



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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       ):
				A_   :     Dict								=     KandinskyImgaImgPipeline
				A_   :     Tuple								=     ['prompt', 'image_embeds', 'negative_image_embeds', 'image']
				A_   :     Union[str, Any]								=     [
				    'prompt',
				    'negative_prompt',
				    'image_embeds',
				    'negative_image_embeds',
				    'image',
				]
				A_   :     List[Any]								=     [
				    'generator',
				    'height',
				    'width',
				    'strength',
				    'guidance_scale',
				    'negative_prompt',
				    'num_inference_steps',
				    'return_dict',
				    'guidance_scale',
				    'num_images_per_prompt',
				    'output_type',
				    'return_dict',
				]
				A_   :     Dict								=     False


				@property
				def __UpperCamelCase						(							self      :  Optional[int]       )		->     Tuple:
											return 32


				@property
				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     str:
											return 32


				@property
				def __UpperCamelCase						(							self      :  Dict       )		->     Optional[int]:
											return self.time_input_dim


				@property
				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     str:
											return self.time_input_dim * 4


				@property
				def __UpperCamelCase						(							self      :  int       )		->     Optional[Any]:
											return 100


				@property
				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     Optional[int]:
											A      						=    XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'       )
											return tokenizer


				@property
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Union[str, Any]:
											torch.manual_seed(0       )
											A      						=    MCLIPConfig(
											    numDims=self.cross_attention_dim						, transformerDimensions=self.text_embedder_hidden_size						, hidden_size=self.text_embedder_hidden_size						, intermediate_size=37						, num_attention_heads=4						, num_hidden_layers=5						, vocab_size=1_005						, )

											A      						=    MultilingualCLIP(lowerCAmelCase_       )
											A      						=    text_encoder.eval()

											return text_encoder


				@property
				def __UpperCamelCase						(							self      :  List[str]       )		->     Tuple:
											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': '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,
											}

											A      						=    UNetaDConditionModel(**lowerCAmelCase_       )
											return model


				@property
				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     int:
											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 __UpperCamelCase						(							self      :  Any       )		->     str:
											torch.manual_seed(0       )
											A      						=    VQModel(**self.dummy_movq_kwargs       )
											return model


				def __UpperCamelCase						(							self      :  int       )		->     str:
											A      						=    self.dummy_text_encoder
											A      						=    self.dummy_tokenizer
											A      						=    self.dummy_unet
											A      						=    self.dummy_movq

											A      						=    {
											    'num_train_timesteps': 1_000,
											    '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      						=    {
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											    'unet': unet,
											    'scheduler': scheduler,
											    'movq': movq,
											}

											return components


				def __UpperCamelCase						(							self      :  Union[str, Any]						, __UpperCamelCase      :  str						, __UpperCamelCase      :  List[Any]=0       )		->     List[str]:
											A      						=    floats_tensor((1, self.cross_attention_dim)						, rng=random.Random(lowerCAmelCase_       )       ).to(lowerCAmelCase_       )
											A      						=    floats_tensor((1, self.cross_attention_dim)						, 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((256, 256)       )

											if str(lowerCAmelCase_       ).startswith('mps'       ):
																		A      						=    torch.manual_seed(lowerCAmelCase_       )
											else:
																		A      						=    torch.Generator(device=lowerCAmelCase_       ).manual_seed(lowerCAmelCase_       )
											A      						=    {
											    'prompt': 'horse',
											    '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 __UpperCamelCase						(							self      :  Optional[int]       )		->     List[str]:
											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_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_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        lowerCAmelCase__					(  unittest.TestCase       ):


				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     Any:
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()


				def __UpperCamelCase						(							self      :  Tuple       )		->     Optional[int]:
											A      						=    load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/kandinsky/kandinsky_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      						=    KandinskyPriorPipeline.from_pretrained(
											    'kandinsky-community/kandinsky-2-1-prior'						, torch_dtype=torch.floataa       )
											pipe_prior.to(lowerCAmelCase_       )

											A      						=    KandinskyImgaImgPipeline.from_pretrained(
											    'kandinsky-community/kandinsky-2-1'						, torch_dtype=torch.floataa       )
											A      						=    pipeline.to(lowerCAmelCase_       )

											pipeline.set_progress_bar_config(disable=lowerCAmelCase_       )

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

											A      						=    pipeline(
											    lowerCAmelCase_						, image=lowerCAmelCase_						, image_embeds=lowerCAmelCase_						, negative_image_embeds=lowerCAmelCase_						, generator=lowerCAmelCase_						, num_inference_steps=100						, height=768						, width=768						, strength=0.2						, output_type='np'						, )

											A      						=    output.images[0]

											assert image.shape == (768, 768, 3)

											assert_mean_pixel_difference(lowerCAmelCase_						, lowerCAmelCase_       )





'''simple docstring'''


import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def 						snake_case_       (UpperCamelCase			:    dict ):





							'''simple docstring'''


							return (data["data"], data["target"])
def 						snake_case_       (UpperCamelCase			:    np.ndarray					,		UpperCamelCase			:    np.ndarray					,		UpperCamelCase			:    np.ndarray ):





							'''simple docstring'''


							_a      	=							XGBRegressor(verbosity=0					,		random_state=42 )
							xgb.fit(UpperCamelCase					,		UpperCamelCase )
							# Predict target for test data
							_a      	=							xgb.predict(UpperCamelCase )
							_a      	=							predictions.reshape(len(UpperCamelCase )					,		1 )
							return predictions
def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							fetch_california_housing()
							_a	,				_a      	=							data_handling(UpperCamelCase )
							_a	,				_a	,				_a	,				_a      	=							train_test_split(
							    UpperCamelCase					,		UpperCamelCase					,		test_size=0.25					,		random_state=1 )
							_a      	=							xgboost(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase )
							# Error printing
							print(f'Mean Absolute Error : {mean_absolute_error(UpperCamelCase					,		UpperCamelCase )}' )
							print(f'Mean Square Error  : {mean_squared_error(UpperCamelCase					,		UpperCamelCase )}' )


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)
				main()

