





import re

from filelock import FileLock


try:
    import nltk

    __lowerCamelCase	:					Tuple			  =					True
except (ImportError, ModuleNotFoundError):
    __lowerCamelCase	:					Any			  =					False

if NLTK_AVAILABLE:
    with FileLock(""".lock""") as lock:
        nltk.download("""punkt""", quiet=True)



def 		A_    (					_lowerCAmelCase					)						->			str:
 re.sub("<n>"							,       ""							,       _lowerCAmelCase					)  # remove pegasus newline char
 assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
 return "\n".join(nltk.sent_tokenize(_lowerCAmelCase					)					)







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

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


__lowerCamelCase	:					List[str]			  =					logging.get_logger(__name__)  # pylint: disable=invalid-name



class  A__       (	__snake_case      ):





      def __init__(			self	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_	,							):
       '''simple docstring'''




       super().__init__()
       self.register_modules(
           vae=A_	,							text_encoder=A_	,							tokenizer=A_	,							unet=A_	,							scheduler=A_	,							safety_checker=A_	,							feature_extractor=A_	,							)





      def 	__UpperCamelCase(			self	,							A_ = "auto"       ):
       '''simple docstring'''




       if slice_size == "auto":
        # half the attention head size is usually a good trade-off between
        # speed and memory
        UpperCamelCase     : Union[str, Any]	     =       self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(A_       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       self.enable_attention_slicing(A_       )





      @torch.no_grad()
      def __call__(			self	,							A_	,							A_ = 512	,							A_ = 512	,							A_ = 50	,							A_ = 7.5	,							A_ = None	,							A_ = 1	,							A_ = 0.0	,							A_ = None	,							A_ = None	,							A_ = "pil"	,							A_ = True	,							A_ = None	,							A_ = 1	,							A_ = None	,							**A_	,							):
       '''simple docstring'''




       if isinstance(A_	,							A_       ):
        UpperCamelCase     : Any	     =       1
       elif isinstance(A_	,							A_       ):
        UpperCamelCase     : Optional[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}."""       )

       if (callback_steps is None) or (
           callback_steps is not None and (not isinstance(A_	,							A_       ) or callback_steps <= 0)
       ):
        raise ValueError(
            F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
            F""" {type(A_       )}."""       )

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

       if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
        UpperCamelCase     : List[str]	     =       self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]       )
        logger.warning(
            "The following part of your input was truncated because CLIP can only handle sequences up to"
            F""" {self.tokenizer.model_max_length} tokens: {removed_text}"""       )
        UpperCamelCase     : Any	     =       text_input_ids[:, : self.tokenizer.model_max_length]

       if text_embeddings is None:
        UpperCamelCase     : str	     =       self.text_encoder(text_input_ids.to(self.device       )       )[0]

       # duplicate text embeddings for each generation per prompt, using mps friendly method
       UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : str	     =       text_embeddings.shape
       UpperCamelCase     : int	     =       text_embeddings.repeat(1	,							A_	,							1       )
       UpperCamelCase     : str	     =       text_embeddings.view(bs_embed * num_images_per_prompt	,							A_	,							-1       )

       # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
       # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
       # corresponds to doing no classifier free guidance.
       UpperCamelCase     : Optional[int]	     =       guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
        UpperCamelCase     : List[str]
        if negative_prompt is None:
         UpperCamelCase     : Tuple	     =       [""]
        elif type(A_       ) is not type(A_       ):
         raise TypeError(
             F"""`negative_prompt` should be the same type to `prompt`, but got {type(A_       )} !="""
             F""" {type(A_       )}."""       )
        elif isinstance(A_	,							A_       ):
         UpperCamelCase     : Optional[int]	     =       [negative_prompt]
        elif batch_size != len(A_       ):
         raise ValueError(
             F"""`negative_prompt`: {negative_prompt} has batch size {len(A_       )}, but `prompt`:"""
             F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
             " the batch size of `prompt`."       )
        else:
         UpperCamelCase     : Union[str, Any]	     =       negative_prompt

        UpperCamelCase     : Tuple	     =       text_input_ids.shape[-1]
        UpperCamelCase     : str	     =       self.tokenizer(
            A_	,							padding="max_length"	,							max_length=A_	,							truncation=A_	,							return_tensors="pt"	,							)
        UpperCamelCase     : str	     =       self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]

        # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
        UpperCamelCase     : Optional[int]	     =       uncond_embeddings.shape[1]
        UpperCamelCase     : Optional[Any]	     =       uncond_embeddings.repeat(A_	,							A_	,							1       )
        UpperCamelCase     : Dict	     =       uncond_embeddings.view(batch_size * num_images_per_prompt	,							A_	,							-1       )

        # For classifier free guidance, we need to do two forward passes.
        # Here we concatenate the unconditional and text embeddings into a single batch
        # to avoid doing two forward passes
        UpperCamelCase     : 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`.
       UpperCamelCase     : Union[str, Any]	     =       (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
       UpperCamelCase     : Tuple	     =       (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
       UpperCamelCase     : Any	     =       text_embeddings.dtype
       if latents is None:
        if self.device.type == "mps":
         # randn does not exist on mps
         UpperCamelCase     : Optional[Any]	     =       torch.randn(
             A_	,							generator=A_	,							device="cpu"	,							dtype=A_       ).to(self.device       )
         UpperCamelCase     : Dict	     =       torch.randn(A_	,							generator=A_	,							device="cpu"	,							dtype=A_       ).to(
             self.device       )
        else:
         UpperCamelCase     : Tuple	     =       torch.randn(
             A_	,							generator=A_	,							device=self.device	,							dtype=A_       )
         UpperCamelCase     : str	     =       torch.randn(A_	,							generator=A_	,							device=self.device	,							dtype=A_       )
       else:
        if latents_reference.shape != latents_shape:
         raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""       )
        UpperCamelCase     : List[Any]	     =       latents_reference.to(self.device       )
        UpperCamelCase     : Optional[Any]	     =       latents.to(self.device       )

       # This is the key part of the pipeline where we
       # try to ensure that the generated images w/ the same seed
       # but different sizes actually result in similar images
       UpperCamelCase     : List[str]	     =       (latents_shape[3] - latents_shape_reference[3]) // 2
       UpperCamelCase     : str	     =       (latents_shape[2] - latents_shape_reference[2]) // 2
       UpperCamelCase     : List[Any]	     =       latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
       UpperCamelCase     : Optional[Any]	     =       latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
       UpperCamelCase     : str	     =       0 if dx < 0 else dx
       UpperCamelCase     : Union[str, Any]	     =       0 if dy < 0 else dy
       UpperCamelCase     : Union[str, Any]	     =       max(-dx	,							0       )
       UpperCamelCase     : Tuple	     =       max(-dy	,							0       )
       # import pdb
       # pdb.set_trace()
       UpperCamelCase     : Optional[Any]	     =       latents_reference[:, :, dy : dy + h, dx : dx + w]

       # set timesteps
       self.scheduler.set_timesteps(A_       )

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

       # scale the initial noise by the standard deviation required by the scheduler
       UpperCamelCase     : str	     =       latents * self.scheduler.init_noise_sigma

       # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
       # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
       # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
       # and should be between [0, 1]
       UpperCamelCase     : List[str]	     =       "eta" in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )
       UpperCamelCase     : str	     =       {}
       if accepts_eta:
        UpperCamelCase     : int	     =       eta

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

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

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

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

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

       UpperCamelCase     : int	     =       1 / 0.1_82_15 * latents
       UpperCamelCase     : Tuple	     =       self.vae.decode(A_       ).sample

       UpperCamelCase     : Any	     =       (image / 2 + 0.5).clamp(0	,							1       )

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

       if self.safety_checker is not None:
        UpperCamelCase     : int	     =       self.feature_extractor(self.numpy_to_pil(A_       )	,							return_tensors="pt"       ).to(
            self.device       )
        UpperCamelCase     ,   UpperCamelCase     : int	     =       self.safety_checker(
            images=A_	,							clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype       )       )
       else:
        UpperCamelCase     : int	     =       None

       if output_type == "pil":
        UpperCamelCase     : Tuple	     =       self.numpy_to_pil(A_       )

       if not return_dict:
        return (image, has_nsfw_concept)

       return StableDiffusionPipelineOutput(images=A_	,							nsfw_content_detected=A_       )
