


'''simple docstring'''





import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
class 	a__				(				nn.Module      ):





			'''simple docstring'''

			def __init__(     self       : Tuple ,						lowerCAmelCase_       : List[Any] ,						lowerCAmelCase_       : List[Any]=1.0  )			->		Dict:
						super().__init__()

						__A=  cut_size
						__A=  cut_power

			def 				lowerCAmelCase    (     self       : Optional[Any] ,						lowerCAmelCase_       : Any ,						lowerCAmelCase_       : int  )			->		Optional[Any]:
						__A,  __A=  pixel_values.shape[2:4]
						__A=  min(lowerCAmelCase_ ,						lowerCAmelCase_  )
						__A=  min(lowerCAmelCase_ ,						lowerCAmelCase_ ,						self.cut_size  )
						__A=  []
						for _ in range(lowerCAmelCase_  ):
									__A=  int(torch.rand([]  ) ** self.cut_power * (max_size - min_size) + min_size  )
									__A=  torch.randint(0 ,						sideX - size + 1 ,						()  )
									__A=  torch.randint(0 ,						sideY - size + 1 ,						()  )
									__A=  pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
									cutouts.append(F.adaptive_avg_poolad(lowerCAmelCase_ ,						self.cut_size  )  )
						return torch.cat(lowerCAmelCase_  )






def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   int,_SCREAMING_SNAKE_CASE				:   str   ):






			"""simple docstring"""




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






def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Union[str, Any],_SCREAMING_SNAKE_CASE				:   Optional[Any]   ):






			"""simple docstring"""




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






class 	a__				(				a_      ):





			'''simple docstring'''

			def __init__(     self       : Dict ,						lowerCAmelCase_       : AutoencoderKL ,						lowerCAmelCase_       : CLIPTextModel ,						lowerCAmelCase_       : CLIPModel ,						lowerCAmelCase_       : CLIPTokenizer ,						lowerCAmelCase_       : UNetaDConditionModel ,						lowerCAmelCase_       : Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler] ,						lowerCAmelCase_       : CLIPImageProcessor ,						)			->		List[str]:
						super().__init__()
						self.register_modules(
						    vae=lowerCAmelCase_ ,						text_encoder=lowerCAmelCase_ ,						clip_model=lowerCAmelCase_ ,						tokenizer=lowerCAmelCase_ ,						unet=lowerCAmelCase_ ,						scheduler=lowerCAmelCase_ ,						feature_extractor=lowerCAmelCase_ ,						)

						__A=  transforms.Normalize(mean=feature_extractor.image_mean ,						std=feature_extractor.image_std  )
						__A=  (
						    feature_extractor.size
						    if isinstance(feature_extractor.size ,						lowerCAmelCase_  )
						    else feature_extractor.size['shortest_edge']
						)
						__A=  MakeCutouts(self.cut_out_size  )

						set_requires_grad(self.text_encoder ,						lowerCAmelCase_  )
						set_requires_grad(self.clip_model ,						lowerCAmelCase_  )
			def 				lowerCAmelCase    (     self       : Union[str, Any] ,						lowerCAmelCase_       : Optional[Union[str, int]] = "auto"  )			->		List[Any]:
						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(lowerCAmelCase_  )
			def 				lowerCAmelCase    (     self       : Dict  )			->		List[Any]:
						self.enable_attention_slicing(lowerCAmelCase_  )
			def 				lowerCAmelCase    (     self       : List[Any]  )			->		List[Any]:
						set_requires_grad(self.vae ,						lowerCAmelCase_  )
			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Dict:
						set_requires_grad(self.vae ,						lowerCAmelCase_  )
			def 				lowerCAmelCase    (     self       : Optional[int]  )			->		Dict:
						set_requires_grad(self.unet ,						lowerCAmelCase_  )
			def 				lowerCAmelCase    (     self       : str  )			->		Tuple:
						set_requires_grad(self.unet ,						lowerCAmelCase_  )
			@torch.enable_grad()
			def 				lowerCAmelCase    (     self       : List[Any] ,						lowerCAmelCase_       : Any ,						lowerCAmelCase_       : Dict ,						lowerCAmelCase_       : Any ,						lowerCAmelCase_       : int ,						lowerCAmelCase_       : Optional[int] ,						lowerCAmelCase_       : Any ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : Optional[int] ,						lowerCAmelCase_       : Optional[int]=True ,						)			->		Tuple:
						__A=  latents.detach().requires_grad_()

						__A=  self.scheduler.scale_model_input(lowerCAmelCase_ ,						lowerCAmelCase_  )

						# predict the noise residual
						__A=  self.unet(lowerCAmelCase_ ,						lowerCAmelCase_ ,						encoder_hidden_states=lowerCAmelCase_  ).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(lowerCAmelCase_  )
									__A=  pred_original_sample * (fac) + latents * (1 - fac)
						elif isinstance(self.scheduler ,						lowerCAmelCase_  ):
									__A=  self.scheduler.sigmas[index]
									__A=  latents - sigma * noise_pred
						else:
									raise ValueError(F"""scheduler type {type(self.scheduler  )} not supported"""  )

						__A=  1 / self.vae.config.scaling_factor * sample
						__A=  self.vae.decode(lowerCAmelCase_  ).sample
						__A=  (image / 2 + 0.5).clamp(0 ,						1  )

						if use_cutouts:
									__A=  self.make_cutouts(lowerCAmelCase_ ,						lowerCAmelCase_  )
						else:
									__A=  transforms.Resize(self.cut_out_size  )(lowerCAmelCase_  )
						__A=  self.normalize(lowerCAmelCase_  ).to(latents.dtype  )

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

						if use_cutouts:
									__A=  spherical_dist_loss(lowerCAmelCase_ ,						lowerCAmelCase_  )
									__A=  dists.view([num_cutouts, sample.shape[0], -1]  )
									__A=  dists.sum(2  ).mean(0  ).sum() * clip_guidance_scale
						else:
									__A=  spherical_dist_loss(lowerCAmelCase_ ,						lowerCAmelCase_  ).mean() * clip_guidance_scale

						__A=  -torch.autograd.grad(lowerCAmelCase_ ,						lowerCAmelCase_  )[0]

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

			@torch.no_grad()
			def __call__(     self       : Union[str, Any] ,						lowerCAmelCase_       : Union[str, List[str]] ,						lowerCAmelCase_       : Optional[int] = 512 ,						lowerCAmelCase_       : Optional[int] = 512 ,						lowerCAmelCase_       : Optional[int] = 50 ,						lowerCAmelCase_       : Optional[float] = 7.5 ,						lowerCAmelCase_       : Optional[int] = 1 ,						lowerCAmelCase_       : float = 0.0 ,						lowerCAmelCase_       : Optional[float] = 100 ,						lowerCAmelCase_       : Optional[Union[str, List[str]]] = None ,						lowerCAmelCase_       : Optional[int] = 4 ,						lowerCAmelCase_       : Optional[bool] = True ,						lowerCAmelCase_       : Optional[torch.Generator] = None ,						lowerCAmelCase_       : Optional[torch.FloatTensor] = None ,						lowerCAmelCase_       : Optional[str] = "pil" ,						lowerCAmelCase_       : bool = True ,						)			->		Tuple:
						if isinstance(lowerCAmelCase_ ,						lowerCAmelCase_  ):
									__A=  1
						elif isinstance(lowerCAmelCase_ ,						lowerCAmelCase_  ):
									__A=  len(lowerCAmelCase_  )
						else:
									raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase_  )}"""  )

						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}."""  )

						# get prompt text embeddings
						__A=  self.tokenizer(
						    lowerCAmelCase_ ,						padding='max_length' ,						max_length=self.tokenizer.model_max_length ,						truncation=lowerCAmelCase_ ,						return_tensors='pt' ,						)
						__A=  self.text_encoder(text_input.input_ids.to(self.device  )  )[0]
						# duplicate text embeddings for each generation per prompt
						__A=  text_embeddings.repeat_interleave(lowerCAmelCase_ ,						dim=0  )

						if clip_guidance_scale > 0:
									if clip_prompt is not None:
												__A=  self.tokenizer(
												    lowerCAmelCase_ ,						padding='max_length' ,						max_length=self.tokenizer.model_max_length ,						truncation=lowerCAmelCase_ ,						return_tensors='pt' ,						).input_ids.to(self.device  )
									else:
												__A=  text_input.input_ids.to(self.device  )
									__A=  self.clip_model.get_text_features(lowerCAmelCase_  )
									__A=  text_embeddings_clip / text_embeddings_clip.norm(p=2 ,						dim=-1 ,						keepdim=lowerCAmelCase_  )
									# duplicate text embeddings clip for each generation per prompt
									__A=  text_embeddings_clip.repeat_interleave(lowerCAmelCase_ ,						dim=0  )

						# 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=  text_input.input_ids.shape[-1]
									__A=  self.tokenizer([''] ,						padding='max_length' ,						max_length=lowerCAmelCase_ ,						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(lowerCAmelCase_ ,						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 * num_images_per_prompt, 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(lowerCAmelCase_ ,						generator=lowerCAmelCase_ ,						device='cpu' ,						dtype=lowerCAmelCase_  ).to(
												    self.device  )
									else:
												__A=  torch.randn(lowerCAmelCase_ ,						generator=lowerCAmelCase_ ,						device=self.device ,						dtype=lowerCAmelCase_  )
						else:
									if latents.shape != latents_shape:
												raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""  )
									__A=  latents.to(self.device  )

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

						self.scheduler.set_timesteps(lowerCAmelCase_ ,						**lowerCAmelCase_  )

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

						for i, t in enumerate(self.progress_bar(lowerCAmelCase_  )  ):
									# 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(lowerCAmelCase_ ,						lowerCAmelCase_  )

									# predict the noise residual
									__A=  self.unet(lowerCAmelCase_ ,						lowerCAmelCase_ ,						encoder_hidden_states=lowerCAmelCase_  ).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(
												    lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						)

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

						# scale and decode the image latents with vae
						__A=  1 / self.vae.config.scaling_factor * latents
						__A=  self.vae.decode(lowerCAmelCase_  ).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(lowerCAmelCase_  )

						if not return_dict:
									return (image, None)

						return StableDiffusionPipelineOutput(images=lowerCAmelCase_ ,						nsfw_content_detected=lowerCAmelCase_  )







'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__      =							logging.get_logger(__name__)

UpperCAmelCase__      =							{
    '''google/mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'''
}
class 	a__				(				a_      ):





			'''simple docstring'''

			A	:							int							= MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
			A	:							Optional[int]							= '''mobilebert'''
			def __init__(     self       : Optional[int] ,						lowerCAmelCase_       : str=30_522 ,						lowerCAmelCase_       : List[Any]=512 ,						lowerCAmelCase_       : Optional[int]=24 ,						lowerCAmelCase_       : str=4 ,						lowerCAmelCase_       : List[str]=512 ,						lowerCAmelCase_       : List[str]="relu" ,						lowerCAmelCase_       : Dict=0.0 ,						lowerCAmelCase_       : List[Any]=0.1 ,						lowerCAmelCase_       : Optional[Any]=512 ,						lowerCAmelCase_       : str=2 ,						lowerCAmelCase_       : Dict=0.02 ,						lowerCAmelCase_       : Any=1E-12 ,						lowerCAmelCase_       : List[Any]=0 ,						lowerCAmelCase_       : Union[str, Any]=128 ,						lowerCAmelCase_       : Any=True ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : List[Any]=128 ,						lowerCAmelCase_       : Dict=False ,						lowerCAmelCase_       : Tuple=True ,						lowerCAmelCase_       : Any=4 ,						lowerCAmelCase_       : Optional[Any]="no_norm" ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : Union[str, Any]=None ,						**lowerCAmelCase_       : Union[str, Any] ,						)			->		List[Any]:
						super().__init__(pad_token_id=lowerCAmelCase_ ,						**lowerCAmelCase_  )

						__A=  vocab_size
						__A=  hidden_size
						__A=  num_hidden_layers
						__A=  num_attention_heads
						__A=  hidden_act
						__A=  intermediate_size
						__A=  hidden_dropout_prob
						__A=  attention_probs_dropout_prob
						__A=  max_position_embeddings
						__A=  type_vocab_size
						__A=  initializer_range
						__A=  layer_norm_eps
						__A=  embedding_size
						__A=  trigram_input
						__A=  use_bottleneck
						__A=  intra_bottleneck_size
						__A=  use_bottleneck_attention
						__A=  key_query_shared_bottleneck
						__A=  num_feedforward_networks
						__A=  normalization_type
						__A=  classifier_activation

						if self.use_bottleneck:
									__A=  intra_bottleneck_size
						else:
									__A=  hidden_size

						__A=  classifier_dropout


class 	a__				(				a_      ):





			'''simple docstring'''

			@property
			def 				lowerCAmelCase    (     self       : List[str]  )			->		Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
									__A=  {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
									__A=  {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						        ('token_type_ids', dynamic_axis),
						    ]  )



