





'''simple docstring'''



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      A_			(	__snake_case     ,      unittest.TestCase	):

			'''simple docstring'''





			_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    a						(       self ):
								return 32




			@property
			def    a						(       self ):
								return 32




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




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




			@property
			def    a						(       self ):
								return 1_00




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

								_UpperCamelCase									= {
								    '''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,
								}

								_UpperCamelCase									= UNetaDConditionModel(**A__ )
								return model




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




			def    a						(       self ):
								_UpperCamelCase									= self.dummy_unet
								_UpperCamelCase									= self.dummy_movq

								_UpperCamelCase									= {
								    '''num_train_timesteps''': 10_00,
								    '''beta_schedule''': '''linear''',
								    '''beta_start''': 0.0_0085,
								    '''beta_end''': 0.012,
								    '''clip_sample''': False,
								    '''set_alpha_to_one''': False,
								    '''steps_offset''': 0,
								    '''prediction_type''': '''epsilon''',
								    '''thresholding''': False,
								}

								_UpperCamelCase									= DDIMScheduler(**A__ )

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

								return components




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

								if str(A__ ).startswith("mps" ):
													_UpperCamelCase									= torch.manual_seed(A__ )
								else:
													_UpperCamelCase									= torch.Generator(device=A__ ).manual_seed(A__ )
								_UpperCamelCase									= {
								    '''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    a						(       self ):
								_UpperCamelCase									= '''cpu'''

								_UpperCamelCase									= self.get_dummy_components()

								_UpperCamelCase									= self.pipeline_class(**A__ )
								_UpperCamelCase									= pipe.to(A__ )

								pipe.set_progress_bar_config(disable=A__ )

								_UpperCamelCase									= pipe(**self.get_dummy_inputs(A__ ) )
								_UpperCamelCase									= output.images

								_UpperCamelCase									= pipe(
								    **self.get_dummy_inputs(A__ )      ,  return_dict=A__      ,  )[0]

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

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

								_UpperCamelCase									= np.array(
								    [0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736] )
								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	):

			'''simple docstring'''





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




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

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

								_UpperCamelCase									= load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/kandinskyv22/hint_image_cat.png" )
								_UpperCamelCase									= torch.from_numpy(np.array(A__ ) ).float() / 2_55.0
								_UpperCamelCase									= hint.permute(2      ,  0      ,  1 ).unsqueeze(0 )

								_UpperCamelCase									= '''A robot, 4k photo'''

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

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

								pipeline.set_progress_bar_config(disable=A__ )

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

								_UpperCamelCase									= pipe_prior(
								    A__      ,  image=A__      ,  strength=0.85      ,  generator=A__      ,  negative_prompt=""      ,  ).to_tuple()

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

								_UpperCamelCase									= output.images[0]

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

								assert_mean_pixel_difference(A__      ,  A__ )











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


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

_UpperCamelCase			:	Optional[Any]								={
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Optional[int]     =   "pegasus"
		__snake_case      :       List[str]     =   ["past_key_values"]
		__snake_case      :       Union[str, Any]     =   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







		def __init__(					self       ,A__=50265       ,A__=1024       ,A__=12       ,A__=4096       ,A__=16       ,A__=12       ,A__=4096       ,A__=16       ,A__=0.0       ,A__=0.0       ,A__=True       ,A__=True       ,A__="gelu"       ,A__=1024       ,A__=0.1       ,A__=0.0       ,A__=0.0       ,A__=0.02       ,A__=0       ,A__=False       ,A__=0       ,A__=1       ,A__=1       ,**A__       ,):
						_A	:				List[str]   =      vocab_size
						_A	:				List[Any]   =      max_position_embeddings
						_A	:				Union[str, Any]   =      d_model
						_A	:				Union[str, Any]   =      encoder_ffn_dim
						_A	:				Union[str, Any]   =      encoder_layers
						_A	:				int   =      encoder_attention_heads
						_A	:				List[Any]   =      decoder_ffn_dim
						_A	:				Optional[int]   =      decoder_layers
						_A	:				Tuple   =      decoder_attention_heads
						_A	:				List[str]   =      dropout
						_A	:				Tuple   =      attention_dropout
						_A	:				Union[str, Any]   =      activation_dropout
						_A	:				int   =      activation_function
						_A	:				Union[str, Any]   =      init_std
						_A	:				Any   =      encoder_layerdrop
						_A	:				int   =      decoder_layerdrop
						_A	:				Tuple   =      use_cache
						_A	:				Dict   =      encoder_layers
						_A	:				List[str]   =      scale_embedding  # scale factor will be sqrt(d_model) if True
						super().__init__(
						    pad_token_id=A__       ,eos_token_id=A__       ,is_encoder_decoder=A__       ,decoder_start_token_id=A__       ,forced_eos_token_id=A__       ,**A__       ,)







		@property
		def  A__      (					self		):
						return self.encoder_attention_heads







		@property
		def  A__      (					self		):
						return self.d_model



