





'''simple docstring'''







from __future__ import annotations

from typing import TypedDict
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            =42
				__a            =42






def  _lowerCamelCase				( lowercase						:      str						)	->	list[str]:
				if not isinstance(lowercase    ,							lowercase						):
								raise TypeError("The parameter s type must be str."						)

				return [s[i:] + s[:i] for i in range(len(lowercase						)						)]






def  _lowerCamelCase				( lowercase						:      str						)	->	BWTTransformDict:
				if not isinstance(lowercase    ,							lowercase						):
								raise TypeError("The parameter s type must be str."						)
				if not s:
								raise ValueError("The parameter s must not be empty."						)

				_a								= all_rotations(lowercase						)
				rotations.sort()  # sort the list of rotations in alphabetically order
				# make a string composed of the last char of each rotation
				_a								= {
				    "bwt_string": "".join([word[-1] for word in rotations]						),
				    "idx_original_string": rotations.index(lowercase						),
				}
				return response






def  _lowerCamelCase				( lowercase						:      str    ,							lowercase						:      int						)	->	str:
				if not isinstance(lowercase    ,							lowercase						):
								raise TypeError("The parameter bwt_string type must be str."						)
				if not bwt_string:
								raise ValueError("The parameter bwt_string must not be empty."						)
				try:
								_a								= int(lowercase						)
				except ValueError:
								raise TypeError(
								    "The parameter idx_original_string type must be int or passive"
								    " of cast to int."						)
				if idx_original_string < 0:
								raise ValueError("The parameter idx_original_string must not be lower than 0."						)
				if idx_original_string >= len(lowercase						):
								raise ValueError(
								    "The parameter idx_original_string must be lower than" " len(bwt_string)."						)

				_a								= [""] * len(lowercase						)
				for _ in range(len(lowercase						)						):
								for i in range(len(lowercase						)						):
												_a								= bwt_string[i] + ordered_rotations[i]
								ordered_rotations.sort()
				return ordered_rotations[idx_original_string]


if __name__ == "__main__":
					lowerCAmelCase_					:    str      	=			'Provide a string that I will generate its BWT transform: '
					lowerCAmelCase_					:    List[str]      	=			input(entry_msg).strip()
					lowerCAmelCase_					:    List[str]      	=			bwt_transform(s)
					print(
					    f"""Burrows Wheeler transform for string '{s}' results """
					    f"""in '{result['bwt_string']}'"""
					)
					lowerCAmelCase_					:    Optional[Any]      	=			reverse_bwt(result['bwt_string'], result['idx_original_string'])
					print(
					    f"""Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' """
					    f"""we get original string '{original_string}'"""
					)




from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel





class   __a			(    __UpperCamelCase      ):
 __lowercase :							Any        = ['vqvae']
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, )					->				Union[str, Any]:




        '''simple docstring'''
        super().__init__()
        self.register_modules(unet=lowerCAmelCase__				, scheduler=lowerCAmelCase__				, mel=lowerCAmelCase__				, vqvae=lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				int:




        '''simple docstring'''
        return 50 if isinstance(self.scheduler				, lowerCAmelCase__ ) else 1_000
 @torch.no_grad()
 def __call__(   self				, lowerCAmelCase__ = 1				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__ = 0				, lowerCAmelCase__ = 0				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__ = 0				, lowerCAmelCase__ = 0				, lowerCAmelCase__ = None				, lowerCAmelCase__ = 0				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__=True				, )					->				Union[
     Union[AudioPipelineOutput, ImagePipelineOutput],
     Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
 ]:




        '''simple docstring'''
        lowercase__:   Union[str, Any]  			=   steps or self.get_default_steps()
        self.scheduler.set_timesteps(lowerCAmelCase__ )
        lowercase__:   Optional[int]  			=   step_generator or generator
        # For backwards compatibility
        if type(self.unet.config.sample_size ) == int:
               lowercase__:   Optional[int]  			=   (self.unet.config.sample_size, self.unet.config.sample_size)
        if noise is None:
               lowercase__:   List[str]  			=   randn_tensor(
                   (
                       batch_size,
                       self.unet.config.in_channels,
                       self.unet.config.sample_size[0],
                       self.unet.config.sample_size[1],
                   )				, generator=lowerCAmelCase__				, device=self.device				, )
        lowercase__:   List[Any]  			=   noise
        lowercase__:   int  			=   None

        if audio_file is not None or raw_audio is not None:
               self.mel.load_audio(lowerCAmelCase__				, lowerCAmelCase__ )
               lowercase__:   int  			=   self.mel.audio_slice_to_image(lowerCAmelCase__ )
               lowercase__:   int  			=   np.frombuffer(input_image.tobytes()				, dtype='uint8' ).reshape(
                   (input_image.height, input_image.width) )
               lowercase__:   str  			=   (input_image / 255) * 2 - 1
               lowercase__:   Union[str, Any]  			=   torch.tensor(input_image[np.newaxis, :, :]				, dtype=torch.float ).to(self.device )

               if self.vqvae is not None:
                      lowercase__:   Optional[int]  			=   self.vqvae.encode(torch.unsqueeze(lowerCAmelCase__				, 0 ) ).latent_dist.sample(
                          generator=lowerCAmelCase__ )[0]
                      lowercase__:   Dict  			=   self.vqvae.config.scaling_factor * input_images

               if start_step > 0:
                      lowercase__:   List[Any]  			=   self.scheduler.add_noise(lowerCAmelCase__				, lowerCAmelCase__				, self.scheduler.timesteps[start_step - 1] )

               lowercase__:   str  			=   (
                   self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
               )
               lowercase__:   Dict  			=   int(mask_start_secs * pixels_per_second )
               lowercase__:   Tuple  			=   int(mask_end_secs * pixels_per_second )
               lowercase__:   List[Any]  			=   self.scheduler.add_noise(lowerCAmelCase__				, lowerCAmelCase__				, torch.tensor(self.scheduler.timesteps[start_step:] ) )

        for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
               if isinstance(self.unet				, lowerCAmelCase__ ):
                      lowercase__:   Union[str, Any]  			=   self.unet(lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )['sample']
               else:
                      lowercase__:   Optional[Any]  			=   self.unet(lowerCAmelCase__				, lowerCAmelCase__ )['sample']

               if isinstance(self.scheduler				, lowerCAmelCase__ ):
                      lowercase__:   List[str]  			=   self.scheduler.step(
                          model_output=lowerCAmelCase__				, timestep=lowerCAmelCase__				, sample=lowerCAmelCase__				, eta=lowerCAmelCase__				, generator=lowerCAmelCase__				, )['prev_sample']
               else:
                      lowercase__:   int  			=   self.scheduler.step(
                          model_output=lowerCAmelCase__				, timestep=lowerCAmelCase__				, sample=lowerCAmelCase__				, generator=lowerCAmelCase__				, )['prev_sample']

               if mask is not None:
                      if mask_start > 0:
                             lowercase__:   List[Any]  			=   mask[:, step, :, :mask_start]
                      if mask_end > 0:
                             lowercase__:   Optional[int]  			=   mask[:, step, :, -mask_end:]

        if self.vqvae is not None:
               # 0.18215 was scaling factor used in training to ensure unit variance
               lowercase__:   Union[str, Any]  			=   1 / self.vqvae.config.scaling_factor * images
               lowercase__:   Optional[Any]  			=   self.vqvae.decode(lowerCAmelCase__ )['sample']

        lowercase__:   Dict  			=   (images / 2 + 0.5).clamp(0				, 1 )
        lowercase__:   List[Any]  			=   images.cpu().permute(0				, 2				, 3				, 1 ).numpy()
        lowercase__:   int  			=   (images * 255).round().astype('uint8' )
        lowercase__:   Optional[Any]  			=   list(
            (Image.fromarray(_[:, :, 0] ) for _ in images)
            if images.shape[3] == 1
            else (Image.fromarray(lowerCAmelCase__				, mode='RGB' ).convert('L' ) for _ in images) )

        lowercase__:   Dict  			=   [self.mel.image_to_audio(lowerCAmelCase__ ) for _ in images]
        if not return_dict:
               return images, (self.mel.get_sample_rate(), audios)

        return BaseOutput(**AudioPipelineOutput(np.array(lowerCAmelCase__ )[:, np.newaxis, :] )				, **ImagePipelineOutput(lowerCAmelCase__ ) )
 @torch.no_grad()
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ = 50 )					->				np.ndarray:




        '''simple docstring'''
        assert isinstance(self.scheduler				, lowerCAmelCase__ )
        self.scheduler.set_timesteps(lowerCAmelCase__ )
        lowercase__:   List[str]  			=   np.array(
            [np.frombuffer(image.tobytes()				, dtype='uint8' ).reshape((1, image.height, image.width) ) for image in images] )
        lowercase__:   str  			=   (sample / 255) * 2 - 1
        lowercase__:   str  			=   torch.Tensor(lowerCAmelCase__ ).to(self.device )

        for t in self.progress_bar(torch.flip(self.scheduler.timesteps				, (0,) ) ):
               lowercase__:   Union[str, Any]  			=   t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
               lowercase__:   Optional[Any]  			=   self.scheduler.alphas_cumprod[t]
               lowercase__:   str  			=   (
                   self.scheduler.alphas_cumprod[prev_timestep]
                   if prev_timestep >= 0
                   else self.scheduler.final_alpha_cumprod
               )
               lowercase__:   str  			=   1 - alpha_prod_t
               lowercase__:   int  			=   self.unet(lowerCAmelCase__				, lowerCAmelCase__ )['sample']
               lowercase__:   int  			=   (1 - alpha_prod_t_prev) ** 0.5 * model_output
               lowercase__:   Optional[int]  			=   (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
               lowercase__:   Any  			=   sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

        return sample
 @staticmethod
 def 		SCREAMING_SNAKE_CASE__   (   lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )					->				torch.Tensor:




        '''simple docstring'''
        lowercase__:   Any  			=   acos(torch.dot(torch.flatten(lowerCAmelCase__ )				, torch.flatten(lowerCAmelCase__ ) ) / torch.norm(lowerCAmelCase__ ) / torch.norm(lowerCAmelCase__ ) )
        return sin((1 - alpha) * theta ) * xa / sin(lowerCAmelCase__ ) + sin(alpha * theta ) * xa / sin(lowerCAmelCase__ )


