




"""simple docstring"""






import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


lowercase__  		=		logging.get_logger(__name__)


lowercase__  		=		{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all LED models at https://huggingface.co/models?filter=LED
lowercase__  		=		{
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

lowercase__  		=		{
    """allenai/led-base-16384""": 1_6384,
}







@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def   _snake_case    (       ):
				_lowerCamelCase  :    List[str]					=					(
				    list(range(ord('!'	)							,					ord('~'	) + 1	)	) + list(range(ord('¡'	)							,					ord('¬'	) + 1	)	) + list(range(ord('®'	)							,					ord('ÿ'	) + 1	)	)
				)
				_lowerCamelCase  :    Any					=					bs[:]
				_lowerCamelCase  :    Optional[Any]					=					0
				for b in range(2**8	):
								if b not in bs:
												bs.append(lowercase__	)
												cs.append(2**8 + n	)
												n += 1
				_lowerCamelCase  :    Dict					=					[chr(lowercase__	) for n in cs]
				return dict(zip(lowercase__							,					lowercase__	)	)







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    Union[str, Any]					=					set()
				_lowerCamelCase  :    Any					=					word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)	)
								_lowerCamelCase  :    Any					=					char
				return pairs






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    VOCAB_FILES_NAMES
			lowerCamelCase__   =    PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__   =    ["""input_ids""", """attention_mask"""]
			def __init__(						self						,   lowercase						,   lowercase						,   lowercase="replace"						,   lowercase="<s>"						,   lowercase="</s>"						,   lowercase="</s>"						,   lowercase="<s>"						,   lowercase="<unk>"						,   lowercase="<pad>"						,   lowercase="<mask>"						,   lowercase=False						,   **lowercase						,   ):
							_lowerCamelCase  :    Dict					=					AddedToken(lowercase						,   lstrip=lowercase						,   rstrip=lowercase						) if isinstance(lowercase						,   lowercase						) else bos_token
							_lowerCamelCase  :    List[str]					=					AddedToken(lowercase						,   lstrip=lowercase						,   rstrip=lowercase						) if isinstance(lowercase						,   lowercase						) else eos_token
							_lowerCamelCase  :    Optional[int]					=					AddedToken(lowercase						,   lstrip=lowercase						,   rstrip=lowercase						) if isinstance(lowercase						,   lowercase						) else sep_token
							_lowerCamelCase  :    Union[str, Any]					=					AddedToken(lowercase						,   lstrip=lowercase						,   rstrip=lowercase						) if isinstance(lowercase						,   lowercase						) else cls_token
							_lowerCamelCase  :    Union[str, Any]					=					AddedToken(lowercase						,   lstrip=lowercase						,   rstrip=lowercase						) if isinstance(lowercase						,   lowercase						) else unk_token
							_lowerCamelCase  :    List[str]					=					AddedToken(lowercase						,   lstrip=lowercase						,   rstrip=lowercase						) if isinstance(lowercase						,   lowercase						) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							_lowerCamelCase  :    Union[str, Any]					=					AddedToken(lowercase						,   lstrip=lowercase						,   rstrip=lowercase						) if isinstance(lowercase						,   lowercase						) else mask_token

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

							with open(lowercase						,   encoding='utf-8'						) as vocab_handle:
											_lowerCamelCase  :    List[str]					=					json.load(lowercase						)
							_lowerCamelCase  :    Union[str, Any]					=					{v: k for k, v in self.encoder.items()}
							_lowerCamelCase  :    Dict					=					errors  # how to handle errors in decoding
							_lowerCamelCase  :    Tuple					=					bytes_to_unicode()
							_lowerCamelCase  :    List[Any]					=					{v: k for k, v in self.byte_encoder.items()}
							with open(lowercase						,   encoding='utf-8'						) as merges_handle:
											_lowerCamelCase  :    int					=					merges_handle.read().split('\n'						)[1:-1]
							_lowerCamelCase  :    Tuple					=					[tuple(merge.split()						) for merge in bpe_merges]
							_lowerCamelCase  :    Tuple					=					dict(zip(lowercase						,   range(len(lowercase						)						)						)						)
							_lowerCamelCase  :    Tuple					=					{}
							_lowerCamelCase  :    Dict					=					add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							_lowerCamelCase  :    str					=					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.bart.tokenization_bart.BartTokenizer.vocab_size
			def       A_					(						self						):
							return len(self.encoder						)
			def       A_					(						self						):
							return dict(self.encoder						,   **self.added_tokens_encoder						)
			def       A_					(						self						,   lowercase						):
							if token in self.cache:
											return self.cache[token]
							_lowerCamelCase  :    List[Any]					=					tuple(lowercase						)
							_lowerCamelCase  :    List[Any]					=					get_pairs(lowercase						)

							if not pairs:
											return token

							while True:
											_lowerCamelCase  :    Union[str, Any]					=					min(lowercase						,   key=lambda lowercase						: self.bpe_ranks.get(lowercase						,   float('inf'						)						)						)
											if bigram not in self.bpe_ranks:
															break
											_lowerCamelCase,				_lowerCamelCase  :    Optional[int]					=					bigram
											_lowerCamelCase  :    Any					=					[]
											_lowerCamelCase  :    List[Any]					=					0
											while i < len(lowercase						):
															try:
																			_lowerCamelCase  :    str					=					word.index(lowercase						,   lowercase						)
															except ValueError:
																			new_word.extend(word[i:]						)
																			break
															else:
																			new_word.extend(word[i:j]						)
																			_lowerCamelCase  :    str					=					j

															if word[i] == first and i < len(lowercase						) - 1 and word[i + 1] == second:
																			new_word.append(first + second						)
																			i += 2
															else:
																			new_word.append(word[i]						)
																			i += 1
											_lowerCamelCase  :    Tuple					=					tuple(lowercase						)
											_lowerCamelCase  :    Tuple					=					new_word
											if len(lowercase						) == 1:
															break
											else:
															_lowerCamelCase  :    List[str]					=					get_pairs(lowercase						)
							_lowerCamelCase  :    List[Any]					=					' '.join(lowercase						)
							_lowerCamelCase  :    str					=					word
							return word
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    Optional[int]					=					[]
							for token in re.findall(self.pat						,   lowercase						):
											_lowerCamelCase  :    int					=					''.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(lowercase						).split(' '						)						)
							return bpe_tokens
			def       A_					(						self						,   lowercase						):
							return self.encoder.get(lowercase						,   self.encoder.get(self.unk_token						)						)
			def       A_					(						self						,   lowercase						):
							return self.decoder.get(lowercase						)
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    List[str]					=					''.join(lowercase						)
							_lowerCamelCase  :    Dict					=					bytearray([self.byte_decoder[c] for c in text]						).decode('utf-8'						,   errors=self.errors						)
							return text
			def       A_					(						self						,   lowercase						,   lowercase = None						):
							if not os.path.isdir(lowercase						):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
											return
							_lowerCamelCase  :    Dict					=					os.path.join(
							    lowercase						,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)
							_lowerCamelCase  :    Any					=					os.path.join(
							    lowercase						,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']						)

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

							_lowerCamelCase  :    List[Any]					=					0
							with open(lowercase						,   '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 lowercase						: 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!'						)
																			_lowerCamelCase  :    Optional[Any]					=					token_index
															writer.write(' '.join(lowercase						) + '\n'						)
															index += 1

							return vocab_file, merge_file
			def       A_					(						self						,   lowercase						,   lowercase = None						):
							if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							_lowerCamelCase  :    Any					=					[self.cls_token_id]
							_lowerCamelCase  :    int					=					[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep
			def       A_					(						self						,   lowercase						,   lowercase = None						,   lowercase = False						):
							if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=lowercase						,   token_ids_a=lowercase						,   already_has_special_tokens=lowercase						)

							if token_ids_a is None:
											return [1] + ([0] * len(lowercase						)) + [1]
							return [1] + ([0] * len(lowercase						)) + [1, 1] + ([0] * len(lowercase						)) + [1]
			def       A_					(						self						,   lowercase						,   lowercase = None						):
							_lowerCamelCase  :    Dict					=					[self.sep_token_id]
							_lowerCamelCase  :    int					=					[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       A_					(						self						,   lowercase						,   lowercase=False						,   **lowercase						):
							_lowerCamelCase  :    Union[str, Any]					=					kwargs.pop('add_prefix_space'						,   self.add_prefix_space						)
							if (is_split_into_words or add_prefix_space) and (len(lowercase						) > 0 and not text[0].isspace()):
											_lowerCamelCase  :    int					=					' ' + text
							return (text, kwargs)






			def       A_					(						self						,   lowercase						,   lowercase = None						,   lowercase = PaddingStrategy.DO_NOT_PAD						,   lowercase = None						,   lowercase = None						,   ):
							_lowerCamelCase  :    Dict					=					super()._pad(
							    encoded_inputs=lowercase						,   max_length=lowercase						,   padding_strategy=lowercase						,   pad_to_multiple_of=lowercase						,   return_attention_mask=lowercase						,   )

							# Load from model defaults
							if return_attention_mask is None:
											_lowerCamelCase  :    List[str]					=					'attention_mask' in self.model_input_names

							if return_attention_mask and "global_attention_mask" in encoded_inputs:
											_lowerCamelCase  :    List[str]					=					encoded_inputs[self.model_input_names[0]]
											# `global_attention_mask` need to have the same length as other (sequential) inputs.
											_lowerCamelCase  :    Any					=					len(encoded_inputs['global_attention_mask']						) != len(lowercase						)

											if needs_to_be_padded:
															_lowerCamelCase  :    int					=					len(lowercase						) - len(encoded_inputs['global_attention_mask']						)

															if self.padding_side == "right":
																			# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																			_lowerCamelCase  :    str					=					(
																			    encoded_inputs['global_attention_mask'] + [-1] * difference
																			)
															elif self.padding_side == "left":
																			_lowerCamelCase  :    Union[str, Any]					=					[-1] * difference + encoded_inputs[
																			    'global_attention_mask'
																			]
															else:
																			raise ValueError('Invalid padding strategy:' + str(self.padding_side						)						)

							return encoded_inputs





"""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 DetrImageProcessor






class lowerCAmelCase__			(  unittest.TestCase		):






			'''simple docstring'''



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






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

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

							return expected_height, expected_width






@require_torch
@require_vision
class lowerCAmelCase__			(  lowercase,			unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__   =    DetrImageProcessor if is_vision_available() else None
			def       A_					(						self						):
							_lowerCamelCase  :    int					=					DetrImageProcessingTester(self						)
			@property
			def       A_					(						self						):
							return self.image_processor_tester.prepare_image_processor_dict()
			def       A_					(						self						):
							_lowerCamelCase  :    str					=					self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(lowercase						,   'image_mean'						)						)
							self.assertTrue(hasattr(lowercase						,   'image_std'						)						)
							self.assertTrue(hasattr(lowercase						,   'do_normalize'						)						)
							self.assertTrue(hasattr(lowercase						,   'do_rescale'						)						)
							self.assertTrue(hasattr(lowercase						,   'rescale_factor'						)						)
							self.assertTrue(hasattr(lowercase						,   'do_resize'						)						)
							self.assertTrue(hasattr(lowercase						,   'size'						)						)
							self.assertTrue(hasattr(lowercase						,   'do_pad'						)						)
			def       A_					(						self						):
							_lowerCamelCase  :    List[str]					=					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						,   lowercase						)

							_lowerCamelCase  :    Union[str, Any]					=					self.image_processing_class.from_dict(
							    self.image_processor_dict						,   size=42						,   max_size=84						,   pad_and_return_pixel_mask=lowercase						)
							self.assertEqual(image_processor.size						,   {'shortest_edge': 42, 'longest_edge': 84}						)
							self.assertEqual(image_processor.do_pad						,   lowercase						)
			def       A_					(						self						):
							pass
			def       A_					(						self						):
							# Initialize image_processing
							_lowerCamelCase  :    Optional[int]					=					self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							_lowerCamelCase  :    List[str]					=					prepare_image_inputs(self.image_processor_tester						,   equal_resolution=lowercase						)
							for image in image_inputs:
											self.assertIsInstance(lowercase						,   Image.Image						)

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

							_lowerCamelCase,				_lowerCamelCase  :    List[Any]					=					self.image_processor_tester.get_expected_values(lowercase						)

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

							# Test batched
							_lowerCamelCase,				_lowerCamelCase  :    str					=					self.image_processor_tester.get_expected_values(lowercase						,   batched=lowercase						)

							_lowerCamelCase  :    Union[str, Any]					=					image_processing(lowercase						,   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						):
							# Initialize image_processing
							_lowerCamelCase  :    Optional[int]					=					self.image_processing_class(**self.image_processor_dict						)
							# create random numpy tensors
							_lowerCamelCase  :    Union[str, Any]					=					prepare_image_inputs(self.image_processor_tester						,   equal_resolution=lowercase						,   numpify=lowercase						)
							for image in image_inputs:
											self.assertIsInstance(lowercase						,   np.ndarray						)

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

							_lowerCamelCase,				_lowerCamelCase  :    List[Any]					=					self.image_processor_tester.get_expected_values(lowercase						)

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

							# Test batched
							_lowerCamelCase  :    str					=					image_processing(lowercase						,   return_tensors='pt'						).pixel_values

							_lowerCamelCase,				_lowerCamelCase  :    Union[str, Any]					=					self.image_processor_tester.get_expected_values(lowercase						,   batched=lowercase						)

							self.assertEqual(
							    encoded_images.shape						,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )						,   )
			def       A_					(						self						):
							# Initialize image_processing
							_lowerCamelCase  :    List[str]					=					self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							_lowerCamelCase  :    Optional[Any]					=					prepare_image_inputs(self.image_processor_tester						,   equal_resolution=lowercase						,   torchify=lowercase						)
							for image in image_inputs:
											self.assertIsInstance(lowercase						,   torch.Tensor						)

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

							_lowerCamelCase,				_lowerCamelCase  :    Optional[Any]					=					self.image_processor_tester.get_expected_values(lowercase						)

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

							# Test batched
							_lowerCamelCase  :    Dict					=					image_processing(lowercase						,   return_tensors='pt'						).pixel_values

							_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					self.image_processor_tester.get_expected_values(lowercase						,   batched=lowercase						)

							self.assertEqual(
							    encoded_images.shape						,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )						,   )
			@slow
			def       A_					(						self						):
							# prepare image and target
							_lowerCamelCase  :    Tuple					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
							with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'						,   'r'						) as f:
											_lowerCamelCase  :    int					=					json.loads(f.read()						)

							_lowerCamelCase  :    str					=					{'image_id': 39769, 'annotations': target}

							# encode them
							_lowerCamelCase  :    str					=					DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'						)
							_lowerCamelCase  :    int					=					image_processing(images=lowercase						,   annotations=lowercase						,   return_tensors='pt'						)

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

							_lowerCamelCase  :    List[str]					=					torch.tensor([0.27_96, 0.31_38, 0.34_81]						)
							self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]						,   lowercase						,   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']						,   lowercase						)						)
							# verify boxes
							_lowerCamelCase  :    int					=					torch.Size([6, 4]						)
							self.assertEqual(encoding['labels'][0]['boxes'].shape						,   lowercase						)
							_lowerCamelCase  :    Tuple					=					torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]						,   lowercase						,   atol=1E-3						)						)
							# verify image_id
							_lowerCamelCase  :    List[Any]					=					torch.tensor([39769]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,   lowercase						)						)
							# verify is_crowd
							_lowerCamelCase  :    str					=					torch.tensor([0, 0, 0, 0, 0, 0]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,   lowercase						)						)
							# verify class_labels
							_lowerCamelCase  :    Dict					=					torch.tensor([75, 75, 63, 65, 17, 17]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,   lowercase						)						)
							# verify orig_size
							_lowerCamelCase  :    Optional[int]					=					torch.tensor([480, 640]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,   lowercase						)						)
							# verify size
							_lowerCamelCase  :    List[str]					=					torch.tensor([800, 1066]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,   lowercase						)						)






			@slow
			def       A_					(						self						):
							# prepare image, target and masks_path
							_lowerCamelCase  :    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:
											_lowerCamelCase  :    List[Any]					=					json.loads(f.read()						)

							_lowerCamelCase  :    str					=					{'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

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

							# encode them
							_lowerCamelCase  :    List[Any]					=					DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'						)
							_lowerCamelCase  :    Dict					=					image_processing(images=lowercase						,   annotations=lowercase						,   masks_path=lowercase						,   return_tensors='pt'						)

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

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

							# verify area
							_lowerCamelCase  :    Optional[Any]					=					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']						,   lowercase						)						)
							# verify boxes
							_lowerCamelCase  :    Dict					=					torch.Size([6, 4]						)
							self.assertEqual(encoding['labels'][0]['boxes'].shape						,   lowercase						)
							_lowerCamelCase  :    Union[str, Any]					=					torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]						,   lowercase						,   atol=1E-3						)						)
							# verify image_id
							_lowerCamelCase  :    List[str]					=					torch.tensor([39769]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,   lowercase						)						)
							# verify is_crowd
							_lowerCamelCase  :    Optional[Any]					=					torch.tensor([0, 0, 0, 0, 0, 0]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,   lowercase						)						)
							# verify class_labels
							_lowerCamelCase  :    List[str]					=					torch.tensor([17, 17, 63, 75, 75, 93]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,   lowercase						)						)
							# verify masks
							_lowerCamelCase  :    List[str]					=					822873
							self.assertEqual(encoding['labels'][0]['masks'].sum().item()						,   lowercase						)
							# verify orig_size
							_lowerCamelCase  :    Optional[Any]					=					torch.tensor([480, 640]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,   lowercase						)						)
							# verify size
							_lowerCamelCase  :    List[str]					=					torch.tensor([800, 1066]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,   lowercase						)						)