

"""simple docstring"""




class        __lowercase     :

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






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[int]   				=   val
   # Number of nodes in left subtree
   UpperCAmelCase__   :					Union[str, Any]   				=   0
   UpperCAmelCase__   :					Any   				=   None
   UpperCAmelCase__   :					Tuple   				=   None
   UpperCAmelCase__   :					List[str]   				=   None




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






   '''simple docstring'''
   assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

   if self.val < other.val:
    UpperCAmelCase__   :					str   				=   self.right
    UpperCAmelCase__   :					Tuple   				=   None
    if self.right:
     UpperCAmelCase__   :					Optional[int]   				=   other
    UpperCAmelCase__   :					Dict   				=   other
    UpperCAmelCase__   :					Tuple   				=   self.left_tree_size * 2 + 1
    return self
   else:
    UpperCAmelCase__   :					Optional[Any]   				=   other.right
    UpperCAmelCase__   :					str   				=   None
    if other.right:
     UpperCAmelCase__   :					Any   				=   self
    UpperCAmelCase__   :					List[str]   				=   self
    UpperCAmelCase__   :					Optional[int]   				=   other.left_tree_size * 2 + 1
    return other







class        __lowercase     :

  def __init__(					self						:     Optional[Any]   ,A						:     Dict=None   ,A						:     List[Any]=None   ,A						:     Any=0      ):






   '''simple docstring'''
   UpperCAmelCase__   :					List[Any]   				=   heap_size
   UpperCAmelCase__   :					Union[str, Any]   				=   bottom_root
   UpperCAmelCase__   :					Dict   				=   min_node

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






   '''simple docstring'''

   # Empty heaps corner cases
   if other.size == 0:
    return None
   if self.size == 0:
    UpperCAmelCase__   :					Dict   				=   other.size
    UpperCAmelCase__   :					List[str]   				=   other.bottom_root
    UpperCAmelCase__   :					Tuple   				=   other.min_node
    return None
   # Update size
   UpperCAmelCase__   :					List[Any]   				=   self.size + other.size

   # Update min.node
   if self.min_node.val > other.min_node.val:
    UpperCAmelCase__   :					Optional[Any]   				=   other.min_node
   # Merge

   # Order roots by left_subtree_size
   UpperCAmelCase__   :					Tuple   				=   []
   UpperCAmelCase__				, UpperCAmelCase__   :					List[Any]   				=   self.bottom_root, other.bottom_root
   while i or j:
    if i and ((not j) or i.left_tree_size < j.left_tree_size):
     combined_roots_list.append((i, True)      )
     UpperCAmelCase__   :					Tuple   				=   i.parent
    else:
     combined_roots_list.append((j, False)      )
     UpperCAmelCase__   :					int   				=   j.parent
        # Insert links between them
   for i in range(len(A      ) - 1      ):
    if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
     UpperCAmelCase__   :					Any   				=   combined_roots_list[i + 1][0]
     UpperCAmelCase__   :					Tuple   				=   combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
   UpperCAmelCase__   :					Any   				=   combined_roots_list[0][0]
   while i.parent:
    if (
        (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
    ) or (
        i.left_tree_size == i.parent.left_tree_size
        and i.left_tree_size != i.parent.parent.left_tree_size
    ):
     # Neighbouring Nodes
     UpperCAmelCase__   :					Union[str, Any]   				=   i.left
     UpperCAmelCase__   :					Optional[int]   				=   i.parent.parent

     # Merging trees
     UpperCAmelCase__   :					List[str]   				=   i.merge_trees(i.parent      )

     # Updating links
     UpperCAmelCase__   :					List[str]   				=   previous_node
     UpperCAmelCase__   :					List[Any]   				=   next_node
     if previous_node:
      UpperCAmelCase__   :					Union[str, Any]   				=   i
     if next_node:
      UpperCAmelCase__   :					Optional[Any]   				=   i
    else:
     UpperCAmelCase__   :					List[Any]   				=   i.parent
        # Updating self.bottom_root
   while i.left:
    UpperCAmelCase__   :					Union[str, Any]   				=   i.left
   UpperCAmelCase__   :					Tuple   				=   i

   # Update other
   UpperCAmelCase__   :					Optional[Any]   				=   self.size
   UpperCAmelCase__   :					str   				=   self.bottom_root
   UpperCAmelCase__   :					Union[str, Any]   				=   self.min_node

   # Return the merged heap
   return self

  def 				__lowercase    (					self						:     Dict   ,A						:     Tuple      ):






   '''simple docstring'''
   if self.size == 0:
    UpperCAmelCase__   :					Tuple   				=   Node(A      )
    UpperCAmelCase__   :					Union[str, Any]   				=   1
    UpperCAmelCase__   :					Any   				=   self.bottom_root
   else:
    # Create new node
    UpperCAmelCase__   :					List[str]   				=   Node(A      )

    # Update size
    self.size += 1

    # update min_node
    if val < self.min_node.val:
     UpperCAmelCase__   :					Optional[Any]   				=   new_node
    # Put new_node as a bottom_root in heap
    UpperCAmelCase__   :					Tuple   				=   new_node
    UpperCAmelCase__   :					List[str]   				=   self.bottom_root
    UpperCAmelCase__   :					Any   				=   new_node

    # Consecutively merge roots with same left_tree_size
    while (
        self.bottom_root.parent
        and self.bottom_root.left_tree_size
        == self.bottom_root.parent.left_tree_size
    ):
     # Next node
     UpperCAmelCase__   :					str   				=   self.bottom_root.parent.parent

     # Merge
     UpperCAmelCase__   :					Any   				=   self.bottom_root.merge_trees(self.bottom_root.parent      )

     # Update Links
     UpperCAmelCase__   :					int   				=   next_node
     UpperCAmelCase__   :					int   				=   None
     if next_node:
      UpperCAmelCase__   :					Optional[Any]   				=   self.bottom_root

  def 				__lowercase    (					self						:     List[Any]      ):






   '''simple docstring'''
   return self.min_node.val

  def 				__lowercase    (					self						:     List[str]      ):






   '''simple docstring'''
   return self.size == 0

  def 				__lowercase    (					self						:     int      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Union[str, Any]   				=   self.min_node.val

   # Last element in heap corner case
   if self.size == 1:
    # Update size
    UpperCAmelCase__   :					List[str]   				=   0

    # Update bottom root
    UpperCAmelCase__   :					Optional[Any]   				=   None

    # Update min_node
    UpperCAmelCase__   :					Optional[int]   				=   None

    return min_value
   # No right subtree corner case
   # The structure of the tree implies that this should be the bottom root
   # and there is at least one other root
   if self.min_node.right is None:
    # Update size
    self.size -= 1

    # Update bottom root
    UpperCAmelCase__   :					Union[str, Any]   				=   self.bottom_root.parent
    UpperCAmelCase__   :					Tuple   				=   None

    # Update min_node
    UpperCAmelCase__   :					Optional[Any]   				=   self.bottom_root
    UpperCAmelCase__   :					Tuple   				=   self.bottom_root.parent
    while i:
     if i.val < self.min_node.val:
      UpperCAmelCase__   :					Optional[int]   				=   i
     UpperCAmelCase__   :					str   				=   i.parent
    return min_value
   # General case
   # Find the BinomialHeap of the right subtree of min_node
   UpperCAmelCase__   :					List[Any]   				=   self.min_node.right
   UpperCAmelCase__   :					Union[str, Any]   				=   None
   UpperCAmelCase__   :					int   				=   bottom_of_new
   UpperCAmelCase__   :					List[Any]   				=   1

   # Size, min_node and bottom_root
   while bottom_of_new.left:
    UpperCAmelCase__   :					str   				=   size_of_new * 2 + 1
    UpperCAmelCase__   :					Tuple   				=   bottom_of_new.left
    if bottom_of_new.val < min_of_new.val:
     UpperCAmelCase__   :					Optional[int]   				=   bottom_of_new
        # Corner case of single root on top left path
   if (not self.min_node.left) and (not self.min_node.parent):
    UpperCAmelCase__   :					str   				=   size_of_new
    UpperCAmelCase__   :					Any   				=   bottom_of_new
    UpperCAmelCase__   :					Any   				=   min_of_new
    # print("Single root, multiple nodes case")
    return min_value
   # Remaining cases
   # Construct heap of right subtree
   UpperCAmelCase__   :					Optional[int]   				=   BinomialHeap(
       bottom_root=A   ,min_node=A   ,heap_size=A      )

   # Update size
   UpperCAmelCase__   :					Optional[int]   				=   self.size - 1 - size_of_new

   # Neighbour nodes
   UpperCAmelCase__   :					List[str]   				=   self.min_node.left
   UpperCAmelCase__   :					Union[str, Any]   				=   self.min_node.parent

   # Initialize new bottom_root and min_node
   UpperCAmelCase__   :					Tuple   				=   previous_node or next_node
   UpperCAmelCase__   :					Optional[Any]   				=   next_node

   # Update links of previous_node and search below for new min_node and
   # bottom_root
   if previous_node:
    UpperCAmelCase__   :					Any   				=   next_node

    # Update bottom_root and search for min_node below
    UpperCAmelCase__   :					Optional[Any]   				=   previous_node
    UpperCAmelCase__   :					str   				=   previous_node
    while self.bottom_root.left:
     UpperCAmelCase__   :					Optional[int]   				=   self.bottom_root.left
     if self.bottom_root.val < self.min_node.val:
      UpperCAmelCase__   :					Optional[int]   				=   self.bottom_root
   if next_node:
    UpperCAmelCase__   :					Optional[Any]   				=   previous_node

    # Search for new min_node above min_node
    UpperCAmelCase__   :					List[str]   				=   next_node
    while i:
     if i.val < self.min_node.val:
      UpperCAmelCase__   :					Optional[Any]   				=   i
     UpperCAmelCase__   :					Tuple   				=   i.parent
        # Merge heaps
   self.merge_heaps(A      )

   return min_value

  def 				__lowercase    (					self						:     List[Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					List[str]   				=   self.bottom_root
   while top_root.parent:
    UpperCAmelCase__   :					Any   				=   top_root.parent
   # preorder
   UpperCAmelCase__   :					Optional[int]   				=   []
   self.__traversal(A   ,A      )
   return heap_pre_order

  def 				__lowercase    (					self						:     Optional[int]   ,A						:     Union[str, Any]   ,A						:     int   ,A						:     Optional[Any]=0      ):






   '''simple docstring'''
   if curr_node:
    preorder.append((curr_node.val, level)      )
    self.__traversal(curr_node.left   ,A   ,level + 1      )
    self.__traversal(curr_node.right   ,A   ,level + 1      )
   else:
    preorder.append(("""#""", level)      )




  def __str__(					self						:     str      ):






   '''simple docstring'''
   if self.is_empty():
    return ""
   UpperCAmelCase__   :					Union[str, Any]   				=   self.pre_order()

   return "\n".join(("""-""" * level + str(A      )) for value, level in preorder_heap      )


# Unit Tests
if __name__ == "__main__":
      import doctest

      doctest.testmod()








"""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()





