

from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


snake_case__     							= logging.get_logger(__name__)  # pylint: disable=invalid-name



class     snake_case__  (      snake_case_  ):
				__lowerCAmelCase		=      ['''safety_checker''', '''feature_extractor''']
				def __init__(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase = True     ,      ):




										"""simple docstring"""




										super().__init__()
										if hasattr(scheduler.config     ,      'steps_offset'      ) and scheduler.config.steps_offset != 1:
																__magic_name__				:							str				=      (
																    F"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
																    F" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
																    'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
																    ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
																    ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
																    ' file'
																)
																deprecate('steps_offset!=1'     ,      '1.0.0'     ,      _UpperCAmelCase     ,      standard_warn=_UpperCAmelCase      )
																__magic_name__				:							List[Any]				=      dict(scheduler.config      )
																__magic_name__				:							Optional[int]				=      1
																__magic_name__				:							Optional[Any]				=      FrozenDict(_UpperCAmelCase      )

										self.register_modules(
										    vae=_UpperCAmelCase     ,      text_encoder=_UpperCAmelCase     ,      tokenizer=_UpperCAmelCase     ,      unet=_UpperCAmelCase     ,      scheduler=_UpperCAmelCase     ,      safety_checker=_UpperCAmelCase     ,      feature_extractor=_UpperCAmelCase     ,      )
										self.register_to_config(requires_safety_checker=_UpperCAmelCase      )
				@property
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										return {k: getattr(self     ,      _UpperCAmelCase      ) for k in self.config.keys() if not k.startswith('_'      )}
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase = "auto"      ):




										"""simple docstring"""




										if slice_size == "auto":
																# half the attention head size is usually a good trade-off between
																# speed and memory
																__magic_name__				:							List[Any]				=      self.unet.config.attention_head_dim // 2
										self.unet.set_attention_slice(_UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										self.enable_attention_slicing(_UpperCAmelCase      )
				@torch.no_grad()
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase = 0.8     ,      _UpperCAmelCase = 50     ,      _UpperCAmelCase = 7.5     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = 1     ,      _UpperCAmelCase = 0.0     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = "pil"     ,      _UpperCAmelCase = True     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = 1     ,      ):




										"""simple docstring"""




										return StableDiffusionInpaintPipelineLegacy(**self.components      )(
										    prompt=_UpperCAmelCase     ,      image=_UpperCAmelCase     ,      mask_image=_UpperCAmelCase     ,      strength=_UpperCAmelCase     ,      num_inference_steps=_UpperCAmelCase     ,      guidance_scale=_UpperCAmelCase     ,      negative_prompt=_UpperCAmelCase     ,      num_images_per_prompt=_UpperCAmelCase     ,      eta=_UpperCAmelCase     ,      generator=_UpperCAmelCase     ,      output_type=_UpperCAmelCase     ,      return_dict=_UpperCAmelCase     ,      callback=_UpperCAmelCase     ,      )
				@torch.no_grad()
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase = 0.8     ,      _UpperCAmelCase = 50     ,      _UpperCAmelCase = 7.5     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = 1     ,      _UpperCAmelCase = 0.0     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = "pil"     ,      _UpperCAmelCase = True     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = 1     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




										return StableDiffusionImgaImgPipeline(**self.components      )(
										    prompt=_UpperCAmelCase     ,      image=_UpperCAmelCase     ,      strength=_UpperCAmelCase     ,      num_inference_steps=_UpperCAmelCase     ,      guidance_scale=_UpperCAmelCase     ,      negative_prompt=_UpperCAmelCase     ,      num_images_per_prompt=_UpperCAmelCase     ,      eta=_UpperCAmelCase     ,      generator=_UpperCAmelCase     ,      output_type=_UpperCAmelCase     ,      return_dict=_UpperCAmelCase     ,      callback=_UpperCAmelCase     ,      callback_steps=_UpperCAmelCase     ,      )





				@torch.no_grad()
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase = 512     ,      _UpperCAmelCase = 512     ,      _UpperCAmelCase = 50     ,      _UpperCAmelCase = 7.5     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = 1     ,      _UpperCAmelCase = 0.0     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = "pil"     ,      _UpperCAmelCase = True     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = 1     ,      ):




										"""simple docstring"""




										return StableDiffusionPipeline(**self.components      )(
										    prompt=_UpperCAmelCase     ,      height=_UpperCAmelCase     ,      width=_UpperCAmelCase     ,      num_inference_steps=_UpperCAmelCase     ,      guidance_scale=_UpperCAmelCase     ,      negative_prompt=_UpperCAmelCase     ,      num_images_per_prompt=_UpperCAmelCase     ,      eta=_UpperCAmelCase     ,      generator=_UpperCAmelCase     ,      latents=_UpperCAmelCase     ,      output_type=_UpperCAmelCase     ,      return_dict=_UpperCAmelCase     ,      callback=_UpperCAmelCase     ,      callback_steps=_UpperCAmelCase     ,      )






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

# Load iris file
snake_case__     							= 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
snake_case__							,     snake_case__							,     snake_case__							,     snake_case__     							= train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

snake_case__     							= KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
snake_case__     							= [[1, 2, 1, 4], [2, 3, 4, 5]]

snake_case__     							= knn.predict(X_new)

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


