





'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
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 DonutImageProcessor
class 			_lowercase						(       unittest.TestCase					):







     def __init__( self    ,   A__    ,   A__=7    ,   A__=3    ,   A__=18    ,   A__=30    ,   A__=4_00    ,   A__=True    ,   A__=None    ,   A__=True    ,   A__=False    ,   A__=True    ,   A__=True    ,   A__=[0.5, 0.5, 0.5]    ,   A__=[0.5, 0.5, 0.5]    ,   ) ->	Dict:
      snake_case				=			parent
      snake_case				=			batch_size
      snake_case				=			num_channels
      snake_case				=			image_size
      snake_case				=			min_resolution
      snake_case				=			max_resolution
      snake_case				=			do_resize
      snake_case				=			size if size is not None else {'''height''': 18, '''width''': 20}
      snake_case				=			do_thumbnail
      snake_case				=			do_align_axis
      snake_case				=			do_pad
      snake_case				=			do_normalize
      snake_case				=			image_mean
      snake_case				=			image_std







     def     UpperCamelCase			( self    ) ->	List[Any]:
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_thumbnail": self.do_thumbnail,
          "do_align_long_axis": self.do_align_axis,
          "do_pad": self.do_pad,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
      }




@require_torch
@require_vision
class 			_lowercase						(       __a				,		unittest.TestCase					):
     _UpperCAmelCase										=					DonutImageProcessor if is_vision_available() else None







     def     UpperCamelCase			( self    ) ->	Tuple:
      snake_case				=			DonutImageProcessingTester(self    )







     @property
     def     UpperCamelCase			( self    ) ->	Dict:
      return self.image_processor_tester.prepare_image_processor_dict()







     def     UpperCamelCase			( self    ) ->	List[Any]:
      snake_case				=			self.image_processing_class(**self.image_processor_dict    )
      self.assertTrue(hasattr(A__    ,   '''do_resize'''    )    )
      self.assertTrue(hasattr(A__    ,   '''size'''    )    )
      self.assertTrue(hasattr(A__    ,   '''do_thumbnail'''    )    )
      self.assertTrue(hasattr(A__    ,   '''do_align_long_axis'''    )    )
      self.assertTrue(hasattr(A__    ,   '''do_pad'''    )    )
      self.assertTrue(hasattr(A__    ,   '''do_normalize'''    )    )
      self.assertTrue(hasattr(A__    ,   '''image_mean'''    )    )
      self.assertTrue(hasattr(A__    ,   '''image_std'''    )    )







     def     UpperCamelCase			( self    ) ->	int:
      snake_case				=			self.image_processing_class.from_dict(self.image_processor_dict    )
      self.assertEqual(image_processor.size    ,   {'''height''': 18, '''width''': 20}    )

      snake_case				=			self.image_processing_class.from_dict(self.image_processor_dict    ,   size=42    )
      self.assertEqual(image_processor.size    ,   {'''height''': 42, '''width''': 42}    )

      # Previous config had dimensions in (width, height) order
      snake_case				=			self.image_processing_class.from_dict(self.image_processor_dict    ,   size=(42, 84)    )
      self.assertEqual(image_processor.size    ,   {'''height''': 84, '''width''': 42}    )







     def     UpperCamelCase			( self    ) ->	List[Any]:
      pass







     @is_flaky()
     def     UpperCamelCase			( self    ) ->	List[str]:
      # 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=A__    )
      for image in image_inputs:
       self.assertIsInstance(A__    ,   Image.Image    )

      # Test not batched input
      snake_case				=			image_processing(image_inputs[0]    ,   return_tensors='''pt'''    ).pixel_values
      self.assertEqual(
          encoded_images.shape    ,   (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )    ,   )

      # Test batched
      snake_case				=			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,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )    ,   )







     @is_flaky()
     def     UpperCamelCase			( self    ) ->	str:
      # 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=A__    ,   numpify=A__    )
      for image in image_inputs:
       self.assertIsInstance(A__    ,   np.ndarray    )

      # Test not batched input
      snake_case				=			image_processing(image_inputs[0]    ,   return_tensors='''pt'''    ).pixel_values
      self.assertEqual(
          encoded_images.shape    ,   (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )    ,   )

      # Test batched
      snake_case				=			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,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )    ,   )







     @is_flaky()
     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      # 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=A__    ,   torchify=A__    )
      for image in image_inputs:
       self.assertIsInstance(A__    ,   torch.Tensor    )

      # Test not batched input
      snake_case				=			image_processing(image_inputs[0]    ,   return_tensors='''pt'''    ).pixel_values
      self.assertEqual(
          encoded_images.shape    ,   (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )    ,   )

      # Test batched
      snake_case				=			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,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )    ,   )








'''simple docstring'''







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

