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


UpperCamelCase  							=						logging.get_logger(__name__)  # pylint: disable=invalid-name



class   snake_case_       (		__A       ):






   def __init__(      self				:     Optional[Any]	,						lowercase_				:     AutoencoderKL	,						lowercase_				:     CLIPTextModel	,						lowercase_				:     CLIPTokenizer	,						lowercase_				:     UNetaDConditionModel	,						lowercase_				:     Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]	,						lowercase_				:     StableDiffusionSafetyChecker	,						lowercase_				:     CLIPImageProcessor	,						)					->						Optional[int]:
       super().__init__()
       self.register_modules(
           vae=lowercase_	,						text_encoder=lowercase_	,						tokenizer=lowercase_	,						unet=lowercase_	,						scheduler=lowercase_	,						safety_checker=lowercase_	,						feature_extractor=lowercase_	,						)






   def       __UpperCamelCase     (      self				:     Tuple	,						lowercase_				:     Optional[Union[str, int]] = "auto"  )					->						Optional[int]:
       if slice_size == "auto":
           # half the attention head size is usually a good trade-off between
           # speed and memory
           lowercase__ :   str    					=				self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(lowercase_  )






   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Tuple:
       self.enable_attention_slicing(lowercase_  )







   @torch.no_grad()
   def __call__(      self				:     int	,						lowercase_				:     Union[str, List[str]]	,						lowercase_				:     int = 5_12	,						lowercase_				:     int = 5_12	,						lowercase_				:     int = 50	,						lowercase_				:     float = 7.5	,						lowercase_				:     Optional[Union[str, List[str]]] = None	,						lowercase_				:     Optional[int] = 1	,						lowercase_				:     float = 0.0	,						lowercase_				:     Optional[torch.Generator] = None	,						lowercase_				:     Optional[torch.FloatTensor] = None	,						lowercase_				:     Optional[str] = "pil"	,						lowercase_				:     bool = True	,						lowercase_				:     Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,						lowercase_				:     int = 1	,						lowercase_				:     Optional[torch.FloatTensor] = None	,						**lowercase_				:     int	,						)					->						List[Any]:

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

       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(lowercase_	,						lowercase_  ) or callback_steps <= 0)
       ):
           raise ValueError(
               F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
               F''' {type(lowercase_  )}.'''  )

       # get prompt text embeddings
       lowercase__ :   List[str]    					=				self.tokenizer(
           lowercase_	,						padding="max_length"	,						max_length=self.tokenizer.model_max_length	,						return_tensors="pt"	,						)
       lowercase__ :   str    					=				text_inputs.input_ids

       if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
           lowercase__ :   Any    					=				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}'''  )
           lowercase__ :   str    					=				text_input_ids[:, : self.tokenizer.model_max_length]

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

       # duplicate text embeddings for each generation per prompt, using mps friendly method
       lowercase__					,   lowercase__					,   lowercase__ :   List[Any]    					=				text_embeddings.shape
       lowercase__ :   Union[str, Any]    					=				text_embeddings.repeat(1	,						lowercase_	,						1  )
       lowercase__ :   Dict    					=				text_embeddings.view(bs_embed * num_images_per_prompt	,						lowercase_	,						-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.
       lowercase__ :   Tuple    					=				guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
           lowercase__ :   List[str]
           if negative_prompt is None:
               lowercase__ :   int    					=				[""]
           elif type(lowercase_  ) is not type(lowercase_  ):
               raise TypeError(
                   F'''`negative_prompt` should be the same type to `prompt`, but got {type(lowercase_  )} !='''
                   F''' {type(lowercase_  )}.'''  )
           elif isinstance(lowercase_	,						lowercase_  ):
               lowercase__ :   str    					=				[negative_prompt]
           elif batch_size != len(lowercase_  ):
               raise ValueError(
                   F'''`negative_prompt`: {negative_prompt} has batch size {len(lowercase_  )}, but `prompt`:'''
                   F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                   " the batch size of `prompt`."  )
           else:
               lowercase__ :   Union[str, Any]    					=				negative_prompt

           lowercase__ :   List[Any]    					=				text_input_ids.shape[-1]
           lowercase__ :   Any    					=				self.tokenizer(
               lowercase_	,						padding="max_length"	,						max_length=lowercase_	,						truncation=lowercase_	,						return_tensors="pt"	,						)
           lowercase__ :   Optional[int]    					=				self.text_encoder(uncond_input.input_ids.to(self.device  )  )[0]

           # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
           lowercase__ :   Union[str, Any]    					=				uncond_embeddings.shape[1]
           lowercase__ :   str    					=				uncond_embeddings.repeat(lowercase_	,						lowercase_	,						1  )
           lowercase__ :   Optional[int]    					=				uncond_embeddings.view(batch_size * num_images_per_prompt	,						lowercase_	,						-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
           lowercase__ :   Optional[Any]    					=				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`.
       lowercase__ :   int    					=				(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
       lowercase__ :   Optional[Any]    					=				(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
       lowercase__ :   Union[str, Any]    					=				text_embeddings.dtype
       if latents is None:
           if self.device.type == "mps":
               # randn does not exist on mps
               lowercase__ :   Tuple    					=				torch.randn(
                   lowercase_	,						generator=lowercase_	,						device="cpu"	,						dtype=lowercase_  ).to(self.device  )
               lowercase__ :   Union[str, Any]    					=				torch.randn(lowercase_	,						generator=lowercase_	,						device="cpu"	,						dtype=lowercase_  ).to(
                   self.device  )
           else:
               lowercase__ :   Any    					=				torch.randn(
                   lowercase_	,						generator=lowercase_	,						device=self.device	,						dtype=lowercase_  )
               lowercase__ :   Tuple    					=				torch.randn(lowercase_	,						generator=lowercase_	,						device=self.device	,						dtype=lowercase_  )
       else:
           if latents_reference.shape != latents_shape:
               raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''  )
           lowercase__ :   Dict    					=				latents_reference.to(self.device  )
           lowercase__ :   str    					=				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
       lowercase__ :   Union[str, Any]    					=				(latents_shape[3] - latents_shape_reference[3]) // 2
       lowercase__ :   str    					=				(latents_shape[2] - latents_shape_reference[2]) // 2
       lowercase__ :   List[Any]    					=				latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
       lowercase__ :   int    					=				latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
       lowercase__ :   Any    					=				0 if dx < 0 else dx
       lowercase__ :   Optional[Any]    					=				0 if dy < 0 else dy
       lowercase__ :   List[Any]    					=				max(-dx	,						0  )
       lowercase__ :   str    					=				max(-dy	,						0  )
       # import pdb
       # pdb.set_trace()
       lowercase__ :   Any    					=				latents_reference[:, :, dy : dy + h, dx : dx + w]

       # set timesteps
       self.scheduler.set_timesteps(lowercase_  )

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

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

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

           # predict the noise residual
           lowercase__ :   Optional[int]    					=				self.unet(lowercase_	,						lowercase_	,						encoder_hidden_states=lowercase_  ).sample

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

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

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

       lowercase__ :   int    					=				1 / 0.1_82_15 * latents
       lowercase__ :   Dict    					=				self.vae.decode(lowercase_  ).sample

       lowercase__ :   Tuple    					=				(image / 2 + 0.5).clamp(0	,						1  )

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

       if self.safety_checker is not None:
           lowercase__ :   List[str]    					=				self.feature_extractor(self.numpy_to_pil(lowercase_  )	,						return_tensors="pt"  ).to(
               self.device  )
           lowercase__					,   lowercase__ :   int    					=				self.safety_checker(
               images=lowercase_	,						clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype  )  )
       else:
           lowercase__ :   List[str]    					=				None

       if output_type == "pil":
           lowercase__ :   List[str]    					=				self.numpy_to_pil(lowercase_  )

       if not return_dict:
           return (image, has_nsfw_concept)

       return StableDiffusionPipelineOutput(images=lowercase_	,						nsfw_content_detected=lowercase_  )







import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
 import ctypes
 import msvcrt  # noqa




 class A  (   ctypes.Structure     ):
       # _fields is a specific attr expected by ctypes
       UpperCamelCase__							:  List[Any]      =[('size', ctypes.c_int), ('visible', ctypes.c_byte)]




def 	a_      (    ):
       '''simple docstring'''





       if os.name == "nt":
              _lowerCamelCase       :		Optional[Any]		=CursorInfo()
              _lowerCamelCase       :		Dict		=ctypes.windll.kernelaa.GetStdHandle(-11     )
              ctypes.windll.kernelaa.GetConsoleCursorInfo(SCREAMING_SNAKE_CASE__		,			ctypes.byref(SCREAMING_SNAKE_CASE__     )     )
              _lowerCamelCase       :		Any		=False
              ctypes.windll.kernelaa.SetConsoleCursorInfo(SCREAMING_SNAKE_CASE__		,			ctypes.byref(SCREAMING_SNAKE_CASE__     )     )
       elif os.name == "posix":
              sys.stdout.write('\033[?25l'     )
              sys.stdout.flush()




def 	a_      (    ):
       '''simple docstring'''





       if os.name == "nt":
              _lowerCamelCase       :		Any		=CursorInfo()
              _lowerCamelCase       :		Optional[Any]		=ctypes.windll.kernelaa.GetStdHandle(-11     )
              ctypes.windll.kernelaa.GetConsoleCursorInfo(SCREAMING_SNAKE_CASE__		,			ctypes.byref(SCREAMING_SNAKE_CASE__     )     )
              _lowerCamelCase       :		Union[str, Any]		=True
              ctypes.windll.kernelaa.SetConsoleCursorInfo(SCREAMING_SNAKE_CASE__		,			ctypes.byref(SCREAMING_SNAKE_CASE__     )     )
       elif os.name == "posix":
              sys.stdout.write('\033[?25h'     )
              sys.stdout.flush()




@contextmanager
def 	a_      (    ):
       '''simple docstring'''





       try:
              hide_cursor()
              yield
       finally:
              show_cursor()


