
"""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  :str					,							lowerCamelCase_  :Optional[Any]					,							lowerCamelCase_  :str=1.0	):





										"""simple docstring"""

										super().__init__()

										lowerCamelCase__     :					List[str]		=cut_size
										lowerCamelCase__     :					str		=cut_power





							def 				UpperCAmelCase__							(  self  :List[Any]					,							lowerCamelCase_  :Optional[Any]					,							lowerCamelCase_  :int	):





										"""simple docstring"""

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






def        lowerCAmelCase_       (		snake_case_		:      str							,   snake_case_		:      List[str]      )       ->List[str]:
			lowerCamelCase__     :					str		=F.normalize(snake_case_							,   dim=-1      )
			lowerCamelCase__     :					int		=F.normalize(snake_case_							,   dim=-1      )
			return (x - y).norm(dim=-1      ).div(2      ).arcsin().pow(2      ).mul(2      )






def        lowerCAmelCase_       (		snake_case_		:      Optional[int]							,   snake_case_		:      Any      )       ->Tuple:
			for param in model.parameters():
						lowerCamelCase__     :					Union[str, Any]		=value

class    A_     (					A__       ):



							"""simple docstring"""






							def __init__(  self  :Optional[int]					,							lowerCamelCase_  :AutoencoderKL					,							lowerCamelCase_  :CLIPTextModel					,							lowerCamelCase_  :CLIPModel					,							lowerCamelCase_  :CLIPTokenizer					,							lowerCamelCase_  :UNetaDConditionModel					,							lowerCamelCase_  :Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]					,							lowerCamelCase_  :CLIPImageProcessor					,							):





										"""simple docstring"""

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

										lowerCamelCase__     :					Any		=transforms.Normalize(mean=feature_extractor.image_mean					,							std=feature_extractor.image_std	)
										lowerCamelCase__     :					List[str]		=(
										    feature_extractor.size
										    if isinstance(feature_extractor.size					,							lowerCamelCase_	)
										    else feature_extractor.size['shortest_edge']
										)
										lowerCamelCase__     :					Any		=MakeCutouts(self.cut_out_size	)

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





							def 				UpperCAmelCase__							(  self  :List[Any]					,							lowerCamelCase_  :Optional[Union[str, int]] = "auto"	):





										"""simple docstring"""

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





							def 				UpperCAmelCase__							(  self  :Dict	):





										"""simple docstring"""

										self.enable_attention_slicing(lowerCamelCase_	)





							def 				UpperCAmelCase__							(  self  :List[str]	):





										"""simple docstring"""

										set_requires_grad(self.vae					,							lowerCamelCase_	)





							def 				UpperCAmelCase__							(  self  :Optional[Any]	):





										"""simple docstring"""

										set_requires_grad(self.vae					,							lowerCamelCase_	)





							def 				UpperCAmelCase__							(  self  :int	):





										"""simple docstring"""

										set_requires_grad(self.unet					,							lowerCamelCase_	)





							def 				UpperCAmelCase__							(  self  :Dict	):





										"""simple docstring"""

										set_requires_grad(self.unet					,							lowerCamelCase_	)





							@torch.enable_grad()
							def 				UpperCAmelCase__							(  self  :Dict					,							lowerCamelCase_  :str					,							lowerCamelCase_  :Any					,							lowerCamelCase_  :List[str]					,							lowerCamelCase_  :Optional[int]					,							lowerCamelCase_  :Optional[int]					,							lowerCamelCase_  :int					,							lowerCamelCase_  :int					,							lowerCamelCase_  :str					,							lowerCamelCase_  :Tuple=True					,							):





										"""simple docstring"""

										lowerCamelCase__     :					Any		=latents.detach().requires_grad_()

										lowerCamelCase__     :					Dict		=self.scheduler.scale_model_input(lowerCamelCase_					,							lowerCamelCase_	)

										# predict the noise residual
										lowerCamelCase__     :					Any		=self.unet(lowerCamelCase_					,							lowerCamelCase_					,							encoder_hidden_states=lowerCamelCase_	).sample

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

													lowerCamelCase__     :					Tuple		=torch.sqrt(lowerCamelCase_	)
													lowerCamelCase__     :					List[Any]		=pred_original_sample * (fac) + latents * (1 - fac)
										elif isinstance(self.scheduler					,							lowerCamelCase_	):
													lowerCamelCase__     :					int		=self.scheduler.sigmas[index]
													lowerCamelCase__     :					Tuple		=latents - sigma * noise_pred
										else:
													raise ValueError(f"""scheduler type {type(self.scheduler	)} not supported"""	)

										lowerCamelCase__     :					Optional[int]		=1 / self.vae.config.scaling_factor * sample
										lowerCamelCase__     :					int		=self.vae.decode(lowerCamelCase_	).sample
										lowerCamelCase__     :					List[str]		=(image / 2 + 0.5).clamp(0					,							1	)

										if use_cutouts:
													lowerCamelCase__     :					List[str]		=self.make_cutouts(lowerCamelCase_					,							lowerCamelCase_	)
										else:
													lowerCamelCase__     :					Union[str, Any]		=transforms.Resize(self.cut_out_size	)(lowerCamelCase_	)
										lowerCamelCase__     :					List[Any]		=self.normalize(lowerCamelCase_	).to(latents.dtype	)

										lowerCamelCase__     :					Optional[Any]		=self.clip_model.get_image_features(lowerCamelCase_	)
										lowerCamelCase__     :					str		=image_embeddings_clip / image_embeddings_clip.norm(p=2					,							dim=-1					,							keepdim=lowerCamelCase_	)

										if use_cutouts:
													lowerCamelCase__     :					Tuple		=spherical_dist_loss(lowerCamelCase_					,							lowerCamelCase_	)
													lowerCamelCase__     :					Optional[int]		=dists.view([num_cutouts, sample.shape[0], -1]	)
													lowerCamelCase__     :					Any		=dists.sum(2	).mean(0	).sum() * clip_guidance_scale
										else:
													lowerCamelCase__     :					Any		=spherical_dist_loss(lowerCamelCase_					,							lowerCamelCase_	).mean() * clip_guidance_scale

										lowerCamelCase__     :					Optional[int]		=-torch.autograd.grad(lowerCamelCase_					,							lowerCamelCase_	)[0]

										if isinstance(self.scheduler					,							lowerCamelCase_	):
													lowerCamelCase__     :					Union[str, Any]		=latents.detach() + grads * (sigma**2)
													lowerCamelCase__     :					Any		=noise_pred_original
										else:
													lowerCamelCase__     :					Optional[int]		=noise_pred_original - torch.sqrt(lowerCamelCase_	) * grads
										return noise_pred, latents





							@torch.no_grad()
							def __call__(  self  :int					,							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					,							):





										"""simple docstring"""

										if isinstance(lowerCamelCase_					,							lowerCamelCase_	):
													lowerCamelCase__     :					Any		=1
										elif isinstance(lowerCamelCase_					,							lowerCamelCase_	):
													lowerCamelCase__     :					Union[str, Any]		=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
										lowerCamelCase__     :					Tuple		=self.tokenizer(
										    lowerCamelCase_					,							padding='max_length'					,							max_length=self.tokenizer.model_max_length					,							truncation=lowerCamelCase_					,							return_tensors='pt'					,							)
										lowerCamelCase__     :					List[str]		=self.text_encoder(text_input.input_ids.to(self.device	)	)[0]
										# duplicate text embeddings for each generation per prompt
										lowerCamelCase__     :					int		=text_embeddings.repeat_interleave(lowerCamelCase_					,							dim=0	)

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

										# set timesteps
										lowerCamelCase__     :					List[Any]		='offset' in set(inspect.signature(self.scheduler.set_timesteps	).parameters.keys()	)
										lowerCamelCase__     :					List[Any]		={}
										if accepts_offset:
													lowerCamelCase__     :					Dict		=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
										lowerCamelCase__     :					Tuple		=self.scheduler.timesteps.to(self.device	)

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

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

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

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

													# perform classifier free guidance
													if do_classifier_free_guidance:
																lowerCamelCase__     :					str		=noise_pred.chunk(2	)
																lowerCamelCase__     :					List[Any]		=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

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

													# compute the previous noisy sample x_t -> x_t-1
													lowerCamelCase__     :					Optional[Any]		=self.scheduler.step(lowerCamelCase_					,							lowerCamelCase_					,							lowerCamelCase_					,							**lowerCamelCase_	).prev_sample

										# scale and decode the image latents with vae
										lowerCamelCase__     :					List[str]		=1 / self.vae.config.scaling_factor * latents
										lowerCamelCase__     :					Dict		=self.vae.decode(lowerCamelCase_	).sample

										lowerCamelCase__     :					List[str]		=(image / 2 + 0.5).clamp(0					,							1	)
										lowerCamelCase__     :					Dict		=image.cpu().permute(0					,							2					,							3					,							1	).numpy()

										if output_type == "pil":
													lowerCamelCase__     :					Dict		=self.numpy_to_pil(lowerCamelCase_	)

										if not return_dict:
													return (image, None)

										return StableDiffusionPipelineOutput(images=lowerCamelCase_					,							nsfw_content_detected=lowerCamelCase_	)

"""simple docstring"""


from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


lowerCAmelCase								=						logging.get_logger(__name__)

lowerCAmelCase								=						{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase								=						{
    """vocab_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """openai/clip-vit-base-patch32""": (
            """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"""
        ),
    },
}

lowerCAmelCase								=						{
    """openai/clip-vit-base-patch32""": 77,
}

class    A_     (					A__       ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_     				=       VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE_     				=       PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE_     				=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE_     				=       ["""input_ids""", """attention_mask"""]
							SCREAMING_SNAKE_CASE_     				=       CLIPTokenizer





							def __init__(  self  :List[str]					,							lowerCamelCase_  :Tuple=None					,							lowerCamelCase_  :Any=None					,							lowerCamelCase_  :Optional[Any]=None					,							lowerCamelCase_  :List[Any]="<|endoftext|>"					,							lowerCamelCase_  :Tuple="<|startoftext|>"					,							lowerCamelCase_  :Dict="<|endoftext|>"					,							lowerCamelCase_  :str="<|endoftext|>"					,							**lowerCamelCase_  :int					,							):





										"""simple docstring"""

										super().__init__(
										    lowerCamelCase_					,							lowerCamelCase_					,							tokenizer_file=lowerCamelCase_					,							unk_token=lowerCamelCase_					,							bos_token=lowerCamelCase_					,							eos_token=lowerCamelCase_					,							pad_token=lowerCamelCase_					,							**lowerCamelCase_					,							)

										if not isinstance(self.backend_tokenizer.pre_tokenizer					,							pre_tokenizers.Sequence	):
													raise ValueError(
													    'The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'
													    ' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'
													    ' to be compatible with this version.The easiest way to do so is'
													    ' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'
													    ' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'
													    ' transformers.'	)

										self._wrap_decode_method_backend_tokenizer()





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





										"""simple docstring"""

										lowerCamelCase__     :					List[str]		=self.backend_tokenizer.decode

										def new_decode_method(*lowerCamelCase_  :Optional[int]					,							**lowerCamelCase_  :List[Any]	):
													lowerCamelCase__     :					Any		=orig_decode_method(*lowerCamelCase_					,							**lowerCamelCase_	)
													lowerCamelCase__     :					Optional[int]		=text.replace(self.backend_tokenizer.model.end_of_word_suffix					,							' '	).strip()
													return text

										lowerCamelCase__     :					Optional[Any]		=new_decode_method





							def 				UpperCAmelCase__							(  self  :List[str]					,							lowerCamelCase_  :List[int]					,							lowerCamelCase_  :Optional[List[int]] = None	):





										"""simple docstring"""

										lowerCamelCase__     :					Any		=[self.bos_token_id]
										lowerCamelCase__     :					Any		=[self.eos_token_id]

										if token_ids_a is None:
													return bos_token + token_ids_a + eos_token
										return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token





							def 				UpperCAmelCase__							(  self  :List[Any]					,							lowerCamelCase_  :List[int]					,							lowerCamelCase_  :Optional[List[int]] = None	):





										"""simple docstring"""

										lowerCamelCase__     :					List[str]		=[self.bos_token_id]
										lowerCamelCase__     :					Optional[Any]		=[self.eos_token_id]

										if token_ids_a is None:
													return len(bos_token + token_ids_a + eos_token	) * [0]
										return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token	) * [0]





							def 				UpperCAmelCase__							(  self  :Tuple					,							lowerCamelCase_  :str					,							lowerCamelCase_  :Optional[str] = None	):





										"""simple docstring"""

										lowerCamelCase__     :					int		=self._tokenizer.model.save(lowerCamelCase_					,							name=lowerCamelCase_	)
										return tuple(lowerCamelCase_	)