





from typing import Any

import numpy as np



def 		A_    (					_lowerCAmelCase					)						->			bool:
 return np.array_equal(__SCREAMING_SNAKE_CASE							,       matrix.conjugate().T					)



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			Any:
 UpperCamelCase     : Optional[int]	     =       v.conjugate().T
 UpperCamelCase     : List[Any]	     =       v_star.dot(__SCREAMING_SNAKE_CASE					)
 assert isinstance(__SCREAMING_SNAKE_CASE							,       np.ndarray					)
 return (v_star_dot.dot(__SCREAMING_SNAKE_CASE					)) / (v_star.dot(__SCREAMING_SNAKE_CASE					))



def 		A_    (					)						->			None:
 UpperCamelCase     : List[str]	     =       np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]					)
 UpperCamelCase     : int	     =       np.array([[1], [2], [3]]					)
 assert is_hermitian(__SCREAMING_SNAKE_CASE					), F"""{a} is not hermitian."""
 print(rayleigh_quotient(__SCREAMING_SNAKE_CASE							,       __SCREAMING_SNAKE_CASE					)					)

 UpperCamelCase     : Union[str, Any]	     =       np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]					)
 assert is_hermitian(__SCREAMING_SNAKE_CASE					), F"""{a} is not hermitian."""
 assert rayleigh_quotient(__SCREAMING_SNAKE_CASE							,       __SCREAMING_SNAKE_CASE					) == float(3					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       tests()







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_       )
