

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 DetrImageProcessor





class        lowerCAmelCase__  (					unittest.TestCase			):



       '''simple docstring'''







       def __init__(							self ,   lowercase__ ,   lowercase__=7 ,   lowercase__=3 ,   lowercase__=3_0 ,   lowercase__=4_0_0 ,   lowercase__=True ,   lowercase__=None ,   lowercase__=True ,   lowercase__=1 / 2_5_5 ,   lowercase__=True ,   lowercase__=[0.5, 0.5, 0.5] ,   lowercase__=[0.5, 0.5, 0.5] ,   lowercase__=True ,   ):







             '''simple docstring'''



             __A    =size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
             __A    =parent
             __A    =batch_size
             __A    =num_channels
             __A    =min_resolution
             __A    =max_resolution
             __A    =do_resize
             __A    =size
             __A    =do_rescale
             __A    =rescale_factor
             __A    =do_normalize
             __A    =image_mean
             __A    =image_std
             __A    =do_pad

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_rescale": self.do_rescale,
                 "rescale_factor": self.rescale_factor,
                 "do_normalize": self.do_normalize,
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_pad": self.do_pad,
             }


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







             '''simple docstring'''



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

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

             return expected_height, expected_width





@require_torch
@require_vision
class        lowerCAmelCase__  (					__magic_name__				,							unittest.TestCase			):



       '''simple docstring'''







       lowercase_						=    DetrImageProcessor if is_vision_available() else None

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =DetrImageProcessingTester(self     )

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return self.image_processor_tester.prepare_image_processor_dict()

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.image_processing_class(**self.image_processor_dict     )
             self.assertTrue(hasattr(lowercase__ ,   '''image_mean'''     )     )
             self.assertTrue(hasattr(lowercase__ ,   '''image_std'''     )     )
             self.assertTrue(hasattr(lowercase__ ,   '''do_normalize'''     )     )
             self.assertTrue(hasattr(lowercase__ ,   '''do_rescale'''     )     )
             self.assertTrue(hasattr(lowercase__ ,   '''rescale_factor'''     )     )
             self.assertTrue(hasattr(lowercase__ ,   '''do_resize'''     )     )
             self.assertTrue(hasattr(lowercase__ ,   '''size'''     )     )
             self.assertTrue(hasattr(lowercase__ ,   '''do_pad'''     )     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.image_processing_class.from_dict(self.image_processor_dict     )
             self.assertEqual(image_processor.size ,   {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}     )
             self.assertEqual(image_processor.do_pad ,   lowercase__     )

             __A    =self.image_processing_class.from_dict(
                 self.image_processor_dict ,   size=4_2 ,   max_size=8_4 ,   pad_and_return_pixel_mask=lowercase__     )
             self.assertEqual(image_processor.size ,   {'''shortest_edge''': 4_2, '''longest_edge''': 8_4}     )
             self.assertEqual(image_processor.do_pad ,   lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             pass

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

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

             __A    ,		__A    =self.image_processor_tester.get_expected_values(lowercase__     )

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

             # Test batched
             __A    ,		__A    =self.image_processor_tester.get_expected_values(lowercase__ ,   batched=lowercase__     )

             __A    =image_processing(lowercase__ ,   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'''



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

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

             __A    ,		__A    =self.image_processor_tester.get_expected_values(lowercase__     )

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

             # Test batched
             __A    =image_processing(lowercase__ ,   return_tensors='''pt'''     ).pixel_values

             __A    ,		__A    =self.image_processor_tester.get_expected_values(lowercase__ ,   batched=lowercase__     )

             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'''



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

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

             __A    ,		__A    =self.image_processor_tester.get_expected_values(lowercase__     )

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

             # Test batched
             __A    =image_processing(lowercase__ ,   return_tensors='''pt'''     ).pixel_values

             __A    ,		__A    =self.image_processor_tester.get_expected_values(lowercase__ ,   batched=lowercase__     )

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

       @slow
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

             __A    ={'''image_id''': 3_9_7_6_9, '''annotations''': target}

             # encode them
             __A    =DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''     )
             __A    =image_processing(images=lowercase__ ,   annotations=lowercase__ ,   return_tensors='''pt'''     )

             # verify pixel values
             __A    =torch.Size([1, 3, 8_0_0, 1_0_6_6]     )
             self.assertEqual(encoding['''pixel_values'''].shape ,   lowercase__     )

             __A    =torch.tensor([0.2796, 0.3138, 0.3481]     )
             self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] ,   lowercase__ ,   atol=1E-4     )     )

             # verify area
             __A    =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'''] ,   lowercase__     )     )
             # verify boxes
             __A    =torch.Size([6, 4]     )
             self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,   lowercase__     )
             __A    =torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]     )
             self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] ,   lowercase__ ,   atol=1E-3     )     )
             # verify image_id
             __A    =torch.tensor([3_9_7_6_9]     )
             self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,   lowercase__     )     )
             # verify is_crowd
             __A    =torch.tensor([0, 0, 0, 0, 0, 0]     )
             self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,   lowercase__     )     )
             # verify class_labels
             __A    =torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]     )
             self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,   lowercase__     )     )
             # verify orig_size
             __A    =torch.tensor([4_8_0, 6_4_0]     )
             self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,   lowercase__     )     )
             # verify size
             __A    =torch.tensor([8_0_0, 1_0_6_6]     )
             self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,   lowercase__     )     )


       @slow
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

             __A    ={'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

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

             # encode them
             __A    =DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''     )
             __A    =image_processing(images=lowercase__ ,   annotations=lowercase__ ,   masks_path=lowercase__ ,   return_tensors='''pt'''     )

             # verify pixel values
             __A    =torch.Size([1, 3, 8_0_0, 1_0_6_6]     )
             self.assertEqual(encoding['''pixel_values'''].shape ,   lowercase__     )

             __A    =torch.tensor([0.2796, 0.3138, 0.3481]     )
             self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] ,   lowercase__ ,   atol=1E-4     )     )

             # verify area
             __A    =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'''] ,   lowercase__     )     )
             # verify boxes
             __A    =torch.Size([6, 4]     )
             self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,   lowercase__     )
             __A    =torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]     )
             self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] ,   lowercase__ ,   atol=1E-3     )     )
             # verify image_id
             __A    =torch.tensor([3_9_7_6_9]     )
             self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,   lowercase__     )     )
             # verify is_crowd
             __A    =torch.tensor([0, 0, 0, 0, 0, 0]     )
             self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,   lowercase__     )     )
             # verify class_labels
             __A    =torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]     )
             self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,   lowercase__     )     )
             # verify masks
             __A    =8_2_2_8_7_3
             self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() ,   lowercase__     )
             # verify orig_size
             __A    =torch.tensor([4_8_0, 6_4_0]     )
             self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,   lowercase__     )     )
             # verify size
             __A    =torch.tensor([8_0_0, 1_0_6_6]     )
             self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,   lowercase__     )     )





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      :					Tuple					    =      [
       '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ASTForAudioClassification''',
       '''ASTModel''',
       '''ASTPreTrainedModel''',
   ]

try:
   if not is_speech_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					Optional[Any]					    =      ['''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__)


