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

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


lowercase_ 				=			logging.get_logger(__name__)

lowercase_ 				=			{
    """vocab_file""": """vocab.txt""",
    """merges_file""": """bpe.codes""",
}

lowercase_ 				=			{
    """vocab_file""": {
        """vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt""",
        """vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt""",
    },
    """merges_file""": {
        """vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes""",
        """vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes""",
    },
}

lowercase_ 				=			{
    """vinai/phobert-base""": 256,
    """vinai/phobert-large""": 256,
}


def 							a__					(							snake_case							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		set()
  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)							)
    __SCREAMING_SNAKE_CASE      :	int					=		char

  __SCREAMING_SNAKE_CASE      :	str					=		set(snake_case							)
  return pairs





class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			VOCAB_FILES_NAMES
     lowerCAmelCase_							   =			PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase_							   =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



     def __init__(				self							:							List[str]     ,						_A							:							Tuple     ,						_A							:							Optional[Any]     ,						_A							:							str="<s>"     ,						_A							:							Any="</s>"     ,						_A							:							List[str]="</s>"     ,						_A							:							str="<s>"     ,						_A							:							Optional[int]="<unk>"     ,						_A							:							Any="<pad>"     ,						_A							:							Dict="<mask>"     ,						**_A							:							Tuple     ,						):







       """simple docstring"""




       super().__init__(
           bos_token=_A     ,						eos_token=_A     ,						unk_token=_A     ,						sep_token=_A     ,						cls_token=_A     ,						pad_token=_A     ,						mask_token=_A     ,						**_A     ,						)

       __SCREAMING_SNAKE_CASE      :	str					=		vocab_file
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		merges_file

       __SCREAMING_SNAKE_CASE      :	List[str]					=		{}
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		0
       __SCREAMING_SNAKE_CASE      :	List[str]					=		1
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		2
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		3

       self.add_from_file(_A )

       __SCREAMING_SNAKE_CASE      :	Tuple					=		{v: k for k, v in self.encoder.items()}

       with open(_A     ,						encoding='''utf-8''' ) as merges_handle:
         __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		merges_handle.read().split('''\n''' )[:-1]
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		[tuple(merge.split()[:-1] ) for merge in merges]
       __SCREAMING_SNAKE_CASE      :	Tuple					=		dict(zip(_A     ,						range(len(_A ) ) ) )
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		{}



     def  UpperCAmelCase__							(				self							:							List[str]     ,						_A							:							List[int]     ,						_A							:							Optional[List[int]] = None ):







       """simple docstring"""




       if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       __SCREAMING_SNAKE_CASE      :	Any					=		[self.cls_token_id]
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		[self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep



     def  UpperCAmelCase__							(				self							:							Optional[Any]     ,						_A							:							List[int]     ,						_A							:							Optional[List[int]] = None     ,						_A							:							bool = False ):







       """simple docstring"""




       if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=_A     ,						token_ids_a=_A     ,						already_has_special_tokens=_A )

       if token_ids_a is None:
         return [1] + ([0] * len(_A )) + [1]
       return [1] + ([0] * len(_A )) + [1, 1] + ([0] * len(_A )) + [1]



     def  UpperCAmelCase__							(				self							:							List[Any]     ,						_A							:							List[int]     ,						_A							:							Optional[List[int]] = None ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	int					=		[self.sep_token_id]
       __SCREAMING_SNAKE_CASE      :	Tuple					=		[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]



     @property
     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




       return len(self.encoder )



     def  UpperCAmelCase__							(				self							:							Tuple ):







       """simple docstring"""




       return dict(self.encoder     ,						**self.added_tokens_encoder )



     def  UpperCAmelCase__							(				self							:							List[str]     ,						_A							:							List[Any] ):







       """simple docstring"""




       if token in self.cache:
         return self.cache[token]
       __SCREAMING_SNAKE_CASE      :	int					=		tuple(_A )
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		tuple(list(word[:-1] ) + [word[-1] + '''</w>'''] )
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		get_pairs(_A )

       if not pairs:
         return token

       while True:
         __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		min(_A     ,						key=lambda _A : self.bpe_ranks.get(_A     ,						float('''inf''' ) ) )
         if bigram not in self.bpe_ranks:
           break
         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Tuple					=		bigram
         __SCREAMING_SNAKE_CASE      :	List[str]					=		[]
         __SCREAMING_SNAKE_CASE      :	List[str]					=		0
         while i < len(_A ):
           try:
             __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		word.index(_A     ,						_A )
           except ValueError:
             new_word.extend(word[i:] )
             break
           else:
             new_word.extend(word[i:j] )
             __SCREAMING_SNAKE_CASE      :	Dict					=		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
         __SCREAMING_SNAKE_CASE      :	List[str]					=		tuple(_A )
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		new_word
         if len(_A ) == 1:
           break
         else:
           __SCREAMING_SNAKE_CASE      :	Tuple					=		get_pairs(_A )
       __SCREAMING_SNAKE_CASE      :	Tuple					=		'''@@ '''.join(_A )
       __SCREAMING_SNAKE_CASE      :	Tuple					=		word[:-4]
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		word
       return word



     def  UpperCAmelCase__							(				self							:							str     ,						_A							:							Optional[int] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	int					=		[]

       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		re.findall(r'''\S+\n?'''     ,						_A )

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



     def  UpperCAmelCase__							(				self							:							List[str]     ,						_A							:							Optional[Any] ):







       """simple docstring"""




       return self.encoder.get(_A     ,						self.encoder.get(self.unk_token ) )



     def  UpperCAmelCase__							(				self							:							Dict     ,						_A							:							int ):







       """simple docstring"""




       return self.decoder.get(_A     ,						self.unk_token )



     def  UpperCAmelCase__							(				self							:							Union[str, Any]     ,						_A							:							str ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[Any]					=		''' '''.join(_A ).replace('''@@ '''     ,						'''''' ).strip()
       return out_string



     def  UpperCAmelCase__							(				self							:							Dict     ,						_A							:							str     ,						_A							:							Optional[str] = None ):







       """simple docstring"""




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

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

       if os.path.abspath(self.merges_file ) != os.path.abspath(_A ):
         copyfile(self.merges_file     ,						_A )

       return out_vocab_file, out_merge_file




     def  UpperCAmelCase__							(				self							:							Optional[Any]     ,						_A							:							str ):







       """simple docstring"""




       if isinstance(_A     ,						_A ):
         try:
           with open(_A     ,						'''r'''     ,						encoding='''utf-8''' ) as fd:
             self.add_from_file(_A )
         except FileNotFoundError as fnfe:
           raise fnfe
         except UnicodeError:
           raise Exception(F'''Incorrect encoding detected in {f}, please rebuild the dataset''' )
         return

       __SCREAMING_SNAKE_CASE      :	Any					=		f.readlines()
       for lineTmp in lines:
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		lineTmp.strip()
         __SCREAMING_SNAKE_CASE      :	Tuple					=		line.rfind(''' ''' )
         if idx == -1:
           raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt>\'''' )
         __SCREAMING_SNAKE_CASE      :	Tuple					=		line[:idx]
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		len(self.encoder )







import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 PIL import Image

			from transformers import YolosImageProcessor


class       __lowerCAmelCase					(       unittest.TestCase      ):


	'''simple docstring'''





	def __init__(						self:						Optional[int],					lowerCamelCase_:						Dict,					lowerCamelCase_:						Tuple=7,					lowerCamelCase_:						Dict=3,					lowerCamelCase_:						Optional[Any]=30,					lowerCamelCase_:						str=400,					lowerCamelCase_:						List[Any]=True,					lowerCamelCase_:						List[Any]=None,					lowerCamelCase_:						Dict=True,					lowerCamelCase_:						Optional[Any]=[0.5, 0.5, 0.5],					lowerCamelCase_:						Dict=[0.5, 0.5, 0.5],					lowerCamelCase_:						Dict=True,					lowerCamelCase_:						List[str]=1 / 255,					lowerCamelCase_:						Dict=True,					):
			# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
			lowercase__					:      str      =		size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
			lowercase__					:      Optional[int]      =		parent
			lowercase__					:      List[Any]      =		batch_size
			lowercase__					:      Optional[int]      =		num_channels
			lowercase__					:      Optional[Any]      =		min_resolution
			lowercase__					:      Union[str, Any]      =		max_resolution
			lowercase__					:      Tuple      =		do_resize
			lowercase__					:      str      =		size
			lowercase__					:      Dict      =		do_normalize
			lowercase__					:      Optional[int]      =		image_mean
			lowercase__					:      Dict      =		image_std
			lowercase__					:      int      =		do_rescale
			lowercase__					:      Any      =		rescale_factor
			lowercase__					:      Union[str, Any]      =		do_pad

	def    snake_case__(						self:						str			):
			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_normalize": self.do_normalize,
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			    "do_rescale": self.do_rescale,
			    "rescale_factor": self.rescale_factor,
			    "do_pad": self.do_pad,
			}





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

			else:
					lowercase__					:      int      =		[]
					for image in image_inputs:
							lowercase__   ,				lowercase__					:      Dict      =		self.get_expected_values([image]			)
							expected_values.append((expected_height, expected_width)			)
					lowercase__					:      int      =		max(lowerCamelCase_,					key=lambda lowerCamelCase_			: item[0]			)[0]
					lowercase__					:      Tuple      =		max(lowerCamelCase_,					key=lambda lowerCamelCase_			: item[1]			)[1]

			return expected_height, expected_width


@require_torch
@require_vision
class       __lowerCAmelCase					(       _UpperCamelCase   ,    unittest.TestCase      ):


	'''simple docstring'''





	_A													=				YolosImageProcessor if is_vision_available() else None

	def    snake_case__(						self:						int			):
			lowercase__					:      Optional[int]      =		YolosImageProcessingTester(self			)

	@property
	def    snake_case__(						self:						Union[str, Any]			):
			return self.image_processor_tester.prepare_image_processor_dict()

	def    snake_case__(						self:						Dict			):
			lowercase__					:      Optional[Any]      =		self.image_processing_class(**self.image_processor_dict			)
			self.assertTrue(hasattr(lowerCamelCase_,					'image_mean'			)			)
			self.assertTrue(hasattr(lowerCamelCase_,					'image_std'			)			)
			self.assertTrue(hasattr(lowerCamelCase_,					'do_normalize'			)			)
			self.assertTrue(hasattr(lowerCamelCase_,					'do_resize'			)			)
			self.assertTrue(hasattr(lowerCamelCase_,					'size'			)			)

	def    snake_case__(						self:						List[Any]			):
			lowercase__					:      Tuple      =		self.image_processing_class.from_dict(self.image_processor_dict			)
			self.assertEqual(image_processor.size,					{'shortest_edge': 18, 'longest_edge': 1333}			)
			self.assertEqual(image_processor.do_pad,					lowerCamelCase_			)

			lowercase__					:      Optional[int]      =		self.image_processing_class.from_dict(
			    self.image_processor_dict,					size=42,					max_size=84,					pad_and_return_pixel_mask=lowerCamelCase_			)
			self.assertEqual(image_processor.size,					{'shortest_edge': 42, 'longest_edge': 84}			)
			self.assertEqual(image_processor.do_pad,					lowerCamelCase_			)

	def    snake_case__(						self:						str			):
			pass

	def    snake_case__(						self:						Optional[Any]			):
			# Initialize image_processing
			lowercase__					:      Optional[int]      =		self.image_processing_class(**self.image_processor_dict			)
			# create random PIL images
			lowercase__					:      Union[str, Any]      =		prepare_image_inputs(self.image_processor_tester,					equal_resolution=lowerCamelCase_			)
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_,					Image.Image			)

			# Test not batched input
			lowercase__					:      Tuple      =		image_processing(image_inputs[0],					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      List[Any]      =		self.image_processor_tester.get_expected_values(lowerCamelCase_			)

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

			# Test batched
			lowercase__   ,				lowercase__					:      Optional[Any]      =		self.image_processor_tester.get_expected_values(lowerCamelCase_,					batched=lowerCamelCase_			)

			lowercase__					:      Dict      =		image_processing(lowerCamelCase_,					return_tensors='pt'			).pixel_values
			self.assertEqual(
			    encoded_images.shape,					(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        expected_height,
			        expected_width,
			    ),					)

	def    snake_case__(						self:						str			):
			# Initialize image_processing
			lowercase__					:      str      =		self.image_processing_class(**self.image_processor_dict			)
			# create random numpy tensors
			lowercase__					:      int      =		prepare_image_inputs(self.image_processor_tester,					equal_resolution=lowerCamelCase_,					numpify=lowerCamelCase_			)
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_,					np.ndarray			)

			# Test not batched input
			lowercase__					:      List[Any]      =		image_processing(image_inputs[0],					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      Optional[int]      =		self.image_processor_tester.get_expected_values(lowerCamelCase_			)

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

			# Test batched
			lowercase__					:      Any      =		image_processing(lowerCamelCase_,					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      Optional[int]      =		self.image_processor_tester.get_expected_values(lowerCamelCase_,					batched=lowerCamelCase_			)

			self.assertEqual(
			    encoded_images.shape,					(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        expected_height,
			        expected_width,
			    ),					)

	def    snake_case__(						self:						Union[str, Any]			):
			# Initialize image_processing
			lowercase__					:      Optional[int]      =		self.image_processing_class(**self.image_processor_dict			)
			# create random PyTorch tensors
			lowercase__					:      Dict      =		prepare_image_inputs(self.image_processor_tester,					equal_resolution=lowerCamelCase_,					torchify=lowerCamelCase_			)
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_,					torch.Tensor			)

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

			lowercase__   ,				lowercase__					:      Union[str, Any]      =		self.image_processor_tester.get_expected_values(lowerCamelCase_			)

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

			# Test batched
			lowercase__					:      List[Any]      =		image_processing(lowerCamelCase_,					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      Union[str, Any]      =		self.image_processor_tester.get_expected_values(lowerCamelCase_,					batched=lowerCamelCase_			)

			self.assertEqual(
			    encoded_images.shape,					(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        expected_height,
			        expected_width,
			    ),					)

	def    snake_case__(						self:						int			):
			# Initialize image_processings
			lowercase__					:      Tuple      =		self.image_processing_class(**self.image_processor_dict			)
			lowercase__					:      List[str]      =		self.image_processing_class(do_resize=lowerCamelCase_,					do_normalize=lowerCamelCase_,					do_rescale=lowerCamelCase_			)
			# create random PyTorch tensors
			lowercase__					:      Tuple      =		prepare_image_inputs(self.image_processor_tester,					equal_resolution=lowerCamelCase_,					torchify=lowerCamelCase_			)
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_,					torch.Tensor			)

			# Test whether the method "pad" and calling the image processor return the same tensors
			lowercase__					:      Optional[int]      =		image_processing_a.pad(lowerCamelCase_,					return_tensors='pt'			)
			lowercase__					:      int      =		image_processing_a(lowerCamelCase_,					return_tensors='pt'			)

			self.assertTrue(
			    torch.allclose(encoded_images_with_method['pixel_values'],					encoded_images['pixel_values'],					atol=1E-4			)			)

	@slow
	def    snake_case__(						self:						List[str]			):
			# prepare image and target
			lowercase__					:      Any      =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
			with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt',					'r'			) as f:
					lowercase__					:      List[str]      =		json.loads(f.read()			)

			lowercase__					:      List[str]      =		{'image_id': 39769, 'annotations': target}

			# encode them
			lowercase__					:      Any      =		YolosImageProcessor.from_pretrained('hustvl/yolos-small'			)
			lowercase__					:      Optional[Any]      =		image_processing(images=lowerCamelCase_,					annotations=lowerCamelCase_,					return_tensors='pt'			)

			# verify pixel values
			lowercase__					:      Optional[Any]      =		torch.Size([1, 3, 800, 1066]			)
			self.assertEqual(encoding['pixel_values'].shape,					lowerCamelCase_			)

			lowercase__					:      int      =		torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]			)
			self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3],					lowerCamelCase_,					atol=1E-4			)			)

			# verify area
			lowercase__					:      Optional[int]      =		torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['area'],					lowerCamelCase_			)			)
			# verify boxes
			lowercase__					:      int      =		torch.Size([6, 4]			)
			self.assertEqual(encoding['labels'][0]['boxes'].shape,					lowerCamelCase_			)
			lowercase__					:      Tuple      =		torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0],					lowerCamelCase_,					atol=1E-3			)			)
			# verify image_id
			lowercase__					:      Tuple      =		torch.tensor([39769]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'],					lowerCamelCase_			)			)
			# verify is_crowd
			lowercase__					:      Union[str, Any]      =		torch.tensor([0, 0, 0, 0, 0, 0]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'],					lowerCamelCase_			)			)
			# verify class_labels
			lowercase__					:      Tuple      =		torch.tensor([75, 75, 63, 65, 17, 17]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'],					lowerCamelCase_			)			)
			# verify orig_size
			lowercase__					:      Optional[int]      =		torch.tensor([480, 640]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'],					lowerCamelCase_			)			)
			# verify size
			lowercase__					:      List[Any]      =		torch.tensor([800, 1066]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['size'],					lowerCamelCase_			)			)





	@slow
	def    snake_case__(						self:						Dict			):
			# prepare image, target and masks_path
			lowercase__					:      Optional[int]      =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
			with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt',					'r'			) as f:
					lowercase__					:      Optional[Any]      =		json.loads(f.read()			)

			lowercase__					:      List[Any]      =		{'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

			lowercase__					:      Optional[int]      =		pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'			)

			# encode them
			lowercase__					:      List[Any]      =		YolosImageProcessor(format='coco_panoptic'			)
			lowercase__					:      Dict      =		image_processing(images=lowerCamelCase_,					annotations=lowerCamelCase_,					masks_path=lowerCamelCase_,					return_tensors='pt'			)

			# verify pixel values
			lowercase__					:      str      =		torch.Size([1, 3, 800, 1066]			)
			self.assertEqual(encoding['pixel_values'].shape,					lowerCamelCase_			)

			lowercase__					:      List[str]      =		torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]			)
			self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3],					lowerCamelCase_,					atol=1E-4			)			)

			# verify area
			lowercase__					:      Optional[int]      =		torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['area'],					lowerCamelCase_			)			)
			# verify boxes
			lowercase__					:      Dict      =		torch.Size([6, 4]			)
			self.assertEqual(encoding['labels'][0]['boxes'].shape,					lowerCamelCase_			)
			lowercase__					:      List[str]      =		torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0],					lowerCamelCase_,					atol=1E-3			)			)
			# verify image_id
			lowercase__					:      Any      =		torch.tensor([39769]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'],					lowerCamelCase_			)			)
			# verify is_crowd
			lowercase__					:      List[str]      =		torch.tensor([0, 0, 0, 0, 0, 0]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'],					lowerCamelCase_			)			)
			# verify class_labels
			lowercase__					:      List[str]      =		torch.tensor([17, 17, 63, 75, 75, 93]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'],					lowerCamelCase_			)			)
			# verify masks
			lowercase__					:      Optional[Any]      =		822873
			self.assertEqual(encoding['labels'][0]['masks'].sum().item(),					lowerCamelCase_			)
			# verify orig_size
			lowercase__					:      Optional[int]      =		torch.tensor([480, 640]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'],					lowerCamelCase_			)			)
			# verify size
			lowercase__					:      Dict      =		torch.tensor([800, 1066]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['size'],					lowerCamelCase_			)			)


