



"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__   ,    unittest.TestCase				):
   __lowerCAmelCase						:     Tuple		=		KandinskyVaaInpaintPipeline
   __lowerCAmelCase						:     int		=		['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
   __lowerCAmelCase						:     Dict		=		[
       'image_embeds',
       'negative_image_embeds',
       'image',
       'mask_image',
   ]
   __lowerCAmelCase						:     Dict		=		[
       'generator',
       'height',
       'width',
       'latents',
       'guidance_scale',
       'num_inference_steps',
       'return_dict',
       'guidance_scale',
       'num_images_per_prompt',
       'output_type',
       'return_dict',
   ]
   __lowerCAmelCase						:     Dict		=		False

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[int]:





    '''simple docstring'''




    return 32

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    return 32

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[int]:





    '''simple docstring'''




    return self.time_input_dim

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    return self.time_input_dim * 4

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    return 100

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    torch.manual_seed(0		)

    UpperCAmelCase			:      Union[str, Any]			=			{
        """in_channels""": 9,
        # Out channels is double in channels because predicts mean and variance
        """out_channels""": 8,
        """addition_embed_type""": """image""",
        """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
        """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
        """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
        """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
        """layers_per_block""": 1,
        """encoder_hid_dim""": self.text_embedder_hidden_size,
        """encoder_hid_dim_type""": """image_proj""",
        """cross_attention_dim""": self.cross_attention_dim,
        """attention_head_dim""": 4,
        """resnet_time_scale_shift""": """scale_shift""",
        """class_embed_type""": None,
    }

    UpperCAmelCase			:      Dict			=			UNetaDConditionModel(**_SCREAMING_SNAKE_CASE		)
    return model

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    return {
        "block_out_channels": [32, 64],
        "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
        "in_channels": 3,
        "latent_channels": 4,
        "layers_per_block": 1,
        "norm_num_groups": 8,
        "norm_type": "spatial",
        "num_vq_embeddings": 12,
        "out_channels": 3,
        "up_block_types": [
            "AttnUpDecoderBlock2D",
            "UpDecoderBlock2D",
        ],
        "vq_embed_dim": 4,
    }

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    torch.manual_seed(0		)
    UpperCAmelCase			:      Any			=			VQModel(**self.dummy_movq_kwargs		)
    return model

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			self.dummy_unet
    UpperCAmelCase			:      List[Any]			=			self.dummy_movq

    UpperCAmelCase			:      Optional[int]			=			DDIMScheduler(
        num_train_timesteps=1000   ,  beta_schedule="""linear"""   ,  beta_start=0.0_0085   ,  beta_end=0.012   ,  clip_sample=_SCREAMING_SNAKE_CASE   ,  set_alpha_to_one=_SCREAMING_SNAKE_CASE   ,  steps_offset=1   ,  prediction_type="""epsilon"""   ,  thresholding=_SCREAMING_SNAKE_CASE   ,  )

    UpperCAmelCase			:      List[Any]			=			{
        """unet""": unet,
        """scheduler""": scheduler,
        """movq""": movq,
    }

    return components

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=0		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			floats_tensor((1, self.text_embedder_hidden_size)   ,  rng=random.Random(_SCREAMING_SNAKE_CASE		)		).to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Union[str, Any]			=			floats_tensor((1, self.text_embedder_hidden_size)   ,  rng=random.Random(seed + 1		)		).to(
        _SCREAMING_SNAKE_CASE		)
    # create init_image
    UpperCAmelCase			:      List[str]			=			floats_tensor((1, 3, 64, 64)   ,  rng=random.Random(_SCREAMING_SNAKE_CASE		)		).to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			image.cpu().permute(0   ,  2   ,  3   ,  1		)[0]
    UpperCAmelCase			:      Dict			=			Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE		)		).convert("""RGB"""		).resize((256, 256)		)
    # create mask
    UpperCAmelCase			:      Optional[Any]			=			np.ones((64, 64)   ,  dtype=np.floataa		)
    UpperCAmelCase			:      Tuple			=			0

    if str(_SCREAMING_SNAKE_CASE		).startswith("""mps"""		):
     UpperCAmelCase			:      int			=			torch.manual_seed(_SCREAMING_SNAKE_CASE		)
    else:
     UpperCAmelCase			:      Dict			=			torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			{
        """image""": init_image,
        """mask_image""": mask,
        """image_embeds""": image_embeds,
        """negative_image_embeds""": negative_image_embeds,
        """generator""": generator,
        """height""": 64,
        """width""": 64,
        """num_inference_steps""": 2,
        """guidance_scale""": 4.0,
        """output_type""": """np""",
    }
    return inputs

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			"""cpu"""

    UpperCAmelCase			:      Dict			=			self.get_dummy_components()

    UpperCAmelCase			:      str			=			self.pipeline_class(**_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Tuple			=			pipe.to(_SCREAMING_SNAKE_CASE		)

    pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[Any]			=			pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)		)
    UpperCAmelCase			:      Optional[int]			=			output.images

    UpperCAmelCase			:      str			=			pipe(
        **self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)   ,  return_dict=_SCREAMING_SNAKE_CASE   ,  )[0]

    UpperCAmelCase			:      Optional[int]			=			image[0, -3:, -3:, -1]
    UpperCAmelCase			:      Any			=			image_from_tuple[0, -3:, -3:, -1]

    print(F"image.shape {image.shape}"		)

    assert image.shape == (1, 64, 64, 3)

    UpperCAmelCase			:      int			=			np.array(
        [0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848]		)

    assert (
        np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
    ), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
    assert (
        np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2
    ), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[int]:





    '''simple docstring'''




    super().test_inference_batch_single_identical(expected_max_diff=3E-3		)



@slow
@require_torch_gpu
class    SCREAMING_SNAKE_CASE__						(      unittest.TestCase				):

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			load_numpy(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
        """/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"""		)

    UpperCAmelCase			:      Union[str, Any]			=			load_image(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""		)
    UpperCAmelCase			:      Any			=			np.ones((768, 768)   ,  dtype=np.floataa		)
    UpperCAmelCase			:      Optional[Any]			=			0

    UpperCAmelCase			:      Union[str, Any]			=			"""a hat"""

    UpperCAmelCase			:      Dict			=			KandinskyVaaPriorPipeline.from_pretrained(
        """kandinsky-community/kandinsky-2-2-prior"""   ,  torch_dtype=torch.floataa		)
    pipe_prior.to(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Tuple			=			KandinskyVaaInpaintPipeline.from_pretrained(
        """kandinsky-community/kandinsky-2-2-decoder-inpaint"""   ,  torch_dtype=torch.floataa		)
    UpperCAmelCase			:      Any			=			pipeline.to(_SCREAMING_SNAKE_CASE		)
    pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[Any]			=			torch.Generator(device="""cpu"""		).manual_seed(0		)
    UpperCAmelCase			:      Union[str, Any]			=			pipe_prior(
        _SCREAMING_SNAKE_CASE   ,  generator=_SCREAMING_SNAKE_CASE   ,  num_inference_steps=5   ,  negative_prompt=""""""   ,  ).to_tuple()

    UpperCAmelCase			:      Optional[Any]			=			pipeline(
        image=_SCREAMING_SNAKE_CASE   ,  mask_image=_SCREAMING_SNAKE_CASE   ,  image_embeds=_SCREAMING_SNAKE_CASE   ,  negative_image_embeds=_SCREAMING_SNAKE_CASE   ,  generator=_SCREAMING_SNAKE_CASE   ,  num_inference_steps=100   ,  height=768   ,  width=768   ,  output_type="""np"""   ,  )

    UpperCAmelCase			:      Dict			=			output.images[0]

    assert image.shape == (768, 768, 3)

    assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


A:     Union[str, Any]										=	{
    "configuration_speecht5": [
        "SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
        "SpeechT5Config",
        "SpeechT5HifiGanConfig",
    ],
    "feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
    "processing_speecht5": ["SpeechT5Processor"],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:     List[str]										=	["SpeechT5Tokenizer"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:     Dict										=	[
          "SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
          "SpeechT5ForSpeechToText",
          "SpeechT5ForSpeechToSpeech",
          "SpeechT5ForTextToSpeech",
          "SpeechT5Model",
          "SpeechT5PreTrainedModel",
          "SpeechT5HifiGan",
      ]

if TYPE_CHECKING:
      from .configuration_speechta import (
          SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
          SpeechTaConfig,
          SpeechTaHifiGanConfig,
      )
      from .feature_extraction_speechta import SpeechTaFeatureExtractor
      from .processing_speechta import SpeechTaProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speechta import SpeechTaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speechta import (
                SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechTaForSpeechToSpeech,
                SpeechTaForSpeechToText,
                SpeechTaForTextToSpeech,
                SpeechTaHifiGan,
                SpeechTaModel,
                SpeechTaPreTrainedModel,
            )

else:
      import sys

      A:     Dict										=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
