






import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowerCamelCase	:							Tuple				=logging.getLogger(__name__)





@dataclass
class 			__a  :
 _lowerCAmelCase       :       str        = field(
     metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}       )
 _lowerCAmelCase       :       Optional[str]        = field(
     default=A__       ,   metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}       )
 _lowerCAmelCase       :       Optional[str]        = field(
     default='''NER'''       ,   metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}       )
 _lowerCAmelCase       :       Optional[str]        = field(
     default=A__       ,   metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}       )
 _lowerCAmelCase       :       bool        = field(default=A__       ,   metadata={'''help''': '''Set this flag to use fast tokenization.'''}       )
 # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
 # or just modify its tokenizer_config.json.
 _lowerCAmelCase       :       Optional[str]        = field(
     default=A__       ,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}       ,   )







@dataclass
class 			__a  :
 _lowerCAmelCase       :       str        = field(
     metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}       )
 _lowerCAmelCase       :       Optional[str]        = field(
     default=A__       ,   metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}       ,   )
 _lowerCAmelCase       :       int        = field(
     default=1_2_8       ,   metadata={
         '''help''': (
             '''The maximum total input sequence length after tokenization. Sequences longer '''
             '''than this will be truncated, sequences shorter will be padded.'''
         )
     }       ,   )
 _lowerCAmelCase       :       bool        = field(
     default=A__       ,   metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}       )







def 		SCREAMING_SNAKE_CASE							(					) ->	Optional[int]:
     # 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.

     UpperCamelCase__			:   Any       				=  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[Any]       				=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
     else:
          UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   Optional[int]       				=  parser.parse_args_into_dataclasses()

     if (
         os.path.exists(training_args.output_dir			)
         and os.listdir(training_args.output_dir			)
         and training_args.do_train
         and not training_args.overwrite_output_dir
     ):
          raise ValueError(
              f'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
              " --overwrite_output_dir to overcome."			)

     UpperCamelCase__			:   Optional[int]       				=  import_module("tasks"			)
     try:
          UpperCamelCase__			:   List[str]       				=  getattr(__lowerCAmelCase					,    model_args.task_type			)
          UpperCamelCase__			:   TokenClassificationTask       				=  token_classification_task_clazz()
     except AttributeError:
          raise ValueError(
              f'Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
              f'Available tasks classes are: {TokenClassificationTask.__subclasses__()}'			)

     # Setup logging
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"					,    datefmt="%m/%d/%Y %H:%M:%S"					,    level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN					,    )
     logger.warning(
         "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"					,    training_args.local_rank					,    training_args.device					,    training_args.n_gpu					,    bool(training_args.local_rank != -1			)					,    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
     set_seed(training_args.seed			)

     # Prepare CONLL-2003 task
     UpperCamelCase__			:   Union[str, Any]       				=  token_classification_task.get_labels(data_args.labels			)
     UpperCamelCase__			:   Dict[int, str]       				=  dict(enumerate(__lowerCAmelCase			)			)
     UpperCamelCase__			:   str       				=  len(__lowerCAmelCase			)

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.

     UpperCamelCase__			:   Any       				=  AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path					,    num_labels=__lowerCAmelCase					,    idalabel=__lowerCAmelCase					,    labelaid={label: i for i, label in enumerate(__lowerCAmelCase			)}					,    cache_dir=model_args.cache_dir					,    )
     UpperCamelCase__			:   Union[str, Any]       				=  AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					,    cache_dir=model_args.cache_dir					,    use_fast=model_args.use_fast					,    )
     UpperCamelCase__			:   List[str]       				=  AutoModelForTokenClassification.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					,    )

     # Get datasets
     UpperCamelCase__			:   Optional[int]       				=  (
         TokenClassificationDataset(
             token_classification_task=__lowerCAmelCase					,    data_dir=data_args.data_dir					,    tokenizer=__lowerCAmelCase					,    labels=__lowerCAmelCase					,    model_type=config.model_type					,    max_seq_length=data_args.max_seq_length					,    overwrite_cache=data_args.overwrite_cache					,    mode=Split.train					,    )
         if training_args.do_train
         else None
     )
     UpperCamelCase__			:   Dict       				=  (
         TokenClassificationDataset(
             token_classification_task=__lowerCAmelCase					,    data_dir=data_args.data_dir					,    tokenizer=__lowerCAmelCase					,    labels=__lowerCAmelCase					,    model_type=config.model_type					,    max_seq_length=data_args.max_seq_length					,    overwrite_cache=data_args.overwrite_cache					,    mode=Split.dev					,    )
         if training_args.do_eval
         else None
     )

     def align_predictions(__lowerCAmelCase					,    __lowerCAmelCase			) -> Tuple[List[int], List[int]]:
          UpperCamelCase__			:   int       				=  np.argmax(__lowerCAmelCase					,    axis=2			)

          UpperCamelCase__				,     UpperCamelCase__			:   Optional[Any]       				=  preds.shape

          UpperCamelCase__			:   List[str]       				=  [[] for _ in range(__lowerCAmelCase			)]
          UpperCamelCase__			:   str       				=  [[] for _ in range(__lowerCAmelCase			)]

          for i in range(__lowerCAmelCase			):
               for j in range(__lowerCAmelCase			):
                    if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
                         out_label_list[i].append(label_map[label_ids[i][j]]			)
                         preds_list[i].append(label_map[preds[i][j]]			)

          return preds_list, out_label_list

     def compute_metrics(__lowerCAmelCase			) -> Dict:
          UpperCamelCase__				,     UpperCamelCase__			:   List[Any]       				=  align_predictions(p.predictions					,    p.label_ids			)
          return {
              "accuracy_score": accuracy_score(__lowerCAmelCase					,    __lowerCAmelCase			),
              "precision": precision_score(__lowerCAmelCase					,    __lowerCAmelCase			),
              "recall": recall_score(__lowerCAmelCase					,    __lowerCAmelCase			),
              "f1": fa_score(__lowerCAmelCase					,    __lowerCAmelCase			),
          }

     # Data collator
     UpperCamelCase__			:   Optional[Any]       				=  DataCollatorWithPadding(__lowerCAmelCase					,    pad_to_multiple_of=8			) if training_args.fpaa else None

     # Initialize our Trainer
     UpperCamelCase__			:   Tuple       				=  Trainer(
         model=__lowerCAmelCase					,    args=__lowerCAmelCase					,    train_dataset=__lowerCAmelCase					,    eval_dataset=__lowerCAmelCase					,    compute_metrics=__lowerCAmelCase					,    data_collator=__lowerCAmelCase					,    )

     # Training
     if training_args.do_train:
          trainer.train(
              model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
          trainer.save_model()
          # For convenience, we also re-save the tokenizer to the same directory,
          # so that you can share your model easily on huggingface.co/models =)
          if trainer.is_world_process_zero():
               tokenizer.save_pretrained(training_args.output_dir			)

    # Evaluation
     UpperCamelCase__			:   List[str]       				=  {}
     if training_args.do_eval:
          logger.info("*** Evaluate ***"			)

          UpperCamelCase__			:   str       				=  trainer.evaluate()

          UpperCamelCase__			:   Any       				=  os.path.join(training_args.output_dir					,    "eval_results.txt"			)
          if trainer.is_world_process_zero():
               with open(__lowerCAmelCase					,    "w"			) as writer:
                    logger.info("***** Eval results *****"			)
                    for key, value in result.items():
                         logger.info("  %s = %s"					,    __lowerCAmelCase					,    __lowerCAmelCase			)
                         writer.write("%s = %s\n" % (key, value)			)

               results.update(__lowerCAmelCase			)

    # Predict
     if training_args.do_predict:
          UpperCamelCase__			:   Tuple       				=  TokenClassificationDataset(
              token_classification_task=__lowerCAmelCase					,    data_dir=data_args.data_dir					,    tokenizer=__lowerCAmelCase					,    labels=__lowerCAmelCase					,    model_type=config.model_type					,    max_seq_length=data_args.max_seq_length					,    overwrite_cache=data_args.overwrite_cache					,    mode=Split.test					,    )

          UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   int       				=  trainer.predict(__lowerCAmelCase			)
          UpperCamelCase__				,     UpperCamelCase__			:   Any       				=  align_predictions(__lowerCAmelCase					,    __lowerCAmelCase			)

          UpperCamelCase__			:   str       				=  os.path.join(training_args.output_dir					,    "test_results.txt"			)
          if trainer.is_world_process_zero():
               with open(__lowerCAmelCase					,    "w"			) as writer:
                    for key, value in metrics.items():
                         logger.info("  %s = %s"					,    __lowerCAmelCase					,    __lowerCAmelCase			)
                         writer.write("%s = %s\n" % (key, value)			)

        # Save predictions
          UpperCamelCase__			:   int       				=  os.path.join(training_args.output_dir					,    "test_predictions.txt"			)
          if trainer.is_world_process_zero():
               with open(__lowerCAmelCase					,    "w"			) as writer:
                    with open(os.path.join(data_args.data_dir					,    "test.txt"			)					,    "r"			) as f:
                         token_classification_task.write_predictions_to_file(__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			)

     return results







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	List[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)						)