"""simple docstring"""
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




@skip_mps
class   __snake_case			(	__lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase			, unittest.TestCase							):
      a__								=						StableDiffusionModelEditingPipeline
      a__								=						TEXT_TO_IMAGE_PARAMS
      a__								=						TEXT_TO_IMAGE_BATCH_PARAMS
      a__								=						TEXT_TO_IMAGE_IMAGE_PARAMS
      a__								=						TEXT_TO_IMAGE_IMAGE_PARAMS



      def     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:	Tuple     				=      UNetaDConditionModel(
            block_out_channels=(32, 64)       ,     layers_per_block=2       ,     sample_size=32       ,     in_channels=4       ,     out_channels=4       ,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,     cross_attention_dim=32       ,     )
        a__:	Tuple     				=      DDIMScheduler()
        torch.manual_seed(0)
        a__:	List[str]     				=      AutoencoderKL(
            block_out_channels=[32, 64]       ,     in_channels=3       ,     out_channels=3       ,     down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,     latent_channels=4       ,     )
        torch.manual_seed(0)
        a__:	Any     				=      CLIPTextConfig(
            bos_token_id=0       ,     eos_token_id=2       ,     hidden_size=32       ,     intermediate_size=37       ,     layer_norm_eps=1e-05       ,     num_attention_heads=4       ,     num_hidden_layers=5       ,     pad_token_id=1       ,     vocab_size=10_00       ,     )
        a__:	str     				=      CLIPTextModel(lowercase)
        a__:	Any     				=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

        a__:	List[Any]     				=      {
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }
        return components



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase=0)      ->     int:





        '''simple docstring'''





        a__:	Union[str, Any]     				=      torch.manual_seed(lowercase)
        a__:	Optional[int]     				=      {
            'prompt': 'A field of roses',
            'generator': generator,
            # Setting height and width to None to prevent OOMs on CPU.
            'height': None,
            'width': None,
            'num_inference_steps': 2,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
        }
        return inputs



      def     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





        a__:	List[str]     				=      'cpu'  # ensure determinism for the device-dependent torch.Generator
        a__:	Union[str, Any]     				=      self.get_dummy_components()
        a__:	List[Any]     				=      StableDiffusionModelEditingPipeline(**lowercase)
        a__:	int     				=      sd_pipe.to(lowercase)
        sd_pipe.set_progress_bar_config(disable=lowercase)

        a__:	str     				=      self.get_dummy_inputs(lowercase)
        a__:	str     				=      sd_pipe(**lowercase).images
        a__:	Optional[Any]     				=      image[0, -3:, -3:, -1]
        assert image.shape == (1, 64, 64, 3)

        a__:	Dict     				=      np.array([0.4755, 0.5132, 0.4976, 0.3904, 0.3554, 0.4765, 0.5139, 0.5158, 0.4889])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2



      def     lowerCamelCase_  (		self)      ->     Tuple:





        '''simple docstring'''





        a__:	List[Any]     				=      'cpu'  # ensure determinism for the device-dependent torch.Generator
        a__:	List[Any]     				=      self.get_dummy_components()
        a__:	Union[str, Any]     				=      StableDiffusionModelEditingPipeline(**lowercase)
        a__:	Union[str, Any]     				=      sd_pipe.to(lowercase)
        sd_pipe.set_progress_bar_config(disable=lowercase)

        a__:	Dict     				=      self.get_dummy_inputs(lowercase)
        a__:	Optional[int]     				=      'french fries'
        a__:	List[str]     				=      sd_pipe(**lowercase       ,     negative_prompt=lowercase)
        a__:	str     				=      output.images
        a__:	Optional[int]     				=      image[0, -3:, -3:, -1]

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

        a__:	List[Any]     				=      np.array([0.4992, 0.5101, 0.5004, 0.3949, 0.3604, 0.4735, 0.5216, 0.5204, 0.4913])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2



      def     lowerCamelCase_  (		self)      ->     List[Any]:





        '''simple docstring'''





        a__:	Optional[Any]     				=      'cpu'  # ensure determinism for the device-dependent torch.Generator
        a__:	str     				=      self.get_dummy_components()
        a__:	List[Any]     				=      EulerAncestralDiscreteScheduler(
            beta_start=0.00085       ,     beta_end=0.012       ,     beta_schedule='scaled_linear')
        a__:	List[Any]     				=      StableDiffusionModelEditingPipeline(**lowercase)
        a__:	str     				=      sd_pipe.to(lowercase)
        sd_pipe.set_progress_bar_config(disable=lowercase)

        a__:	Optional[Any]     				=      self.get_dummy_inputs(lowercase)
        a__:	Optional[Any]     				=      sd_pipe(**lowercase).images
        a__:	List[str]     				=      image[0, -3:, -3:, -1]

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

        a__:	Any     				=      np.array([0.4747, 0.5372, 0.4779, 0.4982, 0.5543, 0.4816, 0.5238, 0.4904, 0.5027])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2



      def     lowerCamelCase_  (		self)      ->     List[Any]:





        '''simple docstring'''





        a__:	str     				=      'cpu'  # ensure determinism for the device-dependent torch.Generator
        a__:	Union[str, Any]     				=      self.get_dummy_components()
        a__:	str     				=      PNDMScheduler()
        a__:	Dict     				=      StableDiffusionModelEditingPipeline(**lowercase)
        a__:	Tuple     				=      sd_pipe.to(lowercase)
        sd_pipe.set_progress_bar_config(disable=lowercase)

        a__:	Optional[Any]     				=      self.get_dummy_inputs(lowercase)
        # the pipeline does not expect pndm so test if it raises error.
        with self.assertRaises(lowercase):
          a__:	str     				=      sd_pipe(**lowercase).images



      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





        super().test_inference_batch_single_identical(expected_max_diff=5e-3)



      def     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





        super().test_attention_slicing_forward_pass(expected_max_diff=5e-3)







@slow
@require_torch_gpu
class   __snake_case			(	unittest.TestCase							):



      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





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



      def     lowerCamelCase_  (		self       ,     lowercase=0)      ->     int:





        '''simple docstring'''





        a__:	Any     				=      torch.manual_seed(lowercase)
        a__:	Any     				=      {
            'prompt': 'A field of roses',
            'generator': generator,
            'num_inference_steps': 3,
            'guidance_scale': 7.5,
            'output_type': 'numpy',
        }
        return inputs



      def     lowerCamelCase_  (		self)      ->     Optional[int]:





        '''simple docstring'''





        a__:	Optional[Any]     				=      'CompVis/stable-diffusion-v1-4'
        a__:	List[str]     				=      StableDiffusionModelEditingPipeline.from_pretrained(lowercase       ,     safety_checker=lowercase)
        pipe.to(lowercase)
        pipe.set_progress_bar_config(disable=lowercase)
        pipe.enable_attention_slicing()

        a__:	Optional[int]     				=      self.get_inputs()
        a__:	Optional[int]     				=      pipe(**lowercase).images
        a__:	int     				=      image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 5_12, 5_12, 3)

        a__:	Optional[Any]     				=      np.array(
            [0.6749496, 0.6386453, 0.51443267, 0.66094905, 0.61921215, 0.5491332, 0.5744417, 0.58075106, 0.5174658])

        assert np.abs(expected_slice - image_slice).max() < 1e-2

        # make sure image changes after editing
        pipe.edit_model('A pack of roses'       ,     'A pack of blue roses')

        a__:	Dict     				=      pipe(**lowercase).images
        a__:	str     				=      image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 5_12, 5_12, 3)

        assert np.abs(expected_slice - image_slice).max() > 1e-1



      def     lowerCamelCase_  (		self)      ->     Optional[int]:





        '''simple docstring'''





        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        a__:	str     				=      'CompVis/stable-diffusion-v1-4'
        a__:	int     				=      DDIMScheduler.from_pretrained(lowercase       ,     subfolder='scheduler')
        a__:	Any     				=      StableDiffusionModelEditingPipeline.from_pretrained(
            lowercase       ,     scheduler=lowercase       ,     safety_checker=lowercase)
        a__:	List[str]     				=      pipe.to(lowercase)
        pipe.set_progress_bar_config(disable=lowercase)
        pipe.enable_attention_slicing(1)
        pipe.enable_sequential_cpu_offload()

        a__:	Dict     				=      self.get_inputs()
        a__:	Tuple     				=      pipe(**lowercase)

        a__:	Any     				=      torch.cuda.max_memory_allocated()
        # make sure that less than 4.4 GB is allocated
        assert mem_bytes < 4.4 * 10**9



"""simple docstring"""
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

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

# KNN

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

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

lowercase__   			=      knn.predict(X_new)

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


