"""simple docstring"""






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

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


__A			   = logging.getLogger(__name__)
__A			   = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__A			   = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class     UpperCAmelCase						:



    """simple docstring"""

    _UpperCAmelCase	:Optional[str]						    = field(
        default=A__    ,metadata={
            "help": (
                "The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch."
            )
        }    ,)
    _UpperCAmelCase	:Optional[str]						    = field(
        default=A__    ,metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(A__  )}    ,)
    _UpperCAmelCase	:Optional[str]						    = field(
        default=A__    ,metadata={
            "help": (
                "Override some existing default config settings when a model is trained from scratch. Example: "
                "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
            )
        }    ,)
    _UpperCAmelCase	:Optional[str]						    = field(
        default=A__    ,metadata={"help": "Pretrained config name or path if not the same as model_name"}  )
    _UpperCAmelCase	:Optional[str]						    = field(
        default=A__    ,metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}  )
    _UpperCAmelCase	:Optional[str]						    = field(
        default=A__    ,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}    ,)
    _UpperCAmelCase	:bool						    = field(
        default=A__    ,metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}    ,)
    _UpperCAmelCase	:str						    = field(
        default="main"    ,metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}    ,)
    _UpperCAmelCase	:bool						    = field(
        default=A__    ,metadata={
            "help": (
                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                "with private models)."
            )
        }    ,)






    def _snake_case			(   self							):






       if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
          raise ValueError(
              '''--config_overrides can\'t be used in combination with --config_name or --model_name_or_path'''							)



@dataclass
class     UpperCAmelCase						:



    """simple docstring"""

    _UpperCAmelCase	:Optional[str]						    = field(
        default=A__    ,metadata={"help": "The name of the dataset to use (via the datasets library)."}  )
    _UpperCAmelCase	:Optional[str]						    = field(
        default=A__    ,metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}  )
    _UpperCAmelCase	:Optional[str]						    = field(default=A__    ,metadata={"help": "The input training data file (a text file)."}  )
    _UpperCAmelCase	:Optional[str]						    = field(
        default=A__    ,metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}    ,)
    _UpperCAmelCase	:Optional[str]						    = field(
        default=A__    ,metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}    ,)
    _UpperCAmelCase	:Optional[str]						    = field(
        default=A__    ,metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}    ,)
    _UpperCAmelCase	:bool						    = field(
        default=A__    ,metadata={"help": "Overwrite the cached training and evaluation sets"}  )
    _UpperCAmelCase	:Optional[int]						    = field(
        default=5    ,metadata={
            "help": "The percentage of the train set used as validation set in case there\'s no validation split"
        }    ,)
    _UpperCAmelCase	:Optional[int]						    = field(
        default=A__    ,metadata={
            "help": (
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated. Default to the max input length of the model."
            )
        }    ,)
    _UpperCAmelCase	:Optional[int]						    = field(
        default=A__    ,metadata={"help": "The number of processes to use for the preprocessing."}    ,)
    _UpperCAmelCase	:float						    = field(
        default=0.15    ,metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}  )
    _UpperCAmelCase	:bool						    = field(
        default=A__    ,metadata={
            "help": (
                "Whether to pad all samples to `max_seq_length`. "
                "If False, will pad the samples dynamically when batching to the maximum length in the batch."
            )
        }    ,)






    def _snake_case			(   self							):






       if self.train_file is not None:
          lowercase__:   Tuple					=      self.train_file.split('''.'''							)[-1]
          assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
       if self.validation_file is not None:
          lowercase__:   Optional[Any]					=      self.validation_file.split('''.'''							)[-1]
          assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  int:
   with open(__lowerCAmelCase       ,    '''r'''       ,    encoding='''utf-8''' ) as f:
      lowercase__:   Dict					=      [json.loads(__lowerCAmelCase ) for line in f.read().splitlines() if (len(__lowerCAmelCase ) > 0 and not line.isspace())]
   assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )

   lowercase__:   Dict					=      {c: dataset[c] for c in dataset.column_names}
   lowercase__:   List[str]					=      refs
   return Dataset.from_dict(__lowerCAmelCase )



def SCREAMING_SNAKE_CASE__			(   )  ->  Dict:
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

   lowercase__:   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.
      lowercase__:   Dict					=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
   else:
      lowercase__:   List[Any]					=      parser.parse_args_into_dataclasses()

   # Detecting last checkpoint.
   lowercase__:   Union[str, Any]					=      None
   if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
      lowercase__:   Any					=      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:
         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.''' )

    # 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 )]       ,    )
   logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )

   # 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}""" )
   # Set the verbosity to info of the Transformers logger (on main process only):
   if is_main_process(training_args.local_rank ):
      transformers.utils.logging.set_verbosity_info()
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()
   logger.info('''Training/evaluation parameters %s'''       ,    __lowerCAmelCase )

   # Set seed before initializing model.
   set_seed(training_args.seed )

   # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
   # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
   # (the dataset will be downloaded automatically from the datasets Hub).
   #
   # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
   # 'text' is found. You can easily tweak this behavior (see below).
   #
   # In distributed training, the load_dataset function guarantee that only one local process can concurrently
   # download the dataset.
   if data_args.dataset_name is not None:
      # Downloading and loading a dataset from the hub.
      lowercase__:   Union[str, Any]					=      load_dataset(data_args.dataset_name       ,    data_args.dataset_config_name )
      if "validation" not in datasets.keys():
         lowercase__:   List[Any]					=      load_dataset(
             data_args.dataset_name       ,    data_args.dataset_config_name       ,    split=F"""train[:{data_args.validation_split_percentage}%]"""       ,    )
         lowercase__:   Dict					=      load_dataset(
             data_args.dataset_name       ,    data_args.dataset_config_name       ,    split=F"""train[{data_args.validation_split_percentage}%:]"""       ,    )
   else:
      lowercase__:   Optional[int]					=      {}
      if data_args.train_file is not None:
         lowercase__:   Optional[Any]					=      data_args.train_file
      if data_args.validation_file is not None:
         lowercase__:   List[Any]					=      data_args.validation_file
      lowercase__:   Dict					=      data_args.train_file.split('''.''' )[-1]
      if extension == "txt":
         lowercase__:   Union[str, Any]					=      "text"
      lowercase__:   Union[str, Any]					=      load_dataset(__lowerCAmelCase       ,    data_files=__lowerCAmelCase )
   # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
   # https://huggingface.co/docs/datasets/loading_datasets.html.

   # Load pretrained model and tokenizer
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.
   lowercase__:   Dict					=      {
       "cache_dir": model_args.cache_dir,
       "revision": model_args.model_revision,
       "use_auth_token": True if model_args.use_auth_token else None,
   }
   if model_args.config_name:
      lowercase__:   Optional[Any]					=      AutoConfig.from_pretrained(model_args.config_name       ,    **__lowerCAmelCase )
   elif model_args.model_name_or_path:
      lowercase__:   Optional[Any]					=      AutoConfig.from_pretrained(model_args.model_name_or_path       ,    **__lowerCAmelCase )
   else:
      lowercase__:   List[Any]					=      CONFIG_MAPPING[model_args.model_type]()
      logger.warning('''You are instantiating a new config instance from scratch.''' )
      if model_args.config_overrides is not None:
         logger.info(F"""Overriding config: {model_args.config_overrides}""" )
         config.update_from_string(model_args.config_overrides )
         logger.info(F"""New config: {config}""" )

   lowercase__:   Optional[int]					=      {
       "cache_dir": model_args.cache_dir,
       "use_fast": model_args.use_fast_tokenizer,
       "revision": model_args.model_revision,
       "use_auth_token": True if model_args.use_auth_token else None,
   }
   if model_args.tokenizer_name:
      lowercase__:   Dict					=      AutoTokenizer.from_pretrained(model_args.tokenizer_name       ,    **__lowerCAmelCase )
   elif model_args.model_name_or_path:
      lowercase__:   List[Any]					=      AutoTokenizer.from_pretrained(model_args.model_name_or_path       ,    **__lowerCAmelCase )
   else:
      raise ValueError(
          '''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
          '''You can do it from another script, save it, and load it from here, using --tokenizer_name.''' )

   if model_args.model_name_or_path:
      lowercase__:   Union[str, Any]					=      AutoModelForMaskedLM.from_pretrained(
          model_args.model_name_or_path       ,    from_tf=bool('''.ckpt''' in model_args.model_name_or_path )       ,    config=__lowerCAmelCase       ,    cache_dir=model_args.cache_dir       ,    revision=model_args.model_revision       ,    use_auth_token=True if model_args.use_auth_token else None       ,    )
   else:
      logger.info('''Training new model from scratch''' )
      lowercase__:   List[Any]					=      AutoModelForMaskedLM.from_config(__lowerCAmelCase )

   model.resize_token_embeddings(len(__lowerCAmelCase ) )

   # Preprocessing the datasets.
   # First we tokenize all the texts.
   if training_args.do_train:
      lowercase__:   Tuple					=      datasets["train"].column_names
   else:
      lowercase__:   Any					=      datasets["validation"].column_names
   lowercase__:   Any					=      "text" if "text" in column_names else column_names[0]

   lowercase__:   Dict					=      "max_length" if data_args.pad_to_max_length else False

   def tokenize_function(__UpperCAmelCase ):
      # Remove empty lines
      lowercase__:   List[str]					=      [line for line in examples["text"] if len(__lowerCAmelCase ) > 0 and not line.isspace()]
      return tokenizer(examples['''text''']       ,    padding=__lowerCAmelCase       ,    truncation=__lowerCAmelCase       ,    max_length=data_args.max_seq_length )

   lowercase__:   Any					=      datasets.map(
       __lowerCAmelCase       ,    batched=__lowerCAmelCase       ,    num_proc=data_args.preprocessing_num_workers       ,    remove_columns=[text_column_name]       ,    load_from_cache_file=not data_args.overwrite_cache       ,    )

   # Add the chinese references if provided
   if data_args.train_ref_file is not None:
      lowercase__:   Union[str, Any]					=      add_chinese_references(tokenized_datasets['''train''']       ,    data_args.train_ref_file )
   if data_args.validation_ref_file is not None:
      lowercase__:   Dict					=      add_chinese_references(
          tokenized_datasets['''validation''']       ,    data_args.validation_ref_file )
   # If we have ref files, need to avoid it removed by trainer
   lowercase__:   Any					=      data_args.train_ref_file or data_args.validation_ref_file
   if has_ref:
      lowercase__:   str					=      False

   # Data collator
   # This one will take care of randomly masking the tokens.
   lowercase__:   List[Any]					=      DataCollatorForWholeWordMask(tokenizer=__lowerCAmelCase       ,    mlm_probability=data_args.mlm_probability )

   # Initialize our Trainer
   lowercase__:   Optional[int]					=      Trainer(
       model=__lowerCAmelCase       ,    args=__lowerCAmelCase       ,    train_dataset=tokenized_datasets['''train'''] if training_args.do_train else None       ,    eval_dataset=tokenized_datasets['''validation'''] if training_args.do_eval else None       ,    tokenizer=__lowerCAmelCase       ,    data_collator=__lowerCAmelCase       ,    )

   # Training
   if training_args.do_train:
      if last_checkpoint is not None:
         lowercase__:   Any					=      last_checkpoint
      elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path ):
         lowercase__:   Tuple					=      model_args.model_name_or_path
      else:
         lowercase__:   Union[str, Any]					=      None
      lowercase__:   Optional[Any]					=      trainer.train(resume_from_checkpoint=__lowerCAmelCase )
      trainer.save_model()  # Saves the tokenizer too for easy upload

      lowercase__:   List[str]					=      os.path.join(training_args.output_dir       ,    '''train_results.txt''' )
      if trainer.is_world_process_zero():
         with open(__lowerCAmelCase       ,    '''w''' ) as writer:
            logger.info('''***** Train results *****''' )
            for key, value in sorted(train_result.metrics.items() ):
               logger.info(F"""  {key} = {value}""" )
               writer.write(F"""{key} = {value}\n""" )

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
         trainer.state.save_to_json(os.path.join(training_args.output_dir       ,    '''trainer_state.json''' ) )

    # Evaluation
   lowercase__:   Tuple					=      {}
   if training_args.do_eval:
      logger.info('''*** Evaluate ***''' )

      lowercase__:   str					=      trainer.evaluate()

      lowercase__:   Any					=      math.exp(eval_output['''eval_loss'''] )
      lowercase__:   Optional[int]					=      perplexity

      lowercase__:   Union[str, Any]					=      os.path.join(training_args.output_dir       ,    '''eval_results_mlm_wwm.txt''' )
      if trainer.is_world_process_zero():
         with open(__lowerCAmelCase       ,    '''w''' ) as writer:
            logger.info('''***** Eval results *****''' )
            for key, value in sorted(results.items() ):
               logger.info(F"""  {key} = {value}""" )
               writer.write(F"""{key} = {value}\n""" )

   return results



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  Union[str, Any]:
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
       main()








import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

 if is_vision_available():
  from transformers import OneFormerImageProcessor
  from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
  from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
 from PIL import Image







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase="shi-labs/oneformer_demo"			) ->	Tuple:
     with open(hf_hub_download(__lowerCAmelCase					,    __lowerCAmelCase					,    repo_type="dataset"			)					,    "r"			) as f:
          UpperCamelCase__			:   Optional[Any]       				=  json.load(__lowerCAmelCase			)
     UpperCamelCase__			:   str       				=  {}
     UpperCamelCase__			:   List[Any]       				=  []
     UpperCamelCase__			:   int       				=  []
     for key, info in class_info.items():
          UpperCamelCase__			:   List[str]       				=  info["name"]
          class_names.append(info["name"]			)
          if info["isthing"]:
               thing_ids.append(int(__lowerCAmelCase			)			)
     UpperCamelCase__			:   Dict       				=  thing_ids
     UpperCamelCase__			:   Optional[int]       				=  class_names
     return metadata





class 			__a  (    unittest.TestCase       ):







 def __init__(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   List[str]=7							,   SCREAMING_SNAKE_CASE					:   Optional[int]=3							,   SCREAMING_SNAKE_CASE					:   Tuple=30							,   SCREAMING_SNAKE_CASE					:   Dict=4_00							,   SCREAMING_SNAKE_CASE					:   Optional[int]=None							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=True							,   SCREAMING_SNAKE_CASE					:   Dict=True							,   SCREAMING_SNAKE_CASE					:   Optional[int]=[0.5, 0.5, 0.5]							,   SCREAMING_SNAKE_CASE					:   List[str]=[0.5, 0.5, 0.5]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=10							,   SCREAMING_SNAKE_CASE					:   int=False							,   SCREAMING_SNAKE_CASE					:   int=2_55							,   SCREAMING_SNAKE_CASE					:   str="shi-labs/oneformer_demo"							,   SCREAMING_SNAKE_CASE					:   List[Any]="ade20k_panoptic.json"							,   SCREAMING_SNAKE_CASE					:   Tuple=10							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  parent
      UpperCamelCase__			:   Optional[int]       				=  batch_size
      UpperCamelCase__			:   Any       				=  num_channels
      UpperCamelCase__			:   Optional[int]       				=  min_resolution
      UpperCamelCase__			:   Union[str, Any]       				=  max_resolution
      UpperCamelCase__			:   Optional[int]       				=  do_resize
      UpperCamelCase__			:   List[Any]       				=  {"shortest_edge": 32, "longest_edge": 13_33} if size is None else size
      UpperCamelCase__			:   Dict       				=  do_normalize
      UpperCamelCase__			:   Optional[int]       				=  image_mean
      UpperCamelCase__			:   Union[str, Any]       				=  image_std
      UpperCamelCase__			:   Union[str, Any]       				=  class_info_file
      UpperCamelCase__			:   Tuple       				=  prepare_metadata(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   str       				=  num_text
      UpperCamelCase__			:   int       				=  repo_path

      # for the post_process_functions
      UpperCamelCase__			:   int       				=  2
      UpperCamelCase__			:   str       				=  10
      UpperCamelCase__			:   Any       				=  10
      UpperCamelCase__			:   Union[str, Any]       				=  3
      UpperCamelCase__			:   List[Any]       				=  4
      UpperCamelCase__			:   Optional[int]       				=  num_labels
      UpperCamelCase__			:   Tuple       				=  do_reduce_labels
      UpperCamelCase__			:   List[str]       				=  ignore_index







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "num_labels": self.num_labels,
          "do_reduce_labels": self.do_reduce_labels,
          "ignore_index": self.ignore_index,
          "class_info_file": self.class_info_file,
          "metadata": self.metadata,
          "num_text": self.num_text,
      }







 def 					__lowercase					(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   List[str]=False						):


      '''simple docstring'''






      if not batched:
           UpperCamelCase__			:   str       				=  image_inputs[0]
           if isinstance(SCREAMING_SNAKE_CASE							,   Image.Image						):
                UpperCamelCase__				,     UpperCamelCase__			:   Union[str, Any]       				=  image.size
           else:
                UpperCamelCase__				,     UpperCamelCase__			:   int       				=  image.shape[1], image.shape[2]
           if w < h:
                UpperCamelCase__			:   Any       				=  int(self.size["shortest_edge"] * h / w						)
                UpperCamelCase__			:   Union[str, Any]       				=  self.size["shortest_edge"]
           elif w > h:
                UpperCamelCase__			:   Union[str, Any]       				=  self.size["shortest_edge"]
                UpperCamelCase__			:   int       				=  int(self.size["shortest_edge"] * w / h						)
           else:
                UpperCamelCase__			:   Optional[Any]       				=  self.size["shortest_edge"]
                UpperCamelCase__			:   str       				=  self.size["shortest_edge"]

      else:
           UpperCamelCase__			:   Tuple       				=  []
           for image in image_inputs:
                UpperCamelCase__				,     UpperCamelCase__			:   Tuple       				=  self.get_expected_values([image]						)
                expected_values.append((expected_height, expected_width)						)
           UpperCamelCase__			:   List[str]       				=  max(SCREAMING_SNAKE_CASE							,   key=lambda SCREAMING_SNAKE_CASE						: item[0]						)[0]
           UpperCamelCase__			:   int       				=  max(SCREAMING_SNAKE_CASE							,   key=lambda SCREAMING_SNAKE_CASE						: item[1]						)[1]

      return expected_height, expected_width







 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      return OneFormerForUniversalSegmentationOutput(
          # +1 for null class
          class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)						)							,   masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)						)							,   )







@require_torch
@require_vision
class 			__a  (    A__       ,   unittest.TestCase       ):
 _lowerCAmelCase       :       Tuple        = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
 # only for test_image_processing_common.test_image_proc_to_json_string
 _lowerCAmelCase       :       List[str]        = image_processing_class







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      UpperCamelCase__			:   Union[str, Any]       				=  OneFormerImageProcessorTester(self						)







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


      '''simple docstring'''






      return self.image_processing_tester.prepare_image_processor_dict()







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  self.image_processing_class(**self.image_processor_dict						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,   "image_mean"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,   "image_std"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,   "do_normalize"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,   "do_resize"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,   "size"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,   "ignore_index"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,   "class_info_file"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,   "num_text"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,   "repo_path"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,   "metadata"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,   "do_reduce_labels"						)						)







 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      pass







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      UpperCamelCase__			:   Optional[int]       				=  prepare_image_inputs(self.image_processing_tester							,   equal_resolution=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   Image.Image						)

      # Test not batched input
      UpperCamelCase__			:   Optional[int]       				=  image_processor(image_inputs[0]							,   ["semantic"]							,   return_tensors="pt"						).pixel_values

      UpperCamelCase__				,     UpperCamelCase__			:   Optional[Any]       				=  self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE						)

      self.assertEqual(
          encoded_images.shape							,   (1, self.image_processing_tester.num_channels, expected_height, expected_width)							,   )

      # Test batched
      UpperCamelCase__				,     UpperCamelCase__			:   Dict       				=  self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE							,   batched=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Dict       				=  image_processor(
          SCREAMING_SNAKE_CASE							,   ["semantic"] * len(SCREAMING_SNAKE_CASE						)							,   return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_images.shape							,   (
              self.image_processing_tester.batch_size,
              self.image_processing_tester.num_channels,
              expected_height,
              expected_width,
          )							,   )







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      UpperCamelCase__			:   Union[str, Any]       				=  prepare_image_inputs(self.image_processing_tester							,   equal_resolution=SCREAMING_SNAKE_CASE							,   numpify=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   np.ndarray						)

      # Test not batched input
      UpperCamelCase__			:   Any       				=  image_processor(image_inputs[0]							,   ["semantic"]							,   return_tensors="pt"						).pixel_values

      UpperCamelCase__				,     UpperCamelCase__			:   Union[str, Any]       				=  self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE						)

      self.assertEqual(
          encoded_images.shape							,   (1, self.image_processing_tester.num_channels, expected_height, expected_width)							,   )

      # Test batched
      UpperCamelCase__				,     UpperCamelCase__			:   Optional[Any]       				=  self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE							,   batched=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Union[str, Any]       				=  image_processor(
          SCREAMING_SNAKE_CASE							,   ["semantic"] * len(SCREAMING_SNAKE_CASE						)							,   return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_images.shape							,   (
              self.image_processing_tester.batch_size,
              self.image_processing_tester.num_channels,
              expected_height,
              expected_width,
          )							,   )







 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      UpperCamelCase__			:   Optional[int]       				=  prepare_image_inputs(self.image_processing_tester							,   equal_resolution=SCREAMING_SNAKE_CASE							,   torchify=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   torch.Tensor						)

      # Test not batched input
      UpperCamelCase__			:   List[Any]       				=  image_processor(image_inputs[0]							,   ["semantic"]							,   return_tensors="pt"						).pixel_values

      UpperCamelCase__				,     UpperCamelCase__			:   Tuple       				=  self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE						)

      self.assertEqual(
          encoded_images.shape							,   (1, self.image_processing_tester.num_channels, expected_height, expected_width)							,   )

      # Test batched
      UpperCamelCase__				,     UpperCamelCase__			:   List[Any]       				=  self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE							,   batched=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Any       				=  image_processor(
          SCREAMING_SNAKE_CASE							,   ["semantic"] * len(SCREAMING_SNAKE_CASE						)							,   return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_images.shape							,   (
              self.image_processing_tester.batch_size,
              self.image_processing_tester.num_channels,
              expected_height,
              expected_width,
          )							,   )







 def 					__lowercase					(		self					:   int							,   SCREAMING_SNAKE_CASE					:   Optional[int]=False							,   SCREAMING_SNAKE_CASE					:   Optional[int]=False							,   SCREAMING_SNAKE_CASE					:   Any="np"						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  self.image_processing_class(**self.image_processor_dict						)
      # prepare image and target
      UpperCamelCase__			:   Any       				=  self.image_processing_tester.num_labels
      UpperCamelCase__			:   Optional[Any]       				=  None
      UpperCamelCase__			:   Optional[Any]       				=  None
      UpperCamelCase__			:   str       				=  prepare_image_inputs(self.image_processing_tester							,   equal_resolution=SCREAMING_SNAKE_CASE						)
      if with_segmentation_maps:
           UpperCamelCase__			:   Tuple       				=  num_labels
           if is_instance_map:
                UpperCamelCase__			:   List[str]       				=  list(range(SCREAMING_SNAKE_CASE						)						) * 2
                UpperCamelCase__			:   Optional[Any]       				=  dict(enumerate(SCREAMING_SNAKE_CASE						)						)
           UpperCamelCase__			:   Union[str, Any]       				=  [
               np.random.randint(0							,   high * 2							,   (img.size[1], img.size[0])						).astype(np.uinta						) for img in image_inputs
           ]
           if segmentation_type == "pil":
                UpperCamelCase__			:   List[str]       				=  [Image.fromarray(SCREAMING_SNAKE_CASE						) for annotation in annotations]

      UpperCamelCase__			:   Optional[int]       				=  image_processor(
          SCREAMING_SNAKE_CASE							,   ["semantic"] * len(SCREAMING_SNAKE_CASE						)							,   SCREAMING_SNAKE_CASE							,   return_tensors="pt"							,   instance_id_to_semantic_id=SCREAMING_SNAKE_CASE							,   pad_and_return_pixel_mask=SCREAMING_SNAKE_CASE							,   )

      return inputs







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      pass







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      def common(SCREAMING_SNAKE_CASE					:   Optional[int]=False							,   SCREAMING_SNAKE_CASE					:   str=None						):
           UpperCamelCase__			:   Any       				=  self.comm_get_image_processor_inputs(
               with_segmentation_maps=SCREAMING_SNAKE_CASE							,   is_instance_map=SCREAMING_SNAKE_CASE							,   segmentation_type=SCREAMING_SNAKE_CASE						)

           UpperCamelCase__			:   str       				=  inputs["mask_labels"]
           UpperCamelCase__			:   Optional[Any]       				=  inputs["class_labels"]
           UpperCamelCase__			:   List[str]       				=  inputs["pixel_values"]
           UpperCamelCase__			:   int       				=  inputs["text_inputs"]

           # check the batch_size
           for mask_label, class_label, text_input in zip(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
                self.assertEqual(mask_label.shape[0]							,   class_label.shape[0]						)
                # this ensure padding has happened
                self.assertEqual(mask_label.shape[1:]							,   pixel_values.shape[2:]						)
                self.assertEqual(len(SCREAMING_SNAKE_CASE						)							,   self.image_processing_tester.num_text						)

      common()
      common(is_instance_map=SCREAMING_SNAKE_CASE						)
      common(is_instance_map=SCREAMING_SNAKE_CASE							,   segmentation_type="pil"						)
      common(is_instance_map=SCREAMING_SNAKE_CASE							,   segmentation_type="pil"						)







 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[int]       				=  np.zeros((20, 50)						)
      UpperCamelCase__			:   int       				=  1
      UpperCamelCase__			:   Dict       				=  1
      UpperCamelCase__			:   Dict       				=  1

      UpperCamelCase__			:   int       				=  binary_mask_to_rle(SCREAMING_SNAKE_CASE						)
      self.assertEqual(len(SCREAMING_SNAKE_CASE						)							,   4						)
      self.assertEqual(rle[0]							,   21						)
      self.assertEqual(rle[1]							,   45						)







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  self.image_processing_class(
          num_labels=self.image_processing_tester.num_classes							,   max_seq_length=77							,   task_seq_length=77							,   class_info_file="ade20k_panoptic.json"							,   num_text=self.image_processing_tester.num_text							,   repo_path="shi-labs/oneformer_demo"							,   )
      UpperCamelCase__			:   int       				=  self.image_processing_tester.get_fake_oneformer_outputs()

      UpperCamelCase__			:   Union[str, Any]       				=  fature_extractor.post_process_semantic_segmentation(SCREAMING_SNAKE_CASE						)

      self.assertEqual(len(SCREAMING_SNAKE_CASE						)							,   self.image_processing_tester.batch_size						)
      self.assertEqual(
          segmentation[0].shape							,   (
              self.image_processing_tester.height,
              self.image_processing_tester.width,
          )							,   )

      UpperCamelCase__			:   List[str]       				=  [(1, 4) for i in range(self.image_processing_tester.batch_size						)]
      UpperCamelCase__			:   Optional[Any]       				=  fature_extractor.post_process_semantic_segmentation(SCREAMING_SNAKE_CASE							,   target_sizes=SCREAMING_SNAKE_CASE						)

      self.assertEqual(segmentation[0].shape							,   target_sizes[0]						)







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  self.image_processing_class(
          num_labels=self.image_processing_tester.num_classes							,   max_seq_length=77							,   task_seq_length=77							,   class_info_file="ade20k_panoptic.json"							,   num_text=self.image_processing_tester.num_text							,   repo_path="shi-labs/oneformer_demo"							,   )
      UpperCamelCase__			:   List[str]       				=  self.image_processing_tester.get_fake_oneformer_outputs()
      UpperCamelCase__			:   Optional[int]       				=  image_processor.post_process_instance_segmentation(SCREAMING_SNAKE_CASE							,   threshold=0						)

      self.assertTrue(len(SCREAMING_SNAKE_CASE						) == self.image_processing_tester.batch_size						)
      for el in segmentation:
           self.assertTrue("segmentation" in el						)
           self.assertTrue("segments_info" in el						)
           self.assertEqual(type(el["segments_info"]						)							,   SCREAMING_SNAKE_CASE						)
           self.assertEqual(
               el["segmentation"].shape							,   (self.image_processing_tester.height, self.image_processing_tester.width)						)







 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  self.image_processing_class(
          num_labels=self.image_processing_tester.num_classes							,   max_seq_length=77							,   task_seq_length=77							,   class_info_file="ade20k_panoptic.json"							,   num_text=self.image_processing_tester.num_text							,   repo_path="shi-labs/oneformer_demo"							,   )
      UpperCamelCase__			:   Any       				=  self.image_processing_tester.get_fake_oneformer_outputs()
      UpperCamelCase__			:   Tuple       				=  image_processor.post_process_panoptic_segmentation(SCREAMING_SNAKE_CASE							,   threshold=0						)

      self.assertTrue(len(SCREAMING_SNAKE_CASE						) == self.image_processing_tester.batch_size						)
      for el in segmentation:
           self.assertTrue("segmentation" in el						)
           self.assertTrue("segments_info" in el						)
           self.assertEqual(type(el["segments_info"]						)							,   SCREAMING_SNAKE_CASE						)
           self.assertEqual(
               el["segmentation"].shape							,   (self.image_processing_tester.height, self.image_processing_tester.width)						)