



import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
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 transformers import OneFormerImageProcessor
												from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
												from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
						from PIL import Image





def    __UpperCamelCase      (    a,     a="shi-labs/oneformer_demo") ->str:






					with open(hf_hub_download(UpperCamelCase__,     UpperCamelCase__,     repo_type="dataset"),     "r") as f:
										lowerCamelCase__       =       json.load(UpperCamelCase__)
					lowerCamelCase__       =       {}
					lowerCamelCase__       =       []
					lowerCamelCase__       =       []
					for key, info in class_info.items():
										lowerCamelCase__       =       info["name"]
										class_names.append(info["name"])
										if info["isthing"]:
															thing_ids.append(int(UpperCamelCase__))
					lowerCamelCase__       =       thing_ids
					lowerCamelCase__       =       class_names
					return metadata

class SCREAMING_SNAKE_CASE_ (					unittest.TestCase   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase=7	,  _lowerCAmelCase=3	,  _lowerCAmelCase=30	,  _lowerCAmelCase=400	,  _lowerCAmelCase=None	,  _lowerCAmelCase=True	,  _lowerCAmelCase=True	,  _lowerCAmelCase=[0.5, 0.5, 0.5]	,  _lowerCAmelCase=[0.5, 0.5, 0.5]	,  _lowerCAmelCase=10	,  _lowerCAmelCase=False	,  _lowerCAmelCase=255	,  _lowerCAmelCase="shi-labs/oneformer_demo"	,  _lowerCAmelCase="ade20k_panoptic.json"	,  _lowerCAmelCase=10	,  ):
										lowerCamelCase__       =       parent
										lowerCamelCase__       =       batch_size
										lowerCamelCase__       =       num_channels
										lowerCamelCase__       =       min_resolution
										lowerCamelCase__       =       max_resolution
										lowerCamelCase__       =       do_resize
										lowerCamelCase__       =       {"shortest_edge": 32, "longest_edge": 1333} if size is None else size
										lowerCamelCase__       =       do_normalize
										lowerCamelCase__       =       image_mean
										lowerCamelCase__       =       image_std
										lowerCamelCase__       =       class_info_file
										lowerCamelCase__       =       prepare_metadata(_lowerCAmelCase	,  _lowerCAmelCase	)
										lowerCamelCase__       =       num_text
										lowerCamelCase__       =       repo_path

										# for the post_process_functions
										lowerCamelCase__       =       2
										lowerCamelCase__       =       10
										lowerCamelCase__       =       10
										lowerCamelCase__       =       3
										lowerCamelCase__       =       4
										lowerCamelCase__       =       num_labels
										lowerCamelCase__       =       do_reduce_labels
										lowerCamelCase__       =       ignore_index





					def 					__magic_name__ ( 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,
										    "num_labels": self.num_labels,
										    "do_reduce_labels": self.do_reduce_labels,
										    "ignore_index": self.ignore_index,
										    "class_info_file": self.class_info_file,
										    "metadata": self.metadata,
										    "num_text": self.num_text,
										}





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

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

										return expected_height, expected_width






					def 					__magic_name__ ( self	):
										return OneFormerForUniversalSegmentationOutput(
										    # +1 for null class
										    class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)	)	,  masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)	)	,  )

@require_torch
@require_vision
class SCREAMING_SNAKE_CASE_ (					lowercase_							,       unittest.TestCase   ):





					"""simple docstring"""


					A__				 =    OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
					# only for test_image_processing_common.test_image_proc_to_json_string
					A__				 =    image_processing_class





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       OneFormerImageProcessorTester(self	)





					@property
					def 					__magic_name__ ( self	):
										return self.image_processing_tester.prepare_image_processor_dict()





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       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	,  "size"	)	)
										self.assertTrue(hasattr(_lowerCAmelCase	,  "ignore_index"	)	)
										self.assertTrue(hasattr(_lowerCAmelCase	,  "class_info_file"	)	)
										self.assertTrue(hasattr(_lowerCAmelCase	,  "num_text"	)	)
										self.assertTrue(hasattr(_lowerCAmelCase	,  "repo_path"	)	)
										self.assertTrue(hasattr(_lowerCAmelCase	,  "metadata"	)	)
										self.assertTrue(hasattr(_lowerCAmelCase	,  "do_reduce_labels"	)	)





					def 					__magic_name__ ( self	):
										pass





					def 					__magic_name__ ( self	):
										# Initialize image_processor
										lowerCamelCase__       =       self.image_processing_class(**self.image_processor_dict	)
										# create random PIL images
										lowerCamelCase__       =       prepare_image_inputs(self.image_processing_tester	,  equal_resolution=_lowerCAmelCase	)
										for image in image_inputs:
															self.assertIsInstance(_lowerCAmelCase	,  Image.Image	)

										# Test not batched input
										lowerCamelCase__       =       image_processor(image_inputs[0]	,  ["semantic"]	,  return_tensors="pt"	).pixel_values

										lowerCamelCase__    ,		lowerCamelCase__       =       self.image_processing_tester.get_expected_values(_lowerCAmelCase	)

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

										# Test batched
										lowerCamelCase__    ,		lowerCamelCase__       =       self.image_processing_tester.get_expected_values(_lowerCAmelCase	,  batched=_lowerCAmelCase	)

										lowerCamelCase__       =       image_processor(
										    _lowerCAmelCase	,  ["semantic"] * len(_lowerCAmelCase	)	,  return_tensors="pt"	).pixel_values
										self.assertEqual(
										    encoded_images.shape	,  (
										        self.image_processing_tester.batch_size,
										        self.image_processing_tester.num_channels,
										        expected_height,
										        expected_width,
										    )	,  )





					def 					__magic_name__ ( self	):
										# Initialize image_processor
										lowerCamelCase__       =       self.image_processing_class(**self.image_processor_dict	)
										# create random numpy tensors
										lowerCamelCase__       =       prepare_image_inputs(self.image_processing_tester	,  equal_resolution=_lowerCAmelCase	,  numpify=_lowerCAmelCase	)
										for image in image_inputs:
															self.assertIsInstance(_lowerCAmelCase	,  np.ndarray	)

										# Test not batched input
										lowerCamelCase__       =       image_processor(image_inputs[0]	,  ["semantic"]	,  return_tensors="pt"	).pixel_values

										lowerCamelCase__    ,		lowerCamelCase__       =       self.image_processing_tester.get_expected_values(_lowerCAmelCase	)

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

										# Test batched
										lowerCamelCase__    ,		lowerCamelCase__       =       self.image_processing_tester.get_expected_values(_lowerCAmelCase	,  batched=_lowerCAmelCase	)

										lowerCamelCase__       =       image_processor(
										    _lowerCAmelCase	,  ["semantic"] * len(_lowerCAmelCase	)	,  return_tensors="pt"	).pixel_values
										self.assertEqual(
										    encoded_images.shape	,  (
										        self.image_processing_tester.batch_size,
										        self.image_processing_tester.num_channels,
										        expected_height,
										        expected_width,
										    )	,  )





					def 					__magic_name__ ( self	):
										# Initialize image_processor
										lowerCamelCase__       =       self.image_processing_class(**self.image_processor_dict	)
										# create random PyTorch tensors
										lowerCamelCase__       =       prepare_image_inputs(self.image_processing_tester	,  equal_resolution=_lowerCAmelCase	,  torchify=_lowerCAmelCase	)
										for image in image_inputs:
															self.assertIsInstance(_lowerCAmelCase	,  torch.Tensor	)

										# Test not batched input
										lowerCamelCase__       =       image_processor(image_inputs[0]	,  ["semantic"]	,  return_tensors="pt"	).pixel_values

										lowerCamelCase__    ,		lowerCamelCase__       =       self.image_processing_tester.get_expected_values(_lowerCAmelCase	)

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

										# Test batched
										lowerCamelCase__    ,		lowerCamelCase__       =       self.image_processing_tester.get_expected_values(_lowerCAmelCase	,  batched=_lowerCAmelCase	)

										lowerCamelCase__       =       image_processor(
										    _lowerCAmelCase	,  ["semantic"] * len(_lowerCAmelCase	)	,  return_tensors="pt"	).pixel_values
										self.assertEqual(
										    encoded_images.shape	,  (
										        self.image_processing_tester.batch_size,
										        self.image_processing_tester.num_channels,
										        expected_height,
										        expected_width,
										    )	,  )





					def 					__magic_name__ ( self	,  _lowerCAmelCase=False	,  _lowerCAmelCase=False	,  _lowerCAmelCase="np"	):
										lowerCamelCase__       =       self.image_processing_class(**self.image_processor_dict	)
										# prepare image and target
										lowerCamelCase__       =       self.image_processing_tester.num_labels
										lowerCamelCase__       =       None
										lowerCamelCase__       =       None
										lowerCamelCase__       =       prepare_image_inputs(self.image_processing_tester	,  equal_resolution=_lowerCAmelCase	)
										if with_segmentation_maps:
															lowerCamelCase__       =       num_labels
															if is_instance_map:
																				lowerCamelCase__       =       list(range(_lowerCAmelCase	)	) * 2
																				lowerCamelCase__       =       dict(enumerate(_lowerCAmelCase	)	)
															lowerCamelCase__       =       [
															    np.random.randint(0	,  high * 2	,  (img.size[1], img.size[0])	).astype(np.uinta	) for img in image_inputs
															]
															if segmentation_type == "pil":
																				lowerCamelCase__       =       [Image.fromarray(_lowerCAmelCase	) for annotation in annotations]

										lowerCamelCase__       =       image_processor(
										    _lowerCAmelCase	,  ["semantic"] * len(_lowerCAmelCase	)	,  _lowerCAmelCase	,  return_tensors="pt"	,  instance_id_to_semantic_id=_lowerCAmelCase	,  pad_and_return_pixel_mask=_lowerCAmelCase	,  )

										return inputs





					def 					__magic_name__ ( self	):
										pass





					def 					__magic_name__ ( self	):
										def common(_lowerCAmelCase=False	,  _lowerCAmelCase=None	):
															lowerCamelCase__       =       self.comm_get_image_processor_inputs(
															    with_segmentation_maps=_lowerCAmelCase	,  is_instance_map=_lowerCAmelCase	,  segmentation_type=_lowerCAmelCase	)

															lowerCamelCase__       =       inputs["mask_labels"]
															lowerCamelCase__       =       inputs["class_labels"]
															lowerCamelCase__       =       inputs["pixel_values"]
															lowerCamelCase__       =       inputs["text_inputs"]

															# check the batch_size
															for mask_label, class_label, text_input in zip(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
																				self.assertEqual(mask_label.shape[0]	,  class_label.shape[0]	)
																				# this ensure padding has happened
																				self.assertEqual(mask_label.shape[1:]	,  pixel_values.shape[2:]	)
																				self.assertEqual(len(_lowerCAmelCase	)	,  self.image_processing_tester.num_text	)

										common()
										common(is_instance_map=_lowerCAmelCase	)
										common(is_instance_map=_lowerCAmelCase	,  segmentation_type="pil"	)
										common(is_instance_map=_lowerCAmelCase	,  segmentation_type="pil"	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       np.zeros((20, 50)	)
										lowerCamelCase__       =       1
										lowerCamelCase__       =       1
										lowerCamelCase__       =       1

										lowerCamelCase__       =       binary_mask_to_rle(_lowerCAmelCase	)
										self.assertEqual(len(_lowerCAmelCase	)	,  4	)
										self.assertEqual(rle[0]	,  21	)
										self.assertEqual(rle[1]	,  45	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.image_processing_class(
										    num_labels=self.image_processing_tester.num_classes	,  max_seq_length=77	,  task_seq_length=77	,  class_info_file="ade20k_panoptic.json"	,  num_text=self.image_processing_tester.num_text	,  repo_path="shi-labs/oneformer_demo"	,  )
										lowerCamelCase__       =       self.image_processing_tester.get_fake_oneformer_outputs()

										lowerCamelCase__       =       fature_extractor.post_process_semantic_segmentation(_lowerCAmelCase	)

										self.assertEqual(len(_lowerCAmelCase	)	,  self.image_processing_tester.batch_size	)
										self.assertEqual(
										    segmentation[0].shape	,  (
										        self.image_processing_tester.height,
										        self.image_processing_tester.width,
										    )	,  )

										lowerCamelCase__       =       [(1, 4) for i in range(self.image_processing_tester.batch_size	)]
										lowerCamelCase__       =       fature_extractor.post_process_semantic_segmentation(_lowerCAmelCase	,  target_sizes=_lowerCAmelCase	)

										self.assertEqual(segmentation[0].shape	,  target_sizes[0]	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.image_processing_class(
										    num_labels=self.image_processing_tester.num_classes	,  max_seq_length=77	,  task_seq_length=77	,  class_info_file="ade20k_panoptic.json"	,  num_text=self.image_processing_tester.num_text	,  repo_path="shi-labs/oneformer_demo"	,  )
										lowerCamelCase__       =       self.image_processing_tester.get_fake_oneformer_outputs()
										lowerCamelCase__       =       image_processor.post_process_instance_segmentation(_lowerCAmelCase	,  threshold=0	)

										self.assertTrue(len(_lowerCAmelCase	) == self.image_processing_tester.batch_size	)
										for el in segmentation:
															self.assertTrue("segmentation" in el	)
															self.assertTrue("segments_info" in el	)
															self.assertEqual(type(el["segments_info"]	)	,  _lowerCAmelCase	)
															self.assertEqual(
															    el["segmentation"].shape	,  (self.image_processing_tester.height, self.image_processing_tester.width)	)






					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.image_processing_class(
										    num_labels=self.image_processing_tester.num_classes	,  max_seq_length=77	,  task_seq_length=77	,  class_info_file="ade20k_panoptic.json"	,  num_text=self.image_processing_tester.num_text	,  repo_path="shi-labs/oneformer_demo"	,  )
										lowerCamelCase__       =       self.image_processing_tester.get_fake_oneformer_outputs()
										lowerCamelCase__       =       image_processor.post_process_panoptic_segmentation(_lowerCAmelCase	,  threshold=0	)

										self.assertTrue(len(_lowerCAmelCase	) == self.image_processing_tester.batch_size	)
										for el in segmentation:
															self.assertTrue("segmentation" in el	)
															self.assertTrue("segments_info" in el	)
															self.assertEqual(type(el["segments_info"]	)	,  _lowerCAmelCase	)
															self.assertEqual(
															    el["segmentation"].shape	,  (self.image_processing_tester.height, self.image_processing_tester.width)	)








import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor

@require_tokenizers
@require_vision
class SCREAMING_SNAKE_CASE_ (					unittest.TestCase   ):





					"""simple docstring"""


					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       tempfile.mkdtemp()

										# fmt: off
										lowerCamelCase__       =       ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
										# fmt: on
										lowerCamelCase__       =       os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES["vocab_file"]	)
										with open(self.vocab_file	,  "w"	,  encoding="utf-8"	) as vocab_writer:
															vocab_writer.write("".join([x + "\n" for x in vocab_tokens]	)	)

										lowerCamelCase__       =       {
										    "do_resize": True,
										    "size": {"height": 18, "width": 18},
										    "do_normalize": True,
										    "image_mean": [0.5, 0.5, 0.5],
										    "image_std": [0.5, 0.5, 0.5],
										}
										lowerCamelCase__       =       os.path.join(self.tmpdirname	,  _lowerCAmelCase	)
										with open(self.image_processor_file	,  "w"	,  encoding="utf-8"	) as fp:
															json.dump(_lowerCAmelCase	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										return BertTokenizer.from_pretrained(self.tmpdirname	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										return ViTImageProcessor.from_pretrained(self.tmpdirname	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										shutil.rmtree(self.tmpdirname	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       [np.random.randint(255	,  size=(3, 30, 400)	,  dtype=np.uinta	)]

										lowerCamelCase__       =       [Image.fromarray(np.moveaxis(_lowerCAmelCase	,  0	,  -1	)	) for x in image_inputs]

										return image_inputs





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_tokenizer()
										lowerCamelCase__       =       self.get_image_processor()

										lowerCamelCase__       =       VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										processor.save_pretrained(self.tmpdirname	)
										lowerCamelCase__       =       VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname	)

										self.assertEqual(processor.tokenizer.get_vocab()	,  tokenizer.get_vocab()	)
										self.assertIsInstance(processor.tokenizer	,  (BertTokenizer, BertTokenizerFast)	)

										self.assertEqual(processor.image_processor.to_json_string()	,  image_processor.to_json_string()	)
										self.assertIsInstance(processor.image_processor	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       VisionTextDualEncoderProcessor(
										    tokenizer=self.get_tokenizer()	,  image_processor=self.get_image_processor()	)
										processor.save_pretrained(self.tmpdirname	)

										lowerCamelCase__       =       self.get_tokenizer(bos_token="(BOS)"	,  eos_token="(EOS)"	)
										lowerCamelCase__       =       self.get_image_processor(do_normalize=_lowerCAmelCase	,  padding_value=1.0	)

										lowerCamelCase__       =       VisionTextDualEncoderProcessor.from_pretrained(
										    self.tmpdirname	,  bos_token="(BOS)"	,  eos_token="(EOS)"	,  do_normalize=_lowerCAmelCase	,  padding_value=1.0	)

										self.assertEqual(processor.tokenizer.get_vocab()	,  tokenizer_add_kwargs.get_vocab()	)
										self.assertIsInstance(processor.tokenizer	,  (BertTokenizer, BertTokenizerFast)	)

										self.assertEqual(processor.image_processor.to_json_string()	,  image_processor_add_kwargs.to_json_string()	)
										self.assertIsInstance(processor.image_processor	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       self.prepare_image_inputs()

										lowerCamelCase__       =       image_processor(_lowerCAmelCase	,  return_tensors="np"	)
										lowerCamelCase__       =       processor(images=_lowerCAmelCase	,  return_tensors="np"	)

										for key in input_feat_extract.keys():
															self.assertAlmostEqual(input_feat_extract[key].sum()	,  input_processor[key].sum()	,  delta=1E-2	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       "lower newer"

										lowerCamelCase__       =       processor(text=_lowerCAmelCase	)

										lowerCamelCase__       =       tokenizer(_lowerCAmelCase	)

										for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]	,  encoded_processor[key]	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       "lower newer"
										lowerCamelCase__       =       self.prepare_image_inputs()

										lowerCamelCase__       =       processor(text=_lowerCAmelCase	,  images=_lowerCAmelCase	)

										self.assertListEqual(list(inputs.keys()	)	,  ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]	)

										# test if it raises when no input is passed
										with self.assertRaises(_lowerCAmelCase	):
															processor()





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										lowerCamelCase__       =       processor.batch_decode(_lowerCAmelCase	)
										lowerCamelCase__       =       tokenizer.batch_decode(_lowerCAmelCase	)

										self.assertListEqual(_lowerCAmelCase	,  _lowerCAmelCase	)






					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       "lower newer"
										lowerCamelCase__       =       self.prepare_image_inputs()

										lowerCamelCase__       =       processor(text=_lowerCAmelCase	,  images=_lowerCAmelCase	)

										self.assertListEqual(list(inputs.keys()	)	,  processor.model_input_names	)

