
'''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 						lowercase__	(  snake_case_,					unittest.TestCase   ):






   '''simple docstring'''







   _snake_case		   =							KandinskyVaaInpaintPipeline
   _snake_case		   =							['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
   _snake_case		   =							[
       '''image_embeds''',
       '''negative_image_embeds''',
       '''image''',
       '''mask_image''',
   ]
   _snake_case		   =							[
       '''generator''',
       '''height''',
       '''width''',
       '''latents''',
       '''guidance_scale''',
       '''num_inference_steps''',
       '''return_dict''',
       '''guidance_scale''',
       '''num_images_per_prompt''',
       '''output_type''',
       '''return_dict''',
   ]
   _snake_case		   =							False







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return 3_2







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return 3_2







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return self.time_input_dim







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return self.time_input_dim * 4







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return 1_0_0







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      torch.manual_seed(0   )

      UpperCamelCase       =  {
          '''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       =  UNetaDConditionModel(**lowerCamelCase__   )
      return model







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return {
          "block_out_channels": [3_2, 6_4],
          "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": 1_2,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      torch.manual_seed(0   )
      UpperCamelCase       =  VQModel(**self.dummy_movq_kwargs   )
      return model







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.dummy_unet
      UpperCamelCase       =  self.dummy_movq

      UpperCamelCase       =  DDIMScheduler(
          num_train_timesteps=1_0_0_0 ,   beta_schedule='''linear''' ,   beta_start=0.00085 ,   beta_end=0.012 ,   clip_sample=lowerCamelCase__ ,   set_alpha_to_one=lowerCamelCase__ ,   steps_offset=1 ,   prediction_type='''epsilon''' ,   thresholding=lowerCamelCase__ ,   )

      UpperCamelCase       =  {
          '''unet''': unet,
          '''scheduler''': scheduler,
          '''movq''': movq,
      }

      return components







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__=0   ):

      '''simple docstring'''







      UpperCamelCase       =  floats_tensor((1, self.text_embedder_hidden_size) ,   rng=random.Random(lowerCamelCase__   )   ).to(lowerCamelCase__   )
      UpperCamelCase       =  floats_tensor((1, self.text_embedder_hidden_size) ,   rng=random.Random(seed + 1   )   ).to(
          lowerCamelCase__   )
      # create init_image
      UpperCamelCase       =  floats_tensor((1, 3, 6_4, 6_4) ,   rng=random.Random(lowerCamelCase__   )   ).to(lowerCamelCase__   )
      UpperCamelCase       =  image.cpu().permute(0 ,   2 ,   3 ,   1   )[0]
      UpperCamelCase       =  Image.fromarray(np.uinta(lowerCamelCase__   )   ).convert('''RGB'''   ).resize((2_5_6, 2_5_6)   )
      # create mask
      UpperCamelCase       =  np.ones((6_4, 6_4) ,   dtype=np.floataa   )
      UpperCamelCase       =  0

      if str(lowerCamelCase__   ).startswith('''mps'''   ):
         UpperCamelCase       =  torch.manual_seed(lowerCamelCase__   )
      else:
         UpperCamelCase       =  torch.Generator(device=lowerCamelCase__   ).manual_seed(lowerCamelCase__   )
      UpperCamelCase       =  {
          '''image''': init_image,
          '''mask_image''': mask,
          '''image_embeds''': image_embeds,
          '''negative_image_embeds''': negative_image_embeds,
          '''generator''': generator,
          '''height''': 6_4,
          '''width''': 6_4,
          '''num_inference_steps''': 2,
          '''guidance_scale''': 4.0,
          '''output_type''': '''np''',
      }
      return inputs







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  '''cpu'''

      UpperCamelCase       =  self.get_dummy_components()

      UpperCamelCase       =  self.pipeline_class(**lowerCamelCase__   )
      UpperCamelCase       =  pipe.to(lowerCamelCase__   )

      pipe.set_progress_bar_config(disable=lowerCamelCase__   )

      UpperCamelCase       =  pipe(**self.get_dummy_inputs(lowerCamelCase__   )   )
      UpperCamelCase       =  output.images

      UpperCamelCase       =  pipe(
          **self.get_dummy_inputs(lowerCamelCase__   ) ,   return_dict=lowerCamelCase__ ,   )[0]

      UpperCamelCase       =  image[0, -3:, -3:, -1]
      UpperCamelCase       =  image_from_tuple[0, -3:, -3:, -1]

      print(f'image.shape {image.shape}'   )

      assert image.shape == (1, 6_4, 6_4, 3)

      UpperCamelCase       =  np.array(
          [0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848]   )

      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       UpperCAmelCase				(			self   ):

      '''simple docstring'''







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




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






   '''simple docstring'''







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







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

      UpperCamelCase       =  load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''   )
      UpperCamelCase       =  np.ones((7_6_8, 7_6_8) ,   dtype=np.floataa   )
      UpperCamelCase       =  0

      UpperCamelCase       =  '''a hat'''

      UpperCamelCase       =  KandinskyVaaPriorPipeline.from_pretrained(
          '''kandinsky-community/kandinsky-2-2-prior''' ,   torch_dtype=torch.floataa   )
      pipe_prior.to(lowerCamelCase__   )

      UpperCamelCase       =  KandinskyVaaInpaintPipeline.from_pretrained(
          '''kandinsky-community/kandinsky-2-2-decoder-inpaint''' ,   torch_dtype=torch.floataa   )
      UpperCamelCase       =  pipeline.to(lowerCamelCase__   )
      pipeline.set_progress_bar_config(disable=lowerCamelCase__   )

      UpperCamelCase       =  torch.Generator(device='''cpu'''   ).manual_seed(0   )
      UpperCamelCase     , UpperCamelCase       =  pipe_prior(
          lowerCamelCase__ ,   generator=lowerCamelCase__ ,   num_inference_steps=5 ,   negative_prompt='''''' ,   ).to_tuple()

      UpperCamelCase       =  pipeline(
          image=lowerCamelCase__ ,   mask_image=lowerCamelCase__ ,   image_embeds=lowerCamelCase__ ,   negative_image_embeds=lowerCamelCase__ ,   generator=lowerCamelCase__ ,   num_inference_steps=1_0_0 ,   height=7_6_8 ,   width=7_6_8 ,   output_type='''np''' ,   )

      UpperCamelCase       =  output.images[0]

      assert image.shape == (7_6_8, 7_6_8, 3)

      assert_mean_pixel_difference(lowerCamelCase__ ,   lowerCamelCase__   )







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


snake_case_ :  List[Any]     =					{
    'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
    'configuration_data2vec_text': [
        'DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecTextConfig',
        'Data2VecTextOnnxConfig',
    ],
    'configuration_data2vec_vision': [
        'DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecVisionConfig',
        'Data2VecVisionOnnxConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case_ :  Union[str, Any]     =					[
       'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
       'Data2VecAudioForAudioFrameClassification',
       'Data2VecAudioForCTC',
       'Data2VecAudioForSequenceClassification',
       'Data2VecAudioForXVector',
       'Data2VecAudioModel',
       'Data2VecAudioPreTrainedModel',
   ]
   snake_case_ :  str     =					[
       'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'Data2VecTextForCausalLM',
       'Data2VecTextForMaskedLM',
       'Data2VecTextForMultipleChoice',
       'Data2VecTextForQuestionAnswering',
       'Data2VecTextForSequenceClassification',
       'Data2VecTextForTokenClassification',
       'Data2VecTextModel',
       'Data2VecTextPreTrainedModel',
   ]
   snake_case_ :  Optional[int]     =					[
       'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
       'Data2VecVisionForImageClassification',
       'Data2VecVisionForMaskedImageModeling',
       'Data2VecVisionForSemanticSegmentation',
       'Data2VecVisionModel',
       'Data2VecVisionPreTrainedModel',
   ]

if is_tf_available():
   snake_case_ :  List[str]     =					[
       'TFData2VecVisionForImageClassification',
       'TFData2VecVisionForSemanticSegmentation',
       'TFData2VecVisionModel',
       'TFData2VecVisionPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
   from .configuration_dataavec_text import (
       DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DataaVecTextConfig,
       DataaVecTextOnnxConfig,
   )
   from .configuration_dataavec_vision import (
       DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DataaVecVisionConfig,
       DataaVecVisionOnnxConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_dataavec_audio import (
          DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
          DataaVecAudioForAudioFrameClassification,
          DataaVecAudioForCTC,
          DataaVecAudioForSequenceClassification,
          DataaVecAudioForXVector,
          DataaVecAudioModel,
          DataaVecAudioPreTrainedModel,
      )
      from .modeling_dataavec_text import (
          DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
          DataaVecTextForCausalLM,
          DataaVecTextForMaskedLM,
          DataaVecTextForMultipleChoice,
          DataaVecTextForQuestionAnswering,
          DataaVecTextForSequenceClassification,
          DataaVecTextForTokenClassification,
          DataaVecTextModel,
          DataaVecTextPreTrainedModel,
      )
      from .modeling_dataavec_vision import (
          DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
          DataaVecVisionForImageClassification,
          DataaVecVisionForMaskedImageModeling,
          DataaVecVisionForSemanticSegmentation,
          DataaVecVisionModel,
          DataaVecVisionPreTrainedModel,
      )
   if is_tf_available():
      from .modeling_tf_dataavec_vision import (
          TFDataaVecVisionForImageClassification,
          TFDataaVecVisionForSemanticSegmentation,
          TFDataaVecVisionModel,
          TFDataaVecVisionPreTrainedModel,
      )

else:
   import sys

   snake_case_ :  int     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





