


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	,   _UpperCAmelCase	,   _UpperCAmelCase=7	,   _UpperCAmelCase=3	,   _UpperCAmelCase=30	,   _UpperCAmelCase=4_00	,   _UpperCAmelCase=True	,   _UpperCAmelCase=None	,   _UpperCAmelCase=True	,   _UpperCAmelCase=1 / 2_55	,   _UpperCAmelCase=True	,   _UpperCAmelCase=[0.5, 0.5, 0.5]	,   _UpperCAmelCase=[0.5, 0.5, 0.5]	,   _UpperCAmelCase=True	,   ):
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       snake_case_ =   size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
       snake_case_ =   parent
       snake_case_ =   batch_size
       snake_case_ =   num_channels
       snake_case_ =   min_resolution
       snake_case_ =   max_resolution
       snake_case_ =   do_resize
       snake_case_ =   size
       snake_case_ =   do_rescale
       snake_case_ =   rescale_factor
       snake_case_ =   do_normalize
       snake_case_ =   image_mean
       snake_case_ =   image_std
       snake_case_ =   do_pad



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

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

       return expected_height, expected_width







@require_torch
@require_vision
class lowerCAmelCase_		(				lowerCamelCase__      ,		unittest.TestCase ):

 '''simple docstring'''

 __snake_case				=						DetrImageProcessor if is_vision_available() else None



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   DetrImageProcessingTester(self      )



 @property
 def 		UpperCamelCase__  (			self      ):
       return self.image_processor_tester.prepare_image_processor_dict()



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   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_rescale'''      )      )
       self.assertTrue(hasattr(_UpperCAmelCase	,   '''rescale_factor'''      )      )
       self.assertTrue(hasattr(_UpperCAmelCase	,   '''do_resize'''      )      )
       self.assertTrue(hasattr(_UpperCAmelCase	,   '''size'''      )      )
       self.assertTrue(hasattr(_UpperCAmelCase	,   '''do_pad'''      )      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.image_processing_class.from_dict(self.image_processor_dict      )
       self.assertEqual(image_processor.size	,   {'''shortest_edge''': 18, '''longest_edge''': 13_33}      )
       self.assertEqual(image_processor.do_pad	,   _UpperCAmelCase      )

       snake_case_ =   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 		UpperCamelCase__  (			self      ):
       pass



 def 		UpperCamelCase__  (			self      ):
       # Initialize image_processing
       snake_case_ =   self.image_processing_class(**self.image_processor_dict      )
       # create random PIL images
       snake_case_ =   prepare_image_inputs(self.image_processor_tester	,   equal_resolution=_UpperCAmelCase      )
       for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase	,   Image.Image      )

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

       snake_case_ ,					snake_case_ =   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
       snake_case_ ,					snake_case_ =   self.image_processor_tester.get_expected_values(_UpperCAmelCase	,   batched=_UpperCAmelCase      )

       snake_case_ =   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 		UpperCamelCase__  (			self      ):
       # Initialize image_processing
       snake_case_ =   self.image_processing_class(**self.image_processor_dict      )
       # create random numpy tensors
       snake_case_ =   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
       snake_case_ =   image_processing(image_inputs[0]	,   return_tensors='''pt'''      ).pixel_values

       snake_case_ ,					snake_case_ =   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
       snake_case_ =   image_processing(_UpperCAmelCase	,   return_tensors='''pt'''      ).pixel_values

       snake_case_ ,					snake_case_ =   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 		UpperCamelCase__  (			self      ):
       # Initialize image_processing
       snake_case_ =   self.image_processing_class(**self.image_processor_dict      )
       # create random PyTorch tensors
       snake_case_ =   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
       snake_case_ =   image_processing(image_inputs[0]	,   return_tensors='''pt'''      ).pixel_values

       snake_case_ ,					snake_case_ =   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
       snake_case_ =   image_processing(_UpperCAmelCase	,   return_tensors='''pt'''      ).pixel_values

       snake_case_ ,					snake_case_ =   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,
           )	,   )



 @slow
 def 		UpperCamelCase__  (			self      ):
       # prepare image and target
       snake_case_ =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''	,   '''r'''      ) as f:
             snake_case_ =   json.loads(f.read()      )

       snake_case_ =   {'''image_id''': 3_97_69, '''annotations''': target}

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

       # verify pixel values
       snake_case_ =   torch.Size([1, 3, 8_00, 10_66]      )
       self.assertEqual(encoding['''pixel_values'''].shape	,   _UpperCAmelCase      )

       snake_case_ =   torch.tensor([0.2_796, 0.3_138, 0.3_481]      )
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]	,   _UpperCAmelCase	,   atol=1E-4      )      )

       # verify area
       snake_case_ =   torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']	,   _UpperCAmelCase      )      )
       # verify boxes
       snake_case_ =   torch.Size([6, 4]      )
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape	,   _UpperCAmelCase      )
       snake_case_ =   torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]	,   _UpperCAmelCase	,   atol=1E-3      )      )
       # verify image_id
       snake_case_ =   torch.tensor([3_97_69]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']	,   _UpperCAmelCase      )      )
       # verify is_crowd
       snake_case_ =   torch.tensor([0, 0, 0, 0, 0, 0]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']	,   _UpperCAmelCase      )      )
       # verify class_labels
       snake_case_ =   torch.tensor([75, 75, 63, 65, 17, 17]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']	,   _UpperCAmelCase      )      )
       # verify orig_size
       snake_case_ =   torch.tensor([4_80, 6_40]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']	,   _UpperCAmelCase      )      )
       # verify size
       snake_case_ =   torch.tensor([8_00, 10_66]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']	,   _UpperCAmelCase      )      )





 @slow
 def 		UpperCamelCase__  (			self      ):
       # prepare image, target and masks_path
       snake_case_ =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''	,   '''r'''      ) as f:
             snake_case_ =   json.loads(f.read()      )

       snake_case_ =   {'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}

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

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

       # verify pixel values
       snake_case_ =   torch.Size([1, 3, 8_00, 10_66]      )
       self.assertEqual(encoding['''pixel_values'''].shape	,   _UpperCAmelCase      )

       snake_case_ =   torch.tensor([0.2_796, 0.3_138, 0.3_481]      )
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]	,   _UpperCAmelCase	,   atol=1E-4      )      )

       # verify area
       snake_case_ =   torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']	,   _UpperCAmelCase      )      )
       # verify boxes
       snake_case_ =   torch.Size([6, 4]      )
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape	,   _UpperCAmelCase      )
       snake_case_ =   torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]	,   _UpperCAmelCase	,   atol=1E-3      )      )
       # verify image_id
       snake_case_ =   torch.tensor([3_97_69]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']	,   _UpperCAmelCase      )      )
       # verify is_crowd
       snake_case_ =   torch.tensor([0, 0, 0, 0, 0, 0]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']	,   _UpperCAmelCase      )      )
       # verify class_labels
       snake_case_ =   torch.tensor([17, 17, 63, 75, 75, 93]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']	,   _UpperCAmelCase      )      )
       # verify masks
       snake_case_ =   82_28_73
       self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()	,   _UpperCAmelCase      )
       # verify orig_size
       snake_case_ =   torch.tensor([4_80, 6_40]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']	,   _UpperCAmelCase      )      )
       # verify size
       snake_case_ =   torch.tensor([8_00, 10_66]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']	,   _UpperCAmelCase      )      )



from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
   from .notes_encoder import SpectrogramNotesEncoder
   from .continous_encoder import SpectrogramContEncoder
   from .pipeline_spectrogram_diffusion import (
       SpectrogramContEncoder,
       SpectrogramDiffusionPipeline,
       TaFilmDecoder,
   )

try:
   if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
   from .midi_utils import MidiProcessor