

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       SCREAMING_SNAKE_CASE__					(__snake_case		,       unittest.TestCase     ):
    __lowerCamelCase						:       Dict														=     KandinskyVaaControlnetPipeline
    __lowerCamelCase						:       List[Any]														=     ["""image_embeds""", """negative_image_embeds""", """hint"""]
    __lowerCamelCase						:       Union[str, Any]														=     ["""image_embeds""", """negative_image_embeds""", """hint"""]
    __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       snake_case_				(      self):
         return 32





    @property
    def       snake_case_				(      self):
         return 32





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





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





    @property
    def       snake_case_				(      self):
         return 100





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

         lowercase__    :   Optional[int]      =       {
             '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,
         }

         lowercase__    :   Optional[int]      =       UNetaDConditionModel(**a)
         return model





    @property
    def       snake_case_				(      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       snake_case_				(      self):
         torch.manual_seed(0)
         lowercase__    :   Optional[int]      =       VQModel(**self.dummy_movq_kwargs)
         return model





    def       snake_case_				(      self):
         lowercase__    :   int      =       self.dummy_unet
         lowercase__    :   Union[str, Any]      =       self.dummy_movq

         lowercase__    :   Dict      =       DDIMScheduler(
             num_train_timesteps=1000						,   beta_schedule='linear'						,   beta_start=0.00_085						,   beta_end=0.012						,   clip_sample=a						,   set_alpha_to_one=a						,   steps_offset=1						,   prediction_type='epsilon'						,   thresholding=a						,   )

         lowercase__    :   Optional[Any]      =       {
             'unet': unet,
             'scheduler': scheduler,
             'movq': movq,
         }
         return components





    def       snake_case_				(      self						,   a						,   a=0):
         lowercase__    :   Dict      =       floats_tensor((1, self.text_embedder_hidden_size)						,   rng=random.Random(a)).to(a)
         lowercase__    :   Tuple      =       floats_tensor((1, self.text_embedder_hidden_size)						,   rng=random.Random(seed + 1)).to(
             a)

         # create hint
         lowercase__    :   Optional[Any]      =       floats_tensor((1, 3, 64, 64)						,   rng=random.Random(a)).to(a)

         if str(a).startswith('mps'):
              lowercase__    :   Dict      =       torch.manual_seed(a)
         else:
              lowercase__    :   Tuple      =       torch.Generator(device=a).manual_seed(a)
         lowercase__    :   Union[str, Any]      =       {
             '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       snake_case_				(      self):
         lowercase__    :   Dict      =       'cpu'

         lowercase__    :   str      =       self.get_dummy_components()

         lowercase__    :   Union[str, Any]      =       self.pipeline_class(**a)
         lowercase__    :   Dict      =       pipe.to(a)

         pipe.set_progress_bar_config(disable=a)

         lowercase__    :   List[Any]      =       pipe(**self.get_dummy_inputs(a))
         lowercase__    :   int      =       output.images

         lowercase__    :   Optional[int]      =       pipe(
             **self.get_dummy_inputs(a)						,   return_dict=a						,   )[0]

         lowercase__    :   str      =       image[0, -3:, -3:, -1]
         lowercase__    :   str      =       image_from_tuple[0, -3:, -3:, -1]

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

         lowercase__    :   List[Any]      =       np.array(
             [0.6_959_826, 0.868_279, 0.7_558_092, 0.68_769_467, 0.85_805_804, 0.65_977_496, 0.44_885_302, 0.5_959_111, 0.4_251_595])

         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       SCREAMING_SNAKE_CASE__					(unittest.TestCase     ):





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





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

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

         lowercase__    :   Union[str, Any]      =       KandinskyVaaPriorPipeline.from_pretrained(
             'kandinsky-community/kandinsky-2-2-prior'						,   torch_dtype=torch.floataa)
         pipe_prior.to(a)

         lowercase__    :   List[Any]      =       KandinskyVaaControlnetPipeline.from_pretrained(
             'kandinsky-community/kandinsky-2-2-controlnet-depth'						,   torch_dtype=torch.floataa)
         lowercase__    :   str      =       pipeline.to(a)
         pipeline.set_progress_bar_config(disable=a)

         lowercase__    :   Optional[Any]      =       'A robot, 4k photo'

         lowercase__    :   Dict      =       torch.Generator(device='cuda').manual_seed(0)
         lowercase__      , lowercase__    :   Tuple      =       pipe_prior(
             a						,   generator=a						,   num_inference_steps=5						,   negative_prompt=''						,   ).to_tuple()

         lowercase__    :   int      =       torch.Generator(device='cuda').manual_seed(0)
         lowercase__    :   List[str]      =       pipeline(
             image_embeds=a						,   negative_image_embeds=a						,   hint=a						,   generator=a						,   num_inference_steps=100						,   output_type='np'						,   )

         lowercase__    :   Tuple      =       output.images[0]

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

         assert_mean_pixel_difference(a						,   a)




def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str	,     SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     if len(SCREAMING_SNAKE_CASE_			) != len(SCREAMING_SNAKE_CASE_			):
          raise ValueError('String lengths must match!'			)

     lowercase__    :   Union[str, Any]      =       0

     for chara, chara in zip(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			):
          if chara != chara:
               count += 1

     return count


if __name__ == "__main__":
       import doctest

       doctest.testmod()

