import random


class   lowercase			:




							def __init__(  self      :    List[Any]		,							_UpperCamelCase      :    list[list[float]]		,							_UpperCamelCase      :    list[int]		,							_UpperCamelCase      :    float = 0.0_1		,							_UpperCamelCase      :    int = 1_000		,							_UpperCamelCase      :    float = -1		,							)    -> None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				sample
												if len(self.sample					) == 0:
																	raise ValueError("Sample data can not be empty"					)
												SCREAMING_SNAKE_CASE				=				target
												if len(self.target					) == 0:
																	raise ValueError("Target data can not be empty"					)
												if len(self.sample					) != len(self.target					):
																	raise ValueError("Sample data and Target data do not have matching lengths"					)
												SCREAMING_SNAKE_CASE				=				learning_rate
												SCREAMING_SNAKE_CASE				=				epoch_number
												SCREAMING_SNAKE_CASE				=				bias
												SCREAMING_SNAKE_CASE				=				len(_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				len(sample[0]					)  # number of columns in dataset
												SCREAMING_SNAKE_CASE				=				[]




							def 		__snake_case(  self      :    Any					)    -> None:



												'''simple docstring'''

												for sample in self.sample:
																	sample.insert(0		,							self.bias					)

												for _ in range(self.col_sample					):
																	self.weight.append(random.random()					)

												self.weight.insert(0		,							self.bias					)

												SCREAMING_SNAKE_CASE				=				0

												while True:
																	SCREAMING_SNAKE_CASE				=				False
																	for i in range(self.number_sample					):
																						SCREAMING_SNAKE_CASE				=				0
																						for j in range(self.col_sample + 1					):
																											SCREAMING_SNAKE_CASE				=				u + self.weight[j] * self.sample[i][j]
																						SCREAMING_SNAKE_CASE				=				self.sign(_UpperCamelCase					)
																						if y != self.target[i]:
																											for j in range(self.col_sample + 1					):
																																SCREAMING_SNAKE_CASE				=				(
																																    self.weight[j]
																																    + self.learning_rate
																																    * (self.target[i] - y)
																																    * self.sample[i][j]
																																)
																											SCREAMING_SNAKE_CASE				=				True
            # print('Epoch: \n',epoch_count)
																	SCREAMING_SNAKE_CASE				=				epoch_count + 1
																	# if you want control the epoch or just by error
																	if not has_misclassified:
																						print(("\nEpoch:\n", epoch_count)					)
																						print("------------------------\n"					)
																						# if epoch_count > self.epoch_number or not error:
																						break




							def 		__snake_case(  self      :    Union[str, Any]		,							_UpperCamelCase      :    list[float]					)    -> None:



												'''simple docstring'''

												if len(self.sample					) == 0:
																	raise ValueError("Sample data can not be empty"					)
												sample.insert(0		,							self.bias					)
												SCREAMING_SNAKE_CASE				=				0
												for i in range(self.col_sample + 1					):
																	SCREAMING_SNAKE_CASE				=				u + self.weight[i] * sample[i]

												SCREAMING_SNAKE_CASE				=				self.sign(_UpperCamelCase					)

												if y == -1:
																	print(("Sample: ", sample)					)
																	print("classification: P1"					)
												else:
																	print(("Sample: ", sample)					)
																	print("classification: P2"					)





							def 		__snake_case(  self      :    Dict		,							_UpperCamelCase      :    float					)    -> int:



												'''simple docstring'''

												return 1 if u >= 0 else -1



_lowerCamelCase    :      Union[str, Any]	 =						[
    [-0.6_508, 0.1_097, 4.0_009],
    [-1.4_492, 0.8_896, 4.4_005],
    [2.0_850, 0.6_876, 12.0_710],
    [0.2_626, 1.1_476, 7.7_985],
    [0.6_418, 1.0_234, 7.0_427],
    [0.2_569, 0.6_730, 8.3_265],
    [1.1_155, 0.6_043, 7.4_446],
    [0.0_914, 0.3_399, 7.0_677],
    [0.0_121, 0.5_256, 4.6_316],
    [-0.0_429, 0.4_660, 5.4_323],
    [0.4_340, 0.6_870, 8.2_287],
    [0.2_735, 1.0_287, 7.1_934],
    [0.4_839, 0.4_851, 7.4_850],
    [0.4_089, -0.1_267, 5.5_019],
    [1.4_391, 0.1_614, 8.5_843],
    [-0.9_115, -0.1_973, 2.1_962],
    [0.3_654, 1.0_475, 7.4_858],
    [0.2_144, 0.7_515, 7.1_699],
    [0.2_013, 1.0_014, 6.5_489],
    [0.6_483, 0.2_183, 5.8_991],
    [-0.1_147, 0.2_242, 7.2_435],
    [-0.7_970, 0.8_795, 3.8_762],
    [-1.0_625, 0.6_366, 2.4_707],
    [0.5_307, 0.1_285, 5.6_883],
    [-1.2_200, 0.7_777, 1.7_252],
    [0.3_957, 0.1_076, 5.6_623],
    [-0.1_013, 0.5_989, 7.1_812],
    [2.4_482, 0.9_455, 11.2_095],
    [2.0_149, 0.6_192, 10.9_263],
    [0.2_012, 0.2_611, 5.4_631],
]

_lowerCamelCase    :      Optional[Any]	 =						[
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							_lowerCamelCase    :      Tuple	 =						Perceptron(
							    sample=samples, target=target, learning_rate=0.01, epoch_number=10_00, bias=-1
							)
							network.training()
							print('''Finished training perceptron''')
							print('''Enter values to predict or q to exit''')
							while True:
														_lowerCamelCase    :      list	 =						[]
														for i in range(len(samples[0])):
																					_lowerCamelCase    :      Dict	 =						input('''value: ''').strip()
																					if user_input == "q":
																												break
																					_lowerCamelCase    :      Any	 =						float(user_input)
																					sample.insert(i, observation)
														network.sort(sample)





import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    require_torch_gpu,
)

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


enable_full_determinism()


class   lowercase			(   a ,				a ,				unittest.TestCase   ):
							lowercase__  :				Optional[Any]									=							StableDiffusionParadigmsPipeline
							lowercase__  :				Optional[Any]									=							TEXT_TO_IMAGE_PARAMS
							lowercase__  :				Union[str, Any]									=							TEXT_TO_IMAGE_BATCH_PARAMS
							lowercase__  :				int									=							TEXT_TO_IMAGE_IMAGE_PARAMS
							lowercase__  :				List[Any]									=							TEXT_TO_IMAGE_IMAGE_PARAMS




							def 		__snake_case(  self      :    Tuple					)    -> List[Any]:



												'''simple docstring'''

												torch.manual_seed(0					)
												SCREAMING_SNAKE_CASE				=				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		,							attention_head_dim=(2, 4)		,							use_linear_projection=_UpperCamelCase		,							)
												SCREAMING_SNAKE_CASE				=				DDIMParallelScheduler(
												    beta_start=0.0_0_0_8_5		,							beta_end=0.0_1_2		,							beta_schedule="scaled_linear"		,							clip_sample=_UpperCamelCase		,							set_alpha_to_one=_UpperCamelCase		,							)
												torch.manual_seed(0					)
												SCREAMING_SNAKE_CASE				=				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		,							sample_size=128		,							)
												torch.manual_seed(0					)
												SCREAMING_SNAKE_CASE				=				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=1_000		,							hidden_act="gelu"		,							projection_dim=512		,							)
												SCREAMING_SNAKE_CASE				=				CLIPTextModel(_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)

												SCREAMING_SNAKE_CASE				=				{
												    "unet": unet,
												    "scheduler": scheduler,
												    "vae": vae,
												    "text_encoder": text_encoder,
												    "tokenizer": tokenizer,
												    "safety_checker": None,
												    "feature_extractor": None,
												}
												return components




							def 		__snake_case(  self      :    Any		,							_UpperCamelCase      :    List[str]		,							_UpperCamelCase      :    Any=0					)    -> Optional[int]:



												'''simple docstring'''

												if str(_UpperCamelCase					).startswith("mps"					):
																	SCREAMING_SNAKE_CASE				=				torch.manual_seed(_UpperCamelCase					)
												else:
																	SCREAMING_SNAKE_CASE				=				torch.Generator(device=_UpperCamelCase					).manual_seed(_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				{
												    "prompt": "a photograph of an astronaut riding a horse",
												    "generator": generator,
												    "num_inference_steps": 10,
												    "guidance_scale": 6.0,
												    "output_type": "numpy",
												    "parallel": 3,
												    "debug": True,
												}
												return inputs




							def 		__snake_case(  self      :    Optional[Any]					)    -> Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				"cpu"  # ensure determinism for the device-dependent torch.Generator
												SCREAMING_SNAKE_CASE				=				self.get_dummy_components()
												SCREAMING_SNAKE_CASE				=				StableDiffusionParadigmsPipeline(**_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				sd_pipe.to(_UpperCamelCase					)
												sd_pipe.set_progress_bar_config(disable=_UpperCamelCase					)

												SCREAMING_SNAKE_CASE				=				self.get_dummy_inputs(_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				sd_pipe(**_UpperCamelCase					).images
												SCREAMING_SNAKE_CASE				=				image[0, -3:, -3:, -1]
												assert image.shape == (1, 64, 64, 3)

												SCREAMING_SNAKE_CASE				=				np.array([0.4_7_7_3, 0.5_4_1_7, 0.4_7_2_3, 0.4_9_2_5, 0.5_6_3_1, 0.4_7_5_2, 0.5_2_4_0, 0.4_9_3_5, 0.5_0_2_3]					)

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




							def 		__snake_case(  self      :    Optional[Any]					)    -> List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				"cpu"  # ensure determinism for the device-dependent torch.Generator
												SCREAMING_SNAKE_CASE				=				self.get_dummy_components()
												torch.manual_seed(0					)
												SCREAMING_SNAKE_CASE				=				DDPMParallelScheduler()
												torch.manual_seed(0					)
												SCREAMING_SNAKE_CASE				=				StableDiffusionParadigmsPipeline(**_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				sd_pipe.to(_UpperCamelCase					)
												sd_pipe.set_progress_bar_config(disable=_UpperCamelCase					)

												SCREAMING_SNAKE_CASE				=				self.get_dummy_inputs(_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				sd_pipe(**_UpperCamelCase					).images
												SCREAMING_SNAKE_CASE				=				image[0, -3:, -3:, -1]
												assert image.shape == (1, 64, 64, 3)

												SCREAMING_SNAKE_CASE				=				np.array([0.3_5_7_3, 0.4_4_2_0, 0.4_9_6_0, 0.4_7_9_9, 0.3_7_9_6, 0.3_8_7_9, 0.4_8_1_9, 0.4_3_6_5, 0.4_4_6_8]					)

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




							def 		__snake_case(  self      :    Tuple					)    -> int:



												'''simple docstring'''

												super().test_inference_batch_consistent(batch_sizes=[1, 2]					)




							def 		__snake_case(  self      :    List[Any]					)    -> Union[str, Any]:



												'''simple docstring'''

												super().test_inference_batch_single_identical(batch_size=2		,							expected_max_diff=3e-3					)





							def 		__snake_case(  self      :    List[Any]					)    -> str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				"cpu"  # ensure determinism for the device-dependent torch.Generator
												SCREAMING_SNAKE_CASE				=				self.get_dummy_components()
												SCREAMING_SNAKE_CASE				=				StableDiffusionParadigmsPipeline(**_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				sd_pipe.to(_UpperCamelCase					)
												sd_pipe.set_progress_bar_config(disable=_UpperCamelCase					)

												SCREAMING_SNAKE_CASE				=				self.get_dummy_inputs(_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				"french fries"
												SCREAMING_SNAKE_CASE				=				sd_pipe(**_UpperCamelCase		,							negative_prompt=_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				output.images
												SCREAMING_SNAKE_CASE				=				image[0, -3:, -3:, -1]

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

												SCREAMING_SNAKE_CASE				=				np.array([0.4_7_7_1, 0.5_4_2_0, 0.4_6_8_3, 0.4_9_1_8, 0.5_6_3_6, 0.4_7_2_5, 0.5_2_3_0, 0.4_9_2_3, 0.5_0_1_5]					)

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



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




							def 		__snake_case(  self      :    List[str]					)    -> List[Any]:



												'''simple docstring'''

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




							def 		__snake_case(  self      :    Optional[Any]		,							_UpperCamelCase      :    int=0					)    -> Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				torch.Generator(device=_UpperCamelCase					).manual_seed(_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				{
												    "prompt": "a photograph of an astronaut riding a horse",
												    "generator": generator,
												    "num_inference_steps": 10,
												    "guidance_scale": 7.5,
												    "output_type": "numpy",
												    "parallel": 3,
												    "debug": True,
												}
												return inputs





							def 		__snake_case(  self      :    Dict					)    -> str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				"stabilityai/stable-diffusion-2-base"
												SCREAMING_SNAKE_CASE				=				DDIMParallelScheduler.from_pretrained(_UpperCamelCase		,							subfolder="scheduler"					)
												SCREAMING_SNAKE_CASE				=				StableDiffusionParadigmsPipeline.from_pretrained(_UpperCamelCase		,							scheduler=_UpperCamelCase		,							safety_checker=_UpperCamelCase					)
												pipe.to(_UpperCamelCase					)
												pipe.set_progress_bar_config(disable=_UpperCamelCase					)
												pipe.enable_attention_slicing()

												SCREAMING_SNAKE_CASE				=				self.get_inputs()
												SCREAMING_SNAKE_CASE				=				pipe(**_UpperCamelCase					).images
												SCREAMING_SNAKE_CASE				=				image[0, -3:, -3:, -1].flatten()

												assert image.shape == (1, 512, 512, 3)

												SCREAMING_SNAKE_CASE				=				np.array([0.9_6_2_2, 0.9_6_0_2, 0.9_7_4_8, 0.9_5_9_1, 0.9_6_3_0, 0.9_6_9_1, 0.9_6_6_1, 0.9_6_3_1, 0.9_7_4_1]					)

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




