




'''simple docstring'''



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			(_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  Union[str, Any]						)-> Optional[Any]:




     '''simple docstring'''





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

     if isinstance(image[0] ,						PIL.Image.Image						):
          __snake_case            =	[np.array(i.resize((w, h) ,						resample=PIL_INTERPOLATION['''lanczos''']						)						)[None, :] for i in image]
          __snake_case            =	np.concatenate(_lowerCamelCase ,						axis=0						)
          __snake_case            =	np.array(_lowerCamelCase						).astype(np.floataa						) / 255.0
          __snake_case            =	image.transpose(0 ,						3 ,						1 ,						2						)
          __snake_case            =	2.0 * image - 1.0
          __snake_case            =	torch.from_numpy(_lowerCamelCase						)
     elif isinstance(image[0] ,						torch.Tensor						):
          __snake_case            =	torch.cat(_lowerCamelCase ,						dim=0						)
     return image







def       _UpperCamelCase			(_lowerCamelCase				:  Dict ,						_lowerCamelCase				:  Optional[int] ,						_lowerCamelCase				:  int ,						_lowerCamelCase				:  Optional[Any]=0.9995						)-> Union[str, Any]:




     '''simple docstring'''





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

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

     if inputs_are_torch:
          __snake_case            =	torch.from_numpy(_lowerCamelCase						).to(_lowerCamelCase						)

     return va







def       _UpperCamelCase			(_lowerCamelCase				:  str ,						_lowerCamelCase				:  Dict						)-> Union[str, Any]:




     '''simple docstring'''





     __snake_case            =	F.normalize(_lowerCamelCase ,						dim=-1						)
     __snake_case            =	F.normalize(_lowerCamelCase ,						dim=-1						)
     return (x - y).norm(dim=-1						).div(2						).arcsin().pow(2						).mul(2						)







def       _UpperCamelCase			(_lowerCamelCase				:  List[str] ,						_lowerCamelCase				:  List[str]						)-> int:




     '''simple docstring'''





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







class      lowerCAmelCase (			__lowerCAmelCase):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	)   ->	str:



            '''simple docstring'''




            super().__init__()
            self.register_modules(
                vae=__SCREAMING_SNAKE_CASE     ,	text_encoder=__SCREAMING_SNAKE_CASE     ,	clip_model=__SCREAMING_SNAKE_CASE     ,	tokenizer=__SCREAMING_SNAKE_CASE     ,	unet=__SCREAMING_SNAKE_CASE     ,	scheduler=__SCREAMING_SNAKE_CASE     ,	feature_extractor=__SCREAMING_SNAKE_CASE     ,	coca_model=__SCREAMING_SNAKE_CASE     ,	coca_tokenizer=__SCREAMING_SNAKE_CASE     ,	coca_transform=__SCREAMING_SNAKE_CASE     ,	)
            __snake_case            =	(
                feature_extractor.size
                if isinstance(feature_extractor.size     ,	__SCREAMING_SNAKE_CASE	)
                else feature_extractor.size['''shortest_edge''']
            )
            __snake_case            =	transforms.Normalize(mean=feature_extractor.image_mean     ,	std=feature_extractor.image_std	)
            set_requires_grad(self.text_encoder     ,	__SCREAMING_SNAKE_CASE	)
            set_requires_grad(self.clip_model     ,	__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE = "auto"	)   ->	List[Any]:



            '''simple docstring'''




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


       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            self.enable_attention_slicing(__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            set_requires_grad(self.vae     ,	__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            set_requires_grad(self.vae     ,	__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            set_requires_grad(self.unet     ,	__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	List[str]:



            '''simple docstring'''




            set_requires_grad(self.unet     ,	__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	min(int(num_inference_steps * strength	)     ,	__SCREAMING_SNAKE_CASE	)

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

            return timesteps, num_inference_steps - t_start


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None	)   ->	str:



            '''simple docstring'''




            if not isinstance(__SCREAMING_SNAKE_CASE     ,	torch.Tensor	):
                 raise ValueError(F'''`image` has to be of type `torch.Tensor` but is {type(__SCREAMING_SNAKE_CASE	)}'''	)

            __snake_case            =	image.to(device=__SCREAMING_SNAKE_CASE     ,	dtype=__SCREAMING_SNAKE_CASE	)

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

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

            __snake_case            =	randn_tensor(init_latents.shape     ,	generator=__SCREAMING_SNAKE_CASE     ,	device=__SCREAMING_SNAKE_CASE     ,	dtype=__SCREAMING_SNAKE_CASE	)

            # get latents
            __snake_case            =	self.scheduler.add_noise(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
            __snake_case            =	init_latents

            return latents


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	str:



            '''simple docstring'''




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


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	int:



            '''simple docstring'''




            __snake_case            =	self.feature_extractor.preprocess(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	torch.from_numpy(clip_image_input['''pixel_values'''][0]	).unsqueeze(0	).to(self.device	).half()
            __snake_case            =	self.clip_model.get_image_features(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	image_embeddings_clip / image_embeddings_clip.norm(p=2     ,	dim=-1     ,	keepdim=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	image_embeddings_clip.repeat_interleave(__SCREAMING_SNAKE_CASE     ,	dim=0	)
            return image_embeddings_clip


       @torch.enable_grad()
       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	)   ->	int:



            '''simple docstring'''




            __snake_case            =	latents.detach().requires_grad_()

            __snake_case            =	self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            # predict the noise residual
            __snake_case            =	self.unet(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	encoder_hidden_states=__SCREAMING_SNAKE_CASE	).sample

            if isinstance(self.scheduler     ,	(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)	):
                 __snake_case            =	self.scheduler.alphas_cumprod[timestep]
                 __snake_case            =	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
                 __snake_case            =	(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

                 __snake_case            =	torch.sqrt(__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	pred_original_sample * (fac) + latents * (1 - fac)
            elif isinstance(self.scheduler     ,	__SCREAMING_SNAKE_CASE	):
                 __snake_case            =	self.scheduler.sigmas[index]
                 __snake_case            =	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
            __snake_case            =	1 / 0.18_215 * sample
            __snake_case            =	self.vae.decode(__SCREAMING_SNAKE_CASE	).sample
            __snake_case            =	(image / 2 + 0.5).clamp(0     ,	1	)

            __snake_case            =	transforms.Resize(self.feature_extractor_size	)(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	self.normalize(__SCREAMING_SNAKE_CASE	).to(latents.dtype	)

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

            __snake_case            =	spherical_dist_loss(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	).mean() * clip_guidance_scale

            __snake_case            =	-torch.autograd.grad(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)[0]

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




       @torch.no_grad()
       def __call__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = 512     ,	__SCREAMING_SNAKE_CASE = 512     ,	__SCREAMING_SNAKE_CASE = 0.6     ,	__SCREAMING_SNAKE_CASE = 50     ,	__SCREAMING_SNAKE_CASE = 7.5     ,	__SCREAMING_SNAKE_CASE = 1     ,	__SCREAMING_SNAKE_CASE = 0.0     ,	__SCREAMING_SNAKE_CASE = 100     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = "pil"     ,	__SCREAMING_SNAKE_CASE = True     ,	__SCREAMING_SNAKE_CASE = 0.8     ,	__SCREAMING_SNAKE_CASE = 0.1     ,	__SCREAMING_SNAKE_CASE = 0.1     ,	)   ->	Any:



            '''simple docstring'''




            if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	) and len(__SCREAMING_SNAKE_CASE	) != batch_size:
                 raise ValueError(F'''You have passed {batch_size} batch_size, but only {len(__SCREAMING_SNAKE_CASE	)} 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(__SCREAMING_SNAKE_CASE     ,	torch.Generator	) and batch_size > 1:
                 __snake_case            =	[generator] + [None] * (batch_size - 1)

            __snake_case            =	[
                ('''model''', self.coca_model is None),
                ('''tokenizer''', self.coca_tokenizer is None),
                ('''transform''', self.coca_transform is None),
            ]
            __snake_case            =	[x[0] for x in coca_is_none if x[1]]
            __snake_case            =	''', '''.join(__SCREAMING_SNAKE_CASE	)
            # generate prompts with coca model if prompt is None
            if content_prompt is None:
                 if len(__SCREAMING_SNAKE_CASE	):
                      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.'''	)
                 __snake_case            =	self.get_image_description(__SCREAMING_SNAKE_CASE	)
            if style_prompt is None:
                 if len(__SCREAMING_SNAKE_CASE	):
                      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.'''	)
                 __snake_case            =	self.get_image_description(__SCREAMING_SNAKE_CASE	)

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

            __snake_case            =	self.tokenizer(
                __SCREAMING_SNAKE_CASE     ,	padding='''max_length'''     ,	max_length=self.tokenizer.model_max_length     ,	truncation=__SCREAMING_SNAKE_CASE     ,	return_tensors='''pt'''     ,	)
            __snake_case            =	self.text_encoder(style_text_input.input_ids.to(self.device	)	)[0]

            __snake_case            =	slerp(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

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

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

            self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)
            # 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	)

            __snake_case       ,     __snake_case            =	self.get_timesteps(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	self.device	)
            __snake_case            =	timesteps[:1].repeat(__SCREAMING_SNAKE_CASE	)

            # Preprocess image
            __snake_case            =	preprocess(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
            __snake_case            =	self.prepare_latents(
                __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	text_embeddings.dtype     ,	self.device     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	preprocess(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
            __snake_case            =	self.prepare_latents(
                __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	text_embeddings.dtype     ,	self.device     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	slerp(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            if clip_guidance_scale > 0:
                 __snake_case            =	self.get_clip_image_embeddings(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	self.get_clip_image_embeddings(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	slerp(
                     __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            # 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.
            __snake_case            =	guidance_scale > 1.0
            # get unconditional embeddings for classifier free guidance
            if do_classifier_free_guidance:
                 __snake_case            =	content_text_input.input_ids.shape[-1]
                 __snake_case            =	self.tokenizer(['''''']     ,	padding='''max_length'''     ,	max_length=__SCREAMING_SNAKE_CASE     ,	return_tensors='''pt'''	)
                 __snake_case            =	self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]
                 # duplicate unconditional embeddings for each generation per prompt
                 __snake_case            =	uncond_embeddings.repeat_interleave(__SCREAMING_SNAKE_CASE     ,	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
                 __snake_case            =	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`.
            __snake_case            =	(batch_size, self.unet.config.in_channels, height // 8, width // 8)
            __snake_case            =	text_embeddings.dtype
            if latents is None:
                 if self.device.type == "mps":
                      # randn does not work reproducibly on mps
                      __snake_case            =	torch.randn(__SCREAMING_SNAKE_CASE     ,	generator=__SCREAMING_SNAKE_CASE     ,	device='''cpu'''     ,	dtype=__SCREAMING_SNAKE_CASE	).to(
                          self.device	)
                 else:
                      __snake_case            =	torch.randn(__SCREAMING_SNAKE_CASE     ,	generator=__SCREAMING_SNAKE_CASE     ,	device=self.device     ,	dtype=__SCREAMING_SNAKE_CASE	)
            else:
                 if latents.shape != latents_shape:
                      raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''	)
                 __snake_case            =	latents.to(self.device	)

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

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

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

                      # predict the noise residual
                      __snake_case            =	self.unet(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	encoder_hidden_states=__SCREAMING_SNAKE_CASE	).sample

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

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

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

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

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

            if output_type == "pil":
                 __snake_case            =	self.numpy_to_pil(__SCREAMING_SNAKE_CASE	)

            if not return_dict:
                 return (image, None)

            return StableDiffusionPipelineOutput(images=__SCREAMING_SNAKE_CASE     ,	nsfw_content_detected=__SCREAMING_SNAKE_CASE	)











"""simple docstring"""

import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union

from .. import config
from .filelock import FileLock
from .logging import get_logger


A_						:    Optional[int]				=      get_logger(__name__)
class     lowerCAmelCase__						:


	'''simple docstring'''







	def __init__(      self				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       Optional[str] = None    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     (
					    os.path.join(_SCREAMING_SNAKE_CASE     ,       config.EXTRACTED_DATASETS_DIR    ) if cache_dir else config.EXTRACTED_DATASETS_PATH
					)
					SCREAMING_SNAKE_CASE						:							Dict							=     Extractor





	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       str    )						->	str:





					"""simple docstring"""


					from .file_utils import hash_url_to_filename

					# Path where we extract compressed archives
					# We extract in the cache dir, and get the extracted path name by hashing the original path"
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     os.path.abspath(_SCREAMING_SNAKE_CASE    )
					return os.path.join(self.extract_dir     ,       hash_url_to_filename(_SCREAMING_SNAKE_CASE    )    )





	def 			_lowerCAmelCase     (      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       bool    )						->	bool:





					"""simple docstring"""


					return force_extract or (
					    not os.path.isfile(_SCREAMING_SNAKE_CASE    ) and not (os.path.isdir(_SCREAMING_SNAKE_CASE    ) and os.listdir(_SCREAMING_SNAKE_CASE    ))
					)







	def 			_lowerCAmelCase     (      self				:       str     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       bool = False    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     self.extractor.infer_extractor_format(_SCREAMING_SNAKE_CASE    )
					if not extractor_format:
									return input_path
					SCREAMING_SNAKE_CASE						:							List[str]							=     self._get_output_path(_SCREAMING_SNAKE_CASE    )
					if self._do_extract(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ):
									self.extractor.extract(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
					return output_path




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	@classmethod
	@abstractmethod
	def 			_lowerCAmelCase     (      cls				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       **_SCREAMING_SNAKE_CASE				:       Tuple    )						->	bool:





					"""simple docstring"""


					...







	@staticmethod
	@abstractmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					...




class     lowerCAmelCase__						(	_lowerCamelCase							,							_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		List[bytes]  						= []





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       int    )						->	Any:





					"""simple docstring"""


					with open(_SCREAMING_SNAKE_CASE     ,       'rb'    ) as f:
									return f.read(_SCREAMING_SNAKE_CASE    )







	@classmethod
	def 			_lowerCAmelCase     (      cls				:       str     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       bytes = b""    )						->	bool:





					"""simple docstring"""


					if not magic_number:
									SCREAMING_SNAKE_CASE						:							Any							=     max(len(_SCREAMING_SNAKE_CASE    ) for cls_magic_number in cls.magic_numbers    )
									try:
													SCREAMING_SNAKE_CASE						:							int							=     cls.read_magic_number(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
									except OSError:
													return False
					return any(magic_number.startswith(_SCREAMING_SNAKE_CASE    ) for cls_magic_number in cls.magic_numbers    )




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	@classmethod
	def 			_lowerCAmelCase     (      cls				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       **_SCREAMING_SNAKE_CASE				:       List[Any]    )						->	bool:





					"""simple docstring"""


					return tarfile.is_tarfile(_SCREAMING_SNAKE_CASE    )





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       Dict    )						->	Dict:





					"""simple docstring"""


					def resolved(_SCREAMING_SNAKE_CASE				:       str    ) -> str:
									return os.path.realpath(os.path.abspath(_SCREAMING_SNAKE_CASE    )    )

					def badpath(_SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       str    ) -> bool:
									# joinpath will ignore base if path is absolute
									return not resolved(os.path.join(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )    ).startswith(_SCREAMING_SNAKE_CASE    )

					def badlink(_SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       str    ) -> bool:
									# Links are interpreted relative to the directory containing the link
									SCREAMING_SNAKE_CASE						:							List[str]							=     resolved(os.path.join(_SCREAMING_SNAKE_CASE     ,       os.path.dirname(info.name    )    )    )
									return badpath(info.linkname     ,       base=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							str							=     resolved(_SCREAMING_SNAKE_CASE    )

					for finfo in members:
									if badpath(finfo.name     ,       _SCREAMING_SNAKE_CASE    ):
													logger.error(f"""Extraction of {finfo.name} is blocked (illegal path)"""    )
									elif finfo.issym() and badlink(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ):
													logger.error(f"""Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}"""    )
									elif finfo.islnk() and badlink(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ):
													logger.error(f"""Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}"""    )
									else:
													yield finfo







	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					os.makedirs(_SCREAMING_SNAKE_CASE     ,       exist_ok=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     tarfile.open(_SCREAMING_SNAKE_CASE    )
					tar_file.extractall(_SCREAMING_SNAKE_CASE     ,       members=TarExtractor.safemembers(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )    )
					tar_file.close()




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Optional[int]  						= [B'''\x1F\x8B''']





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					with gzip.open(_SCREAMING_SNAKE_CASE     ,       'rb'    ) as gzip_file:
									with open(_SCREAMING_SNAKE_CASE     ,       'wb'    ) as extracted_file:
													shutil.copyfileobj(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Optional[Any]  						= [
	    B'''PK\x03\x04''',
	    B'''PK\x05\x06''',  # empty archive
	    B'''PK\x07\x08''',  # spanned archive
	]





	@classmethod
	def 			_lowerCAmelCase     (      cls				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       bytes = b""    )						->	bool:





					"""simple docstring"""


					if super().is_extractable(_SCREAMING_SNAKE_CASE     ,       magic_number=_SCREAMING_SNAKE_CASE    ):
									return True
					try:
									# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
									# From: https://github.com/python/cpython/pull/5053
									from zipfile import (
									    _CD_SIGNATURE,
									    _ECD_DISK_NUMBER,
									    _ECD_DISK_START,
									    _ECD_ENTRIES_TOTAL,
									    _ECD_OFFSET,
									    _ECD_SIZE,
									    _EndRecData,
									    sizeCentralDir,
									    stringCentralDir,
									    structCentralDir,
									)

									with open(_SCREAMING_SNAKE_CASE     ,       'rb'    ) as fp:
													SCREAMING_SNAKE_CASE						:							List[str]							=     _EndRecData(_SCREAMING_SNAKE_CASE    )
													if endrec:
																	if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
																					return True  # Empty zipfiles are still zipfiles
																	elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
																					fp.seek(endrec[_ECD_OFFSET]    )  # Central directory is on the same disk
																					if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
																									SCREAMING_SNAKE_CASE						:							str							=     fp.read(_SCREAMING_SNAKE_CASE    )  # CD is where we expect it to be
																									if len(_SCREAMING_SNAKE_CASE    ) == sizeCentralDir:
																													SCREAMING_SNAKE_CASE						:							Optional[Any]							=     struct.unpack(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )  # CD is the right size
																													if centdir[_CD_SIGNATURE] == stringCentralDir:
																																	return True  # First central directory entry  has correct magic number
									return False
					except Exception:  # catch all errors in case future python versions change the zipfile internals
									return False







	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					os.makedirs(_SCREAMING_SNAKE_CASE     ,       exist_ok=_SCREAMING_SNAKE_CASE    )
					with zipfile.ZipFile(_SCREAMING_SNAKE_CASE     ,       'r'    ) as zip_file:
									zip_file.extractall(_SCREAMING_SNAKE_CASE    )
									zip_file.close()




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Union[str, Any]  						= [B'''\xFD\x37\x7A\x58\x5A\x00''']





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					with lzma.open(_SCREAMING_SNAKE_CASE    ) as compressed_file:
									with open(_SCREAMING_SNAKE_CASE     ,       'wb'    ) as extracted_file:
													shutil.copyfileobj(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Dict  						= [B'''Rar!\x1a\x07\x00''', B'''Rar!\x1a\x07\x01\x00''']  # RAR_ID  # RAR5_ID





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					if not config.RARFILE_AVAILABLE:
									raise ImportError('Please pip install rarfile'    )
					import rarfile

					os.makedirs(_SCREAMING_SNAKE_CASE     ,       exist_ok=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							str							=     rarfile.RarFile(_SCREAMING_SNAKE_CASE    )
					rf.extractall(_SCREAMING_SNAKE_CASE    )
					rf.close()




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Optional[int]  						= [B'''\x28\xb5\x2F\xFD''']





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					if not config.ZSTANDARD_AVAILABLE:
									raise ImportError('Please pip install zstandard'    )
					import zstandard as zstd

					SCREAMING_SNAKE_CASE						:							Any							=     zstd.ZstdDecompressor()
					with open(_SCREAMING_SNAKE_CASE     ,       'rb'    ) as ifh, open(_SCREAMING_SNAKE_CASE     ,       'wb'    ) as ofh:
									dctx.copy_stream(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Union[str, Any]  						= [B'''\x42\x5A\x68''']





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					with bza.open(_SCREAMING_SNAKE_CASE     ,       'rb'    ) as compressed_file:
									with open(_SCREAMING_SNAKE_CASE     ,       'wb'    ) as extracted_file:
													shutil.copyfileobj(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		str  						= [B'''\x37\x7A\xBC\xAF\x27\x1C''']





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					if not config.PY7ZR_AVAILABLE:
									raise ImportError('Please pip install py7zr'    )
					import pyazr

					os.makedirs(_SCREAMING_SNAKE_CASE     ,       exist_ok=_SCREAMING_SNAKE_CASE    )
					with pyazr.SevenZipFile(_SCREAMING_SNAKE_CASE     ,       'r'    ) as archive:
									archive.extractall(_SCREAMING_SNAKE_CASE    )




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		List[str]  						= [B'''\x04\x22\x4D\x18''']





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					if not config.LZ4_AVAILABLE:
									raise ImportError('Please pip install lz4'    )
					import lza.frame

					with lza.frame.open(_SCREAMING_SNAKE_CASE     ,       'rb'    ) as compressed_file:
									with open(_SCREAMING_SNAKE_CASE     ,       'wb'    ) as extracted_file:
													shutil.copyfileobj(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )




class     lowerCAmelCase__						:


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Dict[str, Type[BaseExtractor]]  						= {
	    "tar": TarExtractor,
	    "gzip": GzipExtractor,
	    "zip": ZipExtractor,
	    "xz": XzExtractor,
	    "rar": RarExtractor,
	    "zstd": ZstdExtractor,
	    "bz2": BzipaExtractor,
	    "7z": SevenZipExtractor,  # <Added version="2.4.0"/>
	    "lz4": LzaExtractor,  # <Added version="2.4.0"/>
	}





	@classmethod
	def 			_lowerCAmelCase     (      cls				:       List[str]    )						->	Tuple:





					"""simple docstring"""


					return max(
					    len(_SCREAMING_SNAKE_CASE    )
					    for extractor in cls.extractors.values()
					    if issubclass(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
					    for extractor_magic_number in extractor.magic_numbers    )





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       int    )						->	List[str]:





					"""simple docstring"""


					try:
									return MagicNumberBaseExtractor.read_magic_number(_SCREAMING_SNAKE_CASE     ,       magic_number_length=_SCREAMING_SNAKE_CASE    )
					except OSError:
									return b""





	@classmethod
	def 			_lowerCAmelCase     (      cls				:       Any     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       bool = False    )						->	bool:





					"""simple docstring"""


					warnings.warn(
					    'Method \'is_extractable\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '
					    'Use \'infer_extractor_format\' instead.'     ,       category=_SCREAMING_SNAKE_CASE     ,       )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     cls.infer_extractor_format(_SCREAMING_SNAKE_CASE    )
					if extractor_format:
									return True if not return_extractor else (True, cls.extractors[extractor_format])
					return False if not return_extractor else (False, None)





	@classmethod
	def 			_lowerCAmelCase     (      cls				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	str:  # <Added version="2.4.0"/>





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     cls._get_magic_number_max_length()
					SCREAMING_SNAKE_CASE						:							str							=     cls._read_magic_number(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
					for extractor_format, extractor in cls.extractors.items():
									if extractor.is_extractable(_SCREAMING_SNAKE_CASE     ,       magic_number=_SCREAMING_SNAKE_CASE    ):
													return extractor_format







	@classmethod
	def 			_lowerCAmelCase     (      cls				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Optional[str] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[BaseExtractor] = "deprecated"     ,       )						->	None:





					"""simple docstring"""


					os.makedirs(os.path.dirname(_SCREAMING_SNAKE_CASE    )     ,       exist_ok=_SCREAMING_SNAKE_CASE    )
					# Prevent parallel extractions
					SCREAMING_SNAKE_CASE						:							List[str]							=     str(Path(_SCREAMING_SNAKE_CASE    ).with_suffix('.lock'    )    )
					with FileLock(_SCREAMING_SNAKE_CASE    ):
									shutil.rmtree(_SCREAMING_SNAKE_CASE     ,       ignore_errors=_SCREAMING_SNAKE_CASE    )
									if extractor_format or extractor != "deprecated":
													if extractor != "deprecated" or not isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ):  # passed as positional arg
																	warnings.warn(
																	    'Parameter \'extractor\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '
																	    'Use \'extractor_format\' instead.'     ,       category=_SCREAMING_SNAKE_CASE     ,       )
																	SCREAMING_SNAKE_CASE						:							int							=     extractor if extractor != 'deprecated' else extractor_format
													else:
																	SCREAMING_SNAKE_CASE						:							int							=     cls.extractors[extractor_format]
													return extractor.extract(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
									else:
													warnings.warn(
													    'Parameter \'extractor_format\' was made required in version 2.4.0 and not passing it will raise an '
													    'exception in 3.0.0.'     ,       category=_SCREAMING_SNAKE_CASE     ,       )
													for extractor in cls.extractors.values():
																	if extractor.is_extractable(_SCREAMING_SNAKE_CASE    ):
																					return extractor.extract(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )





