



'''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 YolosImageProcessor



class 							lowercase       (  unittest.TestCase				):


 """simple docstring"""


 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  ,)		->   Optional[int]:
        # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
        _UpperCAmelCase  :						Union[str, Any]			     = size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1_333}
        _UpperCAmelCase  :						Tuple			     = parent
        _UpperCAmelCase  :						Union[str, Any]			     = batch_size
        _UpperCAmelCase  :						Dict			     = num_channels
        _UpperCAmelCase  :						Optional[int]			     = min_resolution
        _UpperCAmelCase  :						Any			     = max_resolution
        _UpperCAmelCase  :						List[Any]			     = do_resize
        _UpperCAmelCase  :						Tuple			     = size
        _UpperCAmelCase  :						int			     = do_normalize
        _UpperCAmelCase  :						Optional[int]			     = image_mean
        _UpperCAmelCase  :						List[str]			     = image_std
        _UpperCAmelCase  :						List[str]			     = do_rescale
        _UpperCAmelCase  :						int			     = rescale_factor
        _UpperCAmelCase  :						Any			     = do_pad







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

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

        return expected_height, expected_width







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


 """simple docstring"""


 UpperCAmelCase							=  YolosImageProcessor if is_vision_available() else None







 def _snake_case (							self							)		->   str:
        _UpperCAmelCase  :						List[str]			     = YolosImageProcessingTester(self							)







 @property
 def _snake_case (							self							)		->   List[str]:
        return self.image_processor_tester.prepare_image_processor_dict()







 def _snake_case (							self							)		->   List[str]:
        _UpperCAmelCase  :						List[str]			     = 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 _snake_case (							self							)		->   Tuple:
        _UpperCAmelCase  :						List[Any]			     = self.image_processing_class.from_dict(self.image_processor_dict							)
        self.assertEqual(image_processor.size  ,{"""shortest_edge""": 18, """longest_edge""": 1_333}							)
        self.assertEqual(image_processor.do_pad  ,a_							)

        _UpperCAmelCase  :						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 _snake_case (							self							)		->   Tuple:
        pass







 def _snake_case (							self							)		->   int:
        # Initialize image_processing
        _UpperCAmelCase  :						Tuple			     = self.image_processing_class(**self.image_processor_dict							)
        # create random PIL images
        _UpperCAmelCase  :						List[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  :						Dict			     = image_processing(image_inputs[0]  ,return_tensors="""pt"""							).pixel_values

        _UpperCAmelCase     ,_UpperCAmelCase  :						Tuple			     = 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  :						int			     = self.image_processor_tester.get_expected_values(a_  ,batched=a_							)

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

        _UpperCAmelCase     ,_UpperCAmelCase  :						Dict			     = 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  :						Optional[int]			     = image_processing(a_  ,return_tensors="""pt"""							).pixel_values

        _UpperCAmelCase     ,_UpperCAmelCase  :						List[str]			     = 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 _snake_case (							self							)		->   List[str]:
        # Initialize image_processing
        _UpperCAmelCase  :						Optional[int]			     = self.image_processing_class(**self.image_processor_dict							)
        # 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 not batched input
        _UpperCAmelCase  :						Dict			     = image_processing(image_inputs[0]  ,return_tensors="""pt"""							).pixel_values

        _UpperCAmelCase     ,_UpperCAmelCase  :						Union[str, 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  :						Union[str, Any]			     = 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 _snake_case (							self							)		->   List[Any]:
        # Initialize image_processings
        _UpperCAmelCase  :						Optional[int]			     = self.image_processing_class(**self.image_processor_dict							)
        _UpperCAmelCase  :						List[Any]			     = self.image_processing_class(do_resize=a_  ,do_normalize=a_  ,do_rescale=a_							)
        # create random PyTorch tensors
        _UpperCAmelCase  :						List[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  :						List[str]			     = image_processing_a.pad(a_  ,return_tensors="""pt"""							)
        _UpperCAmelCase  :						Optional[Any]			     = 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 _snake_case (							self							)		->   Any:
        # prepare image and target
        _UpperCAmelCase  :						Optional[int]			     = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
        with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""  ,"""r"""							) as f:
               _UpperCAmelCase  :						Any			     = json.loads(f.read()							)

        _UpperCAmelCase  :						Optional[Any]			     = {"""image_id""": 39_769, """annotations""": target}

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

        # verify pixel values
        _UpperCAmelCase  :						Tuple			     = torch.Size([1, 3, 800, 1_066]							)
        self.assertEqual(encoding["""pixel_values"""].shape  ,a_							)

        _UpperCAmelCase  :						List[str]			     = torch.tensor([0.2796, 0.3138, 0.3481]							)
        self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]  ,a_  ,atol=1E-4							)							)

        # verify area
        _UpperCAmelCase  :						List[str]			     = torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]							)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]  ,a_							)							)
        # verify boxes
        _UpperCAmelCase  :						Dict			     = torch.Size([6, 4]							)
        self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape  ,a_							)
        _UpperCAmelCase  :						Any			     = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]							)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]  ,a_  ,atol=1E-3							)							)
        # verify image_id
        _UpperCAmelCase  :						Tuple			     = torch.tensor([39_769]							)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]  ,a_							)							)
        # verify is_crowd
        _UpperCAmelCase  :						Tuple			     = torch.tensor([0, 0, 0, 0, 0, 0]							)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]  ,a_							)							)
        # verify class_labels
        _UpperCAmelCase  :						Optional[int]			     = torch.tensor([75, 75, 63, 65, 17, 17]							)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]  ,a_							)							)
        # verify orig_size
        _UpperCAmelCase  :						Any			     = torch.tensor([480, 640]							)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]  ,a_							)							)
        # verify size
        _UpperCAmelCase  :						Any			     = torch.tensor([800, 1_066]							)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]  ,a_							)							)







 @slow
 def _snake_case (							self							)		->   int:
        # prepare image, target and masks_path
        _UpperCAmelCase  :						Tuple			     = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
        with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""  ,"""r"""							) as f:
               _UpperCAmelCase  :						Tuple			     = json.loads(f.read()							)

        _UpperCAmelCase  :						List[str]			     = {"""file_name""": """000000039769.png""", """image_id""": 39_769, """segments_info""": target}

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

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

        # verify pixel values
        _UpperCAmelCase  :						Optional[int]			     = torch.Size([1, 3, 800, 1_066]							)
        self.assertEqual(encoding["""pixel_values"""].shape  ,a_							)

        _UpperCAmelCase  :						Dict			     = torch.tensor([0.2796, 0.3138, 0.3481]							)
        self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]  ,a_  ,atol=1E-4							)							)

        # verify area
        _UpperCAmelCase  :						List[Any]			     = torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]							)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]  ,a_							)							)
        # verify boxes
        _UpperCAmelCase  :						str			     = torch.Size([6, 4]							)
        self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape  ,a_							)
        _UpperCAmelCase  :						Dict			     = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]							)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]  ,a_  ,atol=1E-3							)							)
        # verify image_id
        _UpperCAmelCase  :						List[str]			     = torch.tensor([39_769]							)
        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  :						Optional[Any]			     = torch.tensor([17, 17, 63, 75, 75, 93]							)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]  ,a_							)							)
        # verify masks
        _UpperCAmelCase  :						Tuple			     = 822_873
        self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()  ,a_							)
        # verify orig_size
        _UpperCAmelCase  :						Optional[Any]			     = torch.tensor([480, 640]							)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]  ,a_							)							)
        # verify size
        _UpperCAmelCase  :						int			     = torch.tensor([800, 1_066]							)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]  ,a_							)							)








'''simple docstring'''






import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list



@require_torchaudio
@require_sentencepiece
class 							lowercase       (  unittest.TestCase				):


 """simple docstring"""


 def _snake_case (							self							)		->   Optional[Any]:
        _UpperCAmelCase  :						str			     = """laion/clap-htsat-unfused"""
        _UpperCAmelCase  :						int			     = tempfile.mkdtemp()







 def _snake_case (							self  ,**a_							)		->   str:
        return RobertaTokenizer.from_pretrained(self.checkpoint  ,**a_							)







 def _snake_case (							self  ,**a_							)		->   Tuple:
        return ClapFeatureExtractor.from_pretrained(self.checkpoint  ,**a_							)







 def _snake_case (							self							)		->   int:
        shutil.rmtree(self.tmpdirname							)







 def _snake_case (							self							)		->   Optional[int]:
        _UpperCAmelCase  :						str			     = self.get_tokenizer()
        _UpperCAmelCase  :						Any			     = self.get_feature_extractor()

        _UpperCAmelCase  :						int			     = ClapProcessor(tokenizer=a_  ,feature_extractor=a_							)

        processor.save_pretrained(self.tmpdirname							)
        _UpperCAmelCase  :						List[Any]			     = ClapProcessor.from_pretrained(self.tmpdirname							)

        self.assertEqual(processor.tokenizer.get_vocab()  ,tokenizer.get_vocab()							)
        self.assertIsInstance(processor.tokenizer  ,a_							)

        self.assertEqual(processor.feature_extractor.to_json_string()  ,feature_extractor.to_json_string()							)
        self.assertIsInstance(processor.feature_extractor  ,a_							)







 def _snake_case (							self							)		->   List[Any]:
        _UpperCAmelCase  :						int			     = ClapProcessor(tokenizer=self.get_tokenizer()  ,feature_extractor=self.get_feature_extractor()							)
        processor.save_pretrained(self.tmpdirname							)

        _UpperCAmelCase  :						List[str]			     = self.get_tokenizer(bos_token="""(BOS)"""  ,eos_token="""(EOS)"""							)
        _UpperCAmelCase  :						List[Any]			     = self.get_feature_extractor(do_normalize=a_  ,padding_value=1.0							)

        _UpperCAmelCase  :						Optional[Any]			     = ClapProcessor.from_pretrained(
            self.tmpdirname  ,bos_token="""(BOS)"""  ,eos_token="""(EOS)"""  ,do_normalize=a_  ,padding_value=1.0							)

        self.assertEqual(processor.tokenizer.get_vocab()  ,tokenizer_add_kwargs.get_vocab()							)
        self.assertIsInstance(processor.tokenizer  ,a_							)

        self.assertEqual(processor.feature_extractor.to_json_string()  ,feature_extractor_add_kwargs.to_json_string()							)
        self.assertIsInstance(processor.feature_extractor  ,a_							)







 def _snake_case (							self							)		->   str:
        _UpperCAmelCase  :						Tuple			     = self.get_feature_extractor()
        _UpperCAmelCase  :						Dict			     = self.get_tokenizer()

        _UpperCAmelCase  :						str			     = ClapProcessor(tokenizer=a_  ,feature_extractor=a_							)

        _UpperCAmelCase  :						Tuple			     = floats_list((3, 1_000)							)

        _UpperCAmelCase  :						int			     = feature_extractor(a_  ,return_tensors="""np"""							)
        _UpperCAmelCase  :						Union[str, Any]			     = processor(audios=a_  ,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 _snake_case (							self							)		->   Tuple:
        _UpperCAmelCase  :						List[Any]			     = self.get_feature_extractor()
        _UpperCAmelCase  :						Any			     = self.get_tokenizer()

        _UpperCAmelCase  :						Optional[int]			     = ClapProcessor(tokenizer=a_  ,feature_extractor=a_							)

        _UpperCAmelCase  :						Union[str, Any]			     = """This is a test string"""

        _UpperCAmelCase  :						Optional[Any]			     = processor(text=a_							)

        _UpperCAmelCase  :						Any			     = tokenizer(a_							)

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







 def _snake_case (							self							)		->   List[Any]:
        _UpperCAmelCase  :						str			     = self.get_feature_extractor()
        _UpperCAmelCase  :						List[str]			     = self.get_tokenizer()

        _UpperCAmelCase  :						Any			     = ClapProcessor(tokenizer=a_  ,feature_extractor=a_							)

        _UpperCAmelCase  :						List[str]			     = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _UpperCAmelCase  :						Dict			     = processor.batch_decode(a_							)
        _UpperCAmelCase  :						Any			     = tokenizer.batch_decode(a_							)

        self.assertListEqual(a_  ,a_							)







 def _snake_case (							self							)		->   Dict:
        _UpperCAmelCase  :						List[str]			     = self.get_feature_extractor()
        _UpperCAmelCase  :						int			     = self.get_tokenizer()

        _UpperCAmelCase  :						Dict			     = ClapProcessor(tokenizer=a_  ,feature_extractor=a_							)

        self.assertListEqual(
            processor.model_input_names[2:]  ,feature_extractor.model_input_names  ,msg="""`processor` and `feature_extractor` model input names do not match"""  ,)



