

"""simple docstring"""




import argparse
import collections
import importlib.util
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
__UpperCAmelCase	='src/diffusers'
__UpperCAmelCase	='docs/source/en'
__UpperCAmelCase	='.'
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase ):
 '''simple docstring'''





 with open(__UpperCamelCase							,      """r"""							,      encoding="""utf-8"""							,      newline="""\n""" ) as f:
  UpperCAmelCase__   :					List[Any]   				=   f.readlines()
 # Find the start prompt.
 UpperCAmelCase__   :					Any   				=   0
 while not lines[start_index].startswith(__UpperCamelCase ):
  start_index += 1
 start_index += 1

 UpperCAmelCase__   :					Optional[Any]   				=   start_index
 while not lines[end_index].startswith(__UpperCamelCase ):
  end_index += 1
 end_index -= 1

 while len(lines[start_index] ) <= 1:
  start_index += 1
 while len(lines[end_index] ) <= 1:
  end_index -= 1
 end_index += 1
 return "".join(lines[start_index:end_index] ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
__UpperCAmelCase	='Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
__UpperCAmelCase	=re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
__UpperCAmelCase	=re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__UpperCAmelCase	=re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the diffusers module imported is the one in the repo.
__UpperCAmelCase	=importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(TRANSFORMERS_PATH, '__init__.py'),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
__UpperCAmelCase	=spec.loader.load_module()
def 					lowerCAmelCase  (   __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					List[Any]   				=   re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""							,      __UpperCamelCase )
 return [m.group(0 ) for m in matches]
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					Optional[Any]   				=   2 if text == '''✅''' or text == '''❌''' else len(__UpperCamelCase )
 UpperCAmelCase__   :					Optional[int]   				=   (width - text_length) // 2
 UpperCAmelCase__   :					Tuple   				=   width - text_length - left_indent
 return " " * left_indent + text + " " * right_indent
def 					lowerCAmelCase  (   ):
 '''simple docstring'''





 UpperCAmelCase__   :					Dict   				=   diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 UpperCAmelCase__   :					int   				=   {
     name: config_mapping_names[code]
     for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
     if code in config_mapping_names
 }
 UpperCAmelCase__   :					str   				=   {name: config.replace("""ConfigMixin"""							,      """""" ) for name, config in model_name_to_config.items()}

 # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
 UpperCAmelCase__   :					List[str]   				=   collections.defaultdict(__UpperCamelCase )
 UpperCAmelCase__   :					Union[str, Any]   				=   collections.defaultdict(__UpperCamelCase )
 UpperCAmelCase__   :					Optional[Any]   				=   collections.defaultdict(__UpperCamelCase )
 UpperCAmelCase__   :					List[Any]   				=   collections.defaultdict(__UpperCamelCase )
 UpperCAmelCase__   :					Tuple   				=   collections.defaultdict(__UpperCamelCase )

 # Let's lookup through all diffusers object (once).
 for attr_name in dir(__UpperCamelCase ):
  UpperCAmelCase__   :					Union[str, Any]   				=   None
  if attr_name.endswith("""Tokenizer""" ):
   UpperCAmelCase__   :					str   				=   slow_tokenizers
   UpperCAmelCase__   :					str   				=   attr_name[:-9]
  elif attr_name.endswith("""TokenizerFast""" ):
   UpperCAmelCase__   :					Optional[int]   				=   fast_tokenizers
   UpperCAmelCase__   :					Optional[Any]   				=   attr_name[:-13]
  elif _re_tf_models.match(__UpperCamelCase ) is not None:
   UpperCAmelCase__   :					Union[str, Any]   				=   tf_models
   UpperCAmelCase__   :					Tuple   				=   _re_tf_models.match(__UpperCamelCase ).groups()[0]
  elif _re_flax_models.match(__UpperCamelCase ) is not None:
   UpperCAmelCase__   :					Optional[Any]   				=   flax_models
   UpperCAmelCase__   :					Optional[Any]   				=   _re_flax_models.match(__UpperCamelCase ).groups()[0]
  elif _re_pt_models.match(__UpperCamelCase ) is not None:
   UpperCAmelCase__   :					List[Any]   				=   pt_models
   UpperCAmelCase__   :					int   				=   _re_pt_models.match(__UpperCamelCase ).groups()[0]

  if lookup_dict is not None:
   while len(__UpperCamelCase ) > 0:
    if attr_name in model_name_to_prefix.values():
     UpperCAmelCase__   :					int   				=   True
     break
    # Try again after removing the last word in the name
    UpperCAmelCase__   :					Tuple   				=   ''''''.join(camel_case_split(__UpperCamelCase )[:-1] )

  # Let's build that table!
 UpperCAmelCase__   :					Optional[int]   				=   list(model_name_to_config.keys() )
 model_names.sort(key=str.lower )
 UpperCAmelCase__   :					Optional[Any]   				=   ['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
 # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
 UpperCAmelCase__   :					Optional[Any]   				=   [len(__UpperCamelCase ) + 2 for c in columns]
 UpperCAmelCase__   :					Tuple   				=   max([len(__UpperCamelCase ) for name in model_names] ) + 2

 # Build the table per se
 UpperCAmelCase__   :					List[Any]   				=   '''|''' + '''|'''.join([_center_text(__UpperCamelCase							,      __UpperCamelCase ) for c, w in zip(__UpperCamelCase							,      __UpperCamelCase )] ) + '''|\n'''
 # Use ":-----:" format to center-aligned table cell texts
 table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths] ) + "|\n"

 UpperCAmelCase__   :					Any   				=   {True: '''✅''', False: '''❌'''}
 for name in model_names:
  UpperCAmelCase__   :					str   				=   model_name_to_prefix[name]
  UpperCAmelCase__   :					int   				=   [
      name,
      check[slow_tokenizers[prefix]],
      check[fast_tokenizers[prefix]],
      check[pt_models[prefix]],
      check[tf_models[prefix]],
      check[flax_models[prefix]],
  ]
  table += "|" + "|".join([_center_text(__UpperCamelCase							,      __UpperCamelCase ) for l, w in zip(__UpperCamelCase							,      __UpperCamelCase )] ) + "|\n"
 return table
def 					lowerCAmelCase  (   __UpperCamelCase=False ):
 '''simple docstring'''





 UpperCAmelCase__   :					List[str]   				=   _find_text_in_file(
     filename=os.path.join(__UpperCamelCase							,      """index.mdx""" )							,      start_prompt="""<!--This table is updated automatically from the auto modules"""							,      end_prompt="""<!-- End table-->"""							,      )
 UpperCAmelCase__   :					Optional[Any]   				=   get_model_table_from_auto_modules()

 if current_table != new_table:
  if overwrite:
   with open(os.path.join(__UpperCamelCase							,      """index.mdx""" )							,      """w"""							,      encoding="""utf-8"""							,      newline="""\n""" ) as f:
    f.writelines(lines[:start_index] + [new_table] + lines[end_index:] )
  else:
   raise ValueError(
       """The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this.""" )


if __name__ == "__main__":
      __UpperCAmelCase	=argparse.ArgumentParser()
      parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
      __UpperCAmelCase	=parser.parse_args()

      check_model_table(args.fix_and_overwrite)








"""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()





