





import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation

__magic_name__   :						Optional[Any]    =			logging.get_logger(__name__)


__magic_name__   :						List[str]    =			{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__magic_name__   :						List[Any]    =			{
    """vocab_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"""},
    """merges_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"""},
    """tokenizer_config_file""": {
        """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"""
    },
}

__magic_name__   :						List[Any]    =			{"""facebook/blenderbot-3B""": 128}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				(
      list(range(ord("!"       )					,	ord("~"       ) + 1       )       ) + list(range(ord("¡"       )					,	ord("¬"       ) + 1       )       ) + list(range(ord("®"       )					,	ord("ÿ"       ) + 1       )       )
  )
  _snake_case     		=				bs[:]
  _snake_case     		=				0
  for b in range(2**8       ):
    if b not in bs:
      bs.append(lowerCamelCase__       )
      cs.append(2**8 + n       )
      n += 1
  _snake_case     		=				[chr(lowerCamelCase__       ) for n in cs]
  return dict(zip(lowerCamelCase__					,	lowerCamelCase__       )       )




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				set()
  _snake_case     		=				word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)       )
    _snake_case     		=				char
  return pairs




class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						str      =   VOCAB_FILES_NAMES
     UpperCAmelCase__       :						Tuple      =   PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__       :						Dict      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__       :						Dict      =   ["""input_ids""", """attention_mask"""]

     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase="replace"						,   lowerCamelCase="<s>"						,   lowerCamelCase="</s>"						,   lowerCamelCase="</s>"						,   lowerCamelCase="<s>"						,   lowerCamelCase="<unk>"						,   lowerCamelCase="<pad>"						,   lowerCamelCase="<mask>"						,   lowerCamelCase=False						,   **lowerCamelCase						,   ):
       _snake_case     		=				AddedToken(lowerCamelCase						,   lstrip=lowerCamelCase						,   rstrip=lowerCamelCase	) if isinstance(lowerCamelCase						,   lowerCamelCase	) else bos_token
       _snake_case     		=				AddedToken(lowerCamelCase						,   lstrip=lowerCamelCase						,   rstrip=lowerCamelCase	) if isinstance(lowerCamelCase						,   lowerCamelCase	) else eos_token
       _snake_case     		=				AddedToken(lowerCamelCase						,   lstrip=lowerCamelCase						,   rstrip=lowerCamelCase	) if isinstance(lowerCamelCase						,   lowerCamelCase	) else sep_token
       _snake_case     		=				AddedToken(lowerCamelCase						,   lstrip=lowerCamelCase						,   rstrip=lowerCamelCase	) if isinstance(lowerCamelCase						,   lowerCamelCase	) else cls_token
       _snake_case     		=				AddedToken(lowerCamelCase						,   lstrip=lowerCamelCase						,   rstrip=lowerCamelCase	) if isinstance(lowerCamelCase						,   lowerCamelCase	) else unk_token
       _snake_case     		=				AddedToken(lowerCamelCase						,   lstrip=lowerCamelCase						,   rstrip=lowerCamelCase	) if isinstance(lowerCamelCase						,   lowerCamelCase	) else pad_token

       # Mask token behave like a normal word, i.e. include the space before it
       _snake_case     		=				AddedToken(lowerCamelCase						,   lstrip=lowerCamelCase						,   rstrip=lowerCamelCase	) if isinstance(lowerCamelCase						,   lowerCamelCase	) else mask_token

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

       with open(lowerCamelCase						,   encoding="utf-8"	) as vocab_handle:
         _snake_case     		=				json.load(lowerCamelCase	)
       _snake_case     		=				{v: k for k, v in self.encoder.items()}
       _snake_case     		=				errors  # how to handle errors in decoding
       _snake_case     		=				bytes_to_unicode()
       _snake_case     		=				{v: k for k, v in self.byte_encoder.items()}
       with open(lowerCamelCase						,   encoding="utf-8"	) as merges_handle:
         _snake_case     		=				merges_handle.read().split("\n"	)[1:-1]
       _snake_case     		=				[tuple(merge.split()	) for merge in bpe_merges]
       _snake_case     		=				dict(zip(lowerCamelCase						,   range(len(lowerCamelCase	)	)	)	)
       _snake_case     		=				{}
       _snake_case     		=				add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       _snake_case     		=				re.compile(R"\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"	)

     @property
     # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
     def 	UpperCamelCase(					self	):
       return len(self.encoder	)

     def 	UpperCamelCase(					self	):
       return dict(self.encoder						,   **self.added_tokens_encoder	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):

       if token in self.cache:
         return self.cache[token]
       _snake_case     		=				tuple(lowerCamelCase	)
       _snake_case     		=				get_pairs(lowerCamelCase	)

       if not pairs:
         return token

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

           if word[i] == first and i < len(lowerCamelCase	) - 1 and word[i + 1] == second:
             new_word.append(first + second	)
             i += 2
           else:
             new_word.append(word[i]	)
             i += 1
         _snake_case     		=				tuple(lowerCamelCase	)
         _snake_case     		=				new_word
         if len(lowerCamelCase	) == 1:
           break
         else:
           _snake_case     		=				get_pairs(lowerCamelCase	)
       _snake_case     		=				" ".join(lowerCamelCase	)
       _snake_case     		=				word
       return word

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				[]
       for token in re.findall(self.pat						,   lowerCamelCase	):
         _snake_case     		=				"".join(
             self.byte_encoder[b] for b in token.encode("utf-8"	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
         bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase	).split(" "	)	)
       return bpe_tokens

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       return self.encoder.get(lowerCamelCase						,   self.encoder.get(self.unk_token	)	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       return self.decoder.get(lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				"".join(lowerCamelCase	)
       _snake_case     		=				bytearray([self.byte_decoder[c] for c in text]	).decode("utf-8"						,   errors=self.errors	)
       return text

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None	):

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

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

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

       return vocab_file, merge_file

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None						,   lowerCamelCase = False	):

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

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

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None	):
       _snake_case     		=				[self.sep_token_id]
       _snake_case     		=				[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]

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=False						,   **lowerCamelCase	):
       _snake_case     		=				kwargs.pop("add_prefix_space"						,   self.add_prefix_space	)
       if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase	) > 0 and not text[0].isspace()):
         _snake_case     		=				" " + text
       return (text, kwargs)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None	):
       return token_ids_a + [self.eos_token_id]






     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				[]
       for is_user, text in conversation.iter_texts():
         if is_user:
           # We need to space prefix as it's being done within blenderbot
           inputs.append(" " + text	)
         else:
           # Generated responses should contain them already.
           inputs.append(lowerCamelCase	)

       _snake_case     		=				"  ".join(lowerCamelCase	)
       _snake_case     		=				self.encode(lowerCamelCase	)
       if len(lowerCamelCase	) > self.model_max_length:
         _snake_case     		=				input_ids[-self.model_max_length :]
         logger.warning(F'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'''	)
       return input_ids









'''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       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase=7						,   lowerCamelCase=3						,   lowerCamelCase=30						,   lowerCamelCase=400						,   lowerCamelCase=True						,   lowerCamelCase=None						,   lowerCamelCase=True						,   lowerCamelCase=[0.5, 0.5, 0.5]						,   lowerCamelCase=[0.5, 0.5, 0.5]						,   lowerCamelCase=True						,   lowerCamelCase=1 / 255						,   lowerCamelCase=True						,   ):
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       _snake_case     		=				size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
       _snake_case     		=				parent
       _snake_case     		=				batch_size
       _snake_case     		=				num_channels
       _snake_case     		=				min_resolution
       _snake_case     		=				max_resolution
       _snake_case     		=				do_resize
       _snake_case     		=				size
       _snake_case     		=				do_normalize
       _snake_case     		=				image_mean
       _snake_case     		=				image_std
       _snake_case     		=				do_rescale
       _snake_case     		=				rescale_factor
       _snake_case     		=				do_pad

     def 	UpperCamelCase(					self	):
       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 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=False	):
       if not batched:
         _snake_case     		=				image_inputs[0]
         if isinstance(lowerCamelCase						,   Image.Image	):
           _snake_case		,		_snake_case     		=				image.size
         else:
           _snake_case		,		_snake_case     		=				image.shape[1], image.shape[2]
         if w < h:
           _snake_case     		=				int(self.size["shortest_edge"] * h / w	)
           _snake_case     		=				self.size["shortest_edge"]
         elif w > h:
           _snake_case     		=				self.size["shortest_edge"]
           _snake_case     		=				int(self.size["shortest_edge"] * w / h	)
         else:
           _snake_case     		=				self.size["shortest_edge"]
           _snake_case     		=				self.size["shortest_edge"]

       else:
         _snake_case     		=				[]
         for image in image_inputs:
           _snake_case		,		_snake_case     		=				self.get_expected_values([image]	)
           expected_values.append((expected_height, expected_width)	)
         _snake_case     		=				max(lowerCamelCase						,   key=lambda lowerCamelCase	: item[0]	)[0]
         _snake_case     		=				max(lowerCamelCase						,   key=lambda lowerCamelCase	: item[1]	)[1]

       return expected_height, expected_width







@require_torch
@require_vision
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Optional[Any]      =   DetaImageProcessor if is_vision_available() else None

     def 	UpperCamelCase(					self	):
       _snake_case     		=				DetaImageProcessingTester(self	)

     @property
     def 	UpperCamelCase(					self	):
       return self.image_processor_tester.prepare_image_processor_dict()

     def 	UpperCamelCase(					self	):
       _snake_case     		=				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						,   "do_rescale"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "do_pad"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "size"	)	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.image_processing_class.from_dict(self.image_processor_dict	)
       self.assertEqual(image_processor.size						,   {"shortest_edge": 18, "longest_edge": 1_333}	)
       self.assertEqual(image_processor.do_pad						,   lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       pass

     def 	UpperCamelCase(					self	):
       # Initialize image_processing
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       # create random PIL images
       _snake_case     		=				prepare_image_inputs(self.image_processor_tester						,   equal_resolution=lowerCamelCase	)
       for image in image_inputs:
         self.assertIsInstance(lowerCamelCase						,   Image.Image	)

       # Test not batched input
       _snake_case     		=				image_processing(image_inputs[0]						,   return_tensors="pt"	).pixel_values

       _snake_case		,		_snake_case     		=				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
       _snake_case		,		_snake_case     		=				self.image_processor_tester.get_expected_values(lowerCamelCase						,   batched=lowerCamelCase	)

       _snake_case     		=				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 	UpperCamelCase(					self	):
       # Initialize image_processing
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       # create random numpy tensors
       _snake_case     		=				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
       _snake_case     		=				image_processing(image_inputs[0]						,   return_tensors="pt"	).pixel_values

       _snake_case		,		_snake_case     		=				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
       _snake_case     		=				image_processing(lowerCamelCase						,   return_tensors="pt"	).pixel_values

       _snake_case		,		_snake_case     		=				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 	UpperCamelCase(					self	):
       # Initialize image_processing
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       # create random PyTorch tensors
       _snake_case     		=				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
       _snake_case     		=				image_processing(image_inputs[0]						,   return_tensors="pt"	).pixel_values

       _snake_case		,		_snake_case     		=				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
       _snake_case     		=				image_processing(lowerCamelCase						,   return_tensors="pt"	).pixel_values

       _snake_case		,		_snake_case     		=				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,
           )						,   )

     @slow
     def 	UpperCamelCase(					self	):
       # prepare image and target
       _snake_case     		=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
       with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"						,   "r"	) as f:
         _snake_case     		=				json.loads(f.read()	)

       _snake_case     		=				{"image_id": 39_769, "annotations": target}

       # encode them
       _snake_case     		=				DetaImageProcessor()
       _snake_case     		=				image_processing(images=lowerCamelCase						,   annotations=lowerCamelCase						,   return_tensors="pt"	)

       # verify pixel values
       _snake_case     		=				torch.Size([1, 3, 800, 1_066]	)
       self.assertEqual(encoding["pixel_values"].shape						,   lowerCamelCase	)

       _snake_case     		=				torch.tensor([0.2796, 0.3138, 0.3481]	)
       self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]						,   lowerCamelCase						,   atol=1e-4	)	)

       # verify area
       _snake_case     		=				torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["area"]						,   lowerCamelCase	)	)
       # verify boxes
       _snake_case     		=				torch.Size([6, 4]	)
       self.assertEqual(encoding["labels"][0]["boxes"].shape						,   lowerCamelCase	)
       _snake_case     		=				torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]						,   lowerCamelCase						,   atol=1e-3	)	)
       # verify image_id
       _snake_case     		=				torch.tensor([39_769]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]						,   lowerCamelCase	)	)
       # verify is_crowd
       _snake_case     		=				torch.tensor([0, 0, 0, 0, 0, 0]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]						,   lowerCamelCase	)	)
       # verify class_labels
       _snake_case     		=				torch.tensor([75, 75, 63, 65, 17, 17]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]						,   lowerCamelCase	)	)
       # verify orig_size
       _snake_case     		=				torch.tensor([480, 640]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]						,   lowerCamelCase	)	)
       # verify size
       _snake_case     		=				torch.tensor([800, 1_066]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["size"]						,   lowerCamelCase	)	)






     @slow
     def 	UpperCamelCase(					self	):
       # prepare image, target and masks_path
       _snake_case     		=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
       with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"						,   "r"	) as f:
         _snake_case     		=				json.loads(f.read()	)

       _snake_case     		=				{"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

       _snake_case     		=				pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"	)

       # encode them
       _snake_case     		=				DetaImageProcessor(format="coco_panoptic"	)
       _snake_case     		=				image_processing(images=lowerCamelCase						,   annotations=lowerCamelCase						,   masks_path=lowerCamelCase						,   return_tensors="pt"	)

       # verify pixel values
       _snake_case     		=				torch.Size([1, 3, 800, 1_066]	)
       self.assertEqual(encoding["pixel_values"].shape						,   lowerCamelCase	)

       _snake_case     		=				torch.tensor([0.2796, 0.3138, 0.3481]	)
       self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]						,   lowerCamelCase						,   atol=1e-4	)	)

       # verify area
       _snake_case     		=				torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["area"]						,   lowerCamelCase	)	)
       # verify boxes
       _snake_case     		=				torch.Size([6, 4]	)
       self.assertEqual(encoding["labels"][0]["boxes"].shape						,   lowerCamelCase	)
       _snake_case     		=				torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]						,   lowerCamelCase						,   atol=1e-3	)	)
       # verify image_id
       _snake_case     		=				torch.tensor([39_769]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]						,   lowerCamelCase	)	)
       # verify is_crowd
       _snake_case     		=				torch.tensor([0, 0, 0, 0, 0, 0]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]						,   lowerCamelCase	)	)
       # verify class_labels
       _snake_case     		=				torch.tensor([17, 17, 63, 75, 75, 93]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]						,   lowerCamelCase	)	)
       # verify masks
       _snake_case     		=				822_873
       self.assertEqual(encoding["labels"][0]["masks"].sum().item()						,   lowerCamelCase	)
       # verify orig_size
       _snake_case     		=				torch.tensor([480, 640]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]						,   lowerCamelCase	)	)
       # verify size
       _snake_case     		=				torch.tensor([800, 1_066]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["size"]						,   lowerCamelCase	)	)


