





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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				(  A__				,				unittest.TestCase   ):
  __lowerCamelCase						=     KandinskyVaaControlnetImgaImgPipeline
  __lowerCamelCase						=     ['image_embeds', 'negative_image_embeds', 'image', 'hint']
  __lowerCamelCase						=     ['image_embeds', 'negative_image_embeds', 'image', 'hint']
  __lowerCamelCase						=     [
      'generator',
      'height',
      'width',
      'strength',
      'guidance_scale',
      'num_inference_steps',
      'return_dict',
      'guidance_scale',
      'num_images_per_prompt',
      'output_type',
      'return_dict',
  ]
  __lowerCamelCase						=     False







  @property
  def     UpperCAmelCase	(     self    :Union[str, Any]   ):






     '''simple docstring'''





     return 32







  @property
  def     UpperCAmelCase	(     self    :Any   ):






     '''simple docstring'''





     return 32







  @property
  def     UpperCAmelCase	(     self    :str   ):






     '''simple docstring'''





     return self.time_input_dim







  @property
  def     UpperCAmelCase	(     self    :Dict   ):






     '''simple docstring'''





     return self.time_input_dim * 4







  @property
  def     UpperCAmelCase	(     self    :List[str]   ):






     '''simple docstring'''





     return 1_00







  @property
  def     UpperCAmelCase	(     self    :Dict   ):






     '''simple docstring'''





     torch.manual_seed(0   )

     lowercase__					  =    {
         "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__					  =    UNetaDConditionModel(**__UpperCAmelCase   )
     return model







  @property
  def     UpperCAmelCase	(     self    :Optional[Any]   ):






     '''simple docstring'''





     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     UpperCAmelCase	(     self    :Union[str, Any]   ):






     '''simple docstring'''





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







  def     UpperCAmelCase	(     self    :Dict   ):






     '''simple docstring'''





     lowercase__					  =    self.dummy_unet
     lowercase__					  =    self.dummy_movq

     lowercase__					  =    {
         "num_train_timesteps": 10_00,
         "beta_schedule": "linear",
         "beta_start": 0.00085,
         "beta_end": 0.012,
         "clip_sample": False,
         "set_alpha_to_one": False,
         "steps_offset": 0,
         "prediction_type": "epsilon",
         "thresholding": False,
     }

     lowercase__					  =    DDIMScheduler(**__UpperCAmelCase   )

     lowercase__					  =    {
         "unet": unet,
         "scheduler": scheduler,
         "movq": movq,
     }

     return components







  def     UpperCAmelCase	(     self    :str       ,				_lowercase    :Any       ,				_lowercase    :Any=0   ):






     '''simple docstring'''





     lowercase__					  =    floats_tensor((1, self.text_embedder_hidden_size)       ,				rng=random.Random(__UpperCAmelCase   )   ).to(__UpperCAmelCase   )
     lowercase__					  =    floats_tensor((1, self.text_embedder_hidden_size)       ,				rng=random.Random(seed + 1   )   ).to(
         __UpperCAmelCase   )
     # create init_image
     lowercase__					  =    floats_tensor((1, 3, 64, 64)       ,				rng=random.Random(__UpperCAmelCase   )   ).to(__UpperCAmelCase   )
     lowercase__					  =    image.cpu().permute(0       ,				2       ,				3       ,				1   )[0]
     lowercase__					  =    Image.fromarray(np.uinta(__UpperCAmelCase   )   ).convert("RGB"   ).resize((2_56, 2_56)   )
     # create hint
     lowercase__					  =    floats_tensor((1, 3, 64, 64)       ,				rng=random.Random(__UpperCAmelCase   )   ).to(__UpperCAmelCase   )

     if str(__UpperCAmelCase   ).startswith("mps"   ):
        lowercase__					  =    torch.manual_seed(__UpperCAmelCase   )
     else:
        lowercase__					  =    torch.Generator(device=__UpperCAmelCase   ).manual_seed(__UpperCAmelCase   )
     lowercase__					  =    {
         "image": init_image,
         "image_embeds": image_embeds,
         "negative_image_embeds": negative_image_embeds,
         "hint": hint,
         "generator": generator,
         "height": 64,
         "width": 64,
         "num_inference_steps": 10,
         "guidance_scale": 7.0,
         "strength": 0.2,
         "output_type": "np",
     }
     return inputs







  def     UpperCAmelCase	(     self    :Dict   ):






     '''simple docstring'''





     lowercase__					  =    "cpu"

     lowercase__					  =    self.get_dummy_components()

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

     pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

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

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

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

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

     lowercase__					  =    np.array(
         [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736]   )
     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   ):







  def     UpperCAmelCase	(     self    :List[Any]   ):






     '''simple docstring'''





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







  def     UpperCAmelCase	(     self    :Dict   ):






     '''simple docstring'''





     lowercase__					  =    load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"   )

     lowercase__					  =    load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"   )
     lowercase__					  =    init_image.resize((5_12, 5_12)   )

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

     lowercase__					  =    "A robot, 4k photo"

     lowercase__					  =    KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-2-prior"       ,				torch_dtype=torch.floataa   )
     pipe_prior.to(__UpperCAmelCase   )

     lowercase__					  =    KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-2-controlnet-depth"       ,				torch_dtype=torch.floataa   )
     lowercase__					  =    pipeline.to(__UpperCAmelCase   )

     pipeline.set_progress_bar_config(disable=__UpperCAmelCase   )

     lowercase__					  =    torch.Generator(device="cpu"   ).manual_seed(0   )

     lowercase__  ,				lowercase__					  =    pipe_prior(
         __UpperCAmelCase       ,				image=__UpperCAmelCase       ,				strength=0.85       ,				generator=__UpperCAmelCase       ,				negative_prompt=""       ,				).to_tuple()

     lowercase__					  =    pipeline(
         image=__UpperCAmelCase       ,				image_embeds=__UpperCAmelCase       ,				negative_image_embeds=__UpperCAmelCase       ,				hint=__UpperCAmelCase       ,				generator=__UpperCAmelCase       ,				num_inference_steps=1_00       ,				height=5_12       ,				width=5_12       ,				strength=0.5       ,				output_type="np"       ,				)

     lowercase__					  =    output.images[0]

     assert image.shape == (5_12, 5_12, 3)

     assert_mean_pixel_difference(__UpperCAmelCase       ,				__UpperCAmelCase   )









"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_				:      List[Any]           =			logging.get_logger(__name__)

A_				:      Optional[int]           =			{
    "facebook/dpr-ctx_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-single-nq-base": (
        "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-ctx_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-multiset-base": (
        "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"
    ),
}





class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		Optional[int]       		=				'dpr'

			def __init__(       self					:    Optional[int]     ,					__UpperCAmelCase					:    List[str]=3_0_5_2_2     ,					__UpperCAmelCase					:    int=7_6_8     ,					__UpperCAmelCase					:    Optional[Any]=1_2     ,					__UpperCAmelCase					:    Dict=1_2     ,					__UpperCAmelCase					:    Any=3_0_7_2     ,					__UpperCAmelCase					:    int="gelu"     ,					__UpperCAmelCase					:    int=0.1     ,					__UpperCAmelCase					:    Optional[Any]=0.1     ,					__UpperCAmelCase					:    Any=5_1_2     ,					__UpperCAmelCase					:    Union[str, Any]=2     ,					__UpperCAmelCase					:    Any=0.02     ,					__UpperCAmelCase					:    Dict=1e-12     ,					__UpperCAmelCase					:    Optional[Any]=0     ,					__UpperCAmelCase					:    Union[str, Any]="absolute"     ,					__UpperCAmelCase					:    int = 0     ,					**__UpperCAmelCase					:    Dict     ,					) ->     Tuple:
						super().__init__(pad_token_id=__UpperCAmelCase     ,					**__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   vocab_size
						SCREAMING_SNAKE_CASE__										=   hidden_size
						SCREAMING_SNAKE_CASE__										=   num_hidden_layers
						SCREAMING_SNAKE_CASE__										=   num_attention_heads
						SCREAMING_SNAKE_CASE__										=   hidden_act
						SCREAMING_SNAKE_CASE__										=   intermediate_size
						SCREAMING_SNAKE_CASE__										=   hidden_dropout_prob
						SCREAMING_SNAKE_CASE__										=   attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__										=   max_position_embeddings
						SCREAMING_SNAKE_CASE__										=   type_vocab_size
						SCREAMING_SNAKE_CASE__										=   initializer_range
						SCREAMING_SNAKE_CASE__										=   layer_norm_eps
						SCREAMING_SNAKE_CASE__										=   projection_dim
						SCREAMING_SNAKE_CASE__										=   position_embedding_type


