


'''simple docstring'''






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    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__							(				a__							,							unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  Optional[int]      		= KandinskyVaaInpaintPipeline
							lowercase__     :  Any      		= ["image_embeds", "negative_image_embeds", "image", "mask_image"]
							lowercase__     :  int      		= [
							    "image_embeds",
							    "negative_image_embeds",
							    "image",
							    "mask_image",
							]
							lowercase__     :  List[str]      		= [
							    "generator",
							    "height",
							    "width",
							    "latents",
							    "guidance_scale",
							    "num_inference_steps",
							    "return_dict",
							    "guidance_scale",
							    "num_images_per_prompt",
							    "output_type",
							    "return_dict",
							]
							lowercase__     :  int      		= False







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														return 32







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														return 32







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														return self.time_input_dim







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														return self.time_input_dim * 4







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														return 1_00







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														torch.manual_seed(0  )

														lowerCAmelCase__			  =      {
														    '''in_channels''': 9,
														    # 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,
														}

														lowerCAmelCase__			  =      UNetaDConditionModel(**lowerCamelCase_  )
														return model







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														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     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														torch.manual_seed(0  )
														lowerCAmelCase__			  =      VQModel(**self.dummy_movq_kwargs  )
														return model







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__			  =      self.dummy_unet
														lowerCAmelCase__			  =      self.dummy_movq

														lowerCAmelCase__			  =      DDIMScheduler(
														    num_train_timesteps=10_00   ,       beta_schedule='''linear'''   ,       beta_start=0.00_085   ,       beta_end=0.012   ,       clip_sample=lowerCamelCase_   ,       set_alpha_to_one=lowerCamelCase_   ,       steps_offset=1   ,       prediction_type='''epsilon'''   ,       thresholding=lowerCamelCase_   ,       )

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

														return components







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_=0  )			->				Tuple:
														lowerCAmelCase__			  =      floats_tensor((1, self.text_embedder_hidden_size)   ,       rng=random.Random(lowerCamelCase_  )  ).to(lowerCamelCase_  )
														lowerCAmelCase__			  =      floats_tensor((1, self.text_embedder_hidden_size)   ,       rng=random.Random(seed + 1  )  ).to(
														    lowerCamelCase_  )
														# create init_image
														lowerCAmelCase__			  =      floats_tensor((1, 3, 64, 64)   ,       rng=random.Random(lowerCamelCase_  )  ).to(lowerCamelCase_  )
														lowerCAmelCase__			  =      image.cpu().permute(0   ,       2   ,       3   ,       1  )[0]
														lowerCAmelCase__			  =      Image.fromarray(np.uinta(lowerCamelCase_  )  ).convert('''RGB'''  ).resize((2_56, 2_56)  )
														# create mask
														lowerCAmelCase__			  =      np.ones((64, 64)   ,       dtype=np.floataa  )
														lowerCAmelCase__			  =      0

														if str(lowerCamelCase_  ).startswith('''mps'''  ):
																					lowerCAmelCase__			  =      torch.manual_seed(lowerCamelCase_  )
														else:
																					lowerCAmelCase__			  =      torch.Generator(device=lowerCamelCase_  ).manual_seed(lowerCamelCase_  )
														lowerCAmelCase__			  =      {
														    '''image''': init_image,
														    '''mask_image''': mask,
														    '''image_embeds''': image_embeds,
														    '''negative_image_embeds''': negative_image_embeds,
														    '''generator''': generator,
														    '''height''': 64,
														    '''width''': 64,
														    '''num_inference_steps''': 2,
														    '''guidance_scale''': 4.0,
														    '''output_type''': '''np''',
														}
														return inputs







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      '''cpu'''

														lowerCAmelCase__			  =      self.get_dummy_components()

														lowerCAmelCase__			  =      self.pipeline_class(**lowerCamelCase_  )
														lowerCAmelCase__			  =      pipe.to(lowerCamelCase_  )

														pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      pipe(**self.get_dummy_inputs(lowerCamelCase_  )  )
														lowerCAmelCase__			  =      output.images

														lowerCAmelCase__			  =      pipe(
														    **self.get_dummy_inputs(lowerCamelCase_  )   ,       return_dict=lowerCamelCase_   ,       )[0]

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

														print(F"""image.shape {image.shape}"""  )

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

														lowerCAmelCase__			  =      np.array(
														    [0.50_775_903, 0.49_527_195, 0.48_824_543, 0.50_192_237, 0.48_644_906, 0.49_373_814, 0.4_780_598, 0.47_234_827, 0.48_327_848]  )

														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()}"""







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														super().test_inference_batch_single_identical(expected_max_diff=3e-3  )




@slow
@require_torch_gpu
class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														lowerCAmelCase__			  =      load_numpy(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
														    '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''  )

														lowerCAmelCase__			  =      load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''  )
														lowerCAmelCase__			  =      np.ones((7_68, 7_68)   ,       dtype=np.floataa  )
														lowerCAmelCase__			  =      0

														lowerCAmelCase__			  =      '''a hat'''

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

														lowerCAmelCase__			  =      KandinskyVaaInpaintPipeline.from_pretrained(
														    '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''   ,       torch_dtype=torch.floataa  )
														lowerCAmelCase__			  =      pipeline.to(lowerCamelCase_  )
														pipeline.set_progress_bar_config(disable=lowerCamelCase_  )

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

														lowerCAmelCase__			  =      pipeline(
														    image=lowerCamelCase_   ,       mask_image=lowerCamelCase_   ,       image_embeds=lowerCamelCase_   ,       negative_image_embeds=lowerCamelCase_   ,       generator=lowerCamelCase_   ,       num_inference_steps=1_00   ,       height=7_68   ,       width=7_68   ,       output_type='''np'''   ,       )

														lowerCAmelCase__			  =      output.images[0]

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

														assert_mean_pixel_difference(lowerCamelCase_   ,       lowerCamelCase_  )



from typing import List, Optional

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


lowerCAmelCase       		=    logging.get_logger(__name__)

lowerCAmelCase       		=    {
    """huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json""",
}




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Optional[int]										=						'''autoformer'''
	_lowerCAmelCase     :		Any										=						{
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''encoder_attention_heads''',
	    '''num_hidden_layers''': '''encoder_layers''',
	}


	def __init__(    self				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = "student_t"				,     lowercase__ = "nll"				,     lowercase__ = 1				,     lowercase__ = [1, 2, 3, 4, 5, 6, 7]				,     lowercase__ = True				,     lowercase__ = 0				,     lowercase__ = 0				,     lowercase__ = 0				,     lowercase__ = 0				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = 6_4				,     lowercase__ = 2				,     lowercase__ = 2				,     lowercase__ = 2				,     lowercase__ = 2				,     lowercase__ = 3_2				,     lowercase__ = 3_2				,     lowercase__ = "gelu"				,     lowercase__ = 0.1				,     lowercase__ = 0.1				,     lowercase__ = 0.1				,     lowercase__ = 0.1				,     lowercase__ = 0.1				,     lowercase__ = 1_0_0				,     lowercase__ = 0.0_2				,     lowercase__ = True				,     lowercase__=True				,     lowercase__ = 1_0				,     lowercase__ = 2_5				,     lowercase__ = 3				,     **lowercase__				,     ):
							# time series specific configuration
							__UpperCAmelCase			:							Optional[int]	    =	prediction_length
							__UpperCAmelCase			:							Tuple	    =	context_length if context_length is not None else prediction_length
							__UpperCAmelCase			:							Tuple	    =	distribution_output
							__UpperCAmelCase			:							Tuple	    =	loss
							__UpperCAmelCase			:							Dict	    =	input_size
							__UpperCAmelCase			:							str	    =	num_time_features
							__UpperCAmelCase			:							str	    =	lags_sequence
							__UpperCAmelCase			:							int	    =	scaling
							__UpperCAmelCase			:							int	    =	num_dynamic_real_features
							__UpperCAmelCase			:							List[str]	    =	num_static_real_features
							__UpperCAmelCase			:							List[str]	    =	num_static_categorical_features
							if cardinality is not None and num_static_categorical_features > 0:
													if len(lowercase__) != num_static_categorical_features:
																			raise ValueError(
																			    '''The cardinality should be a list of the same length as `num_static_categorical_features`''')
													__UpperCAmelCase			:							Union[str, Any]	    =	cardinality
							else:
													__UpperCAmelCase			:							List[Any]	    =	[0]
							if embedding_dimension is not None and num_static_categorical_features > 0:
													if len(lowercase__) != num_static_categorical_features:
																			raise ValueError(
																			    '''The embedding dimension should be a list of the same length as `num_static_categorical_features`''')
													__UpperCAmelCase			:							Union[str, Any]	    =	embedding_dimension
							else:
													__UpperCAmelCase			:							int	    =	[min(5_0				,     (cat + 1) // 2) for cat in self.cardinality]
							__UpperCAmelCase			:							Tuple	    =	num_parallel_samples

							# Transformer architecture configuration
							__UpperCAmelCase			:							Dict	    =	input_size * len(self.lags_sequence) + self._number_of_features
							__UpperCAmelCase			:							Optional[int]	    =	d_model
							__UpperCAmelCase			:							List[Any]	    =	encoder_attention_heads
							__UpperCAmelCase			:							int	    =	decoder_attention_heads
							__UpperCAmelCase			:							str	    =	encoder_ffn_dim
							__UpperCAmelCase			:							int	    =	decoder_ffn_dim
							__UpperCAmelCase			:							List[Any]	    =	encoder_layers
							__UpperCAmelCase			:							Tuple	    =	decoder_layers

							__UpperCAmelCase			:							Union[str, Any]	    =	dropout
							__UpperCAmelCase			:							int	    =	attention_dropout
							__UpperCAmelCase			:							int	    =	activation_dropout
							__UpperCAmelCase			:							str	    =	encoder_layerdrop
							__UpperCAmelCase			:							Dict	    =	decoder_layerdrop

							__UpperCAmelCase			:							int	    =	activation_function
							__UpperCAmelCase			:							Optional[int]	    =	init_std

							__UpperCAmelCase			:							Optional[int]	    =	use_cache

							# Autoformer
							__UpperCAmelCase			:							str	    =	label_length
							__UpperCAmelCase			:							int	    =	moving_average
							__UpperCAmelCase			:							Optional[int]	    =	autocorrelation_factor

							super().__init__(is_encoder_decoder=lowercase__				,     **lowercase__)


	@property
	def  A(    self):
							return (
							    sum(self.embedding_dimension)
							    + self.num_dynamic_real_features
							    + self.num_time_features
							    + self.num_static_real_features
							    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
							)

