




'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    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       A   (		__UpperCamelCase					,unittest.TestCase   ):
							lowercase_          =    KandinskyVaaImgaImgPipeline
							lowercase_          =    ['image_embeds', 'negative_image_embeds', 'image']
							lowercase_          =    [
							    'image_embeds',
							    'negative_image_embeds',
							    'image',
							]
							lowercase_          =    [
							    'generator',
							    'height',
							    'width',
							    'strength',
							    'guidance_scale',
							    'num_inference_steps',
							    'return_dict',
							    'guidance_scale',
							    'num_images_per_prompt',
							    'output_type',
							    'return_dict',
							]
							lowercase_          =    False







							@property
							def        __lowerCAmelCase      (  self	:  Any )      ->							List[str]:


														"""simple docstring"""


														return 32







							@property
							def        __lowerCAmelCase      (  self	:  Union[str, Any] )      ->							List[Any]:


														"""simple docstring"""


														return 32







							@property
							def        __lowerCAmelCase      (  self	:  int )      ->							List[str]:


														"""simple docstring"""


														return self.time_input_dim







							@property
							def        __lowerCAmelCase      (  self	:  List[str] )      ->							Tuple:


														"""simple docstring"""


														return self.time_input_dim * 4







							@property
							def        __lowerCAmelCase      (  self	:  Dict )      ->							Tuple:


														"""simple docstring"""


														return 1_00







							@property
							def        __lowerCAmelCase      (  self	:  Optional[Any] )      ->							Dict:


														"""simple docstring"""


														torch.manual_seed(0 )

														_a     					=    {
														    'in_channels': 4,
														    # 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,
														}

														_a     					=    UNetaDConditionModel(**lowerCAmelCase_ )
														return model







							@property
							def        __lowerCAmelCase      (  self	:  Any )      ->							Dict:


														"""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	:  str )      ->							Optional[Any]:


														"""simple docstring"""


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







							def        __lowerCAmelCase      (  self	:  str )      ->							List[Any]:


														"""simple docstring"""


														_a     					=    self.dummy_unet
														_a     					=    self.dummy_movq

														_a     					=    {
														    'num_train_timesteps': 10_00,
														    'beta_schedule': 'linear',
														    'beta_start': 0.0_0_0_8_5,
														    'beta_end': 0.0_1_2,
														    'clip_sample': False,
														    'set_alpha_to_one': False,
														    'steps_offset': 0,
														    'prediction_type': 'epsilon',
														    'thresholding': False,
														}

														_a     					=    DDIMScheduler(**lowerCAmelCase_ )

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

														return components







							def        __lowerCAmelCase      (  self	:  Optional[int]							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  Dict=0 )      ->							Tuple:


														"""simple docstring"""


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

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







							def        __lowerCAmelCase      (  self	:  List[str] )      ->							List[str]:


														"""simple docstring"""


														_a     					=    'cpu'

														_a     					=    self.get_dummy_components()

														_a     					=    self.pipeline_class(**lowerCAmelCase_ )
														_a     					=    pipe.to(lowerCAmelCase_ )

														pipe.set_progress_bar_config(disable=lowerCAmelCase_ )

														_a     					=    pipe(**self.get_dummy_inputs(lowerCAmelCase_ ) )
														_a     					=    output.images

														_a     					=    pipe(
														    **self.get_dummy_inputs(lowerCAmelCase_ )							,      return_dict=lowerCAmelCase_							,      )[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(
														    [0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3] )
														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       A   (		unittest.TestCase   ):







							def        __lowerCAmelCase      (  self	:  Dict )      ->							Dict:


														"""simple docstring"""


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







							def        __lowerCAmelCase      (  self	:  Any )      ->							int:


														"""simple docstring"""


														_a     					=    load_numpy(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
														    '''/kandinskyv22/kandinskyv22_img2img_frog.npy''' )

														_a     					=    load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
														_a     					=    'A red cartoon frog, 4k'

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

														_a     					=    KandinskyVaaImgaImgPipeline.from_pretrained(
														    '''kandinsky-community/kandinsky-2-2-decoder'''							,      torch_dtype=torch.floataa )
														_a     					=    pipeline.to(lowerCAmelCase_ )

														pipeline.set_progress_bar_config(disable=lowerCAmelCase_ )

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

														_a     					=    pipeline(
														    image=lowerCAmelCase_							,      image_embeds=lowerCAmelCase_							,      negative_image_embeds=lowerCAmelCase_							,      generator=lowerCAmelCase_							,      num_inference_steps=1_00							,      height=7_68							,      width=7_68							,      strength=0.2							,      output_type='''np'''							,      )

														_a     					=    output.images[0]

														assert image.shape == (7_68, 7_68, 3)

														assert_mean_pixel_difference(lowerCAmelCase_							,      lowerCAmelCase_ )







'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_snake_case				:				Dict       							=		logging.get_logger(__name__)

_snake_case				:				Optional[Any]       							=		{
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}

class       A   (		_a					,_a   ):
							lowercase_          =    'nat'

							lowercase_          =    {
							    'num_attention_heads': 'num_heads',
							    'num_hidden_layers': 'num_layers',
							}







							def __init__(  self	:  List[str]							,      lowerCAmelCase_	:  str=4							,      lowerCAmelCase_	:  Any=3							,      lowerCAmelCase_	:  List[Any]=64							,      lowerCAmelCase_	:  Dict=[3, 4, 6, 5]							,      lowerCAmelCase_	:  Dict=[2, 4, 8, 16]							,      lowerCAmelCase_	:  str=7							,      lowerCAmelCase_	:  Dict=3.0							,      lowerCAmelCase_	:  int=True							,      lowerCAmelCase_	:  Tuple=0.0							,      lowerCAmelCase_	:  str=0.0							,      lowerCAmelCase_	:  List[str]=0.1							,      lowerCAmelCase_	:  int="gelu"							,      lowerCAmelCase_	:  List[str]=0.0_2							,      lowerCAmelCase_	:  str=1e-5							,      lowerCAmelCase_	:  Tuple=0.0							,      lowerCAmelCase_	:  str=None							,      lowerCAmelCase_	:  int=None							,      **lowerCAmelCase_	:  List[Any]							,      )      ->							Any:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_ )

														_a     					=    patch_size
														_a     					=    num_channels
														_a     					=    embed_dim
														_a     					=    depths
														_a     					=    len(lowerCAmelCase_ )
														_a     					=    num_heads
														_a     					=    kernel_size
														_a     					=    mlp_ratio
														_a     					=    qkv_bias
														_a     					=    hidden_dropout_prob
														_a     					=    attention_probs_dropout_prob
														_a     					=    drop_path_rate
														_a     					=    hidden_act
														_a     					=    layer_norm_eps
														_a     					=    initializer_range
														# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
														# this indicates the channel dimension after the last stage of the model
														_a     					=    int(embed_dim * 2 ** (len(lowerCAmelCase_ ) - 1) )
														_a     					=    layer_scale_init_value
														_a     					=    ['''stem'''] + [F'stage{idx}' for idx in range(1							,      len(lowerCAmelCase_ ) + 1 )]
														_a					,							_a     					=    get_aligned_output_features_output_indices(
														    out_features=lowerCAmelCase_							,      out_indices=lowerCAmelCase_							,      stage_names=self.stage_names )

