






"""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        UpperCamelCase			(	unittest.TestCase      ):


 def __init__(     self		:						Tuple							,						UpperCAmelCase__		:						Tuple							,						UpperCAmelCase__		:						str=7							,						UpperCAmelCase__		:						Tuple=3							,						UpperCAmelCase__		:						List[str]=30							,						UpperCAmelCase__		:						Optional[int]=400							,						UpperCAmelCase__		:						Union[str, Any]=True							,						UpperCAmelCase__		:						List[str]=None							,						UpperCAmelCase__		:						Optional[Any]=True							,						UpperCAmelCase__		:						Union[str, Any]=[0.5, 0.5, 0.5]							,						UpperCAmelCase__		:						List[str]=[0.5, 0.5, 0.5]							,						UpperCAmelCase__		:						List[str]=True							,						UpperCAmelCase__		:						Tuple=1 / 255							,						UpperCAmelCase__		:						Union[str, Any]=True							,						)					->       Tuple:
   # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
   _a  :       Optional[Any]             =	size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
   _a  :       Optional[Any]             =	parent
   _a  :       List[str]             =	batch_size
   _a  :       Optional[Any]             =	num_channels
   _a  :       Optional[Any]             =	min_resolution
   _a  :       str             =	max_resolution
   _a  :       Tuple             =	do_resize
   _a  :       Any             =	size
   _a  :       List[Any]             =	do_normalize
   _a  :       List[Any]             =	image_mean
   _a  :       Union[str, Any]             =	image_std
   _a  :       int             =	do_rescale
   _a  :       Optional[int]             =	rescale_factor
   _a  :       List[Any]             =	do_pad


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

   else:
     _a  :       int             =	[]
     for image in image_inputs:
       _a      ,  _a  :       Any             =	self.get_expected_values([image]							)
       expected_values.append((expected_height, expected_width)							)
     _a  :       Dict             =	max(UpperCAmelCase__							,						key=lambda UpperCAmelCase__							: item[0]							)[0]
     _a  :       Optional[int]             =	max(UpperCAmelCase__							,						key=lambda UpperCAmelCase__							: item[1]							)[1]

   return expected_height, expected_width







@require_torch
@require_vision
class        UpperCamelCase			(	snake_case_		,     unittest.TestCase      ):
 UpperCamelCase					:       int			=					YolosImageProcessor if is_vision_available() else None


 def _lowercase	(     self		:						Dict							)					->       Union[str, Any]:
   _a  :       int             =	YolosImageProcessingTester(self							)


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


 def _lowercase	(     self		:						Optional[Any]							)					->       Optional[Any]:
   _a  :       Any             =	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__							,						"""size"""							)							)


 def _lowercase	(     self		:						Tuple							)					->       List[Any]:
   _a  :       Optional[int]             =	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__							)

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


 def _lowercase	(     self		:						int							)					->       Optional[int]:
   pass


 def _lowercase	(     self		:						Union[str, Any]							)					->       Dict:
   # Initialize image_processing
   _a  :       Optional[int]             =	self.image_processing_class(**self.image_processor_dict							)
   # create random PIL images
   _a  :       Optional[int]             =	prepare_image_inputs(self.image_processor_tester							,						equal_resolution=UpperCAmelCase__							)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase__							,						Image.Image							)

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

   _a      ,  _a  :       Dict             =	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
   _a      ,  _a  :       Union[str, Any]             =	self.image_processor_tester.get_expected_values(UpperCAmelCase__							,						batched=UpperCAmelCase__							)

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

   _a      ,  _a  :       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
   _a  :       int             =	image_processing(UpperCAmelCase__							,						return_tensors="""pt"""							).pixel_values

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

   _a      ,  _a  :       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
   _a  :       str             =	image_processing(UpperCAmelCase__							,						return_tensors="""pt"""							).pixel_values

   _a      ,  _a  :       Dict             =	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 _lowercase	(     self		:						Union[str, Any]							)					->       Optional[Any]:
   # Initialize image_processings
   _a  :       List[Any]             =	self.image_processing_class(**self.image_processor_dict							)
   _a  :       Any             =	self.image_processing_class(do_resize=UpperCAmelCase__							,						do_normalize=UpperCAmelCase__							,						do_rescale=UpperCAmelCase__							)
   # create random PyTorch tensors
   _a  :       Optional[int]             =	prepare_image_inputs(self.image_processor_tester							,						equal_resolution=UpperCAmelCase__							,						torchify=UpperCAmelCase__							)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase__							,						torch.Tensor							)

   # Test whether the method "pad" and calling the image processor return the same tensors
   _a  :       Optional[Any]             =	image_processing_a.pad(UpperCAmelCase__							,						return_tensors="""pt"""							)
   _a  :       str             =	image_processing_a(UpperCAmelCase__							,						return_tensors="""pt"""							)

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


 @slow
 def _lowercase	(     self		:						Optional[Any]							)					->       Tuple:
   # prepare image and target
   _a  :       int             =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
   with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""							,						"""r"""							) as f:
     _a  :       List[str]             =	json.loads(f.read()							)

   _a  :       Optional[int]             =	{"""image_id""": 39769, """annotations""": target}

   # encode them
   _a  :       Any             =	YolosImageProcessor.from_pretrained("""hustvl/yolos-small"""							)
   _a  :       List[Any]             =	image_processing(images=UpperCAmelCase__							,						annotations=UpperCAmelCase__							,						return_tensors="""pt"""							)

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

   _a  :       Optional[int]             =	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
   _a  :       int             =	torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]							)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]							,						UpperCAmelCase__							)							)
   # verify boxes
   _a  :       Dict             =	torch.Size([6, 4]							)
   self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape							,						UpperCAmelCase__							)
   _a  :       List[Any]             =	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
   _a  :       Optional[int]             =	torch.tensor([39769]							)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]							,						UpperCAmelCase__							)							)
   # verify is_crowd
   _a  :       Optional[Any]             =	torch.tensor([0, 0, 0, 0, 0, 0]							)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]							,						UpperCAmelCase__							)							)
   # verify class_labels
   _a  :       Optional[Any]             =	torch.tensor([75, 75, 63, 65, 17, 17]							)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]							,						UpperCAmelCase__							)							)
   # verify orig_size
   _a  :       Optional[int]             =	torch.tensor([480, 640]							)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]							,						UpperCAmelCase__							)							)
   # verify size
   _a  :       List[Any]             =	torch.tensor([800, 1066]							)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]							,						UpperCAmelCase__							)							)







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

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

   _a  :       List[Any]             =	pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""							)

   # encode them
   _a  :       Dict             =	YolosImageProcessor(format="""coco_panoptic"""							)
   _a  :       Union[str, Any]             =	image_processing(images=UpperCAmelCase__							,						annotations=UpperCAmelCase__							,						masks_path=UpperCAmelCase__							,						return_tensors="""pt"""							)

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

   _a  :       Tuple             =	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
   _a  :       Union[str, Any]             =	torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]							)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]							,						UpperCAmelCase__							)							)
   # verify boxes
   _a  :       List[str]             =	torch.Size([6, 4]							)
   self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape							,						UpperCAmelCase__							)
   _a  :       List[str]             =	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
   _a  :       str             =	torch.tensor([39769]							)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]							,						UpperCAmelCase__							)							)
   # verify is_crowd
   _a  :       List[str]             =	torch.tensor([0, 0, 0, 0, 0, 0]							)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]							,						UpperCAmelCase__							)							)
   # verify class_labels
   _a  :       Any             =	torch.tensor([17, 17, 63, 75, 75, 93]							)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]							,						UpperCAmelCase__							)							)
   # verify masks
   _a  :       int             =	822873
   self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()							,						UpperCAmelCase__							)
   # verify orig_size
   _a  :       Any             =	torch.tensor([480, 640]							)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]							,						UpperCAmelCase__							)							)
   # verify size
   _a  :       Any             =	torch.tensor([800, 1066]							)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]							,						UpperCAmelCase__							)							)









"""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        UpperCamelCase			(	unittest.TestCase      ):


 def _lowercase	(     self		:						int							)					->       List[str]:
   _a  :       Any             =	"""laion/clap-htsat-unfused"""
   _a  :       Union[str, Any]             =	tempfile.mkdtemp()


 def _lowercase	(     self		:						List[Any]							,						**UpperCAmelCase__		:						Any							)					->       Dict:
   return RobertaTokenizer.from_pretrained(self.checkpoint							,						**UpperCAmelCase__							)


 def _lowercase	(     self		:						List[Any]							,						**UpperCAmelCase__		:						List[str]							)					->       int:
   return ClapFeatureExtractor.from_pretrained(self.checkpoint							,						**UpperCAmelCase__							)


 def _lowercase	(     self		:						List[Any]							)					->       Tuple:
   shutil.rmtree(self.tmpdirname							)


 def _lowercase	(     self		:						List[str]							)					->       Optional[int]:
   _a  :       List[str]             =	self.get_tokenizer()
   _a  :       Any             =	self.get_feature_extractor()

   _a  :       Optional[Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   processor.save_pretrained(self.tmpdirname							)
   _a  :       List[str]             =	ClapProcessor.from_pretrained(self.tmpdirname							)

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

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


 def _lowercase	(     self		:						Dict							)					->       Optional[int]:
   _a  :       Tuple             =	ClapProcessor(tokenizer=self.get_tokenizer()							,						feature_extractor=self.get_feature_extractor()							)
   processor.save_pretrained(self.tmpdirname							)

   _a  :       Dict             =	self.get_tokenizer(bos_token="""(BOS)"""							,						eos_token="""(EOS)"""							)
   _a  :       Union[str, Any]             =	self.get_feature_extractor(do_normalize=UpperCAmelCase__							,						padding_value=1.0							)

   _a  :       Union[str, Any]             =	ClapProcessor.from_pretrained(
       self.tmpdirname							,						bos_token="""(BOS)"""							,						eos_token="""(EOS)"""							,						do_normalize=UpperCAmelCase__							,						padding_value=1.0							)

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

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


 def _lowercase	(     self		:						List[str]							)					->       Optional[Any]:
   _a  :       Optional[int]             =	self.get_feature_extractor()
   _a  :       Tuple             =	self.get_tokenizer()

   _a  :       List[Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   _a  :       Any             =	floats_list((3, 1000)							)

   _a  :       List[Any]             =	feature_extractor(UpperCAmelCase__							,						return_tensors="""np"""							)
   _a  :       List[str]             =	processor(audios=UpperCAmelCase__							,						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 _lowercase	(     self		:						Tuple							)					->       Optional[int]:
   _a  :       List[str]             =	self.get_feature_extractor()
   _a  :       Any             =	self.get_tokenizer()

   _a  :       Any             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   _a  :       Optional[int]             =	"""This is a test string"""

   _a  :       Tuple             =	processor(text=UpperCAmelCase__							)

   _a  :       int             =	tokenizer(UpperCAmelCase__							)

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


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

   _a  :       List[Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

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

   _a  :       Dict             =	processor.batch_decode(UpperCAmelCase__							)
   _a  :       Any             =	tokenizer.batch_decode(UpperCAmelCase__							)

   self.assertListEqual(UpperCAmelCase__							,						UpperCAmelCase__							)







 def _lowercase	(     self		:						Dict							)					->       List[str]:
   _a  :       str             =	self.get_feature_extractor()
   _a  :       Optional[Any]             =	self.get_tokenizer()

   _a  :       Union[str, Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

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

