




'''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   __UpperCamelCase       ( unittest.TestCase				):






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



              """simple docstring"""







              lowerCamelCase_							=size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
              lowerCamelCase_							=parent
              lowerCamelCase_							=batch_size
              lowerCamelCase_							=num_channels
              lowerCamelCase_							=min_resolution
              lowerCamelCase_							=max_resolution
              lowerCamelCase_							=do_resize
              lowerCamelCase_							=size
              lowerCamelCase_							=do_normalize
              lowerCamelCase_							=image_mean
              lowerCamelCase_							=image_std
              lowerCamelCase_							=do_rescale
              lowerCamelCase_							=rescale_factor
              lowerCamelCase_							=do_pad






       def      lowercase__     (							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      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase=False  ):



              """simple docstring"""







              if not batched:
                     lowerCamelCase_							=image_inputs[0]
                     if isinstance(lowerCAmelCase,		Image.Image  ):
                            lowerCamelCase_,      lowerCamelCase_							=image.size
                     else:
                            lowerCamelCase_,      lowerCamelCase_							=image.shape[1], image.shape[2]
                     if w < h:
                            lowerCamelCase_							=int(self.size['''shortest_edge'''] * h / w  )
                            lowerCamelCase_							=self.size['''shortest_edge''']
                     elif w > h:
                            lowerCamelCase_							=self.size['''shortest_edge''']
                            lowerCamelCase_							=int(self.size['''shortest_edge'''] * w / h  )
                     else:
                            lowerCamelCase_							=self.size['''shortest_edge''']
                            lowerCamelCase_							=self.size['''shortest_edge''']

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

              return expected_height, expected_width





@require_torch
@require_vision
class   __UpperCamelCase       ( lowerCamelCase__			,					unittest.TestCase				):
       lowercase	:    List[str]      						=DetaImageProcessor if is_vision_available() else None






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=DetaImageProcessingTester(self  )






       @property
       def      lowercase__     (							self  ):



              """simple docstring"""







              return self.image_processor_tester.prepare_image_processor_dict()






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=self.image_processing_class(**self.image_processor_dict  )
              self.assertTrue(hasattr(lowerCAmelCase,		'''image_mean'''  )  )
              self.assertTrue(hasattr(lowerCAmelCase,		'''image_std'''  )  )
              self.assertTrue(hasattr(lowerCAmelCase,		'''do_normalize'''  )  )
              self.assertTrue(hasattr(lowerCAmelCase,		'''do_resize'''  )  )
              self.assertTrue(hasattr(lowerCAmelCase,		'''do_rescale'''  )  )
              self.assertTrue(hasattr(lowerCAmelCase,		'''do_pad'''  )  )
              self.assertTrue(hasattr(lowerCAmelCase,		'''size'''  )  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=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,		lowerCAmelCase  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              pass






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=self.image_processing_class(**self.image_processor_dict  )
              # create random PIL images
              lowerCamelCase_							=prepare_image_inputs(self.image_processor_tester,		equal_resolution=lowerCAmelCase  )
              for image in image_inputs:
                     self.assertIsInstance(lowerCAmelCase,		Image.Image  )

              # Test not batched input
              lowerCamelCase_							=image_processing(image_inputs[0],		return_tensors='''pt'''  ).pixel_values

              lowerCamelCase_,      lowerCamelCase_							=self.image_processor_tester.get_expected_values(lowerCAmelCase  )

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

              # Test batched
              lowerCamelCase_,      lowerCamelCase_							=self.image_processor_tester.get_expected_values(lowerCAmelCase,		batched=lowerCAmelCase  )

              lowerCamelCase_							=image_processing(lowerCAmelCase,		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  ):



              """simple docstring"""







              lowerCamelCase_							=self.image_processing_class(**self.image_processor_dict  )
              # create random numpy tensors
              lowerCamelCase_							=prepare_image_inputs(self.image_processor_tester,		equal_resolution=lowerCAmelCase,		numpify=lowerCAmelCase  )
              for image in image_inputs:
                     self.assertIsInstance(lowerCAmelCase,		np.ndarray  )

              # Test not batched input
              lowerCamelCase_							=image_processing(image_inputs[0],		return_tensors='''pt'''  ).pixel_values

              lowerCamelCase_,      lowerCamelCase_							=self.image_processor_tester.get_expected_values(lowerCAmelCase  )

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

              # Test batched
              lowerCamelCase_							=image_processing(lowerCAmelCase,		return_tensors='''pt'''  ).pixel_values

              lowerCamelCase_,      lowerCamelCase_							=self.image_processor_tester.get_expected_values(lowerCAmelCase,		batched=lowerCAmelCase  )

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






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=self.image_processing_class(**self.image_processor_dict  )
              # create random PyTorch tensors
              lowerCamelCase_							=prepare_image_inputs(self.image_processor_tester,		equal_resolution=lowerCAmelCase,		torchify=lowerCAmelCase  )
              for image in image_inputs:
                     self.assertIsInstance(lowerCAmelCase,		torch.Tensor  )

              # Test not batched input
              lowerCamelCase_							=image_processing(image_inputs[0],		return_tensors='''pt'''  ).pixel_values

              lowerCamelCase_,      lowerCamelCase_							=self.image_processor_tester.get_expected_values(lowerCAmelCase  )

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

              # Test batched
              lowerCamelCase_							=image_processing(lowerCAmelCase,		return_tensors='''pt'''  ).pixel_values

              lowerCamelCase_,      lowerCamelCase_							=self.image_processor_tester.get_expected_values(lowerCAmelCase,		batched=lowerCAmelCase  )

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






       @slow
       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
              with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''',		'''r'''  ) as f:
                     lowerCamelCase_							=json.loads(f.read()  )

              lowerCamelCase_							={'''image_id''': 39_769, '''annotations''': target}

              # encode them
              lowerCamelCase_							=DetaImageProcessor()
              lowerCamelCase_							=image_processing(images=lowerCAmelCase,		annotations=lowerCAmelCase,		return_tensors='''pt'''  )

              # verify pixel values
              lowerCamelCase_							=torch.Size([1, 3, 800, 1_066]  )
              self.assertEqual(encoding['''pixel_values'''].shape,		lowerCAmelCase  )

              lowerCamelCase_							=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],		lowerCAmelCase,		atol=1e-4  )  )

              # verify area
              lowerCamelCase_							=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'''],		lowerCAmelCase  )  )
              # verify boxes
              lowerCamelCase_							=torch.Size([6, 4]  )
              self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape,		lowerCAmelCase  )
              lowerCamelCase_							=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],		lowerCAmelCase,		atol=1e-3  )  )
              # verify image_id
              lowerCamelCase_							=torch.tensor([39_769]  )
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''],		lowerCAmelCase  )  )
              # verify is_crowd
              lowerCamelCase_							=torch.tensor([0, 0, 0, 0, 0, 0]  )
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''],		lowerCAmelCase  )  )
              # verify class_labels
              lowerCamelCase_							=torch.tensor([75, 75, 63, 65, 17, 17]  )
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''],		lowerCAmelCase  )  )
              # verify orig_size
              lowerCamelCase_							=torch.tensor([480, 640]  )
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''],		lowerCAmelCase  )  )
              # verify size
              lowerCamelCase_							=torch.tensor([800, 1_066]  )
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''],		lowerCAmelCase  )  )






       @slow
       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
              with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''',		'''r'''  ) as f:
                     lowerCamelCase_							=json.loads(f.read()  )

              lowerCamelCase_							={'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}

              lowerCamelCase_							=pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''  )

              # encode them
              lowerCamelCase_							=DetaImageProcessor(format='''coco_panoptic'''  )
              lowerCamelCase_							=image_processing(images=lowerCAmelCase,		annotations=lowerCAmelCase,		masks_path=lowerCAmelCase,		return_tensors='''pt'''  )

              # verify pixel values
              lowerCamelCase_							=torch.Size([1, 3, 800, 1_066]  )
              self.assertEqual(encoding['''pixel_values'''].shape,		lowerCAmelCase  )

              lowerCamelCase_							=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],		lowerCAmelCase,		atol=1e-4  )  )

              # verify area
              lowerCamelCase_							=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'''],		lowerCAmelCase  )  )
              # verify boxes
              lowerCamelCase_							=torch.Size([6, 4]  )
              self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape,		lowerCAmelCase  )
              lowerCamelCase_							=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],		lowerCAmelCase,		atol=1e-3  )  )
              # verify image_id
              lowerCamelCase_							=torch.tensor([39_769]  )
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''],		lowerCAmelCase  )  )
              # verify is_crowd
              lowerCamelCase_							=torch.tensor([0, 0, 0, 0, 0, 0]  )
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''],		lowerCAmelCase  )  )
              # verify class_labels
              lowerCamelCase_							=torch.tensor([17, 17, 63, 75, 75, 93]  )
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''],		lowerCAmelCase  )  )
              # verify masks
              lowerCamelCase_							=822_873
              self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item(),		lowerCAmelCase  )
              # verify orig_size
              lowerCamelCase_							=torch.tensor([480, 640]  )
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''],		lowerCAmelCase  )  )
              # verify size
              lowerCamelCase_							=torch.tensor([800, 1_066]  )
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''],		lowerCAmelCase  )  )












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


lowerCamelCase	:							int				={
    '''configuration_audio_spectrogram_transformer''': [
        '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ASTConfig''',
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase	:							Union[str, Any]				=[
     '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''ASTForAudioClassification''',
     '''ASTModel''',
     '''ASTPreTrainedModel''',
 ]

try:
 if not is_speech_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase	:							Optional[int]				=['''ASTFeatureExtractor''']

if TYPE_CHECKING:
 from .configuration_audio_spectrogram_transformer import (
     AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ASTConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_audio_spectrogram_transformer import (
      AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      ASTForAudioClassification,
      ASTModel,
      ASTPreTrainedModel,
  )

 try:
  if not is_speech_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
 import sys

 lowerCamelCase	:							Optional[int]				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)