





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_nllb import NllbTokenizer
else:
       _A :   Tuple  			=  None


_A :   Optional[Any]  			=  logging.get_logger(__name__)


_A :   Optional[int]  			=  {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_A :   Optional[Any]  			=  {
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'
        ),
    },
}

_A :   List[str]  			=  {
    'facebook/nllb-large-en-ro': 10_24,
    'facebook/nllb-200-distilled-600M': 10_24,
}

# fmt: off
_A :   Optional[Any]  			=  ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
       _UpperCAmelCase				:  Optional[Any]			   =   VOCAB_FILES_NAMES
       _UpperCAmelCase				:  Any			   =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _UpperCAmelCase				:  Tuple			   =   PRETRAINED_VOCAB_FILES_MAP
       _UpperCAmelCase				:  Union[str, Any]			   =   ["input_ids", "attention_mask"]
       _UpperCAmelCase				:  List[Any]			   =   NllbTokenizer

       _UpperCAmelCase				:  List[int]			   =   []
       _UpperCAmelCase				:  List[int]			   =   []







       def __init__(				self					:     str		, A					:     Any=None		, A					:     int=None		, A					:     Union[str, Any]="<s>"		, A					:     List[Any]="</s>"		, A					:     Union[str, Any]="</s>"		, A					:     Dict="<s>"		, A					:     Optional[Any]="<unk>"		, A					:     int="<pad>"		, A					:     str="<mask>"		, A					:     Optional[int]=None		, A					:     Tuple=None		, A					:     Dict=None		, A					:     Any=False		, **A					:     Optional[Any]		, )       ->Tuple:
          # Mask token behave like a normal word, i.e. include the space before it
          lowerCamelCase__       :						Tuple        =							AddedToken(A		, lstrip=A		, rstrip=A      ) if isinstance(A		, A      ) else mask_token
          lowerCamelCase__       :						int        =							legacy_behaviour
          super().__init__(
              vocab_file=A		, tokenizer_file=A		, bos_token=A		, eos_token=A		, sep_token=A		, cls_token=A		, unk_token=A		, pad_token=A		, mask_token=A		, src_lang=A		, tgt_lang=A		, additional_special_tokens=A		, legacy_behaviour=A		, **A		, )

          lowerCamelCase__       :						Optional[Any]        =							vocab_file
          lowerCamelCase__       :						Tuple        =							False if not self.vocab_file else True

          lowerCamelCase__       :						int        =							FAIRSEQ_LANGUAGE_CODES.copy()

          if additional_special_tokens is not None:
             # Only add those special tokens if they are not already there.
             _additional_special_tokens.extend(
                 [t for t in additional_special_tokens if t not in _additional_special_tokens]      )

          self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens}      )
          lowerCamelCase__       :						Union[str, Any]        =							{
              lang_code: self.convert_tokens_to_ids(A      ) for lang_code in FAIRSEQ_LANGUAGE_CODES
          }

          lowerCamelCase__       :						int        =							src_lang if src_lang is not None else '''eng_Latn'''
          lowerCamelCase__       :						Optional[Any]        =							self.convert_tokens_to_ids(self._src_lang      )
          lowerCamelCase__       :						int        =							tgt_lang
          self.set_src_lang_special_tokens(self._src_lang      )







       @property
       def 						__lowerCamelCase   (				self					:     Any      )       ->str:
          return self._src_lang







       @src_lang.setter
       def 						__lowerCamelCase   (				self					:     int		, A					:     str      )       ->None:
          lowerCamelCase__       :						List[str]        =							new_src_lang
          self.set_src_lang_special_tokens(self._src_lang      )







       def 						__lowerCamelCase   (				self					:     Optional[Any]		, A					:     List[int]		, A					:     Optional[List[int]] = None      )       ->List[int]:
          if token_ids_a is None:
             return self.prefix_tokens + token_ids_a + self.suffix_tokens
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







       def 						__lowerCamelCase   (				self					:     str		, A					:     List[int]		, A					:     Optional[List[int]] = None      )       ->List[int]:
          lowerCamelCase__       :						str        =							[self.sep_token_id]
          lowerCamelCase__       :						Optional[Any]        =							[self.cls_token_id]

          if token_ids_a is None:
             return len(cls + token_ids_a + sep      ) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]







       def 						__lowerCamelCase   (				self					:     List[Any]		, A					:     List[str]		, A					:     str		, A					:     Optional[str]		, A					:     Optional[str]		, **A					:     int      )       ->int:
          if src_lang is None or tgt_lang is None:
             raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''      )
          lowerCamelCase__       :						List[str]        =							src_lang
          lowerCamelCase__       :						Optional[Any]        =							self(A		, add_special_tokens=A		, return_tensors=A		, **A      )
          lowerCamelCase__       :						int        =							self.convert_tokens_to_ids(A      )
          lowerCamelCase__       :						Any        =							tgt_lang_id
          return inputs







       def 						__lowerCamelCase   (				self					:     Optional[int]		, A					:     List[str]		, A					:     str = "eng_Latn"		, A					:     Optional[List[str]] = None		, A					:     str = "fra_Latn"		, **A					:     Optional[int]		, )       ->BatchEncoding:
          lowerCamelCase__       :						Dict        =							src_lang
          lowerCamelCase__       :						List[str]        =							tgt_lang
          return super().prepare_seqaseq_batch(A		, A		, **A      )







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Dict:
          return self.set_src_lang_special_tokens(self.src_lang      )







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Any:
          return self.set_tgt_lang_special_tokens(self.tgt_lang      )







       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     str      )       ->None:
          lowerCamelCase__       :						List[str]        =							self.convert_tokens_to_ids(A      )

          if self.legacy_behaviour:
             lowerCamelCase__       :						Any        =							[]
             lowerCamelCase__       :						Dict        =							[self.eos_token_id, self.cur_lang_code]
          else:
             lowerCamelCase__       :						Optional[int]        =							[self.cur_lang_code]
             lowerCamelCase__       :						Optional[int]        =							[self.eos_token_id]

          lowerCamelCase__       :						Optional[Any]        =							self.convert_ids_to_tokens(self.prefix_tokens      )
          lowerCamelCase__       :						Optional[int]        =							self.convert_ids_to_tokens(self.suffix_tokens      )

          lowerCamelCase__       :						Tuple        =							processors.TemplateProcessing(
              single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str		, pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str		, special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str		, self.prefix_tokens + self.suffix_tokens      )      )		, )







       def 						__lowerCamelCase   (				self					:     str		, A					:     str      )       ->None:
          lowerCamelCase__       :						Optional[Any]        =							self.convert_tokens_to_ids(A      )
          if self.legacy_behaviour:
             lowerCamelCase__       :						Dict        =							[]
             lowerCamelCase__       :						Any        =							[self.eos_token_id, self.cur_lang_code]
          else:
             lowerCamelCase__       :						List[str]        =							[self.cur_lang_code]
             lowerCamelCase__       :						Tuple        =							[self.eos_token_id]

          lowerCamelCase__       :						Optional[Any]        =							self.convert_ids_to_tokens(self.prefix_tokens      )
          lowerCamelCase__       :						Optional[int]        =							self.convert_ids_to_tokens(self.suffix_tokens      )

          lowerCamelCase__       :						List[str]        =							processors.TemplateProcessing(
              single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str		, pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str		, special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str		, self.prefix_tokens + self.suffix_tokens      )      )		, )







       def 						__lowerCamelCase   (				self					:     Any		, A					:     str		, A					:     Optional[str] = None      )       ->Tuple[str]:
          if not self.can_save_slow_tokenizer:
             raise ValueError(
                 '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                 '''tokenizer.'''      )

          if not os.path.isdir(A      ):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory."      )
             return
          lowerCamelCase__       :						int        =							os.path.join(
              A		, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

          if os.path.abspath(self.vocab_file      ) != os.path.abspath(A      ):
             copyfile(self.vocab_file		, A      )

          return (out_vocab_file,)










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 	_a						(   UpperCAmelCase      ,  UpperCAmelCase="shi-labs/oneformer_demo"					)    ->    Union[str, Any]:







   """simple docstring"""
   with open(hf_hub_download(UpperCAmelCase      ,  UpperCAmelCase      ,  repo_type='''dataset'''					)      ,  '''r'''					) as f:
      lowerCamelCase__       :						List[Any]        =							json.load(UpperCAmelCase					)
   lowerCamelCase__       :						Dict        =							{}
   lowerCamelCase__       :						str        =							[]
   lowerCamelCase__       :						Optional[Any]        =							[]
   for key, info in class_info.items():
      lowerCamelCase__       :						Union[str, Any]        =							info['''name''']
      class_names.append(info['''name''']					)
      if info["isthing"]:
         thing_ids.append(int(UpperCAmelCase					)					)
   lowerCamelCase__       :						Optional[int]        =							thing_ids
   lowerCamelCase__       :						Dict        =							class_names
   return metadata


class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







       def __init__(				self					:     Tuple		, A					:     Tuple		, A					:     List[Any]=7		, A					:     str=3		, A					:     List[str]=3_0		, A					:     Optional[int]=4_0_0		, A					:     int=None		, A					:     Tuple=True		, A					:     Dict=True		, A					:     Dict=[0.5, 0.5, 0.5]		, A					:     Tuple=[0.5, 0.5, 0.5]		, A					:     int=1_0		, A					:     List[str]=False		, A					:     Optional[Any]=2_5_5		, A					:     Union[str, Any]="shi-labs/oneformer_demo"		, A					:     Optional[Any]="ade20k_panoptic.json"		, A					:     str=1_0		, )       ->Dict:
          lowerCamelCase__       :						Dict        =							parent
          lowerCamelCase__       :						List[Any]        =							batch_size
          lowerCamelCase__       :						Dict        =							num_channels
          lowerCamelCase__       :						List[str]        =							min_resolution
          lowerCamelCase__       :						str        =							max_resolution
          lowerCamelCase__       :						Optional[Any]        =							do_resize
          lowerCamelCase__       :						Any        =							{'''shortest_edge''': 3_2, '''longest_edge''': 1_3_3_3} if size is None else size
          lowerCamelCase__       :						str        =							do_normalize
          lowerCamelCase__       :						List[str]        =							image_mean
          lowerCamelCase__       :						List[str]        =							image_std
          lowerCamelCase__       :						Optional[int]        =							class_info_file
          lowerCamelCase__       :						Any        =							prepare_metadata(A		, A      )
          lowerCamelCase__       :						str        =							num_text
          lowerCamelCase__       :						Dict        =							repo_path

          # for the post_process_functions
          lowerCamelCase__       :						str        =							2
          lowerCamelCase__       :						Union[str, Any]        =							1_0
          lowerCamelCase__       :						List[Any]        =							1_0
          lowerCamelCase__       :						List[Any]        =							3
          lowerCamelCase__       :						Tuple        =							4
          lowerCamelCase__       :						List[Any]        =							num_labels
          lowerCamelCase__       :						List[Any]        =							do_reduce_labels
          lowerCamelCase__       :						List[Any]        =							ignore_index







       def 						__lowerCamelCase   (				self					:     str      )       ->Dict:
          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					:     List[str]		, A					:     List[Any]		, A					:     Tuple=False      )       ->int:
          if not batched:
             lowerCamelCase__       :						List[Any]        =							image_inputs[0]
             if isinstance(A		, Image.Image      ):
                lowerCamelCase__       ,							lowerCamelCase__       :						str        =							image.size
             else:
                lowerCamelCase__       ,							lowerCamelCase__       :						str        =							image.shape[1], image.shape[2]
             if w < h:
                lowerCamelCase__       :						Any        =							int(self.size['''shortest_edge'''] * h / w      )
                lowerCamelCase__       :						Tuple        =							self.size['''shortest_edge''']
             elif w > h:
                lowerCamelCase__       :						Union[str, Any]        =							self.size['''shortest_edge''']
                lowerCamelCase__       :						Dict        =							int(self.size['''shortest_edge'''] * w / h      )
             else:
                lowerCamelCase__       :						Union[str, Any]        =							self.size['''shortest_edge''']
                lowerCamelCase__       :						Optional[Any]        =							self.size['''shortest_edge''']

          else:
             lowerCamelCase__       :						Any        =							[]
             for image in image_inputs:
                lowerCamelCase__       ,							lowerCamelCase__       :						List[str]        =							self.get_expected_values([image]      )
                expected_values.append((expected_height, expected_width)      )
             lowerCamelCase__       :						Optional[Any]        =							max(A		, key=lambda A      : item[0]      )[0]
             lowerCamelCase__       :						List[str]        =							max(A		, key=lambda A      : item[1]      )[1]

          return expected_height, expected_width







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->List[str]:
          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      (			lowerCAmelCase_					,unittest.TestCase ):
       _UpperCAmelCase				:  Optional[int]			   =   OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
       # only for test_image_processing_common.test_image_proc_to_json_string
       _UpperCAmelCase				:  Dict			   =   image_processing_class







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->str:
          lowerCamelCase__       :						Optional[int]        =							OneFormerImageProcessorTester(self      )







       @property
       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Any:
          return self.image_processing_tester.prepare_image_processor_dict()







       def 						__lowerCamelCase   (				self					:     int      )       ->Tuple:
          lowerCamelCase__       :						List[str]        =							self.image_processing_class(**self.image_processor_dict      )
          self.assertTrue(hasattr(A		, '''image_mean'''      )      )
          self.assertTrue(hasattr(A		, '''image_std'''      )      )
          self.assertTrue(hasattr(A		, '''do_normalize'''      )      )
          self.assertTrue(hasattr(A		, '''do_resize'''      )      )
          self.assertTrue(hasattr(A		, '''size'''      )      )
          self.assertTrue(hasattr(A		, '''ignore_index'''      )      )
          self.assertTrue(hasattr(A		, '''class_info_file'''      )      )
          self.assertTrue(hasattr(A		, '''num_text'''      )      )
          self.assertTrue(hasattr(A		, '''repo_path'''      )      )
          self.assertTrue(hasattr(A		, '''metadata'''      )      )
          self.assertTrue(hasattr(A		, '''do_reduce_labels'''      )      )







       def 						__lowerCamelCase   (				self					:     Any      )       ->Tuple:
          pass







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Optional[int]:
          # Initialize image_processor
          lowerCamelCase__       :						Union[str, Any]        =							self.image_processing_class(**self.image_processor_dict      )
          # create random PIL images
          lowerCamelCase__       :						List[Any]        =							prepare_image_inputs(self.image_processing_tester		, equal_resolution=A      )
          for image in image_inputs:
             self.assertIsInstance(A		, Image.Image      )

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

          lowerCamelCase__       ,							lowerCamelCase__       :						Tuple        =							self.image_processing_tester.get_expected_values(A      )

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

          # Test batched
          lowerCamelCase__       ,							lowerCamelCase__       :						Tuple        =							self.image_processing_tester.get_expected_values(A		, batched=A      )

          lowerCamelCase__       :						List[str]        =							image_processor(
              A		, ['''semantic'''] * len(A      )		, 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      )       ->Tuple:
          # Initialize image_processor
          lowerCamelCase__       :						Union[str, Any]        =							self.image_processing_class(**self.image_processor_dict      )
          # create random numpy tensors
          lowerCamelCase__       :						List[str]        =							prepare_image_inputs(self.image_processing_tester		, equal_resolution=A		, numpify=A      )
          for image in image_inputs:
             self.assertIsInstance(A		, np.ndarray      )

          # Test not batched input
          lowerCamelCase__       :						List[str]        =							image_processor(image_inputs[0]		, ['''semantic''']		, return_tensors='''pt'''      ).pixel_values

          lowerCamelCase__       ,							lowerCamelCase__       :						Dict        =							self.image_processing_tester.get_expected_values(A      )

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

          # Test batched
          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							self.image_processing_tester.get_expected_values(A		, batched=A      )

          lowerCamelCase__       :						int        =							image_processor(
              A		, ['''semantic'''] * len(A      )		, 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					:     int      )       ->str:
          # Initialize image_processor
          lowerCamelCase__       :						Any        =							self.image_processing_class(**self.image_processor_dict      )
          # create random PyTorch tensors
          lowerCamelCase__       :						int        =							prepare_image_inputs(self.image_processing_tester		, equal_resolution=A		, torchify=A      )
          for image in image_inputs:
             self.assertIsInstance(A		, torch.Tensor      )

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

          lowerCamelCase__       ,							lowerCamelCase__       :						int        =							self.image_processing_tester.get_expected_values(A      )

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

          # Test batched
          lowerCamelCase__       ,							lowerCamelCase__       :						Tuple        =							self.image_processing_tester.get_expected_values(A		, batched=A      )

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

          lowerCamelCase__       :						int        =							image_processor(
              A		, ['''semantic'''] * len(A      )		, A		, return_tensors='''pt'''		, instance_id_to_semantic_id=A		, pad_and_return_pixel_mask=A		, )

          return inputs







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







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Any:
          def common(A					:     Dict=False		, A					:     Tuple=None      ):
             lowerCamelCase__       :						str        =							self.comm_get_image_processor_inputs(
                 with_segmentation_maps=A		, is_instance_map=A		, segmentation_type=A      )

             lowerCamelCase__       :						Union[str, Any]        =							inputs['''mask_labels''']
             lowerCamelCase__       :						List[Any]        =							inputs['''class_labels''']
             lowerCamelCase__       :						List[str]        =							inputs['''pixel_values''']
             lowerCamelCase__       :						Union[str, Any]        =							inputs['''text_inputs''']

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

          common()
          common(is_instance_map=A      )
          common(is_instance_map=A		, segmentation_type='''pil'''      )
          common(is_instance_map=A		, segmentation_type='''pil'''      )







       def 						__lowerCamelCase   (				self					:     Any      )       ->Optional[int]:
          lowerCamelCase__       :						List[Any]        =							np.zeros((2_0, 5_0)      )
          lowerCamelCase__       :						Any        =							1
          lowerCamelCase__       :						Union[str, Any]        =							1
          lowerCamelCase__       :						List[str]        =							1

          lowerCamelCase__       :						str        =							binary_mask_to_rle(A      )
          self.assertEqual(len(A      )		, 4      )
          self.assertEqual(rle[0]		, 2_1      )
          self.assertEqual(rle[1]		, 4_5      )







       def 						__lowerCamelCase   (				self					:     int      )       ->Dict:
          lowerCamelCase__       :						int        =							self.image_processing_class(
              num_labels=self.image_processing_tester.num_classes		, max_seq_length=7_7		, task_seq_length=7_7		, class_info_file='''ade20k_panoptic.json'''		, num_text=self.image_processing_tester.num_text		, repo_path='''shi-labs/oneformer_demo'''		, )
          lowerCamelCase__       :						str        =							self.image_processing_tester.get_fake_oneformer_outputs()

          lowerCamelCase__       :						List[Any]        =							fature_extractor.post_process_semantic_segmentation(A      )

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

          lowerCamelCase__       :						Any        =							[(1, 4) for i in range(self.image_processing_tester.batch_size      )]
          lowerCamelCase__       :						Optional[Any]        =							fature_extractor.post_process_semantic_segmentation(A		, target_sizes=A      )

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







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->Tuple:
          lowerCamelCase__       :						Optional[Any]        =							self.image_processing_class(
              num_labels=self.image_processing_tester.num_classes		, max_seq_length=7_7		, task_seq_length=7_7		, class_info_file='''ade20k_panoptic.json'''		, num_text=self.image_processing_tester.num_text		, repo_path='''shi-labs/oneformer_demo'''		, )
          lowerCamelCase__       :						Optional[int]        =							self.image_processing_tester.get_fake_oneformer_outputs()
          lowerCamelCase__       :						Union[str, Any]        =							image_processor.post_process_instance_segmentation(A		, threshold=0      )

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







       def 						__lowerCamelCase   (				self					:     str      )       ->Dict:
          lowerCamelCase__       :						Union[str, Any]        =							self.image_processing_class(
              num_labels=self.image_processing_tester.num_classes		, max_seq_length=7_7		, task_seq_length=7_7		, class_info_file='''ade20k_panoptic.json'''		, num_text=self.image_processing_tester.num_text		, repo_path='''shi-labs/oneformer_demo'''		, )
          lowerCamelCase__       :						Tuple        =							self.image_processing_tester.get_fake_oneformer_outputs()
          lowerCamelCase__       :						List[str]        =							image_processor.post_process_panoptic_segmentation(A		, threshold=0      )

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



