






"""simple docstring"""



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

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


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







class    lowercase__			(							A__   ):


			def __init__(      self     :  str   ,       snake_case__     :  int   ,       snake_case__     :  Tuple   ,       snake_case__     :  Optional[int]   ,       snake_case__     :  List[str]   ,       snake_case__     :  Dict   ,       snake_case__     :  Optional[int]   ,       snake_case__     :  Optional[Any]   ,       snake_case__     :  List[Any]   ,       snake_case__     :  Optional[int]   ,       ):
										super().__init__()

										if safety_checker is None:
																	logger.warning(
																	    F"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
																	    " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
																	    " results in services or applications open to the public. Both the diffusers team and Hugging Face"
																	    " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
																	    " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
																	    " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."					)

										self.register_modules(
										    speech_model=UpperCamelCase_   ,       speech_processor=UpperCamelCase_   ,       vae=UpperCamelCase_   ,       text_encoder=UpperCamelCase_   ,       tokenizer=UpperCamelCase_   ,       unet=UpperCamelCase_   ,       scheduler=UpperCamelCase_   ,       feature_extractor=UpperCamelCase_   ,       )


			def 	UpperCAmelCase__						(      self     :  int   ,       snake_case__     :  int = "auto"					):


										if slice_size == "auto":
																	lowerCamelCase_       :							int									=self.unet.config.attention_head_dim // 2
										self.unet.set_attention_slice(UpperCamelCase_					)


			def 	UpperCAmelCase__						(      self     :  Union[str, Any]					):
										self.enable_attention_slicing(UpperCamelCase_					)


			@torch.no_grad()
			def __call__(      self     :  Tuple   ,       snake_case__     :  List[str]   ,       snake_case__     :  List[Any]=1_6000   ,       snake_case__     :  List[str] = 512   ,       snake_case__     :  Dict = 512   ,       snake_case__     :  str = 50   ,       snake_case__     :  Dict = 7.5   ,       snake_case__     :  Dict = None   ,       snake_case__     :  List[Any] = 1   ,       snake_case__     :  Any = 0.0   ,       snake_case__     :  int = None   ,       snake_case__     :  Optional[int] = None   ,       snake_case__     :  Union[str, Any] = "pil"   ,       snake_case__     :  Tuple = True   ,       snake_case__     :  Optional[Any] = None   ,       snake_case__     :  Optional[Any] = 1   ,       **snake_case__     :  List[Any]   ,       ):
										lowerCamelCase_       :							int									=self.speech_processor.feature_extractor(
										    UpperCamelCase_   ,       return_tensors="pt"   ,       sampling_rate=UpperCamelCase_					).input_features.to(self.device					)
										lowerCamelCase_       :							List[str]									=self.speech_model.generate(UpperCamelCase_   ,       max_length=48_0000					)

										lowerCamelCase_       :							Optional[Any]									=self.speech_processor.tokenizer.batch_decode(UpperCamelCase_   ,       skip_special_tokens=UpperCamelCase_   ,       normalize=UpperCamelCase_					)[
										    0
										]

										if isinstance(UpperCamelCase_   ,       UpperCamelCase_					):
																	lowerCamelCase_       :							Dict									=1
										elif isinstance(UpperCamelCase_   ,       UpperCamelCase_					):
																	lowerCamelCase_       :							Optional[Any]									=len(UpperCamelCase_					)
										else:
																	raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase_					)}"""					)

										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(UpperCamelCase_   ,       UpperCamelCase_					) or callback_steps <= 0)
										):
																	raise ValueError(
																	    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
																	    F""" {type(UpperCamelCase_					)}."""					)

										# get prompt text embeddings
										lowerCamelCase_       :							Optional[int]									=self.tokenizer(
										    UpperCamelCase_   ,       padding="max_length"   ,       max_length=self.tokenizer.model_max_length   ,       return_tensors="pt"   ,       )
										lowerCamelCase_       :							List[Any]									=text_inputs.input_ids

										if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																	lowerCamelCase_       :							Optional[int]									=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}"""					)
																	lowerCamelCase_       :							Tuple									=text_input_ids[:, : self.tokenizer.model_max_length]
										lowerCamelCase_       :							Dict									=self.text_encoder(text_input_ids.to(self.device					)					)[0]

										# duplicate text embeddings for each generation per prompt, using mps friendly method
										lowerCamelCase_       :							Any									=text_embeddings.shape
										lowerCamelCase_       :							Dict									=text_embeddings.repeat(1   ,       UpperCamelCase_   ,       1					)
										lowerCamelCase_       :							Dict									=text_embeddings.view(bs_embed * num_images_per_prompt   ,       UpperCamelCase_   ,       -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.
										lowerCamelCase_       :							Dict									=guidance_scale > 1.0
										# get unconditional embeddings for classifier free guidance
										if do_classifier_free_guidance:
																	lowerCamelCase_       :							List[str]
																	if negative_prompt is None:
																								lowerCamelCase_       :							Dict									=[''''''] * batch_size
																	elif type(UpperCamelCase_					) is not type(UpperCamelCase_					):
																								raise TypeError(
																								    F"""`negative_prompt` should be the same type to `prompt`, but got {type(UpperCamelCase_					)} !="""
																								    F""" {type(UpperCamelCase_					)}."""					)
																	elif isinstance(UpperCamelCase_   ,       UpperCamelCase_					):
																								lowerCamelCase_       :							Tuple									=[negative_prompt]
																	elif batch_size != len(UpperCamelCase_					):
																								raise ValueError(
																								    F"""`negative_prompt`: {negative_prompt} has batch size {len(UpperCamelCase_					)}, but `prompt`:"""
																								    F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
																								    " the batch size of `prompt`."					)
																	else:
																								lowerCamelCase_       :							int									=negative_prompt

																	lowerCamelCase_       :							Optional[Any]									=text_input_ids.shape[-1]
																	lowerCamelCase_       :							List[Any]									=self.tokenizer(
																	    UpperCamelCase_   ,       padding="max_length"   ,       max_length=UpperCamelCase_   ,       truncation=UpperCamelCase_   ,       return_tensors="pt"   ,       )
																	lowerCamelCase_       :							Optional[int]									=self.text_encoder(uncond_input.input_ids.to(self.device					)					)[0]

																	# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																	lowerCamelCase_       :							Tuple									=uncond_embeddings.shape[1]
																	lowerCamelCase_       :							Any									=uncond_embeddings.repeat(1   ,       UpperCamelCase_   ,       1					)
																	lowerCamelCase_       :							List[Any]									=uncond_embeddings.view(batch_size * num_images_per_prompt   ,       UpperCamelCase_   ,       -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
																	lowerCamelCase_       :							Tuple									=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`.
										lowerCamelCase_       :							Dict									=(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
										lowerCamelCase_       :							int									=text_embeddings.dtype
										if latents is None:
																	if self.device.type == "mps":
																								# randn does not exist on mps
																								lowerCamelCase_       :							Optional[int]									=torch.randn(UpperCamelCase_   ,       generator=UpperCamelCase_   ,       device="cpu"   ,       dtype=UpperCamelCase_					).to(
																								    self.device					)
																	else:
																								lowerCamelCase_       :							Tuple									=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}"""					)
																	lowerCamelCase_       :							str									=latents.to(self.device					)

										# set timesteps
										self.scheduler.set_timesteps(UpperCamelCase_					)

										# Some schedulers like PNDM have timesteps as arrays
										# It's more optimized to move all timesteps to correct device beforehand
										lowerCamelCase_       :							List[Any]									=self.scheduler.timesteps.to(self.device					)

										# scale the initial noise by the standard deviation required by the scheduler
										lowerCamelCase_       :							List[str]									=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]
										lowerCamelCase_       :							Tuple									='''eta''' in set(inspect.signature(self.scheduler.step					).parameters.keys()					)
										lowerCamelCase_       :							List[Any]									={}
										if accepts_eta:
																	lowerCamelCase_       :							List[str]									=eta

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

																	# predict the noise residual
																	lowerCamelCase_       :							List[Any]									=self.unet(UpperCamelCase_   ,       UpperCamelCase_   ,       encoder_hidden_states=UpperCamelCase_					).sample

																	# perform guidance
																	if do_classifier_free_guidance:
																								lowerCamelCase_       :							List[Any]									=noise_pred.chunk(2					)
																								lowerCamelCase_       :							int									=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																	# compute the previous noisy sample x_t -> x_t-1
																	lowerCamelCase_       :							Tuple									=self.scheduler.step(UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       **UpperCamelCase_					).prev_sample

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

										lowerCamelCase_       :							Dict									=1 / 0.18_215 * latents
										lowerCamelCase_       :							Dict									=self.vae.decode(UpperCamelCase_					).sample

										lowerCamelCase_       :							Dict									=(image / 2 + 0.5).clamp(0   ,       1					)

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

										if output_type == "pil":
																	lowerCamelCase_       :							Optional[int]									=self.numpy_to_pil(UpperCamelCase_					)

										if not return_dict:
																	return image

										return StableDiffusionPipelineOutput(images=UpperCamelCase_   ,       nsfw_content_detected=UpperCamelCase_					)







'''simple docstring'''
from datetime import datetime as dt
import os

from github import Github


__snake_case							      =     [
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]


def 			a      (			)  ->	List[str]:



					'''simple docstring'''






					UpperCamelCase__   :List[str]												= Github(os.environ['''GITHUB_TOKEN''']   )
					UpperCamelCase__   :Tuple												= g.get_repo('''huggingface/transformers'''   )
					UpperCamelCase__   :Union[str, Any]												= repo.get_issues(state='''open'''   )

					for issue in open_issues:
										UpperCamelCase__   :List[Any]												= sorted([comment for comment in issue.get_comments()] ,					key=lambda __a   : i.created_at ,					reverse=__a   )
										UpperCamelCase__   :List[Any]												= comments[0] if len(__a   ) > 0 else None
										if (
										    last_comment is not None
										    and last_comment.user.login == "github-actions[bot]"
										    and (dt.utcnow() - issue.updated_at).days > 7
										    and (dt.utcnow() - issue.created_at).days >= 30
										    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()   )
										):
															# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
															issue.edit(state='''closed'''   )
										elif (
										    (dt.utcnow() - issue.updated_at).days > 23
										    and (dt.utcnow() - issue.created_at).days >= 30
										    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()   )
										):
															# print(f"Would add stale comment to {issue.number}")
															issue.create_comment(
															    '''This issue has been automatically marked as stale because it has not had '''
															    '''recent activity. If you think this still needs to be addressed '''
															    '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
															    '''[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) '''
															    '''are likely to be ignored.'''   )


if __name__ == "__main__":
		main()