
"""simple docstring"""





import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


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






class     _UpperCAmelCase(    lowerCamelCase	):



		def __init__(					self				,		__a				,		__a				,		__a				,		__a				,		__a				,		__a				,		__a				,		)       ->       str:






							'''simple docstring'''
							super().__init__()
							self.register_modules(
							    vae=__a				,		text_encoder=__a				,		tokenizer=__a				,		unet=__a				,		scheduler=__a				,		safety_checker=__a				,		feature_extractor=__a				,		)



		def 						UpperCAmelCase							(					self				,		__a = "auto")       ->       str:






							'''simple docstring'''
							if slice_size == "auto":
												# half the attention head size is usually a good trade-off between
												# speed and memory
												_UpperCamelCase							 =  self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(__a)



		def 						UpperCAmelCase							(					self)       ->       Optional[Any]:






							'''simple docstring'''
							self.enable_attention_slicing(__a)




		@torch.no_grad()
		def __call__(					self				,		__a				,		__a = 5_12				,		__a = 5_12				,		__a = 50				,		__a = 7.5				,		__a = None				,		__a = 1				,		__a = 0.0				,		__a = None				,		__a = None				,		__a = "pil"				,		__a = True				,		__a = None				,		__a = 1				,		__a = None				,		**__a				,		)       ->       List[str]:






							'''simple docstring'''

							if isinstance(__a				,		__a):
												_UpperCamelCase							 =  1
							elif isinstance(__a				,		__a):
												_UpperCamelCase							 =  len(__a)
							else:
												raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(__a)}''')

							if height % 8 != 0 or width % 8 != 0:
												raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''')

							if (callback_steps is None) or (
							    callback_steps is not None and (not isinstance(__a				,		__a) or callback_steps <= 0)
							):
												raise ValueError(
												    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
												    F''' {type(__a)}.''')

							# get prompt text embeddings
							_UpperCamelCase							 =  self.tokenizer(
							    __a				,		padding='''max_length'''				,		max_length=self.tokenizer.model_max_length				,		return_tensors='''pt'''				,		)
							_UpperCamelCase							 =  text_inputs.input_ids

							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
												_UpperCamelCase							 =  self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
												logger.warning(
												    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
												    F''' {self.tokenizer.model_max_length} tokens: {removed_text}''')
												_UpperCamelCase							 =  text_input_ids[:, : self.tokenizer.model_max_length]

							if text_embeddings is None:
												_UpperCamelCase							 =  self.text_encoder(text_input_ids.to(self.device))[0]

							# duplicate text embeddings for each generation per prompt, using mps friendly method
							_UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase							 =  text_embeddings.shape
							_UpperCamelCase							 =  text_embeddings.repeat(1				,		__a				,		1)
							_UpperCamelCase							 =  text_embeddings.view(bs_embed * num_images_per_prompt				,		__a				,		-1)

							# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
							# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
							# corresponds to doing no classifier free guidance.
							_UpperCamelCase							 =  guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
												_UpperCamelCase							 =  42
												if negative_prompt is None:
																	_UpperCamelCase							 =  ['''''']
												elif type(__a) is not type(__a):
																	raise TypeError(
																	    F'''`negative_prompt` should be the same type to `prompt`, but got {type(__a)} !='''
																	    F''' {type(__a)}.''')
												elif isinstance(__a				,		__a):
																	_UpperCamelCase							 =  [negative_prompt]
												elif batch_size != len(__a):
																	raise ValueError(
																	    F'''`negative_prompt`: {negative_prompt} has batch size {len(__a)}, but `prompt`:'''
																	    F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
																	    ''' the batch size of `prompt`.''')
												else:
																	_UpperCamelCase							 =  negative_prompt

												_UpperCamelCase							 =  text_input_ids.shape[-1]
												_UpperCamelCase							 =  self.tokenizer(
												    __a				,		padding='''max_length'''				,		max_length=__a				,		truncation=__a				,		return_tensors='''pt'''				,		)
												_UpperCamelCase							 =  self.text_encoder(uncond_input.input_ids.to(self.device))[0]

												# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
												_UpperCamelCase							 =  uncond_embeddings.shape[1]
												_UpperCamelCase							 =  uncond_embeddings.repeat(__a				,		__a				,		1)
												_UpperCamelCase							 =  uncond_embeddings.view(batch_size * num_images_per_prompt				,		__a				,		-1)

												# For classifier free guidance, we need to do two forward passes.
												# Here we concatenate the unconditional and text embeddings into a single batch
												# to avoid doing two forward passes
												_UpperCamelCase							 =  torch.cat([uncond_embeddings, text_embeddings])

							# get the initial random noise unless the user supplied it

							# Unlike in other pipelines, latents need to be generated in the target device
							# for 1-to-1 results reproducibility with the CompVis implementation.
							# However this currently doesn't work in `mps`.
							_UpperCamelCase							 =  (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
							_UpperCamelCase							 =  (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
							_UpperCamelCase							 =  text_embeddings.dtype
							if latents is None:
												if self.device.type == "mps":
																	# randn does not exist on mps
																	_UpperCamelCase							 =  torch.randn(
																	    __a				,		generator=__a				,		device='''cpu'''				,		dtype=__a).to(self.device)
																	_UpperCamelCase							 =  torch.randn(__a				,		generator=__a				,		device='''cpu'''				,		dtype=__a).to(
																	    self.device)
												else:
																	_UpperCamelCase							 =  torch.randn(
																	    __a				,		generator=__a				,		device=self.device				,		dtype=__a)
																	_UpperCamelCase							 =  torch.randn(__a				,		generator=__a				,		device=self.device				,		dtype=__a)
							else:
												if latents_reference.shape != latents_shape:
																	raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''')
												_UpperCamelCase							 =  latents_reference.to(self.device)
												_UpperCamelCase							 =  latents.to(self.device)

							# This is the key part of the pipeline where we
							# try to ensure that the generated images w/ the same seed
							# but different sizes actually result in similar images
							_UpperCamelCase							 =  (latents_shape[3] - latents_shape_reference[3]) // 2
							_UpperCamelCase							 =  (latents_shape[2] - latents_shape_reference[2]) // 2
							_UpperCamelCase							 =  latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
							_UpperCamelCase							 =  latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
							_UpperCamelCase							 =  0 if dx < 0 else dx
							_UpperCamelCase							 =  0 if dy < 0 else dy
							_UpperCamelCase							 =  max(-dx				,		0)
							_UpperCamelCase							 =  max(-dy				,		0)
							# import pdb
							# pdb.set_trace()
							_UpperCamelCase							 =  latents_reference[:, :, dy : dy + h, dx : dx + w]

							# set timesteps
							self.scheduler.set_timesteps(__a)

							# Some schedulers like PNDM have timesteps as arrays
							# It's more optimized to move all timesteps to correct device beforehand
							_UpperCamelCase							 =  self.scheduler.timesteps.to(self.device)

							# scale the initial noise by the standard deviation required by the scheduler
							_UpperCamelCase							 =  latents * self.scheduler.init_noise_sigma

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
							# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
							# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
							# and should be between [0, 1]
							_UpperCamelCase							 =  '''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())
							_UpperCamelCase							 =  {}
							if accepts_eta:
												_UpperCamelCase							 =  eta

							for i, t in enumerate(self.progress_bar(__a)):
												# expand the latents if we are doing classifier free guidance
												_UpperCamelCase							 =  torch.cat([latents] * 2) if do_classifier_free_guidance else latents
												_UpperCamelCase							 =  self.scheduler.scale_model_input(__a				,		__a)

												# predict the noise residual
												_UpperCamelCase							 =  self.unet(__a				,		__a				,		encoder_hidden_states=__a).sample

												# perform guidance
												if do_classifier_free_guidance:
																	_UpperCamelCase ,  _UpperCamelCase							 =  noise_pred.chunk(2)
																	_UpperCamelCase							 =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

												# compute the previous noisy sample x_t -> x_t-1
												_UpperCamelCase							 =  self.scheduler.step(__a				,		__a				,		__a				,		**__a).prev_sample

												# call the callback, if provided
												if callback is not None and i % callback_steps == 0:
																	callback(__a				,		__a				,		__a)

							_UpperCamelCase							 =  1 / 0.1_8215 * latents
							_UpperCamelCase							 =  self.vae.decode(__a).sample

							_UpperCamelCase							 =  (image / 2 + 0.5).clamp(0				,		1)

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							_UpperCamelCase							 =  image.cpu().permute(0				,		2				,		3				,		1).float().numpy()

							if self.safety_checker is not None:
												_UpperCamelCase							 =  self.feature_extractor(self.numpy_to_pil(__a)				,		return_tensors='''pt''').to(
												    self.device)
												_UpperCamelCase ,  _UpperCamelCase							 =  self.safety_checker(
												    images=__a				,		clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype))
							else:
												_UpperCamelCase							 =  None

							if output_type == "pil":
												_UpperCamelCase							 =  self.numpy_to_pil(__a)

							if not return_dict:
												return (image, has_nsfw_concept)

							return StableDiffusionPipelineOutput(images=__a				,		nsfw_content_detected=__a)








"""simple docstring"""





# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets



def       lowerCamelCase__		(			__snake_case	)							->		Tuple:


					"""simple docstring"""





					return 1 / (1 + np.exp(-z	))



def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		List[str]:


					"""simple docstring"""





					return (-y * np.log(__snake_case	) - (1 - y) * np.log(1 - h	)).mean()



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case	)							->		int:


					"""simple docstring"""





					_UpperCamelCase							 =  np.dot(__snake_case,     __snake_case	)
					return np.sum(y * scores - np.log(1 + np.exp(__snake_case	)	)	)



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case=7_00_00	)							->		Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase							 =  np.zeros(x.shape[1]	)

					for iterations in range(__snake_case	):
										_UpperCamelCase							 =  np.dot(__snake_case,     __snake_case	)
										_UpperCamelCase							 =  sigmoid_function(__snake_case	)
										_UpperCamelCase							 =  np.dot(x.T,     h - y	) / y.size
										_UpperCamelCase							 =  theta - alpha * gradient  # updating the weights
										_UpperCamelCase							 =  np.dot(__snake_case,     __snake_case	)
										_UpperCamelCase							 =  sigmoid_function(__snake_case	)
										_UpperCamelCase							 =  cost_function(__snake_case,     __snake_case	)
										if iterations % 1_00 == 0:
															print(F'''loss: {j} \t'''	)  # printing the loss after every 100 iterations
					return theta


# In[68]:

if __name__ == "__main__":
				_a														=		datasets.load_iris()
				_a														=		iris.data[:, :2]
				_a														=		(iris.target != 0) * 1

				_a														=		0.1
				_a														=		logistic_reg(alpha, x, y, max_iterations=7_0000)
				print("""theta: """, theta)  # printing the theta i.e our weights vector



				def       lowerCamelCase__		(			__snake_case	)							->		Tuple:


									"""simple docstring"""





									return sigmoid_function(
									    np.dot(__snake_case,     __snake_case	)	)  # predicting the value of probability from the logistic regression algorithm

				plt.figure(figsize=(10, 6))
				plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="""b""", label="""0""")
				plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="""r""", label="""1""")
				((_a)							,							(_a))														=		(x[:, 0].min(), x[:, 0].max())
				((_a)							,							(_a))														=		(x[:, 1].min(), x[:, 1].max())
				((_a)							,							(_a))														=		np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
				_a														=		np.c_[xxa.ravel(), xxa.ravel()]
				_a														=		predict_prob(grid).reshape(xxa.shape)
				plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

				plt.legend()
				plt.show()






