




import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 					snake_case__							(_UpperCamelCase					, unittest.TestCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							List[str]											=      LongformerTokenizer
				SCREAMING_SNAKE_CASE_ :							Optional[int]											=      True
				SCREAMING_SNAKE_CASE_ :							Optional[int]											=      LongformerTokenizerFast
				SCREAMING_SNAKE_CASE_ :							str											=      True





				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  str:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							a					=    [
							    "l",
							    "o",
							    "w",
							    "e",
							    "r",
							    "s",
							    "t",
							    "i",
							    "d",
							    "n",
							    "\u0120",
							    "\u0120l",
							    "\u0120n",
							    "\u0120lo",
							    "\u0120low",
							    "er",
							    "\u0120lowest",
							    "\u0120newer",
							    "\u0120wider",
							    "<unk>",
							]
							a					=    dict(zip(__lowerCamelCase      ,   range(len(__lowerCamelCase							)							)							)							)
							a					=    ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
							a					=    {"unk_token": "<unk>"}

							a					=    os.path.join(self.tmpdirname      ,   VOCAB_FILES_NAMES["vocab_file"]							)
							a					=    os.path.join(self.tmpdirname      ,   VOCAB_FILES_NAMES["merges_file"]							)
							with open(self.vocab_file      ,   "w"      ,   encoding="utf-8"							) as fp:
										fp.write(json.dumps(__lowerCamelCase							) + "\n"							)
							with open(self.merges_file      ,   "w"      ,   encoding="utf-8"							) as fp:
										fp.write("\n".join(__lowerCamelCase							)							)





				def 							__UpperCAmelCase							(   self       :  Dict      ,   **__lowerCamelCase       :  Dict							)      ->  Any:
							kwargs.update(self.special_tokens_map							)
							return self.tokenizer_class.from_pretrained(self.tmpdirname      ,   **__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]      ,   **__lowerCamelCase       :  Any							)      ->  List[Any]:
							kwargs.update(self.special_tokens_map							)
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,   **__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  int      ,   __lowerCamelCase       :  List[Any]							)      ->  Union[str, Any]:
							a					=    "lower newer"
							a					=    "lower newer"
							return input_text, output_text





				def 							__UpperCAmelCase							(   self       :  Optional[Any]							)      ->  Optional[Any]:
							a					=    self.tokenizer_class(self.vocab_file      ,   self.merges_file      ,   **self.special_tokens_map							)
							a					=    "lower newer"
							a					=    ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
							a					=    tokenizer.tokenize(__lowerCamelCase							)  # , add_prefix_space=True)
							self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase							)

							a					=    tokens + [tokenizer.unk_token]
							a					=    [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase							)      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  Tuple:
							a					=    self.get_tokenizer()

							self.assertListEqual(tokenizer.encode("Hello world!"      ,   add_special_tokens=__lowerCamelCase							)      ,   [0, 3_14_14, 2_32, 3_28, 2]							)
							self.assertListEqual(
							    tokenizer.encode("Hello world! cécé herlolip 418"      ,   add_special_tokens=__lowerCamelCase							)      ,   [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]      ,   )





				@slow
				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  List[Any]:
							a					=    self.tokenizer_class.from_pretrained("allenai/longformer-base-4096"							)

							a					=    tokenizer.encode("sequence builders"      ,   add_special_tokens=__lowerCamelCase							)
							a					=    tokenizer.encode("multi-sequence build"      ,   add_special_tokens=__lowerCamelCase							)

							a					=    tokenizer.encode(
							    "sequence builders"      ,   add_special_tokens=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase							)
							a					=    tokenizer.encode(
							    "sequence builders"      ,   "multi-sequence build"      ,   add_special_tokens=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase							)

							a					=    tokenizer.build_inputs_with_special_tokens(__lowerCamelCase							)
							a					=    tokenizer.build_inputs_with_special_tokens(__lowerCamelCase      ,   __lowerCamelCase							)

							assert encoded_sentence == encoded_text_from_decode
							assert encoded_pair == encoded_pair_from_decode





				def 							__UpperCAmelCase							(   self       :  Any							)      ->  str:
							a					=    self.get_tokenizer()

							a					=    "Encode this sequence."
							a					=    tokenizer.byte_encoder[" ".encode("utf-8"							)[0]]

							# Testing encoder arguments
							a					=    tokenizer.encode(__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase							)
							a					=    tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
							self.assertNotEqual(__lowerCamelCase      ,   __lowerCamelCase							)

							a					=    tokenizer.encode(__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase							)
							a					=    tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
							self.assertEqual(__lowerCamelCase      ,   __lowerCamelCase							)

							tokenizer.add_special_tokens({"bos_token": "<s>"}							)
							a					=    tokenizer.encode(__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase							)
							a					=    tokenizer.convert_ids_to_tokens(encoded[1]							)[0]
							self.assertNotEqual(__lowerCamelCase      ,   __lowerCamelCase							)

							# Testing spaces after special tokens
							a					=    "<mask>"
							tokenizer.add_special_tokens(
							    {"mask_token": AddedToken(__lowerCamelCase      ,   lstrip=__lowerCamelCase      ,   rstrip=__lowerCamelCase							)}							)  # mask token has a left space
							a					=    tokenizer.convert_tokens_to_ids(__lowerCamelCase							)

							a					=    "Encode <mask> sequence"
							a					=    "Encode <mask>sequence"

							a					=    tokenizer.encode(__lowerCamelCase							)
							a					=    encoded.index(__lowerCamelCase							)
							a					=    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
							self.assertEqual(__lowerCamelCase      ,   __lowerCamelCase							)

							a					=    tokenizer.encode(__lowerCamelCase							)
							a					=    encoded.index(__lowerCamelCase							)
							a					=    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
							self.assertNotEqual(__lowerCamelCase      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  str							)      ->  List[str]:
							pass





				def 							__UpperCAmelCase							(   self       :  int							)      ->  int:
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
													a					=    self.rust_tokenizer_class.from_pretrained(__lowerCamelCase      ,   **__lowerCamelCase							)
													a					=    self.tokenizer_class.from_pretrained(__lowerCamelCase      ,   **__lowerCamelCase							)
													a					=    "A, <mask> AllenNLP sentence."
													a					=    tokenizer_r.encode_plus(__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase      ,   return_token_type_ids=__lowerCamelCase							)
													a					=    tokenizer_p.encode_plus(__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase      ,   return_token_type_ids=__lowerCamelCase							)

													# token_type_ids should put 0 everywhere
													self.assertEqual(sum(tokens_r["token_type_ids"]							)      ,   sum(tokens_p["token_type_ids"]							)							)

													# attention_mask should put 1 everywhere, so sum over length should be 1
													self.assertEqual(
													    sum(tokens_r["attention_mask"]							) / len(tokens_r["attention_mask"]							)      ,   sum(tokens_p["attention_mask"]							) / len(tokens_p["attention_mask"]							)      ,   )

													a					=    tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]							)
													a					=    tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]							)

													# Rust correctly handles the space before the mask while python doesnt
													self.assertSequenceEqual(tokens_p["input_ids"]      ,   [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]							)
													self.assertSequenceEqual(tokens_r["input_ids"]      ,   [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]							)

													self.assertSequenceEqual(
													    __lowerCamelCase      ,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]							)
													self.assertSequenceEqual(
													    __lowerCamelCase      ,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]							)





				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Union[str, Any]:
							for trim_offsets, add_prefix_space in itertools.product([True, False]      ,   repeat=2							):
										a					=    self.rust_tokenizer_class.from_pretrained(
										    self.tmpdirname      ,   use_fast=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase      ,   trim_offsets=__lowerCamelCase							)

										a					=    json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()							)
										a					=    json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()							)

										self.assertEqual(pre_tokenizer_state["add_prefix_space"]      ,   __lowerCamelCase							)

										self.assertEqual(post_processor_state["add_prefix_space"]      ,   __lowerCamelCase							)
										self.assertEqual(post_processor_state["trim_offsets"]      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Dict:
							# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
							# `trim_offsets`
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
													a					=    "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
													a					=    f"""{text_of_1_token} {text_of_1_token}"""

													a					=    self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase      ,   use_fast=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase      ,   trim_offsets=__lowerCamelCase							)
													a					=    tokenizer_r(__lowerCamelCase      ,   return_offsets_mapping=__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]      ,   (0, len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]      ,   (len(__lowerCamelCase							) + 1, len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))      ,   )

													a					=    self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase      ,   use_fast=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase      ,   trim_offsets=__lowerCamelCase							)
													a					=    tokenizer_r(__lowerCamelCase      ,   return_offsets_mapping=__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]      ,   (0, len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]      ,   (len(__lowerCamelCase							) + 1, len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))      ,   )

													a					=    self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase      ,   use_fast=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase      ,   trim_offsets=__lowerCamelCase							)
													a					=    tokenizer_r(__lowerCamelCase      ,   return_offsets_mapping=__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]      ,   (0, len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]      ,   (len(__lowerCamelCase							), len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))      ,   )

													a					=    self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase      ,   use_fast=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase      ,   trim_offsets=__lowerCamelCase							)
													a					=    tokenizer_r(__lowerCamelCase      ,   return_offsets_mapping=__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]      ,   (0, len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]      ,   (len(__lowerCamelCase							), len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))      ,   )

													a					=    f""" {text}"""

													# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
													#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
													# )
													# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
													# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
													# self.assertEqual(
													#     encoding.offset_mapping[1],
													#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
													# )

													a					=    self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase      ,   use_fast=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase      ,   trim_offsets=__lowerCamelCase							)
													a					=    tokenizer_r(__lowerCamelCase      ,   return_offsets_mapping=__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]      ,   (1, 1 + len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]      ,   (1 + len(__lowerCamelCase							) + 1, 1 + len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))      ,   )

													a					=    self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase      ,   use_fast=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase      ,   trim_offsets=__lowerCamelCase							)
													a					=    tokenizer_r(__lowerCamelCase      ,   return_offsets_mapping=__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]      ,   (0, 1 + len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]      ,   (1 + len(__lowerCamelCase							), 1 + len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))      ,   )

													a					=    self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase      ,   use_fast=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase      ,   trim_offsets=__lowerCamelCase							)
													a					=    tokenizer_r(__lowerCamelCase      ,   return_offsets_mapping=__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]      ,   (0, 1 + len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]      ,   (1 + len(__lowerCamelCase							), 1 + len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))      ,   )












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 					snake_case__							(unittest.TestCase     ):
				"""simple docstring"""



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





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

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

							return expected_height, expected_width




@require_torch
@require_vision
class 					snake_case__							(_UpperCamelCase					, unittest.TestCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							Tuple											=      DetaImageProcessor if is_vision_available() else None





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  List[Any]:
							a					=    DetaImageProcessingTester(self							)





				@property
				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Optional[Any]:
							return self.image_processor_tester.prepare_image_processor_dict()





				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  Tuple:
							a					=    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       :  List[str]							)      ->  Union[str, Any]:
							a					=    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      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Any							)      ->  int:
							pass





				def 							__UpperCAmelCase							(   self       :  Any							)      ->  Any:
							# Initialize image_processing
							a					=    self.image_processing_class(**self.image_processor_dict							)
							# create random PIL images
							a					=    prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=__lowerCamelCase							)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase      ,   Image.Image							)

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

							a					,	a					=    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
							a					,	a					=    self.image_processor_tester.get_expected_values(__lowerCamelCase      ,   batched=__lowerCamelCase							)

							a					=    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       :  Union[str, Any]							)      ->  Optional[Any]:
							# Initialize image_processing
							a					=    self.image_processing_class(**self.image_processor_dict							)
							# create random numpy tensors
							a					=    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
							a					=    image_processing(image_inputs[0]      ,   return_tensors="pt"							).pixel_values

							a					,	a					=    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
							a					=    image_processing(__lowerCamelCase      ,   return_tensors="pt"							).pixel_values

							a					,	a					=    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       :  Any							)      ->  List[str]:
							# Initialize image_processing
							a					=    self.image_processing_class(**self.image_processor_dict							)
							# create random PyTorch tensors
							a					=    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
							a					=    image_processing(image_inputs[0]      ,   return_tensors="pt"							).pixel_values

							a					,	a					=    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
							a					=    image_processing(__lowerCamelCase      ,   return_tensors="pt"							).pixel_values

							a					,	a					=    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       :  Any							)      ->  List[Any]:
							# prepare image and target
							a					=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
							with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"      ,   "r"							) as f:
										a					=    json.loads(f.read()							)

							a					=    {"image_id": 3_97_69, "annotations": target}

							# encode them
							a					=    DetaImageProcessor()
							a					=    image_processing(images=__lowerCamelCase      ,   annotations=__lowerCamelCase      ,   return_tensors="pt"							)

							# verify pixel values
							a					=    torch.Size([1, 3, 8_00, 10_66]							)
							self.assertEqual(encoding["pixel_values"].shape      ,   __lowerCamelCase							)

							a					=    torch.tensor([0.2_796, 0.3_138, 0.3_481]							)
							self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]      ,   __lowerCamelCase      ,   atol=1e-4							)							)

							# verify area
							a					=    torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["area"]      ,   __lowerCamelCase							)							)
							# verify boxes
							a					=    torch.Size([6, 4]							)
							self.assertEqual(encoding["labels"][0]["boxes"].shape      ,   __lowerCamelCase							)
							a					=    torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]      ,   __lowerCamelCase      ,   atol=1e-3							)							)
							# verify image_id
							a					=    torch.tensor([3_97_69]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]      ,   __lowerCamelCase							)							)
							# verify is_crowd
							a					=    torch.tensor([0, 0, 0, 0, 0, 0]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]      ,   __lowerCamelCase							)							)
							# verify class_labels
							a					=    torch.tensor([75, 75, 63, 65, 17, 17]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]      ,   __lowerCamelCase							)							)
							# verify orig_size
							a					=    torch.tensor([4_80, 6_40]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]      ,   __lowerCamelCase							)							)
							# verify size
							a					=    torch.tensor([8_00, 10_66]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"]      ,   __lowerCamelCase							)							)





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

							a					=    {"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

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

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

							# verify pixel values
							a					=    torch.Size([1, 3, 8_00, 10_66]							)
							self.assertEqual(encoding["pixel_values"].shape      ,   __lowerCamelCase							)

							a					=    torch.tensor([0.2_796, 0.3_138, 0.3_481]							)
							self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]      ,   __lowerCamelCase      ,   atol=1e-4							)							)

							# verify area
							a					=    torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["area"]      ,   __lowerCamelCase							)							)
							# verify boxes
							a					=    torch.Size([6, 4]							)
							self.assertEqual(encoding["labels"][0]["boxes"].shape      ,   __lowerCamelCase							)
							a					=    torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]      ,   __lowerCamelCase      ,   atol=1e-3							)							)
							# verify image_id
							a					=    torch.tensor([3_97_69]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]      ,   __lowerCamelCase							)							)
							# verify is_crowd
							a					=    torch.tensor([0, 0, 0, 0, 0, 0]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]      ,   __lowerCamelCase							)							)
							# verify class_labels
							a					=    torch.tensor([17, 17, 63, 75, 75, 93]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]      ,   __lowerCamelCase							)							)
							# verify masks
							a					=    82_28_73
							self.assertEqual(encoding["labels"][0]["masks"].sum().item()      ,   __lowerCamelCase							)
							# verify orig_size
							a					=    torch.tensor([4_80, 6_40]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]      ,   __lowerCamelCase							)							)
							# verify size
							a					=    torch.tensor([8_00, 10_66]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"]      ,   __lowerCamelCase							)							)






