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       snake_case					(      UpperCamelCase_						):
  lowercase_			=     ['vqvae']




  def __init__(      self     :	Tuple     , a_     :	List[Any]     , a_     :	Dict     , a_     :	Union[str, Any]     , a_     :	int     , )->       Optional[int]:




       """simple docstring"""
       super().__init__()
       self.register_modules(unet=_lowerCAmelCase     , scheduler=_lowerCAmelCase     , mel=_lowerCAmelCase     , vqvae=_lowerCAmelCase				)




  def 	__lowercase(      self     :	Any				)->       Dict:




       """simple docstring"""
       return 50 if isinstance(self.scheduler     , _lowerCAmelCase				) else 1000




  @torch.no_grad()
  def __call__(      self     :	Dict     , a_     :	int = 1     , a_     :	Optional[Any] = None     , a_     :	Any = None     , a_     :	Union[str, Any] = 0     , a_     :	Tuple = 0     , a_     :	Optional[int] = None     , a_     :	List[Any] = None     , a_     :	List[str] = 0     , a_     :	Tuple = 0     , a_     :	Tuple = None     , a_     :	Dict = 0     , a_     :	List[Any] = None     , a_     :	str = None     , a_     :	List[Any]=True     , )->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		steps or self.get_default_steps()
       self.scheduler.set_timesteps(_lowerCAmelCase				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		step_generator or generator
       # For backwards compatibility
       if type(self.unet.config.sample_size				) == int:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		(self.unet.config.sample_size, self.unet.config.sample_size)
       if noise is None:
            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		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     , )
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		noise
       SCREAMING_SNAKE_CASE__				:				int			=		None

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

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

            if start_step > 0:
                 SCREAMING_SNAKE_CASE__				:				Dict			=		self.scheduler.add_noise(_lowerCAmelCase     , _lowerCAmelCase     , self.scheduler.timesteps[start_step - 1]				)

            SCREAMING_SNAKE_CASE__				:				List[Any]			=		(
                self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
            )
            SCREAMING_SNAKE_CASE__				:				str			=		int(mask_start_secs * pixels_per_second				)
            SCREAMING_SNAKE_CASE__				:				int			=		int(mask_end_secs * pixels_per_second				)
            SCREAMING_SNAKE_CASE__				:				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				):
                 SCREAMING_SNAKE_CASE__				:				Dict			=		self.unet(_lowerCAmelCase     , _lowerCAmelCase     , _lowerCAmelCase				)['sample']
            else:
                 SCREAMING_SNAKE_CASE__				:				Dict			=		self.unet(_lowerCAmelCase     , _lowerCAmelCase				)['sample']

            if isinstance(self.scheduler     , _lowerCAmelCase				):
                 SCREAMING_SNAKE_CASE__				:				Any			=		self.scheduler.step(
                     model_output=_lowerCAmelCase     , timestep=_lowerCAmelCase     , sample=_lowerCAmelCase     , eta=_lowerCAmelCase     , generator=_lowerCAmelCase     , )['prev_sample']
            else:
                 SCREAMING_SNAKE_CASE__				:				str			=		self.scheduler.step(
                     model_output=_lowerCAmelCase     , timestep=_lowerCAmelCase     , sample=_lowerCAmelCase     , generator=_lowerCAmelCase     , )['prev_sample']

            if mask is not None:
                 if mask_start > 0:
                      SCREAMING_SNAKE_CASE__				:				Optional[int]			=		mask[:, step, :, :mask_start]
                 if mask_end > 0:
                      SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		mask[:, step, :, -mask_end:]

       if self.vqvae is not None:
            # 0.18215 was scaling factor used in training to ensure unit variance
            SCREAMING_SNAKE_CASE__				:				Tuple			=		1 / self.vqvae.config.scaling_factor * images
            SCREAMING_SNAKE_CASE__				:				int			=		self.vqvae.decode(_lowerCAmelCase				)['sample']

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

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		[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 	__lowercase(      self     :	Union[str, Any]     , a_     :	Dict     , a_     :	List[Any] = 50				)->       List[str]:




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

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

       return sample




  @staticmethod
  def 	__lowercase(      a_     :	int     , a_     :	Tuple     , a_     :	List[Any]				)->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		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				)




import math
import sys







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		''
     try:
          with open(lowercase__     ,  'rb' ) as binary_file:
               SCREAMING_SNAKE_CASE__				:				Tuple			=		binary_file.read()
          for dat in data:
               SCREAMING_SNAKE_CASE__				:				Tuple			=		f'''{dat:08b}'''
               result += curr_byte
          return result
     except OSError:
          print('File not accessible' )
          sys.exit()







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				List[str]			=		{'0': '0', '1': '1'}
     SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				int			=		'', ''
     SCREAMING_SNAKE_CASE__				:				Tuple			=		len(lowercase__ )

     for i in range(len(lowercase__ ) ):
          curr_string += data_bits[i]
          if curr_string not in lexicon:
               continue

          SCREAMING_SNAKE_CASE__				:				int			=		lexicon[curr_string]
          result += last_match_id
          SCREAMING_SNAKE_CASE__				:				str			=		last_match_id + '0'

          if math.loga(lowercase__ ).is_integer():
               SCREAMING_SNAKE_CASE__				:				List[str]			=		{}
               for curr_key in list(lowercase__ ):
                    SCREAMING_SNAKE_CASE__				:				Optional[int]			=		lexicon.pop(lowercase__ )
               SCREAMING_SNAKE_CASE__				:				Optional[int]			=		new_lex

          SCREAMING_SNAKE_CASE__				:				Any			=		last_match_id + '1'
          index += 1
          SCREAMING_SNAKE_CASE__				:				Tuple			=		''
     return result







def     _a				(      lowercase__  :			str     ,  lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				str			=		8
     try:
          with open(lowercase__     ,  'wb' ) as opened_file:
               SCREAMING_SNAKE_CASE__				:				Dict			=		[
                   to_write[i : i + byte_length]
                   for i in range(0     ,  len(lowercase__ )     ,  lowercase__ )
               ]

               if len(result_byte_array[-1] ) % byte_length == 0:
                    result_byte_array.append('10000000' )
               else:
                    result_byte_array[-1] += "1" + "0" * (
                        byte_length - len(result_byte_array[-1] ) - 1
                    )

               for elem in result_byte_array[:-1]:
                    opened_file.write(int(lowercase__     ,  2 ).to_bytes(1     ,  byteorder='big' ) )
     except OSError:
          print('File not accessible' )
          sys.exit()







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Dict			=		0
     for letter in data_bits:
          if letter == "1":
               break
          counter += 1

     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		data_bits[counter:]
     SCREAMING_SNAKE_CASE__				:				int			=		data_bits[counter + 1 :]
     return data_bits







def     _a				(      lowercase__  :			str     ,  lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		read_file_binary(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Dict			=		remove_prefix(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Tuple			=		decompress_data(lowercase__ )
     write_file_binary(lowercase__     ,  lowercase__ )


if __name__ == "__main__":
      compress(sys.argv[1], sys.argv[2])



