





import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    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(						__snake_case			, unittest.TestCase	):
     '''simple docstring'''


     __magic_name__   =			KandinskyVaaControlnetPipeline
     __magic_name__   =			['image_embeds', 'negative_image_embeds', 'hint']
     __magic_name__   =			['image_embeds', 'negative_image_embeds', 'hint']
     __magic_name__   =			[
         'generator',
         'height',
         'width',
         'latents',
         'guidance_scale',
         'num_inference_steps',
         'return_dict',
         'guidance_scale',
         'num_images_per_prompt',
         'output_type',
         'return_dict',
     ]
     __magic_name__   =			False




     @property
     def        lowerCAmelCase__		(    self							):
         return 32




     @property
     def        lowerCAmelCase__		(    self							):
         return 32




     @property
     def        lowerCAmelCase__		(    self							):
         return self.time_input_dim




     @property
     def        lowerCAmelCase__		(    self							):
         return self.time_input_dim * 4




     @property
     def        lowerCAmelCase__		(    self							):
         return 100




     @property
     def        lowerCAmelCase__		(    self							):
         torch.manual_seed(0							)

         _A     =							{
             'in_channels': 8,
             # Out channels is double in channels because predicts mean and variance
             'out_channels': 8,
             'addition_embed_type': 'image_hint',
             '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,
         }

         _A     =							UNetaDConditionModel(**snake_case_							)
         return model




     @property
     def        lowerCAmelCase__		(    self							):
         return {
             "block_out_channels": [32, 32, 64, 64],
             "down_block_types": [
                 "DownEncoderBlock2D",
                 "DownEncoderBlock2D",
                 "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
             "vq_embed_dim": 4,
         }




     @property
     def        lowerCAmelCase__		(    self							):
         torch.manual_seed(0							)
         _A     =							VQModel(**self.dummy_movq_kwargs							)
         return model




     def        lowerCAmelCase__		(    self							):
         _A     =							self.dummy_unet
         _A     =							self.dummy_movq

         _A     =							DDIMScheduler(
             num_train_timesteps=1000				,     beta_schedule='linear'				,     beta_start=0.0_0085				,     beta_end=0.012				,     clip_sample=snake_case_				,     set_alpha_to_one=snake_case_				,     steps_offset=1				,     prediction_type='epsilon'				,     thresholding=snake_case_				,     )

         _A     =							{
             'unet': unet,
             'scheduler': scheduler,
             'movq': movq,
         }
         return components




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_=0							):
         _A     =							floats_tensor((1, self.text_embedder_hidden_size)				,     rng=random.Random(snake_case_							)							).to(snake_case_							)
         _A     =							floats_tensor((1, self.text_embedder_hidden_size)				,     rng=random.Random(seed + 1							)							).to(
             snake_case_							)

         # create hint
         _A     =							floats_tensor((1, 3, 64, 64)				,     rng=random.Random(snake_case_							)							).to(snake_case_							)

         if str(snake_case_							).startswith('mps'							):
             _A     =							torch.manual_seed(snake_case_							)
         else:
             _A     =							torch.Generator(device=snake_case_							).manual_seed(snake_case_							)
         _A     =							{
             'image_embeds': image_embeds,
             'negative_image_embeds': negative_image_embeds,
             'hint': hint,
             'generator': generator,
             'height': 64,
             'width': 64,
             'guidance_scale': 4.0,
             'num_inference_steps': 2,
             'output_type': 'np',
         }
         return inputs




     def        lowerCAmelCase__		(    self							):
         _A     =							'cpu'

         _A     =							self.get_dummy_components()

         _A     =							self.pipeline_class(**snake_case_							)
         _A     =							pipe.to(snake_case_							)

         pipe.set_progress_bar_config(disable=snake_case_							)

         _A     =							pipe(**self.get_dummy_inputs(snake_case_							)							)
         _A     =							output.images

         _A     =							pipe(
             **self.get_dummy_inputs(snake_case_							)				,     return_dict=snake_case_				,     )[0]

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

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

         _A     =							np.array(
             [0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595]							)

         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()}"







@slow
@require_torch_gpu
class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     def        lowerCAmelCase__		(    self							):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




     def        lowerCAmelCase__		(    self							):
         _A     =							load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'							)

         _A     =							load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/kandinskyv22/hint_image_cat.png'							)
         _A     =							torch.from_numpy(np.array(snake_case_							)							).float() / 255.0
         _A     =							hint.permute(2				,     0				,     1							).unsqueeze(0							)

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

         _A     =							KandinskyVaaControlnetPipeline.from_pretrained(
             'kandinsky-community/kandinsky-2-2-controlnet-depth'				,     torch_dtype=torch.floataa							)
         _A     =							pipeline.to(snake_case_							)
         pipeline.set_progress_bar_config(disable=snake_case_							)

         _A     =							'A robot, 4k photo'

         _A     =							torch.Generator(device='cuda'							).manual_seed(0							)
         _A,			_A     =							pipe_prior(
             snake_case_				,     generator=snake_case_				,     num_inference_steps=5				,     negative_prompt=''				,     ).to_tuple()

         _A     =							torch.Generator(device='cuda'							).manual_seed(0							)
         _A     =							pipeline(
             image_embeds=snake_case_				,     negative_image_embeds=snake_case_				,     hint=snake_case_				,     generator=snake_case_				,     num_inference_steps=100				,     output_type='np'				,     )

         _A     =							output.images[0]

         assert image.shape == (512, 512, 3)

         assert_mean_pixel_difference(snake_case_				,     snake_case_							)












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A							:			Dict         =			{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			str         =			["BlenderbotTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			str         =			[
     "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "BlenderbotForCausalLM",
     "BlenderbotForConditionalGeneration",
     "BlenderbotModel",
     "BlenderbotPreTrainedModel",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			Union[str, Any]         =			[
     "TFBlenderbotForConditionalGeneration",
     "TFBlenderbotModel",
     "TFBlenderbotPreTrainedModel",
 ]


try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			Optional[Any]         =			[
     "FlaxBlenderbotForConditionalGeneration",
     "FlaxBlenderbotModel",
     "FlaxBlenderbotPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_blenderbot import (
     BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BlenderbotConfig,
     BlenderbotOnnxConfig,
 )
 from .tokenization_blenderbot import BlenderbotTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_blenderbot import (
      BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
      BlenderbotForCausalLM,
      BlenderbotForConditionalGeneration,
      BlenderbotModel,
      BlenderbotPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_blenderbot import (
      TFBlenderbotForConditionalGeneration,
      TFBlenderbotModel,
      TFBlenderbotPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_blenderbot import (
      FlaxBlenderbotForConditionalGeneration,
      FlaxBlenderbotModel,
      FlaxBlenderbotPreTrainedModel,
  )

else:
 import sys

 __A							:			str         =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





