import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase       =		logging.get_logger(__name__)

_lowerCAmelCase       =		{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

_lowerCAmelCase       =		{
    "vocab_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"},
    "merges_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"},
}

_lowerCAmelCase       =		{
    "ctrl": 256,
}

_lowerCAmelCase       =		{
    "Pregnancy": 168_629,
    "Christianity": 7_675,
    "Explain": 106_423,
    "Fitness": 63_440,
    "Saving": 63_163,
    "Ask": 27_171,
    "Ass": 95_985,
    "Joke": 163_509,
    "Questions": 45_622,
    "Thoughts": 49_605,
    "Retail": 52_342,
    "Feminism": 164_338,
    "Writing": 11_992,
    "Atheism": 192_263,
    "Netflix": 48_616,
    "Computing": 39_639,
    "Opinion": 43_213,
    "Alone": 44_967,
    "Funny": 58_917,
    "Gaming": 40_358,
    "Human": 4_088,
    "India": 1_331,
    "Joker": 77_138,
    "Diet": 36_206,
    "Legal": 11_859,
    "Norman": 4_939,
    "Tip": 72_689,
    "Weight": 52_343,
    "Movies": 46_273,
    "Running": 23_425,
    "Science": 2_090,
    "Horror": 37_793,
    "Confession": 60_572,
    "Finance": 12_250,
    "Politics": 16_360,
    "Scary": 191_985,
    "Support": 12_654,
    "Technologies": 32_516,
    "Teenage": 66_160,
    "Event": 32_769,
    "Learned": 67_460,
    "Notion": 182_770,
    "Wikipedia": 37_583,
    "Books": 6_665,
    "Extract": 76_050,
    "Confessions": 102_701,
    "Conspiracy": 75_932,
    "Links": 63_674,
    "Narcissus": 150_425,
    "Relationship": 54_766,
    "Relationships": 134_796,
    "Reviews": 41_671,
    "News": 4_256,
    "Translation": 26_820,
    "multilingual": 128_406,
}
def 					_snake_case	(  __snake_case						):
					_UpperCamelCase			     =     set()
					_UpperCamelCase			     =     word[0]
					for char in word[1:]:
										pairs.add((prev_char, char)						)
										_UpperCamelCase			     =     char

					_UpperCamelCase			     =     set(__snake_case						)
					return pairs







class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     VOCAB_FILES_NAMES
							UpperCAmelCase				   =     PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase				   =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase				   =     CONTROL_CODES







							def __init__(   self      :     Optional[int] ,			_A      :     Dict ,			_A      :     str ,			_A      :     Union[str, Any]="<unk>" ,			**_A      :     List[str]		):
												super().__init__(unk_token=_A ,			**_A		)

												with open(_A ,			encoding='''utf-8'''		) as vocab_handle:
																	_UpperCamelCase			     =     json.load(_A		)
												_UpperCamelCase			     =     {v: k for k, v in self.encoder.items()}
												with open(_A ,			encoding='''utf-8'''		) as merges_handle:
																	_UpperCamelCase			     =     merges_handle.read().split('''\n'''		)[1:-1]
												_UpperCamelCase			     =     [tuple(merge.split()		) for merge in merges]
												_UpperCamelCase			     =     dict(zip(_A ,			range(len(_A		)		)		)		)
												_UpperCamelCase			     =     {}







							@property
							def  UpperCamelCase_						(   self      :     Dict		):
												return len(self.encoder		)







							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												return dict(self.encoder ,			**self.added_tokens_encoder		)







							def  UpperCamelCase_						(   self      :     List[str] ,			_A      :     Optional[int]		):
												if token in self.cache:
																	return self.cache[token]
												_UpperCamelCase			     =     tuple(_A		)
												_UpperCamelCase			     =     tuple(list(word[:-1]		) + [word[-1] + '''</w>''']		)
												_UpperCamelCase			     =     get_pairs(_A		)

												if not pairs:
																	return token

												while True:
																	_UpperCamelCase			     =     min(_A ,			key=lambda _A		: self.bpe_ranks.get(_A ,			float('''inf'''		)		)		)
																	if bigram not in self.bpe_ranks:
																						break
																	_UpperCamelCase     ,    _UpperCamelCase			     =     bigram
																	_UpperCamelCase			     =     []
																	_UpperCamelCase			     =     0
																	while i < len(_A		):
																						try:
																											_UpperCamelCase			     =     word.index(_A ,			_A		)
																						except ValueError:
																											new_word.extend(word[i:]		)
																											break
																						else:
																											new_word.extend(word[i:j]		)
																											_UpperCamelCase			     =     j

																						if word[i] == first and i < len(_A		) - 1 and word[i + 1] == second:
																											new_word.append(first + second		)
																											i += 2
																						else:
																											new_word.append(word[i]		)
																											i += 1
																	_UpperCamelCase			     =     tuple(_A		)
																	_UpperCamelCase			     =     new_word
																	if len(_A		) == 1:
																						break
																	else:
																						_UpperCamelCase			     =     get_pairs(_A		)
												_UpperCamelCase			     =     '''@@ '''.join(_A		)
												_UpperCamelCase			     =     word[:-4]
												_UpperCamelCase			     =     word
												return word







							def  UpperCamelCase_						(   self      :     Tuple ,			_A      :     int		):
												_UpperCamelCase			     =     []

												_UpperCamelCase			     =     re.findall(R'''\S+\n?''' ,			_A		)

												for token in words:
																	split_tokens.extend(list(self.bpe(_A		).split(''' '''		)		)		)
												return split_tokens







							def  UpperCamelCase_						(   self      :     Optional[Any] ,			_A      :     List[str]		):
												return self.encoder.get(_A ,			self.encoder.get(self.unk_token		)		)







							def  UpperCamelCase_						(   self      :     int ,			_A      :     int		):
												return self.decoder.get(_A ,			self.unk_token		)







							def  UpperCamelCase_						(   self      :     str ,			_A      :     Optional[int]		):
												_UpperCamelCase			     =     ''' '''.join(_A		).replace('''@@ ''' ,			''''''		).strip()
												return out_string







							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     str ,			_A      :     Optional[str] = None		):
												if not os.path.isdir(_A		):
																	logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
																	return
												_UpperCamelCase			     =     os.path.join(
												    _A ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)
												_UpperCamelCase			     =     os.path.join(
												    _A ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']		)

												with open(_A ,			'''w''' ,			encoding='''utf-8'''		) as f:
																	f.write(json.dumps(self.encoder ,			indent=2 ,			sort_keys=_A ,			ensure_ascii=_A		) + '''\n'''		)

												_UpperCamelCase			     =     0
												with open(_A ,			'''w''' ,			encoding='''utf-8'''		) as writer:
																	writer.write('''#version: 0.2\n'''		)
																	for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,			key=lambda _A		: kv[1]		):
																						if index != token_index:
																											logger.warning(
																											    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																											    ''' Please check that the tokenizer is not corrupted!'''		)
																											_UpperCamelCase			     =     token_index
																						writer.write(''' '''.join(_A		) + '''\n'''		)
																						index += 1

												return vocab_file, merge_file

							# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
							#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
							#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
							#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
							#     return ''.join(tokens_generated_so_far)








'''simple docstring'''







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





class 						SCREAMING_SNAKE_CASE       (       unittest.TestCase							):






			def __init__(			self   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      List[str]=7	,    __SCREAMING_SNAKE_CASE   :      List[Any]=3	,    __SCREAMING_SNAKE_CASE   :      List[Any]=30	,    __SCREAMING_SNAKE_CASE   :      str=400	,    __SCREAMING_SNAKE_CASE   :      List[str]=None	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]=True	,    __SCREAMING_SNAKE_CASE   :      List[Any]=True	,    __SCREAMING_SNAKE_CASE   :      str=[0.5, 0.5, 0.5]	,    __SCREAMING_SNAKE_CASE   :      Optional[int]=[0.5, 0.5, 0.5]	,    __SCREAMING_SNAKE_CASE   :      Optional[int]=10	,    __SCREAMING_SNAKE_CASE   :      int=False	,    __SCREAMING_SNAKE_CASE   :      Optional[int]=255	,    __SCREAMING_SNAKE_CASE   :      List[Any]="shi-labs/oneformer_demo"	,    __SCREAMING_SNAKE_CASE   :      List[str]="ade20k_panoptic.json"	,    __SCREAMING_SNAKE_CASE   :      List[Any]=10	,    )      ->      Dict:
							a_		:	int									=   parent
							a_		:	Optional[Any]									=   batch_size
							a_		:	str									=   num_channels
							a_		:	Tuple									=   min_resolution
							a_		:	List[Any]									=   max_resolution
							a_		:	List[Any]									=   do_resize
							a_		:	Union[str, Any]									=   {'''shortest_edge''': 32, '''longest_edge''': 1333} if size is None else size
							a_		:	Dict									=   do_normalize
							a_		:	Union[str, Any]									=   image_mean
							a_		:	Dict									=   image_std
							a_		:	int									=   class_info_file
							a_		:	List[Any]									=   prepare_metadata(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
							a_		:	Optional[int]									=   num_text
							a_		:	Any									=   repo_path

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






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

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

							return expected_height, expected_width







			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      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       (       SCREAMING_SNAKE_CASE_						, unittest.TestCase							):
			snake_case__	=					OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
			# only for test_image_processing_common.test_image_proc_to_json_string
			snake_case__	=					image_processing_class






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      List[str]:
							a_		:	Optional[int]									=   OneFormerImageProcessorTester(self       )






			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]       )      ->      Any:
							return self.image_processing_tester.prepare_image_processor_dict()






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      Dict:
							a_		:	List[Any]									=   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       SCREAMING_SNAKE_CASE   (			self   :      Union[str, Any]       )      ->      str:
							pass






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]       )      ->      Tuple:
							# Initialize image_processor
							a_		:	Union[str, Any]									=   self.image_processing_class(**self.image_processor_dict       )
							# create random PIL images
							a_		:	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
							a_		:	Dict									=   image_processor(image_inputs[0]	,    ['''semantic''']	,    return_tensors='''pt'''       ).pixel_values

							a_					,      a_		:	Optional[int]									=   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
							a_					,      a_		:	Union[str, Any]									=   self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,    batched=__SCREAMING_SNAKE_CASE       )

							a_		:	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       SCREAMING_SNAKE_CASE   (			self   :      List[Any]       )      ->      str:
							# Initialize image_processor
							a_		:	Dict									=   self.image_processing_class(**self.image_processor_dict       )
							# create random numpy tensors
							a_		:	Tuple									=   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
							a_		:	List[Any]									=   image_processor(image_inputs[0]	,    ['''semantic''']	,    return_tensors='''pt'''       ).pixel_values

							a_					,      a_		:	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
							a_					,      a_		:	Union[str, Any]									=   self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,    batched=__SCREAMING_SNAKE_CASE       )

							a_		:	Tuple									=   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       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      int:
							# Initialize image_processor
							a_		:	str									=   self.image_processing_class(**self.image_processor_dict       )
							# create random PyTorch tensors
							a_		:	Tuple									=   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
							a_		:	str									=   image_processor(image_inputs[0]	,    ['''semantic''']	,    return_tensors='''pt'''       ).pixel_values

							a_					,      a_		:	int									=   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
							a_					,      a_		:	Tuple									=   self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,    batched=__SCREAMING_SNAKE_CASE       )

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

							a_		:	Dict									=   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       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      Optional[Any]:
							pass






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      List[str]:
							def common(__SCREAMING_SNAKE_CASE   :      int=False	,    __SCREAMING_SNAKE_CASE   :      List[str]=None       ):
											a_		:	Tuple									=   self.comm_get_image_processor_inputs(
											    with_segmentation_maps=__SCREAMING_SNAKE_CASE	,    is_instance_map=__SCREAMING_SNAKE_CASE	,    segmentation_type=__SCREAMING_SNAKE_CASE       )

											a_		:	List[Any]									=   inputs['''mask_labels''']
											a_		:	Any									=   inputs['''class_labels''']
											a_		:	Any									=   inputs['''pixel_values''']
											a_		:	Optional[Any]									=   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       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      str:
							a_		:	int									=   np.zeros((20, 50)       )
							a_		:	Dict									=   1
							a_		:	Optional[Any]									=   1
							a_		:	Dict									=   1

							a_		:	Tuple									=   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       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      int:
							a_		:	List[str]									=   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'''	,    )
							a_		:	Any									=   self.image_processing_tester.get_fake_oneformer_outputs()

							a_		:	str									=   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,
							    )	,    )

							a_		:	Dict									=   [(1, 4) for i in range(self.image_processing_tester.batch_size       )]
							a_		:	List[str]									=   fature_extractor.post_process_semantic_segmentation(__SCREAMING_SNAKE_CASE	,    target_sizes=__SCREAMING_SNAKE_CASE       )

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






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Dict:
							a_		:	Optional[Any]									=   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'''	,    )
							a_		:	Optional[int]									=   self.image_processing_tester.get_fake_oneformer_outputs()
							a_		:	Tuple									=   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       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      Union[str, Any]:
							a_		:	List[Any]									=   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'''	,    )
							a_		:	int									=   self.image_processing_tester.get_fake_oneformer_outputs()
							a_		:	str									=   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)       )




