


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






class      A(  unittest.TestCase		):

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=7					,				SCREAMING_SNAKE_CASE__=3					,				SCREAMING_SNAKE_CASE__=30					,				SCREAMING_SNAKE_CASE__=4_00					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=[0.5, 0.5, 0.5]					,				SCREAMING_SNAKE_CASE__=[0.5, 0.5, 0.5]					,				SCREAMING_SNAKE_CASE__=10					,				SCREAMING_SNAKE_CASE__=False					,				SCREAMING_SNAKE_CASE__=2_55					,				SCREAMING_SNAKE_CASE__="shi-labs/oneformer_demo"					,				SCREAMING_SNAKE_CASE__="ade20k_panoptic.json"					,				SCREAMING_SNAKE_CASE__=10					,				)      ->	Tuple:

					"""simple docstring"""



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

					# for the post_process_functions
					_UpperCamelCase					:Dict					     =	2
					_UpperCamelCase					:List[Any]					     =	10
					_UpperCamelCase					:Any					     =	10
					_UpperCamelCase					:List[Any]					     =	3
					_UpperCamelCase					:List[str]					     =	4
					_UpperCamelCase					:Optional[Any]					     =	num_labels
					_UpperCamelCase					:str					     =	do_reduce_labels
					_UpperCamelCase					:Optional[int]					     =	ignore_index
		def   _UpperCamelCase( self   )      ->	Optional[Any]:

					"""simple docstring"""



					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "num_labels": self.num_labels,
					    "do_reduce_labels": self.do_reduce_labels,
					    "ignore_index": self.ignore_index,
					    "class_info_file": self.class_info_file,
					    "metadata": self.metadata,
					    "num_text": self.num_text,
					}
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=False   )      ->	Tuple:

					"""simple docstring"""



					if not batched:
								_UpperCamelCase					:Tuple					     =	image_inputs[0]
								if isinstance(SCREAMING_SNAKE_CASE__					,				Image.Image   ):
											_UpperCamelCase     ,      _UpperCamelCase					:List[str]					     =	image.size
								else:
											_UpperCamelCase     ,      _UpperCamelCase					:str					     =	image.shape[1], image.shape[2]
								if w < h:
											_UpperCamelCase					:List[Any]					     =	int(self.size['''shortest_edge'''] * h / w   )
											_UpperCamelCase					:int					     =	self.size['''shortest_edge''']
								elif w > h:
											_UpperCamelCase					:str					     =	self.size['''shortest_edge''']
											_UpperCamelCase					:Tuple					     =	int(self.size['''shortest_edge'''] * w / h   )
								else:
											_UpperCamelCase					:List[Any]					     =	self.size['''shortest_edge''']
											_UpperCamelCase					:int					     =	self.size['''shortest_edge''']

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

					return expected_height, expected_width





		def   _UpperCamelCase( 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(  lowerCamelCase__				,							unittest.TestCase		):

		"""simple docstring"""





		A    =							OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
		# only for test_image_processing_common.test_image_proc_to_json_string
		A    =							image_processing_class
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:Optional[Any]					     =	OneFormerImageProcessorTester(self   )
		@property
		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					return self.image_processing_tester.prepare_image_processor_dict()
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, 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   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



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

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	self.image_processing_class(**self.image_processor_dict   )
					# create random PIL images
					_UpperCamelCase					:str					     =	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					:str					     =	image_processor(image_inputs[0]					,				['''semantic''']					,				return_tensors='''pt'''   ).pixel_values

					_UpperCamelCase     ,      _UpperCamelCase					: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
					_UpperCamelCase     ,      _UpperCamelCase					:Optional[Any]					     =	self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE__					,				batched=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[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   _UpperCamelCase( self   )      ->	Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	self.image_processing_class(**self.image_processor_dict   )
					# create random numpy tensors
					_UpperCamelCase					:str					     =	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					:int					     =	image_processor(image_inputs[0]					,				['''semantic''']					,				return_tensors='''pt'''   ).pixel_values

					_UpperCamelCase     ,      _UpperCamelCase					:str					     =	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					:int					     =	self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE__					,				batched=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					: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   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	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					:str					     =	image_processor(image_inputs[0]					,				['''semantic''']					,				return_tensors='''pt'''   ).pixel_values

					_UpperCamelCase     ,      _UpperCamelCase					:Dict					     =	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					:Tuple					     =	self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE__					,				batched=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Optional[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   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__=False					,				SCREAMING_SNAKE_CASE__=False					,				SCREAMING_SNAKE_CASE__="np"   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	self.image_processing_class(**self.image_processor_dict   )
					# prepare image and target
					_UpperCamelCase					:str					     =	self.image_processing_tester.num_labels
					_UpperCamelCase					:Optional[Any]					     =	None
					_UpperCamelCase					:Union[str, Any]					     =	None
					_UpperCamelCase					:Optional[Any]					     =	prepare_image_inputs(self.image_processing_tester					,				equal_resolution=SCREAMING_SNAKE_CASE__   )
					if with_segmentation_maps:
								_UpperCamelCase					:int					     =	num_labels
								if is_instance_map:
											_UpperCamelCase					:List[Any]					     =	list(range(SCREAMING_SNAKE_CASE__   )   ) * 2
											_UpperCamelCase					:Optional[int]					     =	dict(enumerate(SCREAMING_SNAKE_CASE__   )   )
								_UpperCamelCase					: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":
											_UpperCamelCase					:Optional[int]					     =	[Image.fromarray(SCREAMING_SNAKE_CASE__   ) for annotation in annotations]

					_UpperCamelCase					:Union[str, Any]					     =	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   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					pass
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					def common(SCREAMING_SNAKE_CASE__=False					,				SCREAMING_SNAKE_CASE__=None   ):
								_UpperCamelCase					:Union[str, 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					:Union[str, Any]					     =	inputs['''mask_labels''']
								_UpperCamelCase					:List[str]					     =	inputs['''class_labels''']
								_UpperCamelCase					:Optional[int]					     =	inputs['''pixel_values''']
								_UpperCamelCase					:Optional[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   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	np.zeros((20, 50)   )
					_UpperCamelCase					:List[str]					     =	1
					_UpperCamelCase					:str					     =	1
					_UpperCamelCase					:Tuple					     =	1

					_UpperCamelCase					:List[str]					     =	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   _UpperCamelCase( self   )      ->	Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase					: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'''					,				)
					_UpperCamelCase					:List[Any]					     =	self.image_processing_tester.get_fake_oneformer_outputs()

					_UpperCamelCase					:Tuple					     =	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					:Optional[Any]					     =	[(1, 4) for i in range(self.image_processing_tester.batch_size   )]
					_UpperCamelCase					:Dict					     =	fature_extractor.post_process_semantic_segmentation(SCREAMING_SNAKE_CASE__					,				target_sizes=SCREAMING_SNAKE_CASE__   )

					self.assertEqual(segmentation[0].shape					,				target_sizes[0]   )
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					: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'''					,				)
					_UpperCamelCase					:Tuple					     =	self.image_processing_tester.get_fake_oneformer_outputs()
					_UpperCamelCase					:Dict					     =	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   _UpperCamelCase( self   )      ->	List[Any]:

					"""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					:int					     =	self.image_processing_tester.get_fake_oneformer_outputs()
					_UpperCamelCase					:Any					     =	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)   )





"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


UpperCamelCase__				:List[Any]			   =	logging.get_logger(__name__)


UpperCamelCase__				:Tuple			   =	{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
UpperCamelCase__				:List[str]			   =	{
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json""",
    },
}

UpperCamelCase__				:List[Any]			   =	{
    """RUCAIBox/mvp""": 1_024,
}






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							VOCAB_FILES_NAMES
		A    =							PRETRAINED_VOCAB_FILES_MAP
		A    =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A    =							["input_ids", "attention_mask"]
		A    =							MvpTokenizer
		def __init__( self					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__="replace"					,				SCREAMING_SNAKE_CASE__="<s>"					,				SCREAMING_SNAKE_CASE__="</s>"					,				SCREAMING_SNAKE_CASE__="</s>"					,				SCREAMING_SNAKE_CASE__="<s>"					,				SCREAMING_SNAKE_CASE__="<unk>"					,				SCREAMING_SNAKE_CASE__="<pad>"					,				SCREAMING_SNAKE_CASE__="<mask>"					,				SCREAMING_SNAKE_CASE__=False					,				SCREAMING_SNAKE_CASE__=True					,				**SCREAMING_SNAKE_CASE__					,				)      ->	List[str]:

					"""simple docstring"""



					super().__init__(
					    SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				tokenizer_file=SCREAMING_SNAKE_CASE__					,				errors=SCREAMING_SNAKE_CASE__					,				bos_token=SCREAMING_SNAKE_CASE__					,				eos_token=SCREAMING_SNAKE_CASE__					,				sep_token=SCREAMING_SNAKE_CASE__					,				cls_token=SCREAMING_SNAKE_CASE__					,				unk_token=SCREAMING_SNAKE_CASE__					,				pad_token=SCREAMING_SNAKE_CASE__					,				mask_token=SCREAMING_SNAKE_CASE__					,				add_prefix_space=SCREAMING_SNAKE_CASE__					,				trim_offsets=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__					,				)

					_UpperCamelCase					:int					     =	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
					if pre_tok_state.get('''add_prefix_space'''					,				SCREAMING_SNAKE_CASE__   ) != add_prefix_space:
								_UpperCamelCase					:int					     =	getattr(SCREAMING_SNAKE_CASE__					,				pre_tok_state.pop('''type'''   )   )
								_UpperCamelCase					:Optional[int]					     =	add_prefix_space
								_UpperCamelCase					:List[Any]					     =	pre_tok_class(**SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Optional[int]					     =	add_prefix_space

					# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
					_UpperCamelCase					:Any					     =	'''post_processor'''
					_UpperCamelCase					:Optional[Any]					     =	getattr(self.backend_tokenizer					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
					if tokenizer_component_instance:
								_UpperCamelCase					:Optional[Any]					     =	json.loads(tokenizer_component_instance.__getstate__()   )

								# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
								if "sep" in state:
											_UpperCamelCase					:Optional[Any]					     =	tuple(state['''sep''']   )
								if "cls" in state:
											_UpperCamelCase					:Optional[Any]					     =	tuple(state['''cls''']   )

								_UpperCamelCase					:str					     =	False

								if state.get('''add_prefix_space'''					,				SCREAMING_SNAKE_CASE__   ) != add_prefix_space:
											_UpperCamelCase					:Optional[int]					     =	add_prefix_space
											_UpperCamelCase					:Union[str, Any]					     =	True

								if state.get('''trim_offsets'''					,				SCREAMING_SNAKE_CASE__   ) != trim_offsets:
											_UpperCamelCase					:Union[str, Any]					     =	trim_offsets
											_UpperCamelCase					:Optional[Any]					     =	True

								if changes_to_apply:
											_UpperCamelCase					:Union[str, Any]					     =	getattr(SCREAMING_SNAKE_CASE__					,				state.pop('''type'''   )   )
											_UpperCamelCase					:Optional[Any]					     =	component_class(**SCREAMING_SNAKE_CASE__   )
											setattr(self.backend_tokenizer					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
		@property
		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					if self._mask_token is None:
								if self.verbose:
											logger.error('''Using mask_token, but it is not set yet.'''   )
								return None
					return str(self._mask_token   )
		@mask_token.setter
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	AddedToken(SCREAMING_SNAKE_CASE__					,				lstrip=SCREAMING_SNAKE_CASE__					,				rstrip=SCREAMING_SNAKE_CASE__   ) if isinstance(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ) else value
					_UpperCamelCase					:int					     =	value
		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	BatchEncoding:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	kwargs.get('''is_split_into_words'''					,				SCREAMING_SNAKE_CASE__   )

					if is_split_into_words and not self.add_prefix_space:
								raise ValueError(
								    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
								    '''to use it with pretokenized inputs.'''   )

					return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	BatchEncoding:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	kwargs.get('''is_split_into_words'''					,				SCREAMING_SNAKE_CASE__   )

					if is_split_into_words and not self.add_prefix_space:
								raise ValueError(
								    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
								    '''to use it with pretokenized inputs.'''   )

					return super()._encode_plus(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = None   )      ->	Tuple[str]:

					"""simple docstring"""



					_UpperCamelCase					:Optional[Any]					     =	self._tokenizer.model.save(SCREAMING_SNAKE_CASE__					,				name=SCREAMING_SNAKE_CASE__   )
					return tuple(SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=None   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	[self.bos_token_id] + token_ids_a + [self.eos_token_id]
					if token_ids_a is None:
								return output

					return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = None   )      ->	List[int]:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	[self.sep_token_id]
					_UpperCamelCase					:List[str]					     =	[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]

