
'''simple docstring'''


from .pipeline_kandinskya_a import KandinskyVaaPipeline
from .pipeline_kandinsky2_2_controlnet import KandinskyVaaControlnetPipeline
from .pipeline_kandinsky2_2_controlnet_img2img import KandinskyVaaControlnetImgaImgPipeline
from .pipeline_kandinsky2_2_img2img import KandinskyVaaImgaImgPipeline
from .pipeline_kandinsky2_2_inpainting import KandinskyVaaInpaintPipeline
from .pipeline_kandinsky2_2_prior import KandinskyVaaPriorPipeline
from .pipeline_kandinsky2_2_prior_emb2emb import KandinskyVaaPriorEmbaEmbPipeline




'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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 		__lowercase			(					_lowercase ,       unittest.TestCase      ):
			lowerCamelCase     :						List[str]		 =     KandinskyPipeline
			lowerCamelCase     :						List[str]		 =     [
			    "prompt",
			    "image_embeds",
			    "negative_image_embeds",
			]
			lowerCamelCase     :						str		 =     ["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
			lowerCamelCase     :						Dict		 =     [
			    "generator",
			    "height",
			    "width",
			    "latents",
			    "guidance_scale",
			    "negative_prompt",
			    "num_inference_steps",
			    "return_dict",
			    "guidance_scale",
			    "num_images_per_prompt",
			    "output_type",
			    "return_dict",
			]
			lowerCamelCase     :						Union[str, Any]		 =     False

			@property
			def     UpperCAmelCase__	(self ):
								return 3_2

			@property
			def     UpperCAmelCase__	(self ):
								return 3_2

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

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

			@property
			def     UpperCAmelCase__	(self ):
								return 1_0_0

			@property
			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : int       =						XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''' )
								return tokenizer

			@property
			def     UpperCAmelCase__	(self ):
								torch.manual_seed(0 )
								lowerCamelCase_   : Any       =						MCLIPConfig(
								    numDims=self.cross_attention_dim     ,				transformerDimensions=self.text_embedder_hidden_size     ,				hidden_size=self.text_embedder_hidden_size     ,				intermediate_size=3_7     ,				num_attention_heads=4     ,				num_hidden_layers=5     ,				vocab_size=1_0_0_5     ,				)

								lowerCamelCase_   : Union[str, Any]       =						MultilingualCLIP(A )
								lowerCamelCase_   : Dict       =						text_encoder.eval()

								return text_encoder

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

								lowerCamelCase_   : Dict       =						{
								    '''in_channels''': 4,
								    # Out channels is double in channels because predicts mean and variance
								    '''out_channels''': 8,
								    '''addition_embed_type''': '''text_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''': '''text_image_proj''',
								    '''cross_attention_dim''': self.cross_attention_dim,
								    '''attention_head_dim''': 4,
								    '''resnet_time_scale_shift''': '''scale_shift''',
								    '''class_embed_type''': None,
								}

								lowerCamelCase_   : Union[str, Any]       =						UNetaDConditionModel(**A )
								return model

			@property
			def     UpperCAmelCase__	(self ):
								return {
								    "block_out_channels": [3_2, 6_4],
								    "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": 1_2,
								    "out_channels": 3,
								    "up_block_types": [
								        "AttnUpDecoderBlock2D",
								        "UpDecoderBlock2D",
								    ],
								    "vq_embed_dim": 4,
								}

			@property
			def     UpperCAmelCase__	(self ):
								torch.manual_seed(0 )
								lowerCamelCase_   : Dict       =						VQModel(**self.dummy_movq_kwargs )
								return model

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : List[str]       =						self.dummy_text_encoder
								lowerCamelCase_   : Tuple       =						self.dummy_tokenizer
								lowerCamelCase_   : int       =						self.dummy_unet
								lowerCamelCase_   : str       =						self.dummy_movq

								lowerCamelCase_   : str       =						DDIMScheduler(
								    num_train_timesteps=1_0_0_0     ,				beta_schedule='''linear'''     ,				beta_start=0.0_00_85     ,				beta_end=0.0_12     ,				clip_sample=A     ,				set_alpha_to_one=A     ,				steps_offset=1     ,				prediction_type='''epsilon'''     ,				thresholding=A     ,				)

								lowerCamelCase_   : int       =						{
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''movq''': movq,
								}
								return components

			def     UpperCAmelCase__	(self     ,				A     ,				A=0 ):
								lowerCamelCase_   : Union[str, Any]       =						floats_tensor((1, self.cross_attention_dim)     ,				rng=random.Random(A ) ).to(A )
								lowerCamelCase_   : List[Any]       =						floats_tensor((1, self.cross_attention_dim)     ,				rng=random.Random(seed + 1 ) ).to(A )
								if str(A ).startswith('''mps''' ):
													lowerCamelCase_   : List[str]       =						torch.manual_seed(A )
								else:
													lowerCamelCase_   : Optional[Any]       =						torch.Generator(device=A ).manual_seed(A )
								lowerCamelCase_   : Any       =						{
								    '''prompt''': '''horse''',
								    '''image_embeds''': image_embeds,
								    '''negative_image_embeds''': negative_image_embeds,
								    '''generator''': generator,
								    '''height''': 6_4,
								    '''width''': 6_4,
								    '''guidance_scale''': 4.0,
								    '''num_inference_steps''': 2,
								    '''output_type''': '''np''',
								}
								return inputs






			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : int       =						'''cpu'''

								lowerCamelCase_   : Optional[Any]       =						self.get_dummy_components()

								lowerCamelCase_   : Tuple       =						self.pipeline_class(**A )
								lowerCamelCase_   : List[str]       =						pipe.to(A )

								pipe.set_progress_bar_config(disable=A )

								lowerCamelCase_   : Optional[Any]       =						pipe(**self.get_dummy_inputs(A ) )
								lowerCamelCase_   : int       =						output.images

								lowerCamelCase_   : List[Any]       =						pipe(
								    **self.get_dummy_inputs(A )     ,				return_dict=A     ,				)[0]

								lowerCamelCase_   : List[Any]       =						image[0, -3:, -3:, -1]
								lowerCamelCase_   : List[Any]       =						image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 6_4, 6_4, 3)

								lowerCamelCase_   : int       =						np.array(
								    [0.32_86_63, 1.0, 0.23_21_68_73, 1.0, 0.92_71_75_64, 0.4_63_90_46, 0.96_89_47_77, 0.31_71_33_78, 0.6_29_39_53] )

								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 		__lowercase			(					unittest.TestCase      ):

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






			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Tuple       =						load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/kandinsky/kandinsky_text2img_cat_fp16.npy''' )

								lowerCamelCase_   : str       =						KandinskyPriorPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-1-prior'''     ,				torch_dtype=torch.floataa )
								pipe_prior.to(A )

								lowerCamelCase_   : List[Any]       =						KandinskyPipeline.from_pretrained('''kandinsky-community/kandinsky-2-1'''     ,				torch_dtype=torch.floataa )
								lowerCamelCase_   : Tuple       =						pipeline.to(A )
								pipeline.set_progress_bar_config(disable=A )

								lowerCamelCase_   : List[str]       =						'''red cat, 4k photo'''

								lowerCamelCase_   : Optional[Any]       =						torch.Generator(device='''cuda''' ).manual_seed(0 )
								lowerCamelCase_, lowerCamelCase_   : Any       =						pipe_prior(
								    A     ,				generator=A     ,				num_inference_steps=5     ,				negative_prompt=''''''     ,				).to_tuple()

								lowerCamelCase_   : Optional[Any]       =						torch.Generator(device='''cuda''' ).manual_seed(0 )
								lowerCamelCase_   : Optional[Any]       =						pipeline(
								    A     ,				image_embeds=A     ,				negative_image_embeds=A     ,				generator=A     ,				num_inference_steps=1_0_0     ,				output_type='''np'''     ,				)

								lowerCamelCase_   : Union[str, Any]       =						output.images[0]

								assert image.shape == (5_1_2, 5_1_2, 3)

								assert_mean_pixel_difference(A     ,				A )


