

'''simple docstring'''
from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
)


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ..utils.dummy_pt_objects import *  # noqa F403
else:
    from .consistency_models import ConsistencyModelPipeline
    from .dance_diffusion import DanceDiffusionPipeline
    from .ddim import DDIMPipeline
    from .ddpm import DDPMPipeline
    from .dit import DiTPipeline
    from .latent_diffusion import LDMSuperResolutionPipeline
    from .latent_diffusion_uncond import LDMPipeline
    from .pipeline_utils import AudioPipelineOutput, DiffusionPipeline, ImagePipelineOutput
    from .pndm import PNDMPipeline
    from .repaint import RePaintPipeline
    from .score_sde_ve import ScoreSdeVePipeline
    from .stochastic_karras_ve import KarrasVePipeline

try:
    if not (is_torch_available() and is_librosa_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ..utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
    from .audio_diffusion import AudioDiffusionPipeline, Mel

try:
    if not (is_torch_available() and is_transformers_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ..utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
    from .alt_diffusion import AltDiffusionImgaImgPipeline, AltDiffusionPipeline
    from .audioldm import AudioLDMPipeline
    from .controlnet import (
        StableDiffusionControlNetImgaImgPipeline,
        StableDiffusionControlNetInpaintPipeline,
        StableDiffusionControlNetPipeline,
    )
    from .deepfloyd_if import (
        IFImgaImgPipeline,
        IFImgaImgSuperResolutionPipeline,
        IFInpaintingPipeline,
        IFInpaintingSuperResolutionPipeline,
        IFPipeline,
        IFSuperResolutionPipeline,
    )
    from .kandinsky import (
        KandinskyImgaImgPipeline,
        KandinskyInpaintPipeline,
        KandinskyPipeline,
        KandinskyPriorPipeline,
    )
    from .kandinskya_a import (
        KandinskyVaaControlnetImgaImgPipeline,
        KandinskyVaaControlnetPipeline,
        KandinskyVaaImgaImgPipeline,
        KandinskyVaaInpaintPipeline,
        KandinskyVaaPipeline,
        KandinskyVaaPriorEmbaEmbPipeline,
        KandinskyVaaPriorPipeline,
    )
    from .latent_diffusion import LDMTextToImagePipeline
    from .paint_by_example import PaintByExamplePipeline
    from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
    from .shap_e import ShapEImgaImgPipeline, ShapEPipeline
    from .stable_diffusion import (
        CycleDiffusionPipeline,
        StableDiffusionAttendAndExcitePipeline,
        StableDiffusionDepthaImgPipeline,
        StableDiffusionDiffEditPipeline,
        StableDiffusionImageVariationPipeline,
        StableDiffusionImgaImgPipeline,
        StableDiffusionInpaintPipeline,
        StableDiffusionInpaintPipelineLegacy,
        StableDiffusionInstructPixaPixPipeline,
        StableDiffusionLatentUpscalePipeline,
        StableDiffusionLDMaDPipeline,
        StableDiffusionModelEditingPipeline,
        StableDiffusionPanoramaPipeline,
        StableDiffusionParadigmsPipeline,
        StableDiffusionPipeline,
        StableDiffusionPixaPixZeroPipeline,
        StableDiffusionSAGPipeline,
        StableDiffusionUpscalePipeline,
        StableUnCLIPImgaImgPipeline,
        StableUnCLIPPipeline,
    )
    from .stable_diffusion_safe import StableDiffusionPipelineSafe
    from .text_to_video_synthesis import TextToVideoSDPipeline, TextToVideoZeroPipeline, VideoToVideoSDPipeline
    from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline
    from .unidiffuser import ImageTextPipelineOutput, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder
    from .versatile_diffusion import (
        VersatileDiffusionDualGuidedPipeline,
        VersatileDiffusionImageVariationPipeline,
        VersatileDiffusionPipeline,
        VersatileDiffusionTextToImagePipeline,
    )
    from .vq_diffusion import VQDiffusionPipeline


try:
    if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ..utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
    from .stable_diffusion_xl import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
    if not is_onnx_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ..utils.dummy_onnx_objects import *  # noqa F403
else:
    from .onnx_utils import OnnxRuntimeModel

try:
    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ..utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
    from .stable_diffusion import (
        OnnxStableDiffusionImgaImgPipeline,
        OnnxStableDiffusionInpaintPipeline,
        OnnxStableDiffusionInpaintPipelineLegacy,
        OnnxStableDiffusionPipeline,
        OnnxStableDiffusionUpscalePipeline,
        StableDiffusionOnnxPipeline,
    )

try:
    if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ..utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
    from .stable_diffusion import StableDiffusionKDiffusionPipeline

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ..utils.dummy_flax_objects import *  # noqa F403
else:
    from .pipeline_flax_utils import FlaxDiffusionPipeline


try:
    if not (is_flax_available() and is_transformers_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ..utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
    from .controlnet import FlaxStableDiffusionControlNetPipeline
    from .stable_diffusion import (
        FlaxStableDiffusionImgaImgPipeline,
        FlaxStableDiffusionInpaintPipeline,
        FlaxStableDiffusionPipeline,
    )
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 .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline









'''simple docstring'''
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import 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 SegformerImageProcessor


class      a__						(							unittest.TestCase						):


   def __init__(				self    ,       _UpperCamelCase    ,       _UpperCamelCase=7    ,       _UpperCamelCase=3    ,       _UpperCamelCase=30    ,       _UpperCamelCase=400    ,       _UpperCamelCase=True    ,       _UpperCamelCase=None    ,       _UpperCamelCase=True    ,       _UpperCamelCase=[0.5, 0.5, 0.5]    ,       _UpperCamelCase=[0.5, 0.5, 0.5]    ,       _UpperCamelCase=False    ,       ):





      """simple docstring"""





      _lowercase    :   List[str]      =     size if size is not None else {"height": 30, "width": 30}
      _lowercase    :   str      =     parent
      _lowercase    :   Optional[int]      =     batch_size
      _lowercase    :   Optional[int]      =     num_channels
      _lowercase    :   Any      =     min_resolution
      _lowercase    :   Any      =     max_resolution
      _lowercase    :   Tuple      =     do_resize
      _lowercase    :   List[Any]      =     size
      _lowercase    :   Tuple      =     do_normalize
      _lowercase    :   Optional[Any]      =     image_mean
      _lowercase    :   Any      =     image_std
      _lowercase    :   Dict      =     do_reduce_labels






   def 		_lowerCamelCase     (				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_reduce_labels": self.do_reduce_labels,
      }







def       _A	(			)    ->			List[Any]:
   _lowercase    :   List[str]      =     load_dataset("hf-internal-testing/fixtures_ade20k"	,							split="test"  )

   _lowercase    :   Union[str, Any]      =     Image.open(dataset[0]["file"]  )
   _lowercase    :   Optional[int]      =     Image.open(dataset[1]["file"]  )

   return image, map







def       _A	(			)    ->			Optional[int]:
   _lowercase    :   List[Any]      =     load_dataset("hf-internal-testing/fixtures_ade20k"	,							split="test"  )

   _lowercase    :   int      =     Image.open(dataset[0]["file"]  )
   _lowercase    :   str      =     Image.open(dataset[1]["file"]  )
   _lowercase    :   Union[str, Any]      =     Image.open(dataset[2]["file"]  )
   _lowercase    :   Union[str, Any]      =     Image.open(dataset[3]["file"]  )

   return [imagea, imagea], [mapa, mapa]


@require_torch
@require_vision
class      a__						(							lowerCamelCase_						,   unittest.TestCase						):
   _SCREAMING_SNAKE_CASE					:  Union[str, Any]			 =							SegformerImageProcessor if is_vision_available() else None


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Union[str, Any]      =     SegformerImageProcessingTester(self						)


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return self.image_processor_tester.prepare_image_processor_dict()


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   List[str]      =     self.image_processing_class(**self.image_processor_dict						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "do_resize"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "size"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "do_normalize"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "image_mean"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "image_std"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "do_reduce_labels"						)						)


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Dict      =     self.image_processing_class.from_dict(self.image_processor_dict						)
      self.assertEqual(image_processor.size    ,       {"height": 30, "width": 30}						)
      self.assertEqual(image_processor.do_reduce_labels    ,       _UpperCamelCase						)

      _lowercase    :   Tuple      =     self.image_processing_class.from_dict(self.image_processor_dict    ,       size=42    ,       reduce_labels=_UpperCamelCase						)
      self.assertEqual(image_processor.size    ,       {"height": 42, "width": 42}						)
      self.assertEqual(image_processor.do_reduce_labels    ,       _UpperCamelCase						)


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      pass


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   List[Any]      =     self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      _lowercase    :   str      =     prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=_UpperCamelCase						)
      for image in image_inputs:
         self.assertIsInstance(_UpperCamelCase    ,       Image.Image						)

      # Test not batched input
      _lowercase    :   Optional[int]      =     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
      _lowercase    :   int      =     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,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   List[Any]      =     self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      _lowercase    :   List[str]      =     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
      _lowercase    :   Dict      =     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
      _lowercase    :   List[Any]      =     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,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   List[str]      =     self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      _lowercase    :   List[Any]      =     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
      _lowercase    :   Optional[int]      =     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
      _lowercase    :   Dict      =     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,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Union[str, Any]      =     self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      _lowercase    :   Tuple      =     prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=_UpperCamelCase    ,       torchify=_UpperCamelCase						)
      _lowercase    :   Tuple      =     []
      for image in image_inputs:
         self.assertIsInstance(_UpperCamelCase    ,       torch.Tensor						)
         maps.append(torch.zeros(image.shape[-2:]						).long()						)

      # Test not batched input
      _lowercase    :   List[str]      =     image_processing(image_inputs[0]    ,       maps[0]    ,       return_tensors="pt"						)
      self.assertEqual(
          encoding["pixel_values"].shape    ,       (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )
      self.assertEqual(
          encoding["labels"].shape    ,       (
              1,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )
      self.assertEqual(encoding["labels"].dtype    ,       torch.long						)
      self.assertTrue(encoding["labels"].min().item() >= 0						)
      self.assertTrue(encoding["labels"].max().item() <= 255						)

      # Test batched
      _lowercase    :   Optional[int]      =     image_processing(_UpperCamelCase    ,       _UpperCamelCase    ,       return_tensors="pt"						)
      self.assertEqual(
          encoding["pixel_values"].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"],
          )    ,       )
      self.assertEqual(
          encoding["labels"].shape    ,       (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )
      self.assertEqual(encoding["labels"].dtype    ,       torch.long						)
      self.assertTrue(encoding["labels"].min().item() >= 0						)
      self.assertTrue(encoding["labels"].max().item() <= 255						)

      # Test not batched input (PIL images)
      _lowercase  ,   _lowercase    :   Optional[int]      =     prepare_semantic_single_inputs()

      _lowercase    :   int      =     image_processing(_UpperCamelCase    ,       _UpperCamelCase    ,       return_tensors="pt"						)
      self.assertEqual(
          encoding["pixel_values"].shape    ,       (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )
      self.assertEqual(
          encoding["labels"].shape    ,       (
              1,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )
      self.assertEqual(encoding["labels"].dtype    ,       torch.long						)
      self.assertTrue(encoding["labels"].min().item() >= 0						)
      self.assertTrue(encoding["labels"].max().item() <= 255						)

      # Test batched input (PIL images)
      _lowercase  ,   _lowercase    :   Dict      =     prepare_semantic_batch_inputs()

      _lowercase    :   List[str]      =     image_processing(_UpperCamelCase    ,       _UpperCamelCase    ,       return_tensors="pt"						)
      self.assertEqual(
          encoding["pixel_values"].shape    ,       (
              2,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )
      self.assertEqual(
          encoding["labels"].shape    ,       (
              2,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )
      self.assertEqual(encoding["labels"].dtype    ,       torch.long						)
      self.assertTrue(encoding["labels"].min().item() >= 0						)
      self.assertTrue(encoding["labels"].max().item() <= 255						)






   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   str      =     self.image_processing_class(**self.image_processor_dict						)

      # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
      _lowercase  ,   _lowercase    :   str      =     prepare_semantic_single_inputs()
      _lowercase    :   Optional[int]      =     image_processing(_UpperCamelCase    ,       _UpperCamelCase    ,       return_tensors="pt"						)
      self.assertTrue(encoding["labels"].min().item() >= 0						)
      self.assertTrue(encoding["labels"].max().item() <= 150						)

      _lowercase    :   str      =     True
      _lowercase    :   List[str]      =     image_processing(_UpperCamelCase    ,       _UpperCamelCase    ,       return_tensors="pt"						)
      self.assertTrue(encoding["labels"].min().item() >= 0						)
      self.assertTrue(encoding["labels"].max().item() <= 255						)






