





import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


UpperCAmelCase_					:   int 							=						get_logger(__name__)





class      lowercase__    (							enum.Enum  ):
  __UpperCamelCase	     =  """all_checks"""
  __UpperCamelCase	     =  """basic_checks"""
  __UpperCamelCase	     =  """no_checks"""





class      lowercase__    (							__A  ):
  pass





class      lowercase__    (							__A  ):
  pass





class      lowercase__    (							__A  ):
  pass





class      lowercase__    (							__A  ):
  pass






def 							_lowerCAmelCase (  _a   :					Optional[dict]					,   _a   :					dict					,   _a   :					str=None			)	->			Optional[Any]:
     if expected_checksums is None:
          logger.info("""Unable to verify checksums."""			)
          return
     if len(set(_a			) - set(_a			)			) > 0:
          raise ExpectedMoreDownloadedFiles(str(set(_a			) - set(_a			)			)			)
     if len(set(_a			) - set(_a			)			) > 0:
          raise UnexpectedDownloadedFile(str(set(_a			) - set(_a			)			)			)
     lowerCAmelCase_					:       Union[str, Any]    =		[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
     lowerCAmelCase_					:       Optional[Any]    =		""" for """ + verification_name if verification_name is not None else """"""
     if len(_a			) > 0:
          raise NonMatchingChecksumError(
              F'Checksums didn\'t match{for_verification_name}:\n'
              F'{bad_urls}\n'
              """Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"""			)
     logger.info("""All the checksums matched successfully""" + for_verification_name			)





class      lowercase__    (							__A  ):
  pass





class      lowercase__    (							__A  ):
  pass





class      lowercase__    (							__A  ):
  pass





class      lowercase__    (							__A  ):
  pass






def 							_lowerCAmelCase (  _a   :					Optional[dict]					,   _a   :					dict			)	->			str:
     if expected_splits is None:
          logger.info("""Unable to verify splits sizes."""			)
          return
     if len(set(_a			) - set(_a			)			) > 0:
          raise ExpectedMoreSplits(str(set(_a			) - set(_a			)			)			)
     if len(set(_a			) - set(_a			)			) > 0:
          raise UnexpectedSplits(str(set(_a			) - set(_a			)			)			)
     lowerCAmelCase_					:       Optional[int]    =		[
         {"""expected""": expected_splits[name], """recorded""": recorded_splits[name]}
         for name in expected_splits
         if expected_splits[name].num_examples != recorded_splits[name].num_examples
     ]
     if len(_a			) > 0:
          raise NonMatchingSplitsSizesError(str(_a			)			)
     logger.info("""All the splits matched successfully."""			)






def 							_lowerCAmelCase (  _a   :					str					,   _a   :					bool = True			)	->			dict:
     if record_checksum:
          lowerCAmelCase_					:       int    =		shaaaa()
          with open(_a					,   """rb"""			) as f:
               for chunk in iter(lambda: f.read(1 << 20			)					,   B""""""			):
                    m.update(_a			)
               lowerCAmelCase_					:       List[str]    =		m.hexdigest()
     else:
          lowerCAmelCase_					:       Union[str, Any]    =		None
     return {"num_bytes": os.path.getsize(_a			), "checksum": checksum}







def 							_lowerCAmelCase (  _a   :					Tuple			)	->			Optional[int]:
     if dataset_size and config.IN_MEMORY_MAX_SIZE:
          return dataset_size < config.IN_MEMORY_MAX_SIZE
     else:
          return False







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      lowercase__    (							__A  ):
  __UpperCamelCase	     =  ["""vqvae"""]







  def __init__(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						):
       super().__init__()
       self.register_modules(unet=_lowercase    ,						scheduler=_lowercase    ,						mel=_lowercase    ,						vqvae=_lowercase						)







  def 	UpperCAmelCase__		(	self						):
       return 50 if isinstance(self.scheduler    ,						_lowercase						) else 1_000







  @torch.no_grad()
  def __call__(	self    ,						_lowercase = 1    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = 0    ,						_lowercase = 0    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = 0    ,						_lowercase = 0    ,						_lowercase = None    ,						_lowercase = 0    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase=True    ,						):
       lowerCAmelCase_					:       Tuple    =		steps or self.get_default_steps()
       self.scheduler.set_timesteps(_lowercase						)
       lowerCAmelCase_					:       Union[str, Any]    =		step_generator or generator
       # For backwards compatibility
       if type(self.unet.config.sample_size						) == int:
            lowerCAmelCase_					:       Optional[Any]    =		(self.unet.config.sample_size, self.unet.config.sample_size)
       if noise is None:
            lowerCAmelCase_					:       Tuple    =		randn_tensor(
                (
                    batch_size,
                    self.unet.config.in_channels,
                    self.unet.config.sample_size[0],
                    self.unet.config.sample_size[1],
                )    ,						generator=_lowercase    ,						device=self.device    ,						)
       lowerCAmelCase_					:       List[str]    =		noise
       lowerCAmelCase_					:       List[str]    =		None

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

            if self.vqvae is not None:
                 lowerCAmelCase_					:       List[Any]    =		self.vqvae.encode(torch.unsqueeze(_lowercase    ,						0						)						).latent_dist.sample(
                     generator=_lowercase						)[0]
                 lowerCAmelCase_					:       str    =		self.vqvae.config.scaling_factor * input_images

            if start_step > 0:
                 lowerCAmelCase_					:       str    =		self.scheduler.add_noise(_lowercase    ,						_lowercase    ,						self.scheduler.timesteps[start_step - 1]						)

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

       for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]						)						):
            if isinstance(self.unet    ,						_lowercase						):
                 lowerCAmelCase_					:       List[Any]    =		self.unet(_lowercase    ,						_lowercase    ,						_lowercase						)["""sample"""]
            else:
                 lowerCAmelCase_					:       Any    =		self.unet(_lowercase    ,						_lowercase						)["""sample"""]

            if isinstance(self.scheduler    ,						_lowercase						):
                 lowerCAmelCase_					:       str    =		self.scheduler.step(
                     model_output=_lowercase    ,						timestep=_lowercase    ,						sample=_lowercase    ,						eta=_lowercase    ,						generator=_lowercase    ,						)["""prev_sample"""]
            else:
                 lowerCAmelCase_					:       List[str]    =		self.scheduler.step(
                     model_output=_lowercase    ,						timestep=_lowercase    ,						sample=_lowercase    ,						generator=_lowercase    ,						)["""prev_sample"""]

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

       if self.vqvae is not None:
            # 0.18215 was scaling factor used in training to ensure unit variance
            lowerCAmelCase_					:       Dict    =		1 / self.vqvae.config.scaling_factor * images
            lowerCAmelCase_					:       Dict    =		self.vqvae.decode(_lowercase						)["""sample"""]

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

       lowerCAmelCase_					:       str    =		[self.mel.image_to_audio(_lowercase						) for _ in images]
       if not return_dict:
            return images, (self.mel.get_sample_rate(), audios)

       return BaseOutput(**AudioPipelineOutput(np.array(_lowercase						)[:, np.newaxis, :]						)    ,						**ImagePipelineOutput(_lowercase						)						)







  @torch.no_grad()
  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = 50						):
       assert isinstance(self.scheduler    ,						_lowercase						)
       self.scheduler.set_timesteps(_lowercase						)
       lowerCAmelCase_					:       List[Any]    =		np.array(
           [np.frombuffer(image.tobytes()    ,						dtype="""uint8"""						).reshape((1, image.height, image.width)						) for image in images]						)
       lowerCAmelCase_					:       List[str]    =		(sample / 255) * 2 - 1
       lowerCAmelCase_					:       Optional[Any]    =		torch.Tensor(_lowercase						).to(self.device						)

       for t in self.progress_bar(torch.flip(self.scheduler.timesteps    ,						(0,)						)						):
            lowerCAmelCase_					:       int    =		t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
            lowerCAmelCase_					:       Optional[int]    =		self.scheduler.alphas_cumprod[t]
            lowerCAmelCase_					:       Any    =		(
                self.scheduler.alphas_cumprod[prev_timestep]
                if prev_timestep >= 0
                else self.scheduler.final_alpha_cumprod
            )
            lowerCAmelCase_					:       Optional[int]    =		1 - alpha_prod_t
            lowerCAmelCase_					:       Union[str, Any]    =		self.unet(_lowercase    ,						_lowercase						)["""sample"""]
            lowerCAmelCase_					:       int    =		(1 - alpha_prod_t_prev) ** 0.5 * model_output
            lowerCAmelCase_					:       Dict    =		(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
            lowerCAmelCase_					:       Optional[Any]    =		sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

       return sample







  @staticmethod
  def 	UpperCAmelCase__		(	_lowercase    ,						_lowercase    ,						_lowercase						):
       lowerCAmelCase_					:       Optional[int]    =		acos(torch.dot(torch.flatten(_lowercase						)    ,						torch.flatten(_lowercase						)						) / torch.norm(_lowercase						) / torch.norm(_lowercase						)						)
       return sin((1 - alpha) * theta						) * xa / sin(_lowercase						) + sin(alpha * theta						) * xa / sin(_lowercase						)
