"""simple docstring"""






import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer


def 			UpperCAmelCase__       (				lowerCAmelCase__     :int					,		lowerCAmelCase__     :Dict					,		lowerCAmelCase__     :List[str]					,		lowerCAmelCase__     :Optional[int]					,		lowerCAmelCase__     :str=True					,		lowerCAmelCase__     :int="pt"		) ->    Tuple:
      '''simple docstring'''


      lowercase														=	{'''add_prefix_space''': True} if isinstance(__snake_case					,		__snake_case		) and not line.startswith(""" """		) else {}
      lowercase														=	padding_side
      return tokenizer(
          [line]					,		max_length=__snake_case					,		padding="""max_length""" if pad_to_max_length else None					,		truncation=__snake_case					,		return_tensors=__snake_case					,		add_special_tokens=__snake_case					,		**__snake_case					,		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Dict					,		lowerCAmelCase__     :Dict					,		lowerCAmelCase__     :int=None					,		) ->    int:
      '''simple docstring'''


      lowercase														=	input_ids.ne(__snake_case		).any(dim=0		)
      if attention_mask is None:
            return input_ids[:, keep_column_mask]
      else:
            return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])





class  _A						(       _A	):

      def __init__(						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase="train"		,       __lowerCAmelCase=None		,       __lowerCAmelCase=None		,       __lowerCAmelCase=None		,       __lowerCAmelCase=""		,       ):







            """simple docstring"""



            super().__init__()
            lowercase														=	Path(__lowerCAmelCase						).joinpath(type_path + """.source"""						)
            lowercase														=	Path(__lowerCAmelCase						).joinpath(type_path + """.target"""						)
            lowercase														=	self.get_char_lens(self.src_file						)
            lowercase														=	max_source_length
            lowercase														=	max_target_length
            assert min(self.src_lens						) > 0, f'found empty line in {self.src_file}'
            lowercase														=	tokenizer
            lowercase														=	prefix
            if n_obs is not None:
                  lowercase														=	self.src_lens[:n_obs]
            lowercase														=	src_lang
            lowercase														=	tgt_lang

      def __len__(						self						):







            """simple docstring"""



            return len(self.src_lens						)

      def __getitem__(						self		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	index + 1  # linecache starts at 1
            lowercase														=	self.prefix + linecache.getline(str(self.src_file						)		,       __lowerCAmelCase						).rstrip("""\n"""						)
            lowercase														=	linecache.getline(str(self.tgt_file						)		,       __lowerCAmelCase						).rstrip("""\n"""						)
            assert source_line, f'empty source line for index {index}'
            assert tgt_line, f'empty tgt line for index {index}'

            # Need to add eos token manually for T5
            if isinstance(self.tokenizer		,       __lowerCAmelCase						):
                  source_line += self.tokenizer.eos_token
                  tgt_line += self.tokenizer.eos_token

            # Pad source and target to the right
            lowercase														=	(
                self.tokenizer.question_encoder if isinstance(self.tokenizer		,       __lowerCAmelCase						) else self.tokenizer
            )
            lowercase														=	self.tokenizer.generator if isinstance(self.tokenizer		,       __lowerCAmelCase						) else self.tokenizer

            lowercase														=	encode_line(__lowerCAmelCase		,       __lowerCAmelCase		,       self.max_source_length		,       """right"""						)
            lowercase														=	encode_line(__lowerCAmelCase		,       __lowerCAmelCase		,       self.max_target_length		,       """right"""						)

            lowercase														=	source_inputs['''input_ids'''].squeeze()
            lowercase														=	target_inputs['''input_ids'''].squeeze()
            lowercase														=	source_inputs['''attention_mask'''].squeeze()
            return {
                "input_ids": source_ids,
                "attention_mask": src_mask,
                "decoder_input_ids": target_ids,
            }

      @staticmethod
      def 				A__       (						__lowerCAmelCase						):







            """simple docstring"""



            return [len(__lowerCAmelCase						) for x in Path(__lowerCAmelCase						).open().readlines()]







      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	torch.stack([x["""input_ids"""] for x in batch]						)
            lowercase														=	torch.stack([x["""attention_mask"""] for x in batch]						)
            lowercase														=	torch.stack([x["""decoder_input_ids"""] for x in batch]						)
            lowercase														=	(
                self.tokenizer.generator.pad_token_id
                if isinstance(self.tokenizer		,       __lowerCAmelCase						)
                else self.tokenizer.pad_token_id
            )
            lowercase														=	(
                self.tokenizer.question_encoder.pad_token_id
                if isinstance(self.tokenizer		,       __lowerCAmelCase						)
                else self.tokenizer.pad_token_id
            )
            lowercase														=	trim_batch(__lowerCAmelCase		,       __lowerCAmelCase						)
            lowercase														=	trim_batch(__lowerCAmelCase		,       __lowerCAmelCase		,       attention_mask=__lowerCAmelCase						)
            lowercase														=	{
                '''input_ids''': source_ids,
                '''attention_mask''': source_mask,
                '''decoder_input_ids''': y,
            }
            return batch






__lowerCAmelCase    :       Tuple					     =getLogger(__name__)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :List[List]		) ->    List[Any]:
      '''simple docstring'''


      return list(itertools.chain.from_iterable(__snake_case		)		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :str		) ->    Any:
      '''simple docstring'''


      lowercase														=	get_git_info()
      save_json(__snake_case					,		os.path.join(__snake_case					,		"""git_log.json"""		)		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :int					,		lowerCAmelCase__     :List[Any]					,		lowerCAmelCase__     :Any=4					,		**lowerCAmelCase__     :Dict		) ->    Optional[int]:
      '''simple docstring'''


      with open(__snake_case					,		"""w"""		) as f:
            json.dump(__snake_case					,		__snake_case					,		indent=__snake_case					,		**__snake_case		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Tuple		) ->    Dict:
      '''simple docstring'''


      with open(__snake_case		) as f:
            return json.load(__snake_case		)


def 			UpperCAmelCase__       (				) ->    Any:
      '''simple docstring'''


      lowercase														=	git.Repo(search_parent_directories=__snake_case		)
      lowercase														=	{
          '''repo_id''': str(__snake_case		),
          '''repo_sha''': str(repo.head.object.hexsha		),
          '''repo_branch''': str(repo.active_branch		),
          '''hostname''': str(socket.gethostname()		),
      }
      return repo_infos


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Callable					,		lowerCAmelCase__     :Iterable		) ->    List[str]:
      '''simple docstring'''


      return list(map(__snake_case					,		__snake_case		)		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Any					,		lowerCAmelCase__     :List[str]		) ->    str:
      '''simple docstring'''


      with open(__snake_case					,		"""wb"""		) as f:
            return pickle.dump(__snake_case					,		__snake_case		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Any		) ->    Union[str, Any]:
      '''simple docstring'''


      def remove_articles(lowerCAmelCase__     :List[Any]		):
            return re.sub(R"""\b(a|an|the)\b"""					,		""" """					,		__snake_case		)

      def white_space_fix(lowerCAmelCase__     :List[str]		):
            return " ".join(text.split()		)

      def remove_punc(lowerCAmelCase__     :Optional[int]		):
            lowercase														=	set(string.punctuation		)
            return "".join(ch for ch in text if ch not in exclude		)

      def lower(lowerCAmelCase__     :Tuple		):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(__snake_case		)		)		)		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :List[Any]					,		lowerCAmelCase__     :Dict		) ->    Union[str, Any]:
      '''simple docstring'''


      lowercase														=	normalize_answer(__snake_case		).split()
      lowercase														=	normalize_answer(__snake_case		).split()
      lowercase														=	Counter(__snake_case		) & Counter(__snake_case		)
      lowercase														=	sum(common.values()		)
      if num_same == 0:
            return 0
      lowercase														=	1.0 * num_same / len(__snake_case		)
      lowercase														=	1.0 * num_same / len(__snake_case		)
      lowercase														=	(2 * precision * recall) / (precision + recall)
      return fa


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Optional[Any]					,		lowerCAmelCase__     :Any		) ->    int:
      '''simple docstring'''


      return normalize_answer(__snake_case		) == normalize_answer(__snake_case		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :List[str]					,		lowerCAmelCase__     :List[str]		) ->    Optional[Any]:
      '''simple docstring'''


      assert len(__snake_case		) == len(__snake_case		)
      lowercase														=	0
      for hypo, pred in zip(__snake_case					,		__snake_case		):
            em += exact_match_score(__snake_case					,		__snake_case		)
      if len(__snake_case		) > 0:
            em /= len(__snake_case		)
      return {"em": em}


def 			UpperCAmelCase__       (				lowerCAmelCase__     :List[str]		) ->    Any:
      '''simple docstring'''


      return model_prefix.startswith("""rag"""		)






def 			UpperCAmelCase__       (				lowerCAmelCase__     :Optional[int]					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :Union[str, Any]		) ->    Dict:
      '''simple docstring'''


      lowercase														=	{p: p for p in extra_params}
      # T5 models don't have `dropout` param, they have `dropout_rate` instead
      lowercase														=	'''dropout_rate'''
      for p in extra_params:
            if getattr(__snake_case					,		__snake_case					,		__snake_case		):
                  if not hasattr(__snake_case					,		__snake_case		) and not hasattr(__snake_case					,		equivalent_param[p]		):
                        logger.info("""config doesn\'t have a `{}` attribute""".format(__snake_case		)		)
                        delattr(__snake_case					,		__snake_case		)
                        continue
                  lowercase														=	p if hasattr(__snake_case					,		__snake_case		) else equivalent_param[p]
                  setattr(__snake_case					,		__snake_case					,		getattr(__snake_case					,		__snake_case		)		)
                  delattr(__snake_case					,		__snake_case		)
      return hparams, config











"""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 YolosImageProcessor





class 					_UpperCAmelCase       ( unittest.TestCase							):




					def __init__(				self     :						List[Any]    ,						A     :						Any    ,						A     :						Tuple=7    ,						A     :						Tuple=3    ,						A     :						Optional[Any]=30    ,						A     :						List[Any]=4_00    ,						A     :						Tuple=True    ,						A     :						Dict=None    ,						A     :						List[str]=True    ,						A     :						Optional[int]=[0.5, 0.5, 0.5]    ,						A     :						Tuple=[0.5, 0.5, 0.5]    ,						A     :						List[str]=True    ,						A     :						List[Any]=1 / 2_55    ,						A     :						Union[str, Any]=True    ,						)  ->  Tuple:
							# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
							lowercase_ : Optional[int]							       =   size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
							lowercase_ : Optional[int]							       =   parent
							lowercase_ : str							       =   batch_size
							lowercase_ : Tuple							       =   num_channels
							lowercase_ : str							       =   min_resolution
							lowercase_ : Any							       =   max_resolution
							lowercase_ : str							       =   do_resize
							lowercase_ : Any							       =   size
							lowercase_ : Optional[int]							       =   do_normalize
							lowercase_ : List[str]							       =   image_mean
							lowercase_ : Optional[Any]							       =   image_std
							lowercase_ : int							       =   do_rescale
							lowercase_ : List[str]							       =   rescale_factor
							lowercase_ : int							       =   do_pad




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

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

							return expected_height, expected_width





@require_torch
@require_vision
class 					_UpperCAmelCase       ( _A   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				Optional[Any]							    =   YolosImageProcessor if is_vision_available() else None




					def   A  (				self     :						Optional[int]   )  ->  Optional[int]:
							lowercase_ : Optional[Any]							       =   YolosImageProcessingTester(self   )




					@property
					def   A  (				self     :						str   )  ->  Any:
							return self.image_processor_tester.prepare_image_processor_dict()




					def   A  (				self     :						Optional[int]   )  ->  List[str]:
							lowercase_ : Tuple							       =   self.image_processing_class(**self.image_processor_dict   )
							self.assertTrue(hasattr(A    ,						'''image_mean'''   )   )
							self.assertTrue(hasattr(A    ,						'''image_std'''   )   )
							self.assertTrue(hasattr(A    ,						'''do_normalize'''   )   )
							self.assertTrue(hasattr(A    ,						'''do_resize'''   )   )
							self.assertTrue(hasattr(A    ,						'''size'''   )   )




					def   A  (				self     :						Dict   )  ->  Tuple:
							lowercase_ : Dict							       =   self.image_processing_class.from_dict(self.image_processor_dict   )
							self.assertEqual(image_processor.size    ,						{'''shortest_edge''': 18, '''longest_edge''': 13_33}   )
							self.assertEqual(image_processor.do_pad    ,						A   )

							lowercase_ : Tuple							       =   self.image_processing_class.from_dict(
							    self.image_processor_dict    ,						size=42    ,						max_size=84    ,						pad_and_return_pixel_mask=A   )
							self.assertEqual(image_processor.size    ,						{'''shortest_edge''': 42, '''longest_edge''': 84}   )
							self.assertEqual(image_processor.do_pad    ,						A   )




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




					def   A  (				self     :						Tuple   )  ->  int:
							# Initialize image_processing
							lowercase_ : Dict							       =   self.image_processing_class(**self.image_processor_dict   )
							# create random PIL images
							lowercase_ : str							       =   prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=A   )
							for image in image_inputs:
									self.assertIsInstance(A    ,						Image.Image   )

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

							lowercase_       ,					lowercase_ : Union[str, Any]							       =   self.image_processor_tester.get_expected_values(A   )

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

							# Test batched
							lowercase_       ,					lowercase_ : Dict							       =   self.image_processor_tester.get_expected_values(A    ,						batched=A   )

							lowercase_ : str							       =   image_processing(A    ,						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   A  (				self     :						str   )  ->  Any:
							# Initialize image_processing
							lowercase_ : Any							       =   self.image_processing_class(**self.image_processor_dict   )
							# create random numpy tensors
							lowercase_ : List[Any]							       =   prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=A    ,						numpify=A   )
							for image in image_inputs:
									self.assertIsInstance(A    ,						np.ndarray   )

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

							lowercase_       ,					lowercase_ : int							       =   self.image_processor_tester.get_expected_values(A   )

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

							# Test batched
							lowercase_ : Optional[int]							       =   image_processing(A    ,						return_tensors='''pt'''   ).pixel_values

							lowercase_       ,					lowercase_ : List[Any]							       =   self.image_processor_tester.get_expected_values(A    ,						batched=A   )

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




					def   A  (				self     :						Tuple   )  ->  Optional[int]:
							# Initialize image_processing
							lowercase_ : Dict							       =   self.image_processing_class(**self.image_processor_dict   )
							# create random PyTorch tensors
							lowercase_ : Optional[int]							       =   prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=A    ,						torchify=A   )
							for image in image_inputs:
									self.assertIsInstance(A    ,						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(A   )

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

							# Test batched
							lowercase_ : Any							       =   image_processing(A    ,						return_tensors='''pt'''   ).pixel_values

							lowercase_       ,					lowercase_ : List[str]							       =   self.image_processor_tester.get_expected_values(A    ,						batched=A   )

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




					def   A  (				self     :						Tuple   )  ->  Optional[Any]:
							# Initialize image_processings
							lowercase_ : Optional[Any]							       =   self.image_processing_class(**self.image_processor_dict   )
							lowercase_ : Tuple							       =   self.image_processing_class(do_resize=A    ,						do_normalize=A    ,						do_rescale=A   )
							# create random PyTorch tensors
							lowercase_ : Union[str, Any]							       =   prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=A    ,						torchify=A   )
							for image in image_inputs:
									self.assertIsInstance(A    ,						torch.Tensor   )

							# Test whether the method "pad" and calling the image processor return the same tensors
							lowercase_ : Union[str, Any]							       =   image_processing_a.pad(A    ,						return_tensors='''pt'''   )
							lowercase_ : List[Any]							       =   image_processing_a(A    ,						return_tensors='''pt'''   )

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




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

							lowercase_ : Tuple							       =   {'''image_id''': 3_97_69, '''annotations''': target}

							# encode them
							lowercase_ : Union[str, Any]							       =   YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''   )
							lowercase_ : List[Any]							       =   image_processing(images=A    ,						annotations=A    ,						return_tensors='''pt'''   )

							# verify pixel values
							lowercase_ : Union[str, Any]							       =   torch.Size([1, 3, 8_00, 10_66]   )
							self.assertEqual(encoding['''pixel_values'''].shape    ,						A   )

							lowercase_ : Union[str, Any]							       =   torch.tensor([0.2796, 0.3138, 0.3481]   )
							self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,						A    ,						atol=1e-4   )   )

							# verify area
							lowercase_ : Tuple							       =   torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,						A   )   )
							# verify boxes
							lowercase_ : List[str]							       =   torch.Size([6, 4]   )
							self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,						A   )
							lowercase_ : Any							       =   torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,						A    ,						atol=1e-3   )   )
							# verify image_id
							lowercase_ : List[Any]							       =   torch.tensor([3_97_69]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,						A   )   )
							# verify is_crowd
							lowercase_ : Any							       =   torch.tensor([0, 0, 0, 0, 0, 0]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,						A   )   )
							# verify class_labels
							lowercase_ : Optional[Any]							       =   torch.tensor([75, 75, 63, 65, 17, 17]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,						A   )   )
							# verify orig_size
							lowercase_ : List[str]							       =   torch.tensor([4_80, 6_40]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,						A   )   )
							# verify size
							lowercase_ : Optional[Any]							       =   torch.tensor([8_00, 10_66]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,						A   )   )






					@slow
					def   A  (				self     :						List[Any]   )  ->  Dict:
							# prepare image, target and masks_path
							lowercase_ : Tuple							       =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
							with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''    ,						'''r'''   ) as f:
									lowercase_ : str							       =   json.loads(f.read()   )

							lowercase_ : int							       =   {'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}

							lowercase_ : List[Any]							       =   pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''   )

							# encode them
							lowercase_ : int							       =   YolosImageProcessor(format='''coco_panoptic'''   )
							lowercase_ : Any							       =   image_processing(images=A    ,						annotations=A    ,						masks_path=A    ,						return_tensors='''pt'''   )

							# verify pixel values
							lowercase_ : Optional[Any]							       =   torch.Size([1, 3, 8_00, 10_66]   )
							self.assertEqual(encoding['''pixel_values'''].shape    ,						A   )

							lowercase_ : Tuple							       =   torch.tensor([0.2796, 0.3138, 0.3481]   )
							self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,						A    ,						atol=1e-4   )   )

							# verify area
							lowercase_ : List[Any]							       =   torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,						A   )   )
							# verify boxes
							lowercase_ : str							       =   torch.Size([6, 4]   )
							self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,						A   )
							lowercase_ : List[str]							       =   torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,						A    ,						atol=1e-3   )   )
							# verify image_id
							lowercase_ : List[str]							       =   torch.tensor([3_97_69]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,						A   )   )
							# verify is_crowd
							lowercase_ : List[str]							       =   torch.tensor([0, 0, 0, 0, 0, 0]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,						A   )   )
							# verify class_labels
							lowercase_ : Any							       =   torch.tensor([17, 17, 63, 75, 75, 93]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,						A   )   )
							# verify masks
							lowercase_ : Dict							       =   82_28_73
							self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()    ,						A   )
							# verify orig_size
							lowercase_ : Tuple							       =   torch.tensor([4_80, 6_40]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,						A   )   )
							# verify size
							lowercase_ : List[str]							       =   torch.tensor([8_00, 10_66]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,						A   )   )



