





import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader





class      lowercase__    (							__A  ):







  def __init__(	self    ,						_lowercase    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = False    ,						_lowercase = False    ,						_lowercase = None    ,						_lowercase = None    ,						**_lowercase    ,						):
       super().__init__(
           _lowercase    ,						split=_lowercase    ,						features=_lowercase    ,						cache_dir=_lowercase    ,						keep_in_memory=_lowercase    ,						streaming=_lowercase    ,						num_proc=_lowercase    ,						**_lowercase    ,						)
       lowerCAmelCase_					:       Union[str, Any]    =		field
       lowerCAmelCase_					:       List[Any]    =		path_or_paths if isinstance(_lowercase    ,						_lowercase						) else {self.split: path_or_paths}
       lowerCAmelCase_					:       Optional[Any]    =		Json(
           cache_dir=_lowercase    ,						data_files=_lowercase    ,						features=_lowercase    ,						field=_lowercase    ,						**_lowercase    ,						)







  def 	UpperCAmelCase__		(	self						):
       # Build iterable dataset
       if self.streaming:
            lowerCAmelCase_					:       Tuple    =		self.builder.as_streaming_dataset(split=self.split						)
       # Build regular (map-style) dataset
       else:
            lowerCAmelCase_					:       Optional[int]    =		None
            lowerCAmelCase_					:       Any    =		None
            lowerCAmelCase_					:       Optional[int]    =		None
            lowerCAmelCase_					:       Union[str, Any]    =		None

            self.builder.download_and_prepare(
                download_config=_lowercase    ,						download_mode=_lowercase    ,						verification_mode=_lowercase    ,						base_path=_lowercase    ,						num_proc=self.num_proc    ,						)
            lowerCAmelCase_					:       Any    =		self.builder.as_dataset(
                split=self.split    ,						verification_mode=_lowercase    ,						in_memory=self.keep_in_memory						)
       return dataset





class      lowercase__    :







  def __init__(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase = None    ,						_lowercase = None    ,						**_lowercase    ,						):
       if num_proc is not None and num_proc <= 0:
            raise ValueError(F'num_proc {num_proc} must be an integer > 0.'						)

       lowerCAmelCase_					:       Any    =		dataset
       lowerCAmelCase_					:       List[Any]    =		path_or_buf
       lowerCAmelCase_					:       int    =		batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
       lowerCAmelCase_					:       str    =		num_proc
       lowerCAmelCase_					:       Optional[int]    =		"""utf-8"""
       lowerCAmelCase_					:       Dict    =		to_json_kwargs







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       List[str]    =		self.to_json_kwargs.pop("""path_or_buf"""    ,						_lowercase						)
       lowerCAmelCase_					:       Optional[int]    =		self.to_json_kwargs.pop("""orient"""    ,						"""records"""						)
       lowerCAmelCase_					:       str    =		self.to_json_kwargs.pop("""lines"""    ,						True if orient == """records""" else False						)
       lowerCAmelCase_					:       Dict    =		self.to_json_kwargs.pop("""index"""    ,						False if orient in ["""split""", """table"""] else True						)
       lowerCAmelCase_					:       Tuple    =		self.to_json_kwargs.pop("""compression"""    ,						_lowercase						)

       if compression not in [None, "infer", "gzip", "bz2", "xz"]:
            raise NotImplementedError(F'`datasets` currently does not support {compression} compression'						)

       if isinstance(self.path_or_buf    ,						(str, bytes, os.PathLike)						):
            with fsspec.open(self.path_or_buf    ,						"""wb"""    ,						compression=_lowercase						) as buffer:
                 lowerCAmelCase_					:       Dict    =		self._write(file_obj=_lowercase    ,						orient=_lowercase    ,						lines=_lowercase    ,						index=_lowercase    ,						**self.to_json_kwargs						)
       else:
            if compression:
                 raise NotImplementedError(
                     F'The compression parameter is not supported when writing to a buffer, but compression={compression}'
                     """ was passed. Please provide a local path instead."""						)
            lowerCAmelCase_					:       int    =		self._write(
                file_obj=self.path_or_buf    ,						orient=_lowercase    ,						lines=_lowercase    ,						index=_lowercase    ,						**self.to_json_kwargs						)
       return written







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_					:       int    =		args

       lowerCAmelCase_					:       Union[str, Any]    =		query_table(
           table=self.dataset.data    ,						key=slice(_lowercase    ,						offset + self.batch_size						)    ,						indices=self.dataset._indices    ,						)
       lowerCAmelCase_					:       str    =		batch.to_pandas().to_json(
           path_or_buf=_lowercase    ,						orient=_lowercase    ,						lines=_lowercase    ,						index=_lowercase    ,						**_lowercase						)
       if not json_str.endswith("""\n"""						):
            json_str += "\n"
       return json_str.encode(self.encoding						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						**_lowercase    ,						):
       lowerCAmelCase_					:       List[str]    =		0

       if self.num_proc is None or self.num_proc == 1:
            for offset in logging.tqdm(
                range(0    ,						len(self.dataset						)    ,						self.batch_size						)    ,						unit="""ba"""    ,						disable=not logging.is_progress_bar_enabled()    ,						desc="""Creating json from Arrow format"""    ,						):
                 lowerCAmelCase_					:       Tuple    =		self._batch_json((offset, orient, lines, index, to_json_kwargs)						)
                 written += file_obj.write(_lowercase						)
       else:
            lowerCAmelCase_   ,  lowerCAmelCase_					:       Optional[int]    =		len(self.dataset						), self.batch_size
            with multiprocessing.Pool(self.num_proc						) as pool:
                 for json_str in logging.tqdm(
                     pool.imap(
                         self._batch_json    ,						[(offset, orient, lines, index, to_json_kwargs) for offset in range(0    ,						_lowercase    ,						_lowercase						)]    ,						)    ,						total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size    ,						unit="""ba"""    ,						disable=not logging.is_progress_bar_enabled()    ,						desc="""Creating json from Arrow format"""    ,						):
                      written += file_obj.write(_lowercase						)

       return written







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						)
