


"""simple docstring"""





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   UpperCAmelCase_			(					_a				,							unittest.TestCase):
      lowerCamelCase__	:					str										=						KandinskyImgaImgPipeline
      lowerCamelCase__	:					List[str]										=						["prompt", "image_embeds", "negative_image_embeds", "image"]
      lowerCamelCase__	:					Optional[int]										=						[
          "prompt",
          "negative_prompt",
          "image_embeds",
          "negative_image_embeds",
          "image",
      ]
      lowerCamelCase__	:					Union[str, Any]										=						[
          "generator",
          "height",
          "width",
          "strength",
          "guidance_scale",
          "negative_prompt",
          "num_inference_steps",
          "return_dict",
          "guidance_scale",
          "num_images_per_prompt",
          "output_type",
          "return_dict",
      ]
      lowerCamelCase__	:					Optional[int]										=						False


      @property
      def 	_UpperCAmelCase (     self							) ->      List[str]:
             return 3_2


      @property
      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             return 3_2


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


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


      @property
      def 	_UpperCAmelCase (     self							) ->      int:
             return 1_0_0


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


      @property
      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             torch.manual_seed(0							)
             lowercase__    :	Optional[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	,    )

             lowercase__    :	Tuple     					=			MultilingualCLIP(a							)
             lowercase__    :	List[str]     					=			text_encoder.eval()

             return text_encoder


      @property
      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             torch.manual_seed(0							)

             lowercase__    :	List[str]     					=			{
                 '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,
             }

             lowercase__    :	Any     					=			UNetaDConditionModel(**a							)
             return model


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


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__    :	List[Any]     					=			self.dummy_text_encoder
             lowercase__    :	int     					=			self.dummy_tokenizer
             lowercase__    :	Tuple     					=			self.dummy_unet
             lowercase__    :	Tuple     					=			self.dummy_movq

             lowercase__    :	Union[str, Any]     					=			{
                 'num_train_timesteps': 1_0_0_0,
                 'beta_schedule': 'linear',
                 'beta_start': 0.00_085,
                 'beta_end': 0.012,
                 'clip_sample': False,
                 'set_alpha_to_one': False,
                 'steps_offset': 0,
                 'prediction_type': 'epsilon',
                 'thresholding': False,
             }

             lowercase__    :	Dict     					=			DDIMScheduler(**a							)

             lowercase__    :	int     					=			{
                 'text_encoder': text_encoder,
                 'tokenizer': tokenizer,
                 'unet': unet,
                 'scheduler': scheduler,
                 'movq': movq,
             }

             return components


      def 	_UpperCAmelCase (     self	,    a	,    a=0							) ->      Tuple:
             lowercase__    :	Any     					=			floats_tensor((1, self.cross_attention_dim)	,    rng=random.Random(a							)							).to(a							)
             lowercase__    :	List[str]     					=			floats_tensor((1, self.cross_attention_dim)	,    rng=random.Random(seed + 1							)							).to(a							)
             # create init_image
             lowercase__    :	str     					=			floats_tensor((1, 3, 6_4, 6_4)	,    rng=random.Random(a							)							).to(a							)
             lowercase__    :	Optional[int]     					=			image.cpu().permute(0	,    2	,    3	,    1							)[0]
             lowercase__    :	List[Any]     					=			Image.fromarray(np.uinta(a							)							).convert('RGB'							).resize((2_5_6, 2_5_6)							)

             if str(a							).startswith('mps'							):
                    lowercase__    :	Tuple     					=			torch.manual_seed(a							)
             else:
                    lowercase__    :	Tuple     					=			torch.Generator(device=a							).manual_seed(a							)
             lowercase__    :	str     					=			{
                 'prompt': 'horse',
                 'image': init_image,
                 'image_embeds': image_embeds,
                 'negative_image_embeds': negative_image_embeds,
                 'generator': generator,
                 'height': 6_4,
                 'width': 6_4,
                 'num_inference_steps': 1_0,
                 'guidance_scale': 7.0,
                 'strength': 0.2,
                 'output_type': 'np',
             }
             return inputs


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	Any     					=			'cpu'

             lowercase__    :	List[str]     					=			self.get_dummy_components()

             lowercase__    :	List[str]     					=			self.pipeline_class(**a							)
             lowercase__    :	Dict     					=			pipe.to(a							)

             pipe.set_progress_bar_config(disable=a							)

             lowercase__    :	Optional[int]     					=			pipe(**self.get_dummy_inputs(a							)							)
             lowercase__    :	Tuple     					=			output.images

             lowercase__    :	Union[str, Any]     					=			pipe(
                 **self.get_dummy_inputs(a							)	,    return_dict=a	,    )[0]

             lowercase__    :	Dict     					=			image[0, -3:, -3:, -1]
             lowercase__    :	Optional[Any]     					=			image_from_tuple[0, -3:, -3:, -1]

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

             lowercase__    :	Tuple     					=			np.array(
                 [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]							)
             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   UpperCAmelCase_			(					unittest.TestCase):


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


      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	Optional[int]     					=			load_numpy(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                 '/kandinsky/kandinsky_img2img_frog.npy'							)

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

             lowercase__    :	Dict     					=			KandinskyPriorPipeline.from_pretrained(
                 'kandinsky-community/kandinsky-2-1-prior'	,    torch_dtype=torch.floataa							)
             pipe_prior.to(a							)

             lowercase__    :	int     					=			KandinskyImgaImgPipeline.from_pretrained(
                 'kandinsky-community/kandinsky-2-1'	,    torch_dtype=torch.floataa							)
             lowercase__    :	Union[str, Any]     					=			pipeline.to(a							)

             pipeline.set_progress_bar_config(disable=a							)

             lowercase__    :	str     					=			torch.Generator(device='cpu'							).manual_seed(0							)
             lowercase__    :	str     					=			pipe_prior(
                 a	,    generator=a	,    num_inference_steps=5	,    negative_prompt=''	,    ).to_tuple()

             lowercase__    :	Optional[Any]     					=			pipeline(
                 a	,    image=a	,    image_embeds=a	,    negative_image_embeds=a	,    generator=a	,    num_inference_steps=1_0_0	,    height=7_6_8	,    width=7_6_8	,    strength=0.2	,    output_type='np'	,    )

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

             assert image.shape == (7_6_8, 7_6_8, 3)

             assert_mean_pixel_difference(a	,    a							)


"""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        a_  (   _lowerCAmelCase						:				dict  ):





       '''simple docstring'''

       return (data["data"], data["target"])





def        a_  (   _lowerCAmelCase						:				np.ndarray     ,				_lowerCAmelCase						:				np.ndarray     ,				_lowerCAmelCase						:				np.ndarray  ):





       '''simple docstring'''

       lowercase__    :	Any     					=			XGBRegressor(verbosity=0     ,				random_state=42  )
       xgb.fit(_lowerCAmelCase     ,				_lowerCAmelCase  )
       # Predict target for test data
       lowercase__    :	str     					=			xgb.predict(_lowerCAmelCase  )
       lowercase__    :	Union[str, Any]     					=			predictions.reshape(len(_lowerCAmelCase  )     ,				1  )
       return predictions





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	Optional[Any]     					=			fetch_california_housing()
       lowercase__					,   lowercase__    :	str     					=			data_handling(_lowerCAmelCase  )
       lowercase__					,   lowercase__					,   lowercase__					,   lowercase__    :	str     					=			train_test_split(
           _lowerCAmelCase     ,				_lowerCAmelCase     ,				test_size=0.2_5     ,				random_state=1  )
       lowercase__    :	Any     					=			xgboost(_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase  )
       # Error printing
       print(f"""Mean Absolute Error : {mean_absolute_error(_lowerCAmelCase     ,				_lowerCAmelCase  )}"""  )
       print(f"""Mean Square Error  : {mean_squared_error(_lowerCAmelCase     ,				_lowerCAmelCase  )}"""  )


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)
       main()

