



import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)
def  UpperCamelCase						(       _A			:				Optional[Any]			,							_A			:				List[str]			,							_A			:				List[str]    )->       Optional[int]:





						"""simple docstring"""





						if isinstance(_A			,							torch.Tensor    ):
												return image
						elif isinstance(_A			,							PIL.Image.Image    ):
												A__							=       [image]

						if isinstance(image[0]			,							PIL.Image.Image    ):
												A__							=       [np.array(i.resize((w, h)			,							resample=PIL_INTERPOLATION["lanczos"]    )    )[None, :] for i in image]
												A__							=       np.concatenate(_A			,							axis=0    )
												A__							=       np.array(_A    ).astype(np.floataa    ) / 255.0
												A__							=       image.transpose(0			,							3			,							1			,							2    )
												A__							=       2.0 * image - 1.0
												A__							=       torch.from_numpy(_A    )
						elif isinstance(image[0]			,							torch.Tensor    ):
												A__							=       torch.cat(_A			,							dim=0    )
						return image
def  UpperCamelCase						(       _A			:				int			,							_A			:				Optional[Any]			,							_A			:				int			,							_A			:				Union[str, Any]=0.9995    )->       Any:





						"""simple docstring"""





						if not isinstance(_A			,							np.ndarray    ):
												A__							=       True
												A__							=       va.device
												A__							=       va.cpu().numpy()
												A__							=       va.cpu().numpy()

						A__							=       np.sum(va * va / (np.linalg.norm(_A    ) * np.linalg.norm(_A    ))    )
						if np.abs(_A    ) > DOT_THRESHOLD:
												A__							=       (1 - t) * va + t * va
						else:
												A__							=       np.arccos(_A    )
												A__							=       np.sin(_A    )
												A__							=       theta_a * t
												A__							=       np.sin(_A    )
												A__							=       np.sin(theta_a - theta_t    ) / sin_theta_a
												A__							=       sin_theta_t / sin_theta_a
												A__							=       sa * va + sa * va

						if inputs_are_torch:
												A__							=       torch.from_numpy(_A    ).to(_A    )

						return va
def  UpperCamelCase						(       _A			:				Optional[Any]			,							_A			:				Optional[int]    )->       Union[str, Any]:





						"""simple docstring"""





						A__							=       F.normalize(_A			,							dim=-1    )
						A__							=       F.normalize(_A			,							dim=-1    )
						return (x - y).norm(dim=-1    ).div(2    ).arcsin().pow(2    ).mul(2    )
def  UpperCamelCase						(       _A			:				Dict			,							_A			:				List[Any]    )->       Any:





						"""simple docstring"""





						for param in model.parameters():
												A__							=       value






class      UpperCamelCase    (						_UpperCAmelCase     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__=None			,	UpperCAmelCase__=None			,	UpperCAmelCase__=None			,	):
							super().__init__()
							self.register_modules(
							    vae=UpperCAmelCase__			,	text_encoder=UpperCAmelCase__			,	clip_model=UpperCAmelCase__			,	tokenizer=UpperCAmelCase__			,	unet=UpperCAmelCase__			,	scheduler=UpperCAmelCase__			,	feature_extractor=UpperCAmelCase__			,	coca_model=UpperCAmelCase__			,	coca_tokenizer=UpperCAmelCase__			,	coca_transform=UpperCAmelCase__			,	)
							A__							=       (
							    feature_extractor.size
							    if isinstance(feature_extractor.size			,	UpperCAmelCase__		)
							    else feature_extractor.size["shortest_edge"]
							)
							A__							=       transforms.Normalize(mean=feature_extractor.image_mean			,	std=feature_extractor.image_std		)
							set_requires_grad(self.text_encoder			,	UpperCAmelCase__		)
							set_requires_grad(self.clip_model			,	UpperCAmelCase__		)



	def 			__A							(       self			,	UpperCAmelCase__ = "auto"		):
							if slice_size == "auto":
													# half the attention head size is usually a good trade-off between
													# speed and memory
													A__							=       self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(UpperCAmelCase__		)



	def 			__A							(       self		):
							self.enable_attention_slicing(UpperCAmelCase__		)



	def 			__A							(       self		):
							set_requires_grad(self.vae			,	UpperCAmelCase__		)



	def 			__A							(       self		):
							set_requires_grad(self.vae			,	UpperCAmelCase__		)



	def 			__A							(       self		):
							set_requires_grad(self.unet			,	UpperCAmelCase__		)



	def 			__A							(       self		):
							set_requires_grad(self.unet			,	UpperCAmelCase__		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							# get the original timestep using init_timestep
							A__							=       min(int(num_inference_steps * strength		)			,	UpperCAmelCase__		)

							A__							=       max(num_inference_steps - init_timestep			,	0		)
							A__							=       self.scheduler.timesteps[t_start:]

							return timesteps, num_inference_steps - t_start



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__=None		):
							if not isinstance(UpperCAmelCase__			,	torch.Tensor		):
													raise ValueError(F"""`image` has to be of type `torch.Tensor` but is {type(UpperCAmelCase__		)}"""		)

							A__							=       image.to(device=UpperCAmelCase__			,	dtype=UpperCAmelCase__		)

							if isinstance(UpperCAmelCase__			,	UpperCAmelCase__		):
													A__							=       [
													    self.vae.encode(image[i : i + 1]		).latent_dist.sample(generator[i]		) for i in range(UpperCAmelCase__		)
													]
													A__							=       torch.cat(UpperCAmelCase__			,	dim=0		)
							else:
													A__							=       self.vae.encode(UpperCAmelCase__		).latent_dist.sample(UpperCAmelCase__		)

							# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
							A__							=       0.18_215 * init_latents
							A__							=       init_latents.repeat_interleave(UpperCAmelCase__			,	dim=0		)

							A__							=       randn_tensor(init_latents.shape			,	generator=UpperCAmelCase__			,	device=UpperCAmelCase__			,	dtype=UpperCAmelCase__		)

							# get latents
							A__							=       self.scheduler.add_noise(UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		)
							A__							=       init_latents

							return latents



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       self.coca_transform(UpperCAmelCase__		).unsqueeze(0		)
							with torch.no_grad(), torch.cuda.amp.autocast():
													A__							=       self.coca_model.generate(transformed_image.to(device=self.device			,	dtype=self.coca_model.dtype		)		)
							A__							=       self.coca_tokenizer.decode(generated[0].cpu().numpy()		)
							return generated.split("<end_of_text>"		)[0].replace("<start_of_text>"			,	""		).rstrip(" .,"		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       self.feature_extractor.preprocess(UpperCAmelCase__		)
							A__							=       torch.from_numpy(clip_image_input["pixel_values"][0]		).unsqueeze(0		).to(self.device		).half()
							A__							=       self.clip_model.get_image_features(UpperCAmelCase__		)
							A__							=       image_embeddings_clip / image_embeddings_clip.norm(p=2			,	dim=-1			,	keepdim=UpperCAmelCase__		)
							A__							=       image_embeddings_clip.repeat_interleave(UpperCAmelCase__			,	dim=0		)
							return image_embeddings_clip



	@torch.enable_grad()
	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	):
							A__							=       latents.detach().requires_grad_()

							A__							=       self.scheduler.scale_model_input(UpperCAmelCase__			,	UpperCAmelCase__		)

							# predict the noise residual
							A__							=       self.unet(UpperCAmelCase__			,	UpperCAmelCase__			,	encoder_hidden_states=UpperCAmelCase__		).sample

							if isinstance(self.scheduler			,	(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)		):
													A__							=       self.scheduler.alphas_cumprod[timestep]
													A__							=       1 - alpha_prod_t
													# compute predicted original sample from predicted noise also called
													# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
													A__							=       (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

													A__							=       torch.sqrt(UpperCAmelCase__		)
													A__							=       pred_original_sample * (fac) + latents * (1 - fac)
							elif isinstance(self.scheduler			,	UpperCAmelCase__		):
													A__							=       self.scheduler.sigmas[index]
													A__							=       latents - sigma * noise_pred
							else:
													raise ValueError(F"""scheduler type {type(self.scheduler		)} not supported"""		)

							# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
							A__							=       1 / 0.18_215 * sample
							A__							=       self.vae.decode(UpperCAmelCase__		).sample
							A__							=       (image / 2 + 0.5).clamp(0			,	1		)

							A__							=       transforms.Resize(self.feature_extractor_size		)(UpperCAmelCase__		)
							A__							=       self.normalize(UpperCAmelCase__		).to(latents.dtype		)

							A__							=       self.clip_model.get_image_features(UpperCAmelCase__		)
							A__							=       image_embeddings_clip / image_embeddings_clip.norm(p=2			,	dim=-1			,	keepdim=UpperCAmelCase__		)

							A__							=       spherical_dist_loss(UpperCAmelCase__			,	UpperCAmelCase__		).mean() * clip_guidance_scale

							A__							=       -torch.autograd.grad(UpperCAmelCase__			,	UpperCAmelCase__		)[0]

							if isinstance(self.scheduler			,	UpperCAmelCase__		):
													A__							=       latents.detach() + grads * (sigma**2)
													A__							=       noise_pred_original
							else:
													A__							=       noise_pred_original - torch.sqrt(UpperCAmelCase__		) * grads
							return noise_pred, latents



	@torch.no_grad()
	def __call__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = 512			,	UpperCAmelCase__ = 512			,	UpperCAmelCase__ = 0.6			,	UpperCAmelCase__ = 50			,	UpperCAmelCase__ = 7.5			,	UpperCAmelCase__ = 1			,	UpperCAmelCase__ = 0.0			,	UpperCAmelCase__ = 100			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = "pil"			,	UpperCAmelCase__ = True			,	UpperCAmelCase__ = 0.8			,	UpperCAmelCase__ = 0.1			,	UpperCAmelCase__ = 0.1			,	):
							if isinstance(UpperCAmelCase__			,	UpperCAmelCase__		) and len(UpperCAmelCase__		) != batch_size:
													raise ValueError(F"""You have passed {batch_size} batch_size, but only {len(UpperCAmelCase__		)} generators."""		)

							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 isinstance(UpperCAmelCase__			,	torch.Generator		) and batch_size > 1:
													A__							=       [generator] + [None] * (batch_size - 1)

							A__							=       [
							    ("model", self.coca_model is None),
							    ("tokenizer", self.coca_tokenizer is None),
							    ("transform", self.coca_transform is None),
							]
							A__							=       [x[0] for x in coca_is_none if x[1]]
							A__							=       ", ".join(UpperCAmelCase__		)
							# generate prompts with coca model if prompt is None
							if content_prompt is None:
													if len(UpperCAmelCase__		):
																			raise ValueError(
																			    F"""Content prompt is None and CoCa [{coca_is_none_str}] is None."""
																			    F"""Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."""		)
													A__							=       self.get_image_description(UpperCAmelCase__		)
							if style_prompt is None:
													if len(UpperCAmelCase__		):
																			raise ValueError(
																			    F"""Style prompt is None and CoCa [{coca_is_none_str}] is None."""
																			    F""" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."""		)
													A__							=       self.get_image_description(UpperCAmelCase__		)

							# get prompt text embeddings for content and style
							A__							=       self.tokenizer(
							    UpperCAmelCase__			,	padding="max_length"			,	max_length=self.tokenizer.model_max_length			,	truncation=UpperCAmelCase__			,	return_tensors="pt"			,	)
							A__							=       self.text_encoder(content_text_input.input_ids.to(self.device		)		)[0]

							A__							=       self.tokenizer(
							    UpperCAmelCase__			,	padding="max_length"			,	max_length=self.tokenizer.model_max_length			,	truncation=UpperCAmelCase__			,	return_tensors="pt"			,	)
							A__							=       self.text_encoder(style_text_input.input_ids.to(self.device		)		)[0]

							A__							=       slerp(UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		)

							# duplicate text embeddings for each generation per prompt
							A__							=       text_embeddings.repeat_interleave(UpperCAmelCase__			,	dim=0		)

							# set timesteps
							A__							=       "offset" in set(inspect.signature(self.scheduler.set_timesteps		).parameters.keys()		)
							A__							=       {}
							if accepts_offset:
													A__							=       1

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

							A__						,       A__							=       self.get_timesteps(UpperCAmelCase__			,	UpperCAmelCase__			,	self.device		)
							A__							=       timesteps[:1].repeat(UpperCAmelCase__		)

							# Preprocess image
							A__							=       preprocess(UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		)
							A__							=       self.prepare_latents(
							    UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	text_embeddings.dtype			,	self.device			,	UpperCAmelCase__		)

							A__							=       preprocess(UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		)
							A__							=       self.prepare_latents(
							    UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	text_embeddings.dtype			,	self.device			,	UpperCAmelCase__		)

							A__							=       slerp(UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		)

							if clip_guidance_scale > 0:
													A__							=       self.get_clip_image_embeddings(UpperCAmelCase__			,	UpperCAmelCase__		)
													A__							=       self.get_clip_image_embeddings(UpperCAmelCase__			,	UpperCAmelCase__		)
													A__							=       slerp(
													    UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		)

							# 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.
							A__							=       guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
													A__							=       content_text_input.input_ids.shape[-1]
													A__							=       self.tokenizer([""]			,	padding="max_length"			,	max_length=UpperCAmelCase__			,	return_tensors="pt"		)
													A__							=       self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]
													# duplicate unconditional embeddings for each generation per prompt
													A__							=       uncond_embeddings.repeat_interleave(UpperCAmelCase__			,	dim=0		)

													# 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
													A__							=       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`.
							A__							=       (batch_size, self.unet.config.in_channels, height // 8, width // 8)
							A__							=       text_embeddings.dtype
							if latents is None:
													if self.device.type == "mps":
																			# randn does not work reproducibly on mps
																			A__							=       torch.randn(UpperCAmelCase__			,	generator=UpperCAmelCase__			,	device="cpu"			,	dtype=UpperCAmelCase__		).to(
																			    self.device		)
													else:
																			A__							=       torch.randn(UpperCAmelCase__			,	generator=UpperCAmelCase__			,	device=self.device			,	dtype=UpperCAmelCase__		)
							else:
													if latents.shape != latents_shape:
																			raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""		)
													A__							=       latents.to(self.device		)

							# scale the initial noise by the standard deviation required by the scheduler
							A__							=       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]
							A__							=       "eta" in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
							A__							=       {}
							if accepts_eta:
													A__							=       eta

							# check if the scheduler accepts generator
							A__							=       "generator" in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
							if accepts_generator:
													A__							=       generator

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

																			# predict the noise residual
																			A__							=       self.unet(UpperCAmelCase__			,	UpperCAmelCase__			,	encoder_hidden_states=UpperCAmelCase__		).sample

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

																			# perform clip guidance
																			if clip_guidance_scale > 0:
																									A__							=       (
																									    text_embeddings.chunk(2		)[1] if do_classifier_free_guidance else text_embeddings
																									)
																									A__						,       A__							=       self.cond_fn(
																									    UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	)

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

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
							A__							=       1 / 0.18_215 * latents
							A__							=       self.vae.decode(UpperCAmelCase__		).sample

							A__							=       (image / 2 + 0.5).clamp(0			,	1		)
							A__							=       image.cpu().permute(0			,	2			,	3			,	1		).numpy()

							if output_type == "pil":
													A__							=       self.numpy_to_pil(UpperCAmelCase__		)

							if not return_dict:
													return (image, None)

							return StableDiffusionPipelineOutput(images=UpperCAmelCase__			,	nsfw_content_detected=UpperCAmelCase__		)










import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def  UpperCamelCase						(       _A			:				List[Any]			,							_A			:				List[str]=7    )->       Optional[Any]:





						"""simple docstring"""





						A__							=       None
						if token is not None:
												A__							=       {"Accept": "application/vnd.github+json", "Authorization": f"""Bearer {token}"""}

						# The id of a workflow (not of a workflow run)
						A__							=       "636036"

						A__							=       f"""https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"""
						# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
						url += f"""?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"""

						A__							=       requests.get(_A			,							headers=_A    ).json()

						return result["workflow_runs"]
def  UpperCamelCase						(       _A			:				str    )->       Dict:





						"""simple docstring"""





						A__							=       get_daily_ci_runs(_A    )
						A__							=       None
						for workflow_run in workflow_runs:
												if workflow_run["status"] == "completed":
																		A__							=       workflow_run["id"]
																		break

						return workflow_run_id
def  UpperCamelCase						(       _A			:				int			,							_A			:				List[str]			,							_A			:				str    )->       Any:





						"""simple docstring"""





						A__							=       get_last_daily_ci_runs(_A    )
						if workflow_run_id is not None:
												A__							=       get_artifacts_links(worflow_run_id=_A			,							token=_A    )
												for artifact_name in artifact_names:
																		if artifact_name in artifacts_links:
																								A__							=       artifacts_links[artifact_name]
																								download_artifact(
																								    artifact_name=_A			,							artifact_url=_A			,							output_dir=_A			,							token=_A    )
def  UpperCamelCase						(       _A			:				Optional[Any]			,							_A			:				Any			,							_A			:				List[Any]    )->       Optional[int]:





						"""simple docstring"""





						get_last_daily_ci_artifacts(_A			,							_A			,							_A    )

						A__							=       {}
						for artifact_name in artifact_names:
												A__							=       os.path.join(_A			,							f"""{artifact_name}.zip"""    )
												if os.path.isfile(_A    ):
																		A__							=       {}
																		with zipfile.ZipFile(_A    ) as z:
																								for filename in z.namelist():
																														if not os.path.isdir(_A    ):
																																				# read the file
																																				with z.open(_A    ) as f:
																																										A__							=       f.read().decode("UTF-8"    )

						return results





