"""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  _snake_case     (					unittest.TestCase		):






						def __init__(       self      :			Tuple							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			List[Any]=7							,					UpperCAmelCase      :			Optional[int]=3							,					UpperCAmelCase      :			int=30							,					UpperCAmelCase      :			Optional[int]=400							,					UpperCAmelCase      :			Optional[Any]=True							,					UpperCAmelCase      :			Optional[Any]=None							,					UpperCAmelCase      :			Tuple=True							,					UpperCAmelCase      :			Optional[int]=[0.5, 0.5, 0.5]							,					UpperCAmelCase      :			int=[0.5, 0.5, 0.5]							,					UpperCAmelCase      :			int=True							,					UpperCAmelCase      :			List[str]=1 / 255							,					UpperCAmelCase      :			str=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					:		Any		   =     parent
													__lowerCamelCase					:		Optional[int]		   =     batch_size
													__lowerCamelCase					:		Dict		   =     num_channels
													__lowerCamelCase					:		List[Any]		   =     min_resolution
													__lowerCamelCase					:		List[str]		   =     max_resolution
													__lowerCamelCase					:		Optional[int]		   =     do_resize
													__lowerCamelCase					:		Dict		   =     size
													__lowerCamelCase					:		Optional[Any]		   =     do_normalize
													__lowerCamelCase					:		Dict		   =     image_mean
													__lowerCamelCase					:		int		   =     image_std
													__lowerCamelCase					:		Optional[int]		   =     do_rescale
													__lowerCamelCase					:		List[str]		   =     rescale_factor
													__lowerCamelCase					:		Union[str, Any]		   =     do_pad






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

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

													return expected_height, expected_width

@require_torch
@require_vision
class  _snake_case     (					a__				, unittest.TestCase		):
						snake_case__			  =			DetaImageProcessor if is_vision_available() else None






						def       lowerCamelCase__     (       self      :			Tuple    ):
													__lowerCamelCase					:		Optional[int]		   =     DetaImageProcessingTester(self    )






						@property
						def       lowerCamelCase__     (       self      :			int    ):
													return self.image_processor_tester.prepare_image_processor_dict()






						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													__lowerCamelCase					:		List[str]		   =     self.image_processing_class(**self.image_processor_dict    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"image_mean"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"image_std"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_normalize"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_resize"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_rescale"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_pad"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"size"    )    )






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		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							,					UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Dict    ):
													pass






						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													# Initialize image_processing
													__lowerCamelCase					:		Any		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random PIL images
													__lowerCamelCase					:		str		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					Image.Image    )

													# Test not batched input
													__lowerCamelCase					:		Union[str, Any]		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values

													__lowerCamelCase					:		Optional[int]		   =     self.image_processor_tester.get_expected_values(UpperCAmelCase    )

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

													# Test batched
													__lowerCamelCase					:		Tuple		   =     self.image_processor_tester.get_expected_values(UpperCAmelCase							,					batched=UpperCAmelCase    )

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

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

													__lowerCamelCase					:		Any		   =     self.image_processor_tester.get_expected_values(UpperCAmelCase    )

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

													# Test batched
													__lowerCamelCase					:		List[str]		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values

													__lowerCamelCase					:		Tuple		   =     self.image_processor_tester.get_expected_values(UpperCAmelCase							,					batched=UpperCAmelCase    )

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






						def       lowerCamelCase__     (       self      :			int    ):
													# Initialize image_processing
													__lowerCamelCase					:		List[str]		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random PyTorch tensors
													__lowerCamelCase					:		str		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase							,					torchify=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					torch.Tensor    )

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

													__lowerCamelCase					:		List[Any]		   =     self.image_processor_tester.get_expected_values(UpperCAmelCase    )

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

													# Test batched
													__lowerCamelCase					:		List[Any]		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values

													__lowerCamelCase					:		List[Any]		   =     self.image_processor_tester.get_expected_values(UpperCAmelCase							,					batched=UpperCAmelCase    )

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






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

													__lowerCamelCase					:		Dict		   =     {"image_id": 39769, "annotations": target}

													# encode them
													__lowerCamelCase					:		Dict		   =     DetaImageProcessor()
													__lowerCamelCase					:		Tuple		   =     image_processing(images=UpperCAmelCase							,					annotations=UpperCAmelCase							,					return_tensors="pt"    )

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

													__lowerCamelCase					:		List[Any]		   =     torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]    )
													self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,					UpperCAmelCase							,					atol=1E-4    )    )

													# verify area
													__lowerCamelCase					:		List[Any]		   =     torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["area"]							,					UpperCAmelCase    )    )
													# verify boxes
													__lowerCamelCase					:		Optional[Any]		   =     torch.Size([6, 4]    )
													self.assertEqual(encoding["labels"][0]["boxes"].shape							,					UpperCAmelCase    )
													__lowerCamelCase					:		int		   =     torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,					UpperCAmelCase							,					atol=1E-3    )    )
													# verify image_id
													__lowerCamelCase					:		Union[str, Any]		   =     torch.tensor([39769]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,					UpperCAmelCase    )    )
													# verify is_crowd
													__lowerCamelCase					:		Any		   =     torch.tensor([0, 0, 0, 0, 0, 0]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,					UpperCAmelCase    )    )
													# verify class_labels
													__lowerCamelCase					:		Tuple		   =     torch.tensor([75, 75, 63, 65, 17, 17]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,					UpperCAmelCase    )    )
													# verify orig_size
													__lowerCamelCase					:		Tuple		   =     torch.tensor([480, 640]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,					UpperCAmelCase    )    )
													# verify size
													__lowerCamelCase					:		Union[str, Any]		   =     torch.tensor([800, 1066]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,					UpperCAmelCase    )    )







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

													__lowerCamelCase					:		Any		   =     {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

													__lowerCamelCase					:		Tuple		   =     pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"    )

													# encode them
													__lowerCamelCase					:		Optional[int]		   =     DetaImageProcessor(format="coco_panoptic"    )
													__lowerCamelCase					:		Tuple		   =     image_processing(images=UpperCAmelCase							,					annotations=UpperCAmelCase							,					masks_path=UpperCAmelCase							,					return_tensors="pt"    )

													# verify pixel values
													__lowerCamelCase					:		int		   =     torch.Size([1, 3, 800, 1066]    )
													self.assertEqual(encoding["pixel_values"].shape							,					UpperCAmelCase    )

													__lowerCamelCase					:		Dict		   =     torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]    )
													self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,					UpperCAmelCase							,					atol=1E-4    )    )

													# verify area
													__lowerCamelCase					:		Optional[Any]		   =     torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["area"]							,					UpperCAmelCase    )    )
													# verify boxes
													__lowerCamelCase					:		Any		   =     torch.Size([6, 4]    )
													self.assertEqual(encoding["labels"][0]["boxes"].shape							,					UpperCAmelCase    )
													__lowerCamelCase					:		int		   =     torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,					UpperCAmelCase							,					atol=1E-3    )    )
													# verify image_id
													__lowerCamelCase					:		List[Any]		   =     torch.tensor([39769]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,					UpperCAmelCase    )    )
													# verify is_crowd
													__lowerCamelCase					:		Any		   =     torch.tensor([0, 0, 0, 0, 0, 0]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,					UpperCAmelCase    )    )
													# verify class_labels
													__lowerCamelCase					:		int		   =     torch.tensor([17, 17, 63, 75, 75, 93]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,					UpperCAmelCase    )    )
													# verify masks
													__lowerCamelCase					:		List[Any]		   =     822873
													self.assertEqual(encoding["labels"][0]["masks"].sum().item()							,					UpperCAmelCase    )
													# verify orig_size
													__lowerCamelCase					:		List[str]		   =     torch.tensor([480, 640]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,					UpperCAmelCase    )    )
													# verify size
													__lowerCamelCase					:		Union[str, Any]		   =     torch.tensor([800, 1066]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,					UpperCAmelCase    )    )






"""simple docstring"""





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
				import torch


if is_vision_available():
				from PIL import Image

				from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class  _snake_case     (					unittest.TestCase		):
						snake_case__			  =			ViTImageProcessor if is_vision_available() else None






						@property
						def       lowerCamelCase__     (       self      :			List[Any]    ):
													return self.image_processor_tester.prepare_image_processor_dict()






						def       lowerCamelCase__     (       self      :			Tuple    ):
													__lowerCamelCase					:		str		   =     (3, 32, 128)
													__lowerCamelCase					:		Optional[int]		   =     tempfile.mkdtemp()

													# fmt: off
													__lowerCamelCase					:		Tuple		   =     ["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
													# fmt: on
													__lowerCamelCase					:		Dict		   =     dict(zip(UpperCAmelCase							,					range(len(UpperCAmelCase    )    )    )    )

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

													__lowerCamelCase					:		int		   =     {
													    "do_normalize": False,
													    "do_resize": True,
													    "image_processor_type": "ViTImageProcessor",
													    "resample": 3,
													    "size": {"height": 32, "width": 128},
													}
													__lowerCamelCase					:		Dict		   =     os.path.join(self.tmpdirname							,					UpperCAmelCase    )
													with open(self.image_processor_file							,					"w"							,					encoding="utf-8"    ) as fp:
																				json.dump(UpperCAmelCase							,					UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Tuple							,					**UpperCAmelCase      :			Optional[int]    ):
													return MgpstrTokenizer.from_pretrained(self.tmpdirname							,					**UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			List[str]							,					**UpperCAmelCase      :			List[str]    ):
													return ViTImageProcessor.from_pretrained(self.tmpdirname							,					**UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Tuple    ):
													shutil.rmtree(self.tmpdirname    )






						def       lowerCamelCase__     (       self      :			str    ):
													__lowerCamelCase					:		Any		   =     np.random.randint(255							,					size=(3, 30, 400)							,					dtype=np.uinta    )

													__lowerCamelCase					:		Union[str, Any]		   =     Image.fromarray(np.moveaxis(UpperCAmelCase							,					0							,					-1    )    )

													return image_input






						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     self.get_tokenizer()
													__lowerCamelCase					:		Any		   =     self.get_image_processor()

													__lowerCamelCase					:		List[Any]		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )
													processor.save_pretrained(self.tmpdirname    )
													__lowerCamelCase					:		str		   =     MgpstrProcessor.from_pretrained(self.tmpdirname							,					use_fast=UpperCAmelCase    )

													self.assertEqual(processor.char_tokenizer.get_vocab()							,					tokenizer.get_vocab()    )
													self.assertIsInstance(processor.char_tokenizer							,					UpperCAmelCase    )

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






						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													__lowerCamelCase					:		Any		   =     self.get_tokenizer()
													__lowerCamelCase					:		Dict		   =     self.get_image_processor()

													__lowerCamelCase					:		Optional[int]		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )
													processor.save_pretrained(self.tmpdirname    )

													__lowerCamelCase					:		Dict		   =     self.get_tokenizer(bos_token="(BOS)"							,					eos_token="(EOS)"    )
													__lowerCamelCase					:		Any		   =     self.get_image_processor(do_normalize=UpperCAmelCase							,					padding_value=1.0    )

													__lowerCamelCase					:		List[str]		   =     MgpstrProcessor.from_pretrained(
													    self.tmpdirname							,					bos_token="(BOS)"							,					eos_token="(EOS)"							,					do_normalize=UpperCAmelCase							,					padding_value=1.0    )

													self.assertEqual(processor.char_tokenizer.get_vocab()							,					tokenizer_add_kwargs.get_vocab()    )
													self.assertIsInstance(processor.char_tokenizer							,					UpperCAmelCase    )

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






						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													__lowerCamelCase					:		List[str]		   =     self.get_image_processor()
													__lowerCamelCase					:		Dict		   =     self.get_tokenizer()

													__lowerCamelCase					:		Optional[int]		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )

													__lowerCamelCase					:		Optional[int]		   =     self.prepare_image_inputs()

													__lowerCamelCase					:		Optional[int]		   =     image_processor(UpperCAmelCase							,					return_tensors="np"    )
													__lowerCamelCase					:		Tuple		   =     processor(images=UpperCAmelCase							,					return_tensors="np"    )

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






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase					:		int		   =     self.get_image_processor()
													__lowerCamelCase					:		Any		   =     self.get_tokenizer()

													__lowerCamelCase					:		int		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )

													__lowerCamelCase					:		Optional[Any]		   =     "test"

													__lowerCamelCase					:		List[Any]		   =     processor(text=UpperCAmelCase    )

													__lowerCamelCase					:		Optional[int]		   =     tokenizer(UpperCAmelCase    )
													for key in encoded_tok.keys():
																				self.assertListEqual(encoded_tok[key]							,					encoded_processor[key]    )






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     self.get_image_processor()
													__lowerCamelCase					:		Optional[Any]		   =     self.get_tokenizer()

													__lowerCamelCase					:		Optional[int]		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )

													__lowerCamelCase					:		str		   =     "test"
													__lowerCamelCase					:		Union[str, Any]		   =     self.prepare_image_inputs()

													__lowerCamelCase					:		Optional[Any]		   =     processor(text=UpperCAmelCase							,					images=UpperCAmelCase    )

													self.assertListEqual(list(inputs.keys()    )							,					["pixel_values", "labels"]    )

													# test if it raises when no input is passed
													with pytest.raises(UpperCAmelCase    ):
																				processor()






						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													__lowerCamelCase					:		Any		   =     self.get_image_processor()
													__lowerCamelCase					:		Optional[Any]		   =     self.get_tokenizer()

													__lowerCamelCase					:		Dict		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )

													__lowerCamelCase					:		Optional[Any]		   =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

													__lowerCamelCase					:		List[Any]		   =     processor.char_decode(UpperCAmelCase    )
													__lowerCamelCase					:		Dict		   =     tokenizer.batch_decode(UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     [seq.replace(" "							,					""    ) for seq in decoded_tok]

													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		str		   =     self.get_image_processor()
													__lowerCamelCase					:		str		   =     self.get_tokenizer()

													__lowerCamelCase					:		Union[str, Any]		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )

													__lowerCamelCase					:		Tuple		   =     None
													__lowerCamelCase					:		int		   =     self.prepare_image_inputs()

													__lowerCamelCase					:		List[str]		   =     processor(text=UpperCAmelCase							,					images=UpperCAmelCase    )

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







						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													__lowerCamelCase					:		Optional[Any]		   =     self.get_image_processor()
													__lowerCamelCase					:		Tuple		   =     self.get_tokenizer()

													__lowerCamelCase					:		List[Any]		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )

													__lowerCamelCase					:		Optional[int]		   =     torch.randn(1							,					27							,					38    )
													__lowerCamelCase					:		List[str]		   =     torch.randn(1							,					27							,					50257    )
													__lowerCamelCase					:		Union[str, Any]		   =     torch.randn(1							,					27							,					30522    )

													__lowerCamelCase					:		str		   =     processor.batch_decode([char_input, bpe_input, wp_input]    )

													self.assertListEqual(list(results.keys()    )							,					["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"]    )