpperCAmelCase__   :					Dict   				=   torch.stack(__UpperCamelCase )
  UpperCAmelCase__   :					Tuple   				=   torch.stack(__UpperCamelCase )

  return encoding

 def preprocess_val(__UpperCamelCase ):
  UpperCAmelCase__   :					List[str]   				=   []
  UpperCAmelCase__   :					Optional[Any]   				=   []
  for image, target in zip(example_batch["""image"""]							,      example_batch["""label"""] ):
   UpperCAmelCase__				, UpperCAmelCase__   :					Any   				=   val_transforms(image.convert("""RGB""" )							,      __UpperCamelCase )
   pixel_values.append(__UpperCamelCase )
   labels.append(__UpperCamelCase )

  UpperCAmelCase__   :					Dict   				=   {}
  UpperCAmelCase__   :					int   				=   torch.stack(__UpperCamelCase )
  UpperCAmelCase__   :					List[Any]   				=   torch.stack(__UpperCamelCase )

  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:
   UpperCAmelCase__   :					Optional[Any]   				=   (
       dataset["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
   )
  # Set the training transforms
  dataset["train"].set_transform(__UpperCamelCase )

 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:
   UpperCAmelCase__   :					List[str]   				=   (
       dataset["""validation"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
   )
  # Set the validation transforms
  dataset["validation"].set_transform(__UpperCamelCase )

 # Initalize our trainer
 UpperCAmelCase__   :					Any   				=   Trainer(
     model=__UpperCamelCase							,      args=__UpperCamelCase							,      train_dataset=dataset["""train"""] if training_args.do_train else None							,      eval_dataset=dataset["""validation"""] if training_args.do_eval else None							,      compute_metrics=__UpperCamelCase							,      tokenizer=__UpperCamelCase							,      data_collator=__UpperCamelCase							,      )

 # Training
 if training_args.do_train:
  UpperCAmelCase__   :					str   				=   None
  if training_args.resume_from_checkpoint is not None:
   UpperCAmelCase__   :					Dict   				=   training_args.resume_from_checkpoint
  elif last_checkpoint is not None:
   UpperCAmelCase__   :					Optional[int]   				=   last_checkpoint
  UpperCAmelCase__   :					Dict   				=   trainer.train(resume_from_checkpoint=__UpperCamelCase )
  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:
  UpperCAmelCase__   :					Dict   				=   trainer.evaluate()
  trainer.log_metrics("""eval"""							,      __UpperCamelCase )
  trainer.save_metrics("""eval"""							,      __UpperCamelCase )

 # Write model card and (optionally) push to hub
 UpperCAmelCase__   :					int   				=   {
     """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(**__UpperCamelCase )
 else:
  trainer.create_model_card(**__UpperCamelCase )


if __name__ == "__main__":
      main()





