


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   ):






		def __init__(  self	,     __UpperCamelCase	,     __UpperCamelCase=1.0							)->			List[str]:
						'''simple docstring'''


						super().__init__()

						A__     :							Union[str, Any]   				=cut_size
						A__     :							Optional[int]   				=cut_power






		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase							)->			Union[str, Any]:
						'''simple docstring'''


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







def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							Union[str, Any]:
				A__     :							str   				=F.normalize(UpperCamelCase__,	dim=-1	)
				A__     :							Dict   				=F.normalize(UpperCamelCase__,	dim=-1	)
				return (x - y).norm(dim=-1	).div(2	).arcsin().pow(2	).mul(2	)



def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							Dict:






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




class 			a      (      UpperCamelCase_   ):






		def __init__(  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     )->			Optional[Any]:
						'''simple docstring'''


						super().__init__()
						self.register_modules(
						    vae=a_	,     text_encoder=a_	,     clip_model=a_	,     tokenizer=a_	,     unet=a_	,     scheduler=a_	,     feature_extractor=a_	,     )

						A__     :							Dict   				=transforms.Normalize(mean=feature_extractor.image_mean	,     std=feature_extractor.image_std							)
						A__     :							str   				=(
						    feature_extractor.size
						    if isinstance(feature_extractor.size	,     a_							)
						    else feature_extractor.size['''shortest_edge''']
						)
						A__     :							Optional[Any]   				=MakeCutouts(self.cut_out_size							)

						set_requires_grad(self.text_encoder	,     a_							)
						set_requires_grad(self.clip_model	,     a_							)






		def       lowerCAmelCase_  (  self	,     __UpperCamelCase = "auto"							)->			str:
						'''simple docstring'''


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






		def       lowerCAmelCase_  (  self							)->			str:
						'''simple docstring'''


						self.enable_attention_slicing(a_							)






		def       lowerCAmelCase_  (  self							)->			Optional[int]:
						'''simple docstring'''


						set_requires_grad(self.vae	,     a_							)






		def       lowerCAmelCase_  (  self							)->			Tuple:
						'''simple docstring'''


						set_requires_grad(self.vae	,     a_							)






		def       lowerCAmelCase_  (  self							)->			int:
						'''simple docstring'''


						set_requires_grad(self.unet	,     a_							)






		def       lowerCAmelCase_  (  self							)->			List[Any]:
						'''simple docstring'''


						set_requires_grad(self.unet	,     a_							)






		@torch.enable_grad()
		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase=True	,     )->			Dict:
						'''simple docstring'''


						A__     :							Optional[Any]   				=latents.detach().requires_grad_()

						A__     :							int   				=self.scheduler.scale_model_input(a_	,     a_							)

						# predict the noise residual
						A__     :							Union[str, Any]   				=self.unet(a_	,     a_	,     encoder_hidden_states=a_							).sample

						if isinstance(self.scheduler	,     (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)							):
										A__     :							Union[str, Any]   				=self.scheduler.alphas_cumprod[timestep]
										A__     :							Optional[int]   				=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__     :							Union[str, Any]   				=(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

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

						A__     :							Optional[int]   				=1 / self.vae.config.scaling_factor * sample
						A__     :							Dict   				=self.vae.decode(a_							).sample
						A__     :							Union[str, Any]   				=(image / 2 + 0.5).clamp(0	,     1							)

						if use_cutouts:
										A__     :							str   				=self.make_cutouts(a_	,     a_							)
						else:
										A__     :							Dict   				=transforms.Resize(self.cut_out_size							)(a_							)
						A__     :							Union[str, Any]   				=self.normalize(a_							).to(latents.dtype							)

						A__     :							Optional[Any]   				=self.clip_model.get_image_features(a_							)
						A__     :							Dict   				=image_embeddings_clip / image_embeddings_clip.norm(p=2	,     dim=-1	,     keepdim=a_							)

						if use_cutouts:
										A__     :							Union[str, Any]   				=spherical_dist_loss(a_	,     a_							)
										A__     :							List[str]   				=dists.view([num_cutouts, sample.shape[0], -1]							)
										A__     :							int   				=dists.sum(2							).mean(0							).sum() * clip_guidance_scale
						else:
										A__     :							int   				=spherical_dist_loss(a_	,     a_							).mean() * clip_guidance_scale

						A__     :							Union[str, Any]   				=-torch.autograd.grad(a_	,     a_							)[0]

						if isinstance(self.scheduler	,     a_							):
										A__     :							Any   				=latents.detach() + grads * (sigma**2)
										A__     :							Dict   				=noise_pred_original
						else:
										A__     :							Optional[Any]   				=noise_pred_original - torch.sqrt(a_							) * grads
						return noise_pred, latents






		@torch.no_grad()
		def __call__(  self	,     __UpperCamelCase	,     __UpperCamelCase = 5_12	,     __UpperCamelCase = 5_12	,     __UpperCamelCase = 50	,     __UpperCamelCase = 7.5	,     __UpperCamelCase = 1	,     __UpperCamelCase = 0.0	,     __UpperCamelCase = 1_00	,     __UpperCamelCase = None	,     __UpperCamelCase = 4	,     __UpperCamelCase = True	,     __UpperCamelCase = None	,     __UpperCamelCase = None	,     __UpperCamelCase = "pil"	,     __UpperCamelCase = True	,     )->			str:
						'''simple docstring'''


						if isinstance(a_	,     a_							):
										A__     :							List[str]   				=1
						elif isinstance(a_	,     a_							):
										A__     :							Union[str, Any]   				=len(a_							)
						else:
										raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(a_							)}'							)

						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__     :							Optional[int]   				=self.tokenizer(
						    a_	,     padding='''max_length'''	,     max_length=self.tokenizer.model_max_length	,     truncation=a_	,     return_tensors='''pt'''	,     )
						A__     :							Optional[int]   				=self.text_encoder(text_input.input_ids.to(self.device							)							)[0]
						# duplicate text embeddings for each generation per prompt
						A__     :							Optional[int]   				=text_embeddings.repeat_interleave(a_	,     dim=0							)

						if clip_guidance_scale > 0:
										if clip_prompt is not None:
														A__     :							str   				=self.tokenizer(
														    a_	,     padding='''max_length'''	,     max_length=self.tokenizer.model_max_length	,     truncation=a_	,     return_tensors='''pt'''	,     ).input_ids.to(self.device							)
										else:
														A__     :							Tuple   				=text_input.input_ids.to(self.device							)
										A__     :							Tuple   				=self.clip_model.get_text_features(a_							)
										A__     :							int   				=text_embeddings_clip / text_embeddings_clip.norm(p=2	,     dim=-1	,     keepdim=a_							)
										# duplicate text embeddings clip for each generation per prompt
										A__     :							Dict   				=text_embeddings_clip.repeat_interleave(a_	,     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__     :							Dict   				=guidance_scale > 1.0
						# get unconditional embeddings for classifier free guidance
						if do_classifier_free_guidance:
										A__     :							int   				=text_input.input_ids.shape[-1]
										A__     :							str   				=self.tokenizer(['''''']	,     padding='''max_length'''	,     max_length=a_	,     return_tensors='''pt'''							)
										A__     :							Dict   				=self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]
										# duplicate unconditional embeddings for each generation per prompt
										A__     :							List[Any]   				=uncond_embeddings.repeat_interleave(a_	,     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__     :							List[str]   				=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__     :							str   				=(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
						A__     :							List[Any]   				=text_embeddings.dtype
						if latents is None:
										if self.device.type == "mps":
														# randn does not work reproducibly on mps
														A__     :							str   				=torch.randn(a_	,     generator=a_	,     device='''cpu'''	,     dtype=a_							).to(
														    self.device							)
										else:
														A__     :							List[str]   				=torch.randn(a_	,     generator=a_	,     device=self.device	,     dtype=a_							)
						else:
										if latents.shape != latents_shape:
														raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'							)
										A__     :							Any   				=latents.to(self.device							)

						# set timesteps
						A__     :							Dict   				='''offset''' in set(inspect.signature(self.scheduler.set_timesteps							).parameters.keys()							)
						A__     :							List[str]   				={}
						if accepts_offset:
										A__     :							Optional[int]   				=1

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

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

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

						# check if the scheduler accepts generator
						A__     :							int   				='''generator''' in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
						if accepts_generator:
										A__     :							Union[str, Any]   				=generator

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

										# predict the noise residual
										A__     :							int   				=self.unet(a_	,     a_	,     encoder_hidden_states=a_							).sample

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

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

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

						# scale and decode the image latents with vae
						A__     :							List[Any]   				=1 / self.vae.config.scaling_factor * latents
						A__     :							Optional[int]   				=self.vae.decode(a_							).sample

						A__     :							Optional[int]   				=(image / 2 + 0.5).clamp(0	,     1							)
						A__     :							Any   				=image.cpu().permute(0	,     2	,     3	,     1							).numpy()

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

						if not return_dict:
										return (image, None)

						return StableDiffusionPipelineOutput(images=a_	,     nsfw_content_detected=a_							)







import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor



def   SCREAMING_SNAKE_CASE__						(							)			->							Tuple:
				A__     :							List[Any]   				=set()
				# Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
				for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
								A__     :							Optional[int]   				=getattr(transformers.models.auto,	snake_case_,	snake_case_	)
								if module is None:
												continue
								# all mappings in a single auto modeling file
								A__     :							Optional[int]   				=[
								    x
								    for x in dir(snake_case_	)
								    if x.endswith('''_MAPPING_NAMES'''	)
								    and (x.startswith('''MODEL_'''	) or x.startswith('''TF_MODEL_'''	) or x.startswith('''FLAX_MODEL_'''	))
								]
								for name in mapping_names:
												A__     :							Any   				=getattr(snake_case_,	snake_case_	)
												if mapping is not None:
																for v in mapping.values():
																				if isinstance(snake_case_,	(list, tuple)	):
																								model_names.update(snake_case_	)
																				elif isinstance(snake_case_,	snake_case_	):
																								model_names.add(snake_case_	)

				return sorted(snake_case_	)



def   SCREAMING_SNAKE_CASE__						(							)			->							Optional[Any]:
				# All model names defined in auto mappings
				A__     :							int   				=set(get_all_model_names()	)

				with open('''tests/utils/tiny_model_summary.json'''	) as fp:
								A__     :							Union[str, Any]   				=json.load(snake_case_	)
				A__     :							int   				=set()
				for model_base_name in tiny_model_info:
								tiny_models_names.update(tiny_model_info[model_base_name]['''model_classes''']	)

				# Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
				A__     :							Optional[Any]   				=model_names.difference(snake_case_	)
				for model_name in copy.copy(snake_case_	):
								if not model_name.startswith('''TF'''	) and f'TF{model_name}' in not_on_hub:
												tiny_models_names.remove(snake_case_	)
								elif model_name.startswith('''TF'''	) and model_name[2:] in not_on_hub:
												tiny_models_names.remove(snake_case_	)

				return sorted(snake_case_	)



def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							Tuple:
				A__     :							Dict   				=COMPOSITE_MODELS.values()

				# All tiny model base names on Hub
				A__     :							Union[str, Any]   				=get_all_model_names()
				A__     :							Optional[Any]   				=hf_api.list_models(
				    filter=ModelFilter(
				        author='''hf-internal-testing''',	)	)
				A__     :							str   				=set()
				for x in models:
								A__     :							List[Any]   				=x.modelId
								A__				,  A__     :							List[str]   				=model.split('''/'''	)
								if not model.startswith('''tiny-random-'''	):
												continue
								A__     :							str   				=model.replace('''tiny-random-''',	''''''	)
								if not model[0].isupper():
												continue
								if model not in model_names and model not in special_models:
												continue
								_models.add(snake_case_	)

				A__     :							Dict   				=sorted(_models	)
				# All tiny model names on Hub
				A__     :							Optional[int]   				={}
				for model in models:
								A__     :							Optional[int]   				=f'hf-internal-testing/tiny-random-{model}'
								A__     :							str   				=model.split('''-'''	)[0]
								try:
												A__     :							int   				=hf_api.repo_info(snake_case_	)
												A__     :							Optional[int]   				={
												    '''tokenizer_classes''': set(),
												    '''processor_classes''': set(),
												    '''model_classes''': set(),
												    '''sha''': repo_info.sha,
												}
								except Exception:
												continue
								try:
												time.sleep(1	)
												A__     :							List[str]   				=AutoTokenizer.from_pretrained(snake_case_	)
												content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__	)
								except Exception:
												pass
								try:
												time.sleep(1	)
												A__     :							int   				=AutoTokenizer.from_pretrained(snake_case_,	use_fast=snake_case_	)
												content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__	)
								except Exception:
												pass
								try:
												time.sleep(1	)
												A__     :							int   				=AutoImageProcessor.from_pretrained(snake_case_	)
												content["processor_classes"].add(img_p.__class__.__name__	)
								except Exception:
												pass
								try:
												time.sleep(1	)
												A__     :							Optional[int]   				=AutoFeatureExtractor.from_pretrained(snake_case_	)
												if not isinstance(snake_case_,	snake_case_	):
																content["processor_classes"].add(feat_p.__class__.__name__	)
								except Exception:
												pass
								try:
												time.sleep(1	)
												A__     :							Tuple   				=getattr(snake_case_,	snake_case_	)
												A__     :							Tuple   				=model_class.from_pretrained(snake_case_	)
												content["model_classes"].add(m.__class__.__name__	)
								except Exception:
												pass
								try:
												time.sleep(1	)
												A__     :							int   				=getattr(snake_case_,	f'TF{model}'	)
												A__     :							Union[str, Any]   				=model_class.from_pretrained(snake_case_	)
												content["model_classes"].add(m.__class__.__name__	)
								except Exception:
												pass

								A__     :							List[str]   				=sorted(content['''tokenizer_classes''']	)
								A__     :							Tuple   				=sorted(content['''processor_classes''']	)
								A__     :							Optional[Any]   				=sorted(content['''model_classes''']	)

								A__     :							List[Any]   				=content
								with open(os.path.join(snake_case_,	'''hub_tiny_model_summary.json'''	),	'''w'''	) as fp:
												json.dump(snake_case_,	snake_case_,	ensure_ascii=snake_case_,	indent=4	)


if __name__ == "__main__":
						__lowerCamelCase       :       Any				   =			argparse.ArgumentParser()
						parser.add_argument("--num_workers", default=1, type=int, help="The number of workers to run.")
						__lowerCamelCase       :       int				   =			parser.parse_args()

						# This has to be `spawn` to avoid hanging forever!
						multiprocessing.set_start_method("spawn")

						__lowerCamelCase       :       Any				   =			"tiny_models"
						__lowerCamelCase       :       Optional[int]				   =			True
						__lowerCamelCase       :       int				   =			None
						__lowerCamelCase       :       str				   =			get_tiny_model_names_from_repo()
						__lowerCamelCase       :       List[Any]				   =			True
						__lowerCamelCase       :       List[Any]				   =			True
						__lowerCamelCase       :       Optional[Any]				   =			"hf-internal-testing"

						create_tiny_models(
						    output_path,
						    all,
						    model_types,
						    models_to_skip,
						    no_check,
						    upload,
						    organization,
						    token=os.environ.get("TOKEN", None),
						    num_workers=args.num_workers,
						)



