






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 							__A	(     a ,	unittest.TestCase					):





				"""simple docstring"""







				UpperCamelCase__					: str     					=KandinskyPipeline
				UpperCamelCase__					: Dict     					=[
				    """prompt""",
				    """image_embeds""",
				    """negative_image_embeds""",
				]
				UpperCamelCase__					: int     					=["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
				UpperCamelCase__					: Optional[Any]     					=[
				    """generator""",
				    """height""",
				    """width""",
				    """latents""",
				    """guidance_scale""",
				    """negative_prompt""",
				    """num_inference_steps""",
				    """return_dict""",
				    """guidance_scale""",
				    """num_images_per_prompt""",
				    """output_type""",
				    """return_dict""",
				]
				UpperCamelCase__					: Optional[int]     					=False


				@property
				def  __lowercase				(					self       ):




									"""simple docstring"""

									return 32


				@property
				def  __lowercase				(					self       ):




									"""simple docstring"""

									return 32


				@property
				def  __lowercase				(					self       ):




									"""simple docstring"""

									return self.time_input_dim


				@property
				def  __lowercase				(					self       ):




									"""simple docstring"""

									return self.time_input_dim * 4


				@property
				def  __lowercase				(					self       ):




									"""simple docstring"""

									return 100


				@property
				def  __lowercase				(					self       ):




									"""simple docstring"""

									__UpperCamelCase :   Optional[int]									=XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'       )
									return tokenizer


				@property
				def  __lowercase				(					self       ):




									"""simple docstring"""

									torch.manual_seed(0       )
									__UpperCamelCase :   Optional[int]									=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=1005				,    )

									__UpperCamelCase :   Optional[int]									=MultilingualCLIP(lowerCamelCase__       )
									__UpperCamelCase :   Optional[Any]									=text_encoder.eval()

									return text_encoder


				@property
				def  __lowercase				(					self       ):




									"""simple docstring"""

									torch.manual_seed(0       )

									__UpperCamelCase :   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,
									}

									__UpperCamelCase :   Optional[Any]									=UNetaDConditionModel(**lowerCamelCase__       )
									return model


				@property
				def  __lowercase				(					self       ):




									"""simple docstring"""

									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  __lowercase				(					self       ):




									"""simple docstring"""

									torch.manual_seed(0       )
									__UpperCamelCase :   Union[str, Any]									=VQModel(**self.dummy_movq_kwargs       )
									return model


				def  __lowercase				(					self       ):




									"""simple docstring"""

									__UpperCamelCase :   List[Any]									=self.dummy_text_encoder
									__UpperCamelCase :   str									=self.dummy_tokenizer
									__UpperCamelCase :   Optional[Any]									=self.dummy_unet
									__UpperCamelCase :   List[Any]									=self.dummy_movq

									__UpperCamelCase :   int									=DDIMScheduler(
									    num_train_timesteps=1000				,    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__				,    )

									__UpperCamelCase :   str									={
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'unet': unet,
									    'scheduler': scheduler,
									    'movq': movq,
									}
									return components


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__=0       ):




									"""simple docstring"""

									__UpperCamelCase :   Optional[int]									=floats_tensor((1, self.cross_attention_dim)				,    rng=random.Random(lowerCamelCase__       )       ).to(lowerCamelCase__       )
									__UpperCamelCase :   Optional[Any]									=floats_tensor((1, self.cross_attention_dim)				,    rng=random.Random(seed + 1       )       ).to(lowerCamelCase__       )
									if str(lowerCamelCase__       ).startswith('mps'       ):
														__UpperCamelCase :   Union[str, Any]									=torch.manual_seed(lowerCamelCase__       )
									else:
														__UpperCamelCase :   Optional[int]									=torch.Generator(device=lowerCamelCase__       ).manual_seed(lowerCamelCase__       )
									__UpperCamelCase :   Optional[int]									={
									    'prompt': 'horse',
									    'image_embeds': image_embeds,
									    'negative_image_embeds': negative_image_embeds,
									    'generator': generator,
									    'height': 64,
									    'width': 64,
									    'guidance_scale': 4.0,
									    'num_inference_steps': 2,
									    'output_type': 'np',
									}
									return inputs







				def  __lowercase				(					self       ):




									"""simple docstring"""

									__UpperCamelCase :   Union[str, Any]									='cpu'

									__UpperCamelCase :   Any									=self.get_dummy_components()

									__UpperCamelCase :   Any									=self.pipeline_class(**lowerCamelCase__       )
									__UpperCamelCase :   str									=pipe.to(lowerCamelCase__       )

									pipe.set_progress_bar_config(disable=lowerCamelCase__       )

									__UpperCamelCase :   Dict									=pipe(**self.get_dummy_inputs(lowerCamelCase__       )       )
									__UpperCamelCase :   List[str]									=output.images

									__UpperCamelCase :   str									=pipe(
									    **self.get_dummy_inputs(lowerCamelCase__       )				,    return_dict=lowerCamelCase__				,    )[0]

									__UpperCamelCase :   int									=image[0, -3:, -3:, -1]
									__UpperCamelCase :   Tuple									=image_from_tuple[0, -3:, -3:, -1]

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

									__UpperCamelCase :   int									=np.array(
									    [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]       )

									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  __lowercase				(					self       ):




									"""simple docstring"""

									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







				def  __lowercase				(					self       ):




									"""simple docstring"""

									__UpperCamelCase :   List[str]									=load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/kandinsky/kandinsky_text2img_cat_fp16.npy'       )

									__UpperCamelCase :   Union[str, Any]									=KandinskyPriorPipeline.from_pretrained(
									    'kandinsky-community/kandinsky-2-1-prior'				,    torch_dtype=torch.floataa       )
									pipe_prior.to(lowerCamelCase__       )

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

									__UpperCamelCase :   str									='red cat, 4k photo'

									__UpperCamelCase :   Dict									=torch.Generator(device='cuda'       ).manual_seed(0       )
									__UpperCamelCase						,							__UpperCamelCase :   List[str]									=pipe_prior(
									    lowerCamelCase__				,    generator=lowerCamelCase__				,    num_inference_steps=5				,    negative_prompt=''				,    ).to_tuple()

									__UpperCamelCase :   Union[str, Any]									=torch.Generator(device='cuda'       ).manual_seed(0       )
									__UpperCamelCase :   List[str]									=pipeline(
									    lowerCamelCase__				,    image_embeds=lowerCamelCase__				,    negative_image_embeds=lowerCamelCase__				,    generator=lowerCamelCase__				,    num_inference_steps=100				,    output_type='np'				,    )

									__UpperCamelCase :   Any									=output.images[0]

									assert image.shape == (512, 512, 3)

									assert_mean_pixel_difference(lowerCamelCase__				,    lowerCamelCase__       )









from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
A_			:Union[str, Any]	    =		load_iris()
iris.keys()


print(f"Target names: \n {iris.target_names} ")
print(f"\n Features: \n {iris.feature_names}")

# Train set e Test set
A_    ,A_    ,A_    ,A_			:Tuple	    =		train_test_split(
    iris['''data'''], iris['''target'''], random_state=4
)

# KNN

A_			:Any	    =		KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
A_			:Optional[Any]	    =		[[1, 2, 1, 4], [2, 3, 4, 5]]

A_			:Union[str, Any]	    =		knn.predict(X_new)

print(
    f"\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"
    f" {iris['target_names'][prediction]}"
)

