






'''simple docstring'''
import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader





def  _a(  UpperCamelCase__				:						Features							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				List[str]									=np.inf

      def set_batch_size(UpperCamelCase__				:						FeatureType							) -> None:
            nonlocal batch_size
            if isinstance(UpperCamelCase__,	UpperCamelCase__							):
                  SCREAMING_SNAKE_CASE__     :				str									=min(UpperCamelCase__,	config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS							)
            elif isinstance(UpperCamelCase__,	UpperCamelCase__							):
                  SCREAMING_SNAKE_CASE__     :				int									=min(UpperCamelCase__,	config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS							)
            elif isinstance(UpperCamelCase__,	UpperCamelCase__							) and feature.dtype == "binary":
                  SCREAMING_SNAKE_CASE__     :				Dict									=min(UpperCamelCase__,	config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS							)

      _visit(UpperCamelCase__,	UpperCamelCase__							)

      return None if batch_size is np.inf else batch_size






class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):




 def __init__(      self	:							Any					,      __lowercase	:							NestedDataStructureLike[PathLike]					,      __lowercase	:							Optional[NamedSplit] = None					,      __lowercase	:							Optional[Features] = None					,      __lowercase	:							str = None					,      __lowercase	:							bool = False					,      __lowercase	:							bool = False					,      __lowercase	:							Optional[int] = None					,      **__lowercase	:							str					,      )	->						Optional[int]:
       super().__init__(
           __lowercase					,      split=__lowercase					,      features=__lowercase					,      cache_dir=__lowercase					,      keep_in_memory=__lowercase					,      streaming=__lowercase					,      num_proc=__lowercase					,      **__lowercase					,      )
       SCREAMING_SNAKE_CASE__     :				Dict									=path_or_paths if isinstance(__lowercase					,      __lowercase      ) else {self.split: path_or_paths}
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=_PACKAGED_DATASETS_MODULES['''parquet'''][1]
       SCREAMING_SNAKE_CASE__     :				List[str]									=Parquet(
           cache_dir=__lowercase					,      data_files=__lowercase					,      features=__lowercase					,      hash=__lowercase					,      **__lowercase					,      )




 def      __magic_name__	(      self	:							Optional[Any]      )	->						List[Any]:
       # Build iterable dataset
       if self.streaming:
             SCREAMING_SNAKE_CASE__     :				Tuple									=self.builder.as_streaming_dataset(split=self.split      )
       # Build regular (map-style) dataset
       else:
             SCREAMING_SNAKE_CASE__     :				Any									=None
             SCREAMING_SNAKE_CASE__     :				Any									=None
             SCREAMING_SNAKE_CASE__     :				Tuple									=None
             SCREAMING_SNAKE_CASE__     :				Tuple									=None

             self.builder.download_and_prepare(
                 download_config=__lowercase					,      download_mode=__lowercase					,      verification_mode=__lowercase					,      base_path=__lowercase					,      num_proc=self.num_proc					,      )
             SCREAMING_SNAKE_CASE__     :				Dict									=self.builder.as_dataset(
                 split=self.split					,      verification_mode=__lowercase					,      in_memory=self.keep_in_memory      )
       return dataset






class      __SCREAMING_SNAKE_CASE      :




 def __init__(      self	:							Dict					,      __lowercase	:							Dataset					,      __lowercase	:							Union[PathLike, BinaryIO]					,      __lowercase	:							Optional[int] = None					,      **__lowercase	:							int					,      )	->						Optional[int]:
       SCREAMING_SNAKE_CASE__     :				List[Any]									=dataset
       SCREAMING_SNAKE_CASE__     :				str									=path_or_buf
       SCREAMING_SNAKE_CASE__     :				int									=batch_size or get_writer_batch_size(dataset.features      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=parquet_writer_kwargs




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						int:
       SCREAMING_SNAKE_CASE__     :				Tuple									=self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

       if isinstance(self.path_or_buf					,      (str, bytes, os.PathLike)      ):
             with open(self.path_or_buf					,      '''wb+'''      ) as buffer:
                   SCREAMING_SNAKE_CASE__     :				Optional[int]									=self._write(file_obj=__lowercase					,      batch_size=__lowercase					,      **self.parquet_writer_kwargs      )
       else:
             SCREAMING_SNAKE_CASE__     :				int									=self._write(file_obj=self.path_or_buf					,      batch_size=__lowercase					,      **self.parquet_writer_kwargs      )
       return written




 def      __magic_name__	(      self	:							List[Any]					,      __lowercase	:							BinaryIO					,      __lowercase	:							int					,      **__lowercase	:							Tuple      )	->						int:
       SCREAMING_SNAKE_CASE__     :				int									=0
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=parquet_writer_kwargs.pop('''path_or_buf'''					,      __lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.dataset.features.arrow_schema

       SCREAMING_SNAKE_CASE__     :				List[Any]									=pq.ParquetWriter(__lowercase					,      schema=__lowercase					,      **__lowercase      )

       for offset in logging.tqdm(
           range(0					,      len(self.dataset      )					,      __lowercase      )					,      unit='''ba'''					,      disable=not logging.is_progress_bar_enabled()					,      desc='''Creating parquet from Arrow format'''					,      ):
             SCREAMING_SNAKE_CASE__     :				Dict									=query_table(
                 table=self.dataset._data					,      key=slice(__lowercase					,      offset + batch_size      )					,      indices=self.dataset._indices if self.dataset._indices is not None else None					,      )
             writer.write_table(__lowercase      )
             written += batch.nbytes
       writer.close()
       return written







'''simple docstring'''
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      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
 snake_case_  							=							["""vqvae"""]




 def __init__(      self	:							int					,      __lowercase	:							AutoencoderKL					,      __lowercase	:							UNetaDConditionModel					,      __lowercase	:							Mel					,      __lowercase	:							Union[DDIMScheduler, DDPMScheduler]					,      )	->						int:
       super().__init__()
       self.register_modules(unet=__lowercase					,      scheduler=__lowercase					,      mel=__lowercase					,      vqvae=__lowercase      )




 def      __magic_name__	(      self	:							List[str]      )	->						int:
       return 50 if isinstance(self.scheduler					,      __lowercase      ) else 10_00




 @torch.no_grad()
 def __call__(      self	:							Dict					,      __lowercase	:							int = 1					,      __lowercase	:							str = None					,      __lowercase	:							np.ndarray = None					,      __lowercase	:							int = 0					,      __lowercase	:							int = 0					,      __lowercase	:							int = None					,      __lowercase	:							torch.Generator = None					,      __lowercase	:							float = 0					,      __lowercase	:							float = 0					,      __lowercase	:							torch.Generator = None					,      __lowercase	:							float = 0					,      __lowercase	:							torch.Tensor = None					,      __lowercase	:							torch.Tensor = None					,      __lowercase	:							Dict=True					,      )	->						Union[
     Union[AudioPipelineOutput, ImagePipelineOutput],
     Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
 ]:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=steps or self.get_default_steps()
       self.scheduler.set_timesteps(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=step_generator or generator
       # For backwards compatibility
       if type(self.unet.config.sample_size      ) == int:
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=(self.unet.config.sample_size, self.unet.config.sample_size)
       if noise is None:
             SCREAMING_SNAKE_CASE__     :				Optional[int]									=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					,      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=noise
       SCREAMING_SNAKE_CASE__     :				List[str]									=None

       if audio_file is not None or raw_audio is not None:
             self.mel.load_audio(__lowercase					,      __lowercase      )
             SCREAMING_SNAKE_CASE__     :				Dict									=self.mel.audio_slice_to_image(__lowercase      )
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=np.frombuffer(input_image.tobytes()					,      dtype='''uint8'''      ).reshape(
                 (input_image.height, input_image.width)      )
             SCREAMING_SNAKE_CASE__     :				int									=(input_image / 2_55) * 2 - 1
             SCREAMING_SNAKE_CASE__     :				Optional[int]									=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(__lowercase					,      0      )      ).latent_dist.sample(
                       generator=__lowercase      )[0]
                   SCREAMING_SNAKE_CASE__     :				int									=self.vqvae.config.scaling_factor * input_images

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

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

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

             if mask is not None:
                   if mask_start > 0:
                         SCREAMING_SNAKE_CASE__     :				Optional[Any]									=mask[:, step, :, :mask_start]
                   if mask_end > 0:
                         SCREAMING_SNAKE_CASE__     :				List[str]									=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__     :				str									=1 / self.vqvae.config.scaling_factor * images
             SCREAMING_SNAKE_CASE__     :				int									=self.vqvae.decode(__lowercase      )['''sample''']

       SCREAMING_SNAKE_CASE__     :				List[str]									=(images / 2 + 0.5).clamp(0					,      1      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=images.cpu().permute(0					,      2					,      3					,      1      ).numpy()
       SCREAMING_SNAKE_CASE__     :				Dict									=(images * 2_55).round().astype('''uint8'''      )
       SCREAMING_SNAKE_CASE__     :				Any									=list(
           (Image.fromarray(_[:, :, 0]      ) for _ in images)
           if images.shape[3] == 1
           else (Image.fromarray(__lowercase					,      mode='''RGB'''      ).convert('''L'''      ) for _ in images)      )

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=[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      __magic_name__	(      self	:							Optional[int]					,      __lowercase	:							List[Image.Image]					,      __lowercase	:							int = 50      )	->						np.ndarray:
       assert isinstance(self.scheduler					,      __lowercase      )
       self.scheduler.set_timesteps(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=np.array(
           [np.frombuffer(image.tobytes()					,      dtype='''uint8'''      ).reshape((1, image.height, image.width)      ) for image in images]      )
       SCREAMING_SNAKE_CASE__     :				str									=(sample / 2_55) * 2 - 1
       SCREAMING_SNAKE_CASE__     :				str									=torch.Tensor(__lowercase      ).to(self.device      )

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

       return sample




 @staticmethod
 def      __magic_name__	(      __lowercase	:							torch.Tensor					,      __lowercase	:							torch.Tensor					,      __lowercase	:							float      )	->						torch.Tensor:
       SCREAMING_SNAKE_CASE__     :				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      )