





"""simple docstring"""





import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


__A											=						logging.get_logger(__name__)

__A											=						"""▁"""

__A											=						{
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
}

__A											=						{
    """vocab_file""": {
        """facebook/s2t-small-librispeech-asr""": (
            """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"""
        ),
    },
    """spm_file""": {
        """facebook/s2t-small-librispeech-asr""": (
            """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"""
        )
    },
}

__A											=						{
    """facebook/s2t-small-librispeech-asr""": 1024,
}

__A											=						["""pt""", """fr""", """ru""", """nl""", """ro""", """it""", """es""", """de"""]

__A											=						{"""mustc""": MUSTC_LANGS}






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:Tuple						    =   VOCAB_FILES_NAMES
       __magic_name__	:Union[str, Any]						    =   PRETRAINED_VOCAB_FILES_MAP
       __magic_name__	:Tuple						    =   MAX_MODEL_INPUT_SIZES
       __magic_name__	:Optional[Any]						    =   ["""input_ids""", """attention_mask"""]

       __magic_name__	:List[int]						    =   []






       def __init__(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase="<s>"       ,					__UpperCAmelCase="</s>"       ,					__UpperCAmelCase="<pad>"       ,					__UpperCAmelCase="<unk>"       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase = None       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=__UpperCAmelCase       ,					eos_token=__UpperCAmelCase       ,					unk_token=__UpperCAmelCase       ,					pad_token=__UpperCAmelCase       ,					do_upper_case=__UpperCAmelCase       ,					do_lower_case=__UpperCAmelCase       ,					tgt_lang=__UpperCAmelCase       ,					lang_codes=__UpperCAmelCase       ,					sp_model_kwargs=self.sp_model_kwargs       ,					**__UpperCAmelCase       ,					)
         lowerCAmelCase__     :Optional[int]											=				do_upper_case
         lowerCAmelCase__     :Optional[Any]											=				do_lower_case

         lowerCAmelCase__     :Any											=				load_json(__UpperCAmelCase )
         lowerCAmelCase__     :List[Any]											=				{v: k for k, v in self.encoder.items()}
         lowerCAmelCase__     :Optional[Any]											=				spm_file
         lowerCAmelCase__     :Optional[Any]											=				load_spm(__UpperCAmelCase       ,					self.sp_model_kwargs )

         if lang_codes is not None:
           lowerCAmelCase__     :Optional[Any]											=				lang_codes
           lowerCAmelCase__     :int											=				LANGUAGES[lang_codes]
           lowerCAmelCase__     :str											=				[F"<lang:{lang}>" for lang in self.langs]
           lowerCAmelCase__     :List[str]											=				{lang: self.sp_model.PieceToId(F"<lang:{lang}>" ) for lang in self.langs}

           lowerCAmelCase__     :Any											=				self.lang_tokens
           lowerCAmelCase__     :str											=				tgt_lang if tgt_lang is not None else self.langs[0]

           self.set_tgt_lang_special_tokens(self._tgt_lang )
         else:
           lowerCAmelCase__     :List[str]											=				{}






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return len(self.encoder )






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return self._tgt_lang






       @tgt_lang.setter
       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				new_tgt_lang
         self.set_tgt_lang_special_tokens(__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				self.lang_code_to_id[tgt_lang]
         lowerCAmelCase__     :Any											=				[lang_code_id]






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         return self.sp_model.encode(__UpperCAmelCase       ,					out_type=__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         return self.encoder.get(__UpperCAmelCase       ,					self.encoder[self.unk_token] )






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

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






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				[]
         lowerCAmelCase__     :List[str]											=				''
         for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
             lowerCAmelCase__     :Union[str, Any]											=				self.sp_model.decode(__UpperCAmelCase )
             out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
             lowerCAmelCase__     :Any											=				[]
           else:
             current_sub_tokens.append(__UpperCAmelCase )
         lowerCAmelCase__     :Optional[Any]											=				self.sp_model.decode(__UpperCAmelCase )
         out_string += decoded.upper() if self.do_upper_case else decoded
         return out_string.strip()






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=None ):



         '''simple docstring'''

         if token_ids_a is None:
           return self.prefix_tokens + token_ids_a + [self.eos_token_id]
         # 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.eos_token_id]






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = False ):



         '''simple docstring'''

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

         lowerCAmelCase__     :Any											=				[1] * len(self.prefix_tokens )
         lowerCAmelCase__     :Tuple											=				[1]
         if token_ids_a is None:
           return prefix_ones + ([0] * len(__UpperCAmelCase )) + suffix_ones
         return prefix_ones + ([0] * len(__UpperCAmelCase )) + ([0] * len(__UpperCAmelCase )) + suffix_ones






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				self.encoder.copy()
         vocab.update(self.added_tokens_encoder )
         return vocab






       def __getstate__(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				self.__dict__.copy()
         lowerCAmelCase__     :Tuple											=				None
         return state






       def __setstate__(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				d

         # for backward compatibility
         if not hasattr(self       ,					'sp_model_kwargs' ):
           lowerCAmelCase__     :List[str]											=				{}

         lowerCAmelCase__     :Union[str, Any]											=				load_spm(self.spm_file       ,					self.sp_model_kwargs )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				Path(__UpperCAmelCase )
         assert save_dir.is_dir(), F"{save_directory} should be a directory"
         lowerCAmelCase__     :List[str]											=				save_dir / (
             (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
         )
         lowerCAmelCase__     :Optional[Any]											=				save_dir / (
             (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
         )

         save_json(self.encoder       ,					__UpperCAmelCase )

         if os.path.abspath(self.spm_file ) != os.path.abspath(__UpperCAmelCase ) and os.path.isfile(self.spm_file ):
           copyfile(self.spm_file       ,					__UpperCAmelCase )
         elif not os.path.isfile(self.spm_file ):
           with open(__UpperCAmelCase       ,					'wb' ) as fi:
             lowerCAmelCase__     :Dict											=				self.sp_model.serialized_model_proto()
             fi.write(__UpperCAmelCase )

         return (str(__UpperCAmelCase ), str(__UpperCAmelCase ))






def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->sentencepiece.SentencePieceProcessor:





  """simple docstring"""


  lowerCAmelCase__     :List[Any]											=				sentencepiece.SentencePieceProcessor(**_SCREAMING_SNAKE_CASE     )
  spm.Load(str(_SCREAMING_SNAKE_CASE     )     )
  return spm
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->Union[Dict, List]:





  """simple docstring"""


  with open(_SCREAMING_SNAKE_CASE	,  'r'     ) as f:
    return json.load(_SCREAMING_SNAKE_CASE     )




def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->None:





  """simple docstring"""


  with open(_SCREAMING_SNAKE_CASE	,  'w'     ) as f:
    json.dump(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  indent=2     )







"""simple docstring"""





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 DetaImageProcessor






class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def __init__(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=7       ,					__UpperCAmelCase=3       ,					__UpperCAmelCase=3_0       ,					__UpperCAmelCase=4_0_0       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=[0.5, 0.5, 0.5]       ,					__UpperCAmelCase=[0.5, 0.5, 0.5]       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=1 / 2_5_5       ,					__UpperCAmelCase=True       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
         lowerCAmelCase__     :List[Any]											=				parent
         lowerCAmelCase__     :int											=				batch_size
         lowerCAmelCase__     :Union[str, Any]											=				num_channels
         lowerCAmelCase__     :Any											=				min_resolution
         lowerCAmelCase__     :Dict											=				max_resolution
         lowerCAmelCase__     :Dict											=				do_resize
         lowerCAmelCase__     :Optional[Any]											=				size
         lowerCAmelCase__     :List[str]											=				do_normalize
         lowerCAmelCase__     :str											=				image_mean
         lowerCAmelCase__     :Tuple											=				image_std
         lowerCAmelCase__     :Dict											=				do_rescale
         lowerCAmelCase__     :Tuple											=				rescale_factor
         lowerCAmelCase__     :Optional[int]											=				do_pad






       def 	snake_case		(       self ):



         '''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,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_pad": self.do_pad,
         }






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=False ):



         '''simple docstring'''

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

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

         return expected_height, expected_width






@require_torch
@require_vision
class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:Any						    =   DetaImageProcessor if is_vision_available() else None






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				DetaImageProcessingTester(self )






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return self.image_processor_tester.prepare_image_processor_dict()






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				self.image_processing_class(**self.image_processor_dict )
         self.assertTrue(hasattr(__UpperCAmelCase       ,					'image_mean' ) )
         self.assertTrue(hasattr(__UpperCAmelCase       ,					'image_std' ) )
         self.assertTrue(hasattr(__UpperCAmelCase       ,					'do_normalize' ) )
         self.assertTrue(hasattr(__UpperCAmelCase       ,					'do_resize' ) )
         self.assertTrue(hasattr(__UpperCAmelCase       ,					'do_rescale' ) )
         self.assertTrue(hasattr(__UpperCAmelCase       ,					'do_pad' ) )
         self.assertTrue(hasattr(__UpperCAmelCase       ,					'size' ) )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				self.image_processing_class.from_dict(self.image_processor_dict )
         self.assertEqual(image_processor.size       ,					{'shortest_edge': 1_8, 'longest_edge': 1_3_3_3} )
         self.assertEqual(image_processor.do_pad       ,					__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         pass






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				self.image_processing_class(**self.image_processor_dict )
         # create random PIL images
         lowerCAmelCase__     :Any											=				prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=__UpperCAmelCase )
         for image in image_inputs:
           self.assertIsInstance(__UpperCAmelCase       ,					Image.Image )

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

         lowerCAmelCase__				,							lowerCAmelCase__     :Optional[int]											=				self.image_processor_tester.get_expected_values(__UpperCAmelCase )

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

         # Test batched
         lowerCAmelCase__				,							lowerCAmelCase__     :Tuple											=				self.image_processor_tester.get_expected_values(__UpperCAmelCase       ,					batched=__UpperCAmelCase )

         lowerCAmelCase__     :Optional[Any]											=				image_processing(__UpperCAmelCase       ,					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 ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				self.image_processing_class(**self.image_processor_dict )
         # create random numpy tensors
         lowerCAmelCase__     :Dict											=				prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=__UpperCAmelCase       ,					numpify=__UpperCAmelCase )
         for image in image_inputs:
           self.assertIsInstance(__UpperCAmelCase       ,					np.ndarray )

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

         lowerCAmelCase__				,							lowerCAmelCase__     :List[Any]											=				self.image_processor_tester.get_expected_values(__UpperCAmelCase )

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

         # Test batched
         lowerCAmelCase__     :Tuple											=				image_processing(__UpperCAmelCase       ,					return_tensors='pt' ).pixel_values

         lowerCAmelCase__				,							lowerCAmelCase__     :List[str]											=				self.image_processor_tester.get_expected_values(__UpperCAmelCase       ,					batched=__UpperCAmelCase )

         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 ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				self.image_processing_class(**self.image_processor_dict )
         # create random PyTorch tensors
         lowerCAmelCase__     :List[Any]											=				prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=__UpperCAmelCase       ,					torchify=__UpperCAmelCase )
         for image in image_inputs:
           self.assertIsInstance(__UpperCAmelCase       ,					torch.Tensor )

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

         lowerCAmelCase__				,							lowerCAmelCase__     :Optional[int]											=				self.image_processor_tester.get_expected_values(__UpperCAmelCase )

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

         # Test batched
         lowerCAmelCase__     :str											=				image_processing(__UpperCAmelCase       ,					return_tensors='pt' ).pixel_values

         lowerCAmelCase__				,							lowerCAmelCase__     :List[Any]											=				self.image_processor_tester.get_expected_values(__UpperCAmelCase       ,					batched=__UpperCAmelCase )

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






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
         with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'       ,					'r' ) as f:
           lowerCAmelCase__     :Dict											=				json.loads(f.read() )

         lowerCAmelCase__     :int											=				{'image_id': 3_9_7_6_9, 'annotations': target}

         # encode them
         lowerCAmelCase__     :int											=				DetaImageProcessor()
         lowerCAmelCase__     :List[Any]											=				image_processing(images=__UpperCAmelCase       ,					annotations=__UpperCAmelCase       ,					return_tensors='pt' )

         # verify pixel values
         lowerCAmelCase__     :str											=				torch.Size([1, 3, 8_0_0, 1_0_6_6] )
         self.assertEqual(encoding['pixel_values'].shape       ,					__UpperCAmelCase )

         lowerCAmelCase__     :Optional[int]											=				torch.tensor([0.27_96, 0.31_38, 0.34_81] )
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,					__UpperCAmelCase       ,					atol=1E-4 ) )

         # verify area
         lowerCAmelCase__     :Dict											=				torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,					__UpperCAmelCase ) )
         # verify boxes
         lowerCAmelCase__     :Tuple											=				torch.Size([6, 4] )
         self.assertEqual(encoding['labels'][0]['boxes'].shape       ,					__UpperCAmelCase )
         lowerCAmelCase__     :Dict											=				torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,					__UpperCAmelCase       ,					atol=1E-3 ) )
         # verify image_id
         lowerCAmelCase__     :Any											=				torch.tensor([3_9_7_6_9] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,					__UpperCAmelCase ) )
         # verify is_crowd
         lowerCAmelCase__     :Union[str, Any]											=				torch.tensor([0, 0, 0, 0, 0, 0] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,					__UpperCAmelCase ) )
         # verify class_labels
         lowerCAmelCase__     :Optional[Any]											=				torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,					__UpperCAmelCase ) )
         # verify orig_size
         lowerCAmelCase__     :str											=				torch.tensor([4_8_0, 6_4_0] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,					__UpperCAmelCase ) )
         # verify size
         lowerCAmelCase__     :Any											=				torch.tensor([8_0_0, 1_0_6_6] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,					__UpperCAmelCase ) )






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
         with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'       ,					'r' ) as f:
           lowerCAmelCase__     :Dict											=				json.loads(f.read() )

         lowerCAmelCase__     :Dict											=				{'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

         lowerCAmelCase__     :Union[str, Any]											=				pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )

         # encode them
         lowerCAmelCase__     :Dict											=				DetaImageProcessor(format='coco_panoptic' )
         lowerCAmelCase__     :Optional[int]											=				image_processing(images=__UpperCAmelCase       ,					annotations=__UpperCAmelCase       ,					masks_path=__UpperCAmelCase       ,					return_tensors='pt' )

         # verify pixel values
         lowerCAmelCase__     :str											=				torch.Size([1, 3, 8_0_0, 1_0_6_6] )
         self.assertEqual(encoding['pixel_values'].shape       ,					__UpperCAmelCase )

         lowerCAmelCase__     :Union[str, Any]											=				torch.tensor([0.27_96, 0.31_38, 0.34_81] )
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,					__UpperCAmelCase       ,					atol=1E-4 ) )

         # verify area
         lowerCAmelCase__     :Tuple											=				torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,					__UpperCAmelCase ) )
         # verify boxes
         lowerCAmelCase__     :int											=				torch.Size([6, 4] )
         self.assertEqual(encoding['labels'][0]['boxes'].shape       ,					__UpperCAmelCase )
         lowerCAmelCase__     :Optional[Any]											=				torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,					__UpperCAmelCase       ,					atol=1E-3 ) )
         # verify image_id
         lowerCAmelCase__     :Optional[int]											=				torch.tensor([3_9_7_6_9] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,					__UpperCAmelCase ) )
         # verify is_crowd
         lowerCAmelCase__     :Tuple											=				torch.tensor([0, 0, 0, 0, 0, 0] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,					__UpperCAmelCase ) )
         # verify class_labels
         lowerCAmelCase__     :List[str]											=				torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,					__UpperCAmelCase ) )
         # verify masks
         lowerCAmelCase__     :Optional[int]											=				8_2_2_8_7_3
         self.assertEqual(encoding['labels'][0]['masks'].sum().item()       ,					__UpperCAmelCase )
         # verify orig_size
         lowerCAmelCase__     :Optional[int]											=				torch.tensor([4_8_0, 6_4_0] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,					__UpperCAmelCase ) )
         # verify size
         lowerCAmelCase__     :Union[str, Any]											=				torch.tensor([8_0_0, 1_0_6_6] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,					__UpperCAmelCase ) )
