

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 YolosImageProcessor

class 			a					(		unittest.TestCase			):


						def __init__(    self  ,   A_  ,   A_=7  ,   A_=3  ,   A_=30  ,   A_=400  ,   A_=True  ,   A_=None  ,   A_=True  ,   A_=[0.5, 0.5, 0.5]  ,   A_=[0.5, 0.5, 0.5]  ,   A_=True  ,   A_=1 / 255  ,   A_=True  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
										_UpperCAmelCase    :						Union[str, Any]							=      parent
										_UpperCAmelCase    :						Optional[int]							=      batch_size
										_UpperCAmelCase    :						Any							=      num_channels
										_UpperCAmelCase    :						int							=      min_resolution
										_UpperCAmelCase    :						Dict							=      max_resolution
										_UpperCAmelCase    :						Tuple							=      do_resize
										_UpperCAmelCase    :						List[str]							=      size
										_UpperCAmelCase    :						Any							=      do_normalize
										_UpperCAmelCase    :						Optional[Any]							=      image_mean
										_UpperCAmelCase    :						Optional[int]							=      image_std
										_UpperCAmelCase    :						str							=      do_rescale
										_UpperCAmelCase    :						str							=      rescale_factor
										_UpperCAmelCase    :						List[Any]							=      do_pad


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										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  ,   A_  ,   A_=False		):







										'''simple docstring'''


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

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

										return expected_height, expected_width







@require_torch
@require_vision
class 			a					(		UpperCAmelCase							,	unittest.TestCase			):
						_lowercase        =     YolosImageProcessor if is_vision_available() else None


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      YolosImageProcessingTester(self		)


						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return self.image_processor_tester.prepare_image_processor_dict()


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      self.image_processing_class(**self.image_processor_dict		)
										self.assertTrue(hasattr(A_  ,   "image_mean"		)		)
										self.assertTrue(hasattr(A_  ,   "image_std"		)		)
										self.assertTrue(hasattr(A_  ,   "do_normalize"		)		)
										self.assertTrue(hasattr(A_  ,   "do_resize"		)		)
										self.assertTrue(hasattr(A_  ,   "size"		)		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      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  ,   A_		)

										_UpperCAmelCase    :						Optional[Any]							=      self.image_processing_class.from_dict(
										    self.image_processor_dict  ,   size=42  ,   max_size=84  ,   pad_and_return_pixel_mask=A_		)
										self.assertEqual(image_processor.size  ,   {"shortest_edge": 42, "longest_edge": 84}		)
										self.assertEqual(image_processor.do_pad  ,   A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      self.image_processing_class(**self.image_processor_dict		)
										# create random PIL images
										_UpperCAmelCase    :						Union[str, Any]							=      prepare_image_inputs(self.image_processor_tester  ,   equal_resolution=A_		)
										for image in image_inputs:
														self.assertIsInstance(A_  ,   Image.Image		)

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

										_UpperCAmelCase						,							_UpperCAmelCase    :						str							=      self.image_processor_tester.get_expected_values(A_		)

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

										# Test batched
										_UpperCAmelCase						,							_UpperCAmelCase    :						Tuple							=      self.image_processor_tester.get_expected_values(A_  ,   batched=A_		)

										_UpperCAmelCase    :						Any							=      image_processing(A_  ,   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		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      self.image_processing_class(**self.image_processor_dict		)
										# create random numpy tensors
										_UpperCAmelCase    :						Dict							=      prepare_image_inputs(self.image_processor_tester  ,   equal_resolution=A_  ,   numpify=A_		)
										for image in image_inputs:
														self.assertIsInstance(A_  ,   np.ndarray		)

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

										_UpperCAmelCase						,							_UpperCAmelCase    :						Any							=      self.image_processor_tester.get_expected_values(A_		)

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

										# Test batched
										_UpperCAmelCase    :						Any							=      image_processing(A_  ,   return_tensors="pt"		).pixel_values

										_UpperCAmelCase						,							_UpperCAmelCase    :						Optional[Any]							=      self.image_processor_tester.get_expected_values(A_  ,   batched=A_		)

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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      self.image_processing_class(**self.image_processor_dict		)
										# create random PyTorch tensors
										_UpperCAmelCase    :						Optional[int]							=      prepare_image_inputs(self.image_processor_tester  ,   equal_resolution=A_  ,   torchify=A_		)
										for image in image_inputs:
														self.assertIsInstance(A_  ,   torch.Tensor		)

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

										_UpperCAmelCase						,							_UpperCAmelCase    :						List[str]							=      self.image_processor_tester.get_expected_values(A_		)

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

										# Test batched
										_UpperCAmelCase    :						Tuple							=      image_processing(A_  ,   return_tensors="pt"		).pixel_values

										_UpperCAmelCase						,							_UpperCAmelCase    :						Dict							=      self.image_processor_tester.get_expected_values(A_  ,   batched=A_		)

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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.image_processing_class(**self.image_processor_dict		)
										_UpperCAmelCase    :						Union[str, Any]							=      self.image_processing_class(do_resize=A_  ,   do_normalize=A_  ,   do_rescale=A_		)
										# create random PyTorch tensors
										_UpperCAmelCase    :						Any							=      prepare_image_inputs(self.image_processor_tester  ,   equal_resolution=A_  ,   torchify=A_		)
										for image in image_inputs:
														self.assertIsInstance(A_  ,   torch.Tensor		)

										# Test whether the method "pad" and calling the image processor return the same tensors
										_UpperCAmelCase    :						Optional[int]							=      image_processing_a.pad(A_  ,   return_tensors="pt"		)
										_UpperCAmelCase    :						List[str]							=      image_processing_a(A_  ,   return_tensors="pt"		)

										self.assertTrue(
										    torch.allclose(encoded_images_with_method["pixel_values"]  ,   encoded_images["pixel_values"]  ,   atol=1e-4		)		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
										with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"  ,   "r"		) as f:
														_UpperCAmelCase    :						Optional[Any]							=      json.loads(f.read()		)

										_UpperCAmelCase    :						Union[str, Any]							=      {"image_id": 39769, "annotations": target}

										# encode them
										_UpperCAmelCase    :						Union[str, Any]							=      YolosImageProcessor.from_pretrained("hustvl/yolos-small"		)
										_UpperCAmelCase    :						Optional[int]							=      image_processing(images=A_  ,   annotations=A_  ,   return_tensors="pt"		)

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

										_UpperCAmelCase    :						Optional[Any]							=      torch.tensor([0.27_96, 0.31_38, 0.34_81]		)
										self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]  ,   A_  ,   atol=1e-4		)		)

										# verify area
										_UpperCAmelCase    :						Optional[int]							=      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"]  ,   A_		)		)
										# verify boxes
										_UpperCAmelCase    :						Union[str, Any]							=      torch.Size([6, 4]		)
										self.assertEqual(encoding["labels"][0]["boxes"].shape  ,   A_		)
										_UpperCAmelCase    :						List[str]							=      torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]		)
										self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]  ,   A_  ,   atol=1e-3		)		)
										# verify image_id
										_UpperCAmelCase    :						Optional[int]							=      torch.tensor([39769]		)
										self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]  ,   A_		)		)
										# verify is_crowd
										_UpperCAmelCase    :						List[str]							=      torch.tensor([0, 0, 0, 0, 0, 0]		)
										self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]  ,   A_		)		)
										# verify class_labels
										_UpperCAmelCase    :						int							=      torch.tensor([75, 75, 63, 65, 17, 17]		)
										self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]  ,   A_		)		)
										# verify orig_size
										_UpperCAmelCase    :						Optional[int]							=      torch.tensor([480, 640]		)
										self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]  ,   A_		)		)
										# verify size
										_UpperCAmelCase    :						Any							=      torch.tensor([800, 1066]		)
										self.assertTrue(torch.allclose(encoding["labels"][0]["size"]  ,   A_		)		)





						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
										with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"  ,   "r"		) as f:
														_UpperCAmelCase    :						int							=      json.loads(f.read()		)

										_UpperCAmelCase    :						List[Any]							=      {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

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

										# encode them
										_UpperCAmelCase    :						Optional[Any]							=      YolosImageProcessor(format="coco_panoptic"		)
										_UpperCAmelCase    :						Optional[Any]							=      image_processing(images=A_  ,   annotations=A_  ,   masks_path=A_  ,   return_tensors="pt"		)

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

										_UpperCAmelCase    :						List[Any]							=      torch.tensor([0.27_96, 0.31_38, 0.34_81]		)
										self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]  ,   A_  ,   atol=1e-4		)		)

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





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
					from transformers import TvltImageProcessor

if is_speech_available():
					from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class 			a					(		unittest.TestCase			):


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      "ZinengTang/tvlt-base"
										_UpperCAmelCase    :						int							=      tempfile.mkdtemp()


						def     _UpperCAmelCase			(    self  ,   **A_		):







										'''simple docstring'''


										return TvltImageProcessor.from_pretrained(self.checkpoint  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   **A_		):







										'''simple docstring'''


										return TvltFeatureExtractor.from_pretrained(self.checkpoint  ,   **A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										shutil.rmtree(self.tmpdirname		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      self.get_image_processor()
										_UpperCAmelCase    :						Optional[int]							=      self.get_feature_extractor()

										_UpperCAmelCase    :						Tuple							=      TvltProcessor(image_processor=A_  ,   feature_extractor=A_		)
										processor.save_pretrained(self.tmpdirname		)
										_UpperCAmelCase    :						str							=      TvltProcessor.from_pretrained(self.tmpdirname		)

										self.assertIsInstance(processor.feature_extractor  ,   A_		)
										self.assertIsInstance(processor.image_processor  ,   A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      self.get_image_processor()
										_UpperCAmelCase    :						int							=      self.get_feature_extractor()

										_UpperCAmelCase    :						Tuple							=      TvltProcessor(image_processor=A_  ,   feature_extractor=A_		)

										_UpperCAmelCase    :						List[str]							=      np.ones([12000]		)

										_UpperCAmelCase    :						int							=      feature_extractor(A_  ,   return_tensors="np"		)
										_UpperCAmelCase    :						int							=      processor(audio=A_  ,   return_tensors="np"		)

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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      self.get_image_processor()
										_UpperCAmelCase    :						int							=      self.get_feature_extractor()

										_UpperCAmelCase    :						Optional[Any]							=      TvltProcessor(image_processor=A_  ,   feature_extractor=A_		)

										_UpperCAmelCase    :						Union[str, Any]							=      np.ones([3, 224, 224]		)

										_UpperCAmelCase    :						Tuple							=      image_processor(A_  ,   return_tensors="np"		)
										_UpperCAmelCase    :						List[str]							=      processor(images=A_  ,   return_tensors="np"		)

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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      self.get_image_processor()
										_UpperCAmelCase    :						Any							=      self.get_feature_extractor()

										_UpperCAmelCase    :						Dict							=      TvltProcessor(image_processor=A_  ,   feature_extractor=A_		)

										_UpperCAmelCase    :						str							=      np.ones([12000]		)
										_UpperCAmelCase    :						Optional[Any]							=      np.ones([3, 224, 224]		)

										_UpperCAmelCase    :						List[Any]							=      processor(audio=A_  ,   images=A_		)

										self.assertListEqual(list(inputs.keys()		)  ,   ["audio_values", "audio_mask", "pixel_values", "pixel_mask"]		)

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





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.get_image_processor()
										_UpperCAmelCase    :						int							=      self.get_feature_extractor()

										_UpperCAmelCase    :						str							=      TvltProcessor(image_processor=A_  ,   feature_extractor=A_		)

										self.assertListEqual(
										    processor.model_input_names  ,   image_processor.model_input_names + feature_extractor.model_input_names  ,   msg="`processor` and `image_processor`+`feature_extractor` model input names do not match"  ,   )


