


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


lowerCamelCase__							:						Dict							     =   logging.getLogger(__name__)
lowerCamelCase__							:						Union[str, Any]							     =   list(MODEL_FOR_MASKED_LM_MAPPING.keys())
lowerCamelCase__							:						Any							     =   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class 		__magic_name__						:





     '''simple docstring'''



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

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       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 		__magic_name__						:





     '''simple docstring'''



     __lowercase				:							Optional[str]	   =       field(
         default=snake_case_   ,metadata={'help': 'The name of the dataset to use (via the datasets library).'}				)
     __lowercase				:							Optional[str]	   =       field(
         default=snake_case_   ,metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}				)
     __lowercase				:							Optional[str]	   =       field(default=snake_case_   ,metadata={'help': 'The input training data file (a text file).'}				)
     __lowercase				:							Optional[str]	   =       field(
         default=snake_case_   ,metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}   ,)
     __lowercase				:							Optional[str]	   =       field(
         default=snake_case_   ,metadata={'help': 'An optional input train ref data file for whole word masking in Chinese.'}   ,)
     __lowercase				:							Optional[str]	   =       field(
         default=snake_case_   ,metadata={'help': 'An optional input validation ref data file for whole word masking in Chinese.'}   ,)
     __lowercase				:							bool	   =       field(
         default=snake_case_   ,metadata={'help': 'Overwrite the cached training and evaluation sets'}				)
     __lowercase				:							Optional[int]	   =       field(
         default=5   ,metadata={
             'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
         }   ,)
     __lowercase				:							Optional[int]	   =       field(
         default=snake_case_   ,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.'
             )
         }   ,)
     __lowercase				:							Optional[int]	   =       field(
         default=snake_case_   ,metadata={'help': 'The number of processes to use for the preprocessing.'}   ,)
     __lowercase				:							float	   =       field(
         default=0.15   ,metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'}				)
     __lowercase				:							bool	   =       field(
         default=snake_case_   ,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 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       if self.train_file is not None:
         snake_case__						= 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:
         snake_case__						= 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	(			__lowerCAmelCase							,   __lowerCAmelCase				)    ->				Dict:
  with open(__lowerCAmelCase							,   '''r'''							,   encoding='''utf-8'''				) as f:
    snake_case__						= [json.loads(__lowerCAmelCase				) for line in f.read().splitlines() if (len(__lowerCAmelCase				) > 0 and not line.isspace())]
  assert len(__lowerCAmelCase				) == len(__lowerCAmelCase				)

  snake_case__						= {c: dataset[c] for c in dataset.column_names}
  snake_case__						= refs
  return Dataset.from_dict(__lowerCAmelCase				)







def SCREAMING_SNAKE_CASE	(			)    ->				List[Any]:
  # 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.

  snake_case__						= 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.
    snake_case__				,				snake_case__				,				snake_case__						= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
  else:
    snake_case__				,				snake_case__				,				snake_case__						= parser.parse_args_into_dataclasses()

  # Detecting last checkpoint.
  snake_case__						= None
  if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
    snake_case__						= 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.
    snake_case__						= load_dataset(data_args.dataset_name							,   data_args.dataset_config_name				)
    if "validation" not in datasets.keys():
      snake_case__						= load_dataset(
          data_args.dataset_name							,   data_args.dataset_config_name							,   split=F"""train[:{data_args.validation_split_percentage}%]"""							,   )
      snake_case__						= load_dataset(
          data_args.dataset_name							,   data_args.dataset_config_name							,   split=F"""train[{data_args.validation_split_percentage}%:]"""							,   )
  else:
    snake_case__						= {}
    if data_args.train_file is not None:
      snake_case__						= data_args.train_file
    if data_args.validation_file is not None:
      snake_case__						= data_args.validation_file
    snake_case__						= data_args.train_file.split('''.'''				)[-1]
    if extension == "txt":
      snake_case__						= '''text'''
    snake_case__						= 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.
  snake_case__						= {
      '''cache_dir''': model_args.cache_dir,
      '''revision''': model_args.model_revision,
      '''use_auth_token''': True if model_args.use_auth_token else None,
  }
  if model_args.config_name:
    snake_case__						= AutoConfig.from_pretrained(model_args.config_name							,   **__lowerCAmelCase				)
  elif model_args.model_name_or_path:
    snake_case__						= AutoConfig.from_pretrained(model_args.model_name_or_path							,   **__lowerCAmelCase				)
  else:
    snake_case__						= 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}"""				)

  snake_case__						= {
      '''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:
    snake_case__						= AutoTokenizer.from_pretrained(model_args.tokenizer_name							,   **__lowerCAmelCase				)
  elif model_args.model_name_or_path:
    snake_case__						= 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:
    snake_case__						= 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'''				)
    snake_case__						= AutoModelForMaskedLM.from_config(__lowerCAmelCase				)

  model.resize_token_embeddings(len(__lowerCAmelCase				)				)

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

  snake_case__						= '''max_length''' if data_args.pad_to_max_length else False

  def tokenize_function(__lowerCAmelCase				):
    # Remove empty lines
    snake_case__						= [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				)

  snake_case__						= 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:
    snake_case__						= add_chinese_references(tokenized_datasets['''train''']							,   data_args.train_ref_file				)
  if data_args.validation_ref_file is not None:
    snake_case__						= add_chinese_references(
        tokenized_datasets['''validation''']							,   data_args.validation_ref_file				)
  # If we have ref files, need to avoid it removed by trainer
  snake_case__						= data_args.train_ref_file or data_args.validation_ref_file
  if has_ref:
    snake_case__						= False

  # Data collator
  # This one will take care of randomly masking the tokens.
  snake_case__						= DataCollatorForWholeWordMask(tokenizer=__lowerCAmelCase							,   mlm_probability=data_args.mlm_probability				)

  # Initialize our Trainer
  snake_case__						= 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:
      snake_case__						= last_checkpoint
    elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path				):
      snake_case__						= model_args.model_name_or_path
    else:
      snake_case__						= None
    snake_case__						= trainer.train(resume_from_checkpoint=__lowerCAmelCase				)
    trainer.save_model()  # Saves the tokenizer too for easy upload

    snake_case__						= 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
  snake_case__						= {}
  if training_args.do_eval:
    logger.info('''*** Evaluate ***'''				)

    snake_case__						= trainer.evaluate()

    snake_case__						= math.exp(eval_output['''eval_loss''']				)
    snake_case__						= perplexity

    snake_case__						= 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	(			__lowerCAmelCase				)    ->				List[str]:
  # 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 (							__snake_case   ,      __snake_case="shi-labs/oneformer_demo" )				->						Any:
       with open(hf_hub_download(__snake_case   ,      __snake_case   ,      repo_type="""dataset""" )   ,      """r""" ) as f:
              _UpperCAmelCase							=    json.load(__snake_case )
       _UpperCAmelCase							=    {}
       _UpperCAmelCase							=    []
       _UpperCAmelCase							=    []
       for key, info in class_info.items():
              _UpperCAmelCase							=    info["""name"""]
              class_names.append(info["""name"""] )
              if info["isthing"]:
                     thing_ids.append(int(__snake_case ) )
       _UpperCAmelCase							=    thing_ids
       _UpperCAmelCase							=    class_names
       return metadata
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  def __init__(      self		:			Optional[Any]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			List[Any]=7       ,				lowerCamelCase		:			str=3       ,				lowerCamelCase		:			Union[str, Any]=30       ,				lowerCamelCase		:			Optional[int]=400       ,				lowerCamelCase		:			Union[str, Any]=None       ,				lowerCamelCase		:			int=True       ,				lowerCamelCase		:			List[str]=True       ,				lowerCamelCase		:			Tuple=[0.5, 0.5, 0.5]       ,				lowerCamelCase		:			Tuple=[0.5, 0.5, 0.5]       ,				lowerCamelCase		:			Tuple=10       ,				lowerCamelCase		:			str=False       ,				lowerCamelCase		:			Union[str, Any]=255       ,				lowerCamelCase		:			Tuple="shi-labs/oneformer_demo"       ,				lowerCamelCase		:			Tuple="ade20k_panoptic.json"       ,				lowerCamelCase		:			Optional[Any]=10       ,				)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    min_resolution
         _UpperCAmelCase							=    max_resolution
         _UpperCAmelCase							=    do_resize
         _UpperCAmelCase							=    {"""shortest_edge""": 32, """longest_edge""": 1333} if size is None else size
         _UpperCAmelCase							=    do_normalize
         _UpperCAmelCase							=    image_mean
         _UpperCAmelCase							=    image_std
         _UpperCAmelCase							=    class_info_file
         _UpperCAmelCase							=    prepare_metadata(lowerCamelCase       ,				lowerCamelCase							)
         _UpperCAmelCase							=    num_text
         _UpperCAmelCase							=    repo_path

         # for the post_process_functions
         _UpperCAmelCase							=    2
         _UpperCAmelCase							=    10
         _UpperCAmelCase							=    10
         _UpperCAmelCase							=    3
         _UpperCAmelCase							=    4
         _UpperCAmelCase							=    num_labels
         _UpperCAmelCase							=    do_reduce_labels
         _UpperCAmelCase							=    ignore_index


  def  lowerCamelCase       (      self		:			int							)						->	Union[str, Any]:


         """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  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			Tuple=False							)						->	Any:


         """simple docstring"""
         if not batched:
                _UpperCAmelCase							=    image_inputs[0]
                if isinstance(lowerCamelCase       ,				Image.Image							):
                       _UpperCAmelCase	,			_UpperCAmelCase							=    image.size
                else:
                       _UpperCAmelCase	,			_UpperCAmelCase							=    image.shape[1], image.shape[2]
                if w < h:
                       _UpperCAmelCase							=    int(self.size["""shortest_edge"""] * h / w							)
                       _UpperCAmelCase							=    self.size["""shortest_edge"""]
                elif w > h:
                       _UpperCAmelCase							=    self.size["""shortest_edge"""]
                       _UpperCAmelCase							=    int(self.size["""shortest_edge"""] * w / h							)
                else:
                       _UpperCAmelCase							=    self.size["""shortest_edge"""]
                       _UpperCAmelCase							=    self.size["""shortest_edge"""]

         else:
                _UpperCAmelCase							=    []
                for image in image_inputs:
                       _UpperCAmelCase	,			_UpperCAmelCase							=    self.get_expected_values([image]							)
                       expected_values.append((expected_height, expected_width)							)
                _UpperCAmelCase							=    max(lowerCamelCase       ,				key=lambda lowerCamelCase							: item[0]							)[0]
                _UpperCAmelCase							=    max(lowerCamelCase       ,				key=lambda lowerCamelCase							: item[1]							)[1]

         return expected_height, expected_width





  def  lowerCamelCase       (      self		:			str							)						->	Optional[int]:


         """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 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
  # only for test_image_processing_common.test_image_proc_to_json_string
  _lowerCamelCase    =  image_processing_class


  def  lowerCamelCase       (      self		:			Dict							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    OneFormerImageProcessorTester(self							)


  @property
  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	Dict:


         """simple docstring"""
         return self.image_processing_tester.prepare_image_processor_dict()


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""image_mean"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""image_std"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""do_normalize"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""do_resize"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""size"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""ignore_index"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""class_info_file"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""num_text"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""repo_path"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""metadata"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""do_reduce_labels"""							)							)


  def  lowerCamelCase       (      self		:			Tuple							)						->	Any:


         """simple docstring"""
         pass


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Optional[int]:


         """simple docstring"""
         # Initialize image_processor
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         # create random PIL images
         _UpperCAmelCase							=    prepare_image_inputs(self.image_processing_tester       ,				equal_resolution=lowerCamelCase							)
         for image in image_inputs:
                self.assertIsInstance(lowerCamelCase       ,				Image.Image							)

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

         _UpperCAmelCase	,			_UpperCAmelCase							=    self.image_processing_tester.get_expected_values(lowerCamelCase							)

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

         # Test batched
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.image_processing_tester.get_expected_values(lowerCamelCase       ,				batched=lowerCamelCase							)

         _UpperCAmelCase							=    image_processor(
             lowerCamelCase       ,				["""semantic"""] * len(lowerCamelCase							)       ,				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  lowerCamelCase       (      self		:			List[Any]							)						->	List[Any]:


         """simple docstring"""
         # Initialize image_processor
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         # create random numpy tensors
         _UpperCAmelCase							=    prepare_image_inputs(self.image_processing_tester       ,				equal_resolution=lowerCamelCase       ,				numpify=lowerCamelCase							)
         for image in image_inputs:
                self.assertIsInstance(lowerCamelCase       ,				np.ndarray							)

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

         _UpperCAmelCase	,			_UpperCAmelCase							=    self.image_processing_tester.get_expected_values(lowerCamelCase							)

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

         # Test batched
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.image_processing_tester.get_expected_values(lowerCamelCase       ,				batched=lowerCamelCase							)

         _UpperCAmelCase							=    image_processor(
             lowerCamelCase       ,				["""semantic"""] * len(lowerCamelCase							)       ,				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  lowerCamelCase       (      self		:			List[str]							)						->	Tuple:


         """simple docstring"""
         # Initialize image_processor
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         # create random PyTorch tensors
         _UpperCAmelCase							=    prepare_image_inputs(self.image_processing_tester       ,				equal_resolution=lowerCamelCase       ,				torchify=lowerCamelCase							)
         for image in image_inputs:
                self.assertIsInstance(lowerCamelCase       ,				torch.Tensor							)

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

         _UpperCAmelCase	,			_UpperCAmelCase							=    self.image_processing_tester.get_expected_values(lowerCamelCase							)

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

         # Test batched
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.image_processing_tester.get_expected_values(lowerCamelCase       ,				batched=lowerCamelCase							)

         _UpperCAmelCase							=    image_processor(
             lowerCamelCase       ,				["""semantic"""] * len(lowerCamelCase							)       ,				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  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			Tuple=False       ,				lowerCamelCase		:			Union[str, Any]=False       ,				lowerCamelCase		:			Union[str, Any]="np"							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         # prepare image and target
         _UpperCAmelCase							=    self.image_processing_tester.num_labels
         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    prepare_image_inputs(self.image_processing_tester       ,				equal_resolution=lowerCamelCase							)
         if with_segmentation_maps:
                _UpperCAmelCase							=    num_labels
                if is_instance_map:
                       _UpperCAmelCase							=    list(range(lowerCamelCase							)							) * 2
                       _UpperCAmelCase							=    dict(enumerate(lowerCamelCase							)							)
                _UpperCAmelCase							=    [
                    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							=    [Image.fromarray(lowerCamelCase							) for annotation in annotations]

         _UpperCAmelCase							=    image_processor(
             lowerCamelCase       ,				["""semantic"""] * len(lowerCamelCase							)       ,				lowerCamelCase       ,				return_tensors="""pt"""       ,				instance_id_to_semantic_id=lowerCamelCase       ,				pad_and_return_pixel_mask=lowerCamelCase       ,				)

         return inputs


  def  lowerCamelCase       (      self		:			Any							)						->	Dict:


         """simple docstring"""
         pass


  def  lowerCamelCase       (      self		:			Dict							)						->	List[Any]:


         """simple docstring"""
         def common(lowerCamelCase		:			List[Any]=False       ,				lowerCamelCase		:			List[Any]=None							):
                _UpperCAmelCase							=    self.comm_get_image_processor_inputs(
                    with_segmentation_maps=lowerCamelCase       ,				is_instance_map=lowerCamelCase       ,				segmentation_type=lowerCamelCase							)

                _UpperCAmelCase							=    inputs["""mask_labels"""]
                _UpperCAmelCase							=    inputs["""class_labels"""]
                _UpperCAmelCase							=    inputs["""pixel_values"""]
                _UpperCAmelCase							=    inputs["""text_inputs"""]

                # check the batch_size
                for mask_label, class_label, text_input in zip(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase							):
                       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(lowerCamelCase							)       ,				self.image_processing_tester.num_text							)

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


  def  lowerCamelCase       (      self		:			int							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    np.zeros((20, 50)							)
         _UpperCAmelCase							=    1
         _UpperCAmelCase							=    1
         _UpperCAmelCase							=    1

         _UpperCAmelCase							=    binary_mask_to_rle(lowerCamelCase							)
         self.assertEqual(len(lowerCamelCase							)       ,				4							)
         self.assertEqual(rle[0]       ,				21							)
         self.assertEqual(rle[1]       ,				45							)


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    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							=    self.image_processing_tester.get_fake_oneformer_outputs()

         _UpperCAmelCase							=    fature_extractor.post_process_semantic_segmentation(lowerCamelCase							)

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

         _UpperCAmelCase							=    [(1, 4) for i in range(self.image_processing_tester.batch_size							)]
         _UpperCAmelCase							=    fature_extractor.post_process_semantic_segmentation(lowerCamelCase       ,				target_sizes=lowerCamelCase							)

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


  def  lowerCamelCase       (      self		:			Dict							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    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							=    self.image_processing_tester.get_fake_oneformer_outputs()
         _UpperCAmelCase							=    image_processor.post_process_instance_segmentation(lowerCamelCase       ,				threshold=0							)

         self.assertTrue(len(lowerCamelCase							) == 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"""]							)       ,				lowerCamelCase							)
                self.assertEqual(
                    el["""segmentation"""].shape       ,				(self.image_processing_tester.height, self.image_processing_tester.width)							)





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


         """simple docstring"""
         _UpperCAmelCase							=    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							=    self.image_processing_tester.get_fake_oneformer_outputs()
         _UpperCAmelCase							=    image_processor.post_process_panoptic_segmentation(lowerCamelCase       ,				threshold=0							)

         self.assertTrue(len(lowerCamelCase							) == 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"""]							)       ,				lowerCamelCase							)
                self.assertEqual(
                    el["""segmentation"""].shape       ,				(self.image_processing_tester.height, self.image_processing_tester.width)							)