
"""simple docstring"""







import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		List[Any]		    =   np.array([[1, item, train_mtch[i]] for i, item in enumerate(_UpperCamelCase	)]	)
	__lowerCAmelCase    :		Optional[Any]		    =   np.array(_UpperCamelCase	)
	__lowerCAmelCase    :		List[str]		    =   np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() ,						_UpperCamelCase	)	) ,						x.transpose()	) ,						_UpperCamelCase	)
	return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]	)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		str		    =   (1, 2, 1)
	__lowerCAmelCase    :		int		    =   (1, 1, 0, 7)
	__lowerCAmelCase    :		List[Any]		    =   SARIMAX(
	    _UpperCamelCase ,						exog=_UpperCamelCase ,						order=_UpperCamelCase ,						seasonal_order=_UpperCamelCase	)
	__lowerCAmelCase    :		Dict		    =   model.fit(disp=_UpperCamelCase ,						maxiter=600 ,						method='nm'	)
	__lowerCAmelCase    :		Tuple		    =   model_fit.predict(1 ,						len(_UpperCamelCase	) ,						exog=[test_match]	)
	return result[0]
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		str		    =   SVR(kernel='rbf' ,						C=1 ,						gamma=0.1 ,						epsilon=0.1	)
	regressor.fit(_UpperCamelCase ,						_UpperCamelCase	)
	__lowerCAmelCase    :		Dict		    =   regressor.predict(_UpperCamelCase	)
	return y_pred[0]
def 					__lowerCAmelCase       (_UpperCamelCase	):
	train_user.sort()
	__lowerCAmelCase    :		List[Any]		    =   np.percentile(_UpperCamelCase ,						25	)
	__lowerCAmelCase    :		List[Any]		    =   np.percentile(_UpperCamelCase ,						75	)
	__lowerCAmelCase    :		int		    =   qa - qa
	__lowerCAmelCase    :		Tuple		    =   qa - (iqr * 0.1)
	return low_lim
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Any		    =   0
	__lowerCAmelCase    :		List[str]		    =   0
	for i in list_vote:
		if i > actual_result:
			__lowerCAmelCase    :		List[str]		    =   not_safe + 1
		else:
			if abs(abs(_UpperCamelCase	) - abs(_UpperCamelCase	)	) <= 0.1:
				safe += 1
			else:
				not_safe += 1
	return safe > not_safe


if __name__ == "__main__":
							# data_input_df = pd.read_csv("ex_data.csv", header=None)
							lowerCamelCase__           =	[[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 1.0, 4]]
							lowerCamelCase__           =	pd.DataFrame(
							    data_input, columns=["""total_user""", """total_even""", """days"""]
							)
							lowerCamelCase__           =	Normalizer().fit_transform(data_input_df.values)
							# split data
							lowerCamelCase__           =	normalize_df[:, 2].tolist()
							lowerCamelCase__           =	normalize_df[:, 0].tolist()
							lowerCamelCase__           =	normalize_df[:, 1].tolist()

							# for svr (input variable = total date and total match)
							lowerCamelCase__           =	normalize_df[:, [1, 2]].tolist()
							lowerCamelCase__           =	x[: len(x) - 1]
							lowerCamelCase__           =	x[len(x) - 1 :]

							# for linear regression & sarimax
							lowerCamelCase__           =	total_date[: len(total_date) - 1]
							lowerCamelCase__           =	total_user[: len(total_user) - 1]
							lowerCamelCase__           =	total_match[: len(total_match) - 1]

							lowerCamelCase__           =	total_date[len(total_date) - 1 :]
							lowerCamelCase__           =	total_user[len(total_user) - 1 :]
							lowerCamelCase__           =	total_match[len(total_match) - 1 :]

							# voting system with forecasting
							lowerCamelCase__           =	[
							    linear_regression_prediction(
							        trn_date, trn_user, trn_match, tst_date, tst_match
							    ),
							    sarimax_predictor(trn_user, trn_match, tst_match),
							    support_vector_regressor(x_train, x_test, trn_user),
							]

							# check the safety of today's data
							lowerCamelCase__           =	"""""" if data_safety_checker(res_vote, tst_user) else """not """
							print("""Today's data is {not_str}safe.""")

"""simple docstring"""







from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

lowerCamelCase__           =	"""
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")

        >>> repo = \"openai/shap-e-img2img\"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"
        >>> image = load_image(image_url).convert(\"RGB\")

        >>> images = pipe(
        ...     image,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")
        ```
"""




@dataclass
class 				A__ (			_lowerCamelCase):
		A_		: Union[PIL.Image.Image, np.ndarray]




class 				A__ (			_lowerCamelCase):

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	):
			super().__init__()

			self.register_modules(
			    prior=_SCREAMING_SNAKE_CASE ,	image_encoder=_SCREAMING_SNAKE_CASE ,	image_processor=_SCREAMING_SNAKE_CASE ,	scheduler=_SCREAMING_SNAKE_CASE ,	renderer=_SCREAMING_SNAKE_CASE ,	)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			if latents is None:
				__lowerCAmelCase    :		List[str]		    =   randn_tensor(_SCREAMING_SNAKE_CASE ,	generator=_SCREAMING_SNAKE_CASE ,	device=_SCREAMING_SNAKE_CASE ,	dtype=_SCREAMING_SNAKE_CASE				)
			else:
				if latents.shape != shape:
					raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"				)
				__lowerCAmelCase    :		Any		    =   latents.to(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Optional[int]		    =   latents * scheduler.init_noise_sigma
			return latents

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE=0				):
			if is_accelerate_available():
				from accelerate import cpu_offload
			else:
				raise ImportError('Please install accelerate via `pip install accelerate`'				)

			__lowerCAmelCase    :		Tuple		    =   torch.device(f"cuda:{gpu_id}"				)

			__lowerCAmelCase    :		Union[str, Any]		    =   [self.image_encoder, self.prior]
			for cpu_offloaded_model in models:
				if cpu_offloaded_model is not None:
					cpu_offload(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		@property
		def 							__lowerCamelCase							(   self				):
			if self.device != torch.device('meta'				) or not hasattr(self.image_encoder ,	'_hf_hook'				):
				return self.device
			for module in self.image_encoder.modules():
				if (
				    hasattr(_SCREAMING_SNAKE_CASE ,	'_hf_hook'				)
				    and hasattr(module._hf_hook ,	'execution_device'				)
				    and module._hf_hook.execution_device is not None
				):
					return torch.device(module._hf_hook.execution_device				)
			return self.device

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	):
			if isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) and isinstance(image[0] ,	torch.Tensor				):
				__lowerCAmelCase    :		str		    =   torch.cat(_SCREAMING_SNAKE_CASE ,	axis=0				) if image[0].ndim == 4 else torch.stack(_SCREAMING_SNAKE_CASE ,	axis=0				)

			if not isinstance(_SCREAMING_SNAKE_CASE ,	torch.Tensor				):
				__lowerCAmelCase    :		Optional[int]		    =   self.image_processor(_SCREAMING_SNAKE_CASE ,	return_tensors='pt'				).pixel_values[0].unsqueeze(0				)

			__lowerCAmelCase    :		Dict		    =   image.to(dtype=self.image_encoder.dtype ,	device=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		List[str]		    =   self.image_encoder(_SCREAMING_SNAKE_CASE				)['last_hidden_state']
			__lowerCAmelCase    :		Optional[int]		    =   image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

			__lowerCAmelCase    :		Tuple		    =   image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE ,	dim=0				)

			if do_classifier_free_guidance:
				__lowerCAmelCase    :		List[Any]		    =   torch.zeros_like(_SCREAMING_SNAKE_CASE				)

				# 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
				__lowerCAmelCase    :		Any		    =   torch.cat([negative_image_embeds, image_embeds]				)

			return image_embeds







		@torch.no_grad()
		@replace_example_docstring(_SCREAMING_SNAKE_CASE				)
		def __call__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = 1 ,	_SCREAMING_SNAKE_CASE = 25 ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = 4.0 ,	_SCREAMING_SNAKE_CASE = 64 ,	_SCREAMING_SNAKE_CASE = "pil" ,	_SCREAMING_SNAKE_CASE = True ,	):

			if isinstance(_SCREAMING_SNAKE_CASE ,	PIL.Image.Image				):
				__lowerCAmelCase    :		Union[str, Any]		    =   1
			elif isinstance(_SCREAMING_SNAKE_CASE ,	torch.Tensor				):
				__lowerCAmelCase    :		Tuple		    =   image.shape[0]
			elif isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) and isinstance(image[0] ,	(torch.Tensor, PIL.Image.Image)				):
				__lowerCAmelCase    :		Any		    =   len(_SCREAMING_SNAKE_CASE				)
			else:
				raise ValueError(
				    f"`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(_SCREAMING_SNAKE_CASE				)}"				)

			__lowerCAmelCase    :		Optional[Any]		    =   self._execution_device

			__lowerCAmelCase    :		Optional[Any]		    =   batch_size * num_images_per_prompt

			__lowerCAmelCase    :		Any		    =   guidance_scale > 1.0
			__lowerCAmelCase    :		List[Any]		    =   self._encode_image(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

			# prior

			self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE ,	device=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[str]		    =   self.scheduler.timesteps

			__lowerCAmelCase    :		Optional[int]		    =   self.prior.config.num_embeddings
			__lowerCAmelCase    :		List[str]		    =   self.prior.config.embedding_dim

			__lowerCAmelCase    :		Any		    =   self.prepare_latents(
			    (batch_size, num_embeddings * embedding_dim) ,	image_embeds.dtype ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	self.scheduler ,	)

			# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
			__lowerCAmelCase    :		str		    =   latents.reshape(latents.shape[0] ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

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

				__lowerCAmelCase    :		Tuple		    =   self.prior(
				    _SCREAMING_SNAKE_CASE ,	timestep=_SCREAMING_SNAKE_CASE ,	proj_embedding=_SCREAMING_SNAKE_CASE ,	).predicted_image_embedding

				# remove the variance
				__lowerCAmelCase    ,     __lowerCAmelCase    :		Tuple		    =   noise_pred.split(
				    scaled_model_input.shape[2] ,	dim=2				)  # batch_size, num_embeddings, embedding_dim

				if do_classifier_free_guidance is not None:
					__lowerCAmelCase    ,     __lowerCAmelCase    :		List[Any]		    =   noise_pred.chunk(2				)
					__lowerCAmelCase    :		Optional[int]		    =   noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

				__lowerCAmelCase    :		Dict		    =   self.scheduler.step(
				    _SCREAMING_SNAKE_CASE ,	timestep=_SCREAMING_SNAKE_CASE ,	sample=_SCREAMING_SNAKE_CASE ,	).prev_sample

			if output_type == "latent":
				return ShapEPipelineOutput(images=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		List[Any]		    =   []
			for i, latent in enumerate(_SCREAMING_SNAKE_CASE				):
				print()
				__lowerCAmelCase    :		int		    =   self.renderer.decode(
				    latent[None, :] ,	_SCREAMING_SNAKE_CASE ,	size=_SCREAMING_SNAKE_CASE ,	ray_batch_size=40_96 ,	n_coarse_samples=64 ,	n_fine_samples=1_28 ,	)

				images.append(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		List[Any]		    =   torch.stack(_SCREAMING_SNAKE_CASE				)

			if output_type not in ["np", "pil"]:
				raise ValueError(f"Only the output types `pil` and `np` are supported not output_type={output_type}"				)

			__lowerCAmelCase    :		int		    =   images.cpu().numpy()

			if output_type == "pil":
				__lowerCAmelCase    :		Dict		    =   [self.numpy_to_pil(_SCREAMING_SNAKE_CASE				) for image in images]

			# Offload last model to CPU
			if hasattr(self ,	'final_offload_hook'				) and self.final_offload_hook is not None:
				self.final_offload_hook.offload()

			if not return_dict:
				return (images,)

			return ShapEPipelineOutput(images=_SCREAMING_SNAKE_CASE				)