



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  __lowerCAmelCase     (	lowerCAmelCase__  ,	unittest.TestCase   ):
 lowerCAmelCase__       					=  KandinskyVaaInpaintPipeline
 lowerCAmelCase__       					=  ["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
 lowerCAmelCase__       					=  [
     """image_embeds""",
     """negative_image_embeds""",
     """image""",
     """mask_image""",
 ]
 lowerCAmelCase__       					=  [
     """generator""",
     """height""",
     """width""",
     """latents""",
     """guidance_scale""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 lowerCAmelCase__       					=  False




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return 32




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return 32




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return self.time_input_dim




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return self.time_input_dim * 4




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return 100




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    torch.manual_seed(0				)

    __lowerCamelCase													=    {
        '''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,
    }

    __lowerCamelCase													=    UNetaDConditionModel(**__UpperCAmelCase				)
    return model




 @property
 def        lowerCamelCase      (						self				):



    '''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        lowerCamelCase      (						self				):



    '''simple docstring'''
    torch.manual_seed(0				)
    __lowerCamelCase													=    VQModel(**self.dummy_movq_kwargs				)
    return model




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.dummy_unet
    __lowerCamelCase													=    self.dummy_movq

    __lowerCamelCase													=    DDIMScheduler(
        num_train_timesteps=1000	,				beta_schedule='''linear'''	,				beta_start=0.00_085	,				beta_end=0.012	,				clip_sample=__UpperCAmelCase	,				set_alpha_to_one=__UpperCAmelCase	,				steps_offset=1	,				prediction_type='''epsilon'''	,				thresholding=__UpperCAmelCase	,				)

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

    return components




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase=0				):



    '''simple docstring'''
    __lowerCamelCase													=    floats_tensor((1, self.text_embedder_hidden_size)	,				rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)
    __lowerCamelCase													=    floats_tensor((1, self.text_embedder_hidden_size)	,				rng=random.Random(seed + 1				)				).to(
        __UpperCAmelCase				)
    # create init_image
    __lowerCamelCase													=    floats_tensor((1, 3, 64, 64)	,				rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)
    __lowerCamelCase													=    image.cpu().permute(0	,				2	,				3	,				1				)[0]
    __lowerCamelCase													=    Image.fromarray(np.uinta(__UpperCAmelCase				)				).convert('''RGB'''				).resize((256, 256)				)
    # create mask
    __lowerCamelCase													=    np.ones((64, 64)	,				dtype=np.floataa				)
    __lowerCamelCase													=    0

    if str(__UpperCAmelCase				).startswith('''mps'''				):
       __lowerCamelCase													=    torch.manual_seed(__UpperCAmelCase				)
    else:
       __lowerCamelCase													=    torch.Generator(device=__UpperCAmelCase				).manual_seed(__UpperCAmelCase				)
    __lowerCamelCase													=    {
        '''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        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    '''cpu'''

    __lowerCamelCase													=    self.get_dummy_components()

    __lowerCamelCase													=    self.pipeline_class(**__UpperCAmelCase				)
    __lowerCamelCase													=    pipe.to(__UpperCAmelCase				)

    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    __lowerCamelCase													=    pipe(**self.get_dummy_inputs(__UpperCAmelCase				)				)
    __lowerCamelCase													=    output.images

    __lowerCamelCase													=    pipe(
        **self.get_dummy_inputs(__UpperCAmelCase				)	,				return_dict=__UpperCAmelCase	,				)[0]

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

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

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

    __lowerCamelCase													=    np.array(
        [0.50_775_903, 0.49_527_195, 0.48_824_543, 0.50_192_237, 0.48_644_906, 0.49_373_814, 0.4_780_598, 0.47_234_827, 0.48_327_848]				)

    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        lowerCamelCase      (						self				):



    '''simple docstring'''
    super().test_inference_batch_single_identical(expected_max_diff=3E-3				)



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




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()




 def        lowerCamelCase      (						self				):



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

    __lowerCamelCase													=    load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''				)
    __lowerCamelCase													=    np.ones((768, 768)	,				dtype=np.floataa				)
    __lowerCamelCase													=    0

    __lowerCamelCase													=    '''a hat'''

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

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

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

    __lowerCamelCase													=    pipeline(
        image=__UpperCAmelCase	,				mask_image=__UpperCAmelCase	,				image_embeds=__UpperCAmelCase	,				negative_image_embeds=__UpperCAmelCase	,				generator=__UpperCAmelCase	,				num_inference_steps=100	,				height=768	,				width=768	,				output_type='''np'''	,				)

    __lowerCamelCase													=    output.images[0]

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

    assert_mean_pixel_difference(__UpperCAmelCase	,				__UpperCAmelCase				)











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_			=					{
    """configuration_clipseg""": [
        """CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """CLIPSegConfig""",
        """CLIPSegTextConfig""",
        """CLIPSegVisionConfig""",
    ],
    """processing_clipseg""": ["""CLIPSegProcessor"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_			=					[
      """CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """CLIPSegModel""",
      """CLIPSegPreTrainedModel""",
      """CLIPSegTextModel""",
      """CLIPSegVisionModel""",
      """CLIPSegForImageSegmentation""",
  ]

if TYPE_CHECKING:
  from .configuration_clipseg import (
      CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
      CLIPSegConfig,
      CLIPSegTextConfig,
      CLIPSegVisionConfig,
  )
  from .processing_clipseg import CLIPSegProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_clipseg import (
        CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
        CLIPSegForImageSegmentation,
        CLIPSegModel,
        CLIPSegPreTrainedModel,
        CLIPSegTextModel,
        CLIPSegVisionModel,
    )

else:
  import sys

  a_			=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






