





'''simple docstring'''




import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
  import sqlitea

  import sqlalchemy







class   A_	(  lowerCAmelCase_			):




 def __init__(					self       :    List[Any]	,			snake_case_       :    Union[str, "sqlalchemy.sql.Selectable"]	,			snake_case_       :    Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]	,			snake_case_       :    Optional[Features] = None	,			snake_case_       :    str = None	,			snake_case_       :    bool = False	,			**snake_case_       :    int	,			):
      super().__init__(features=snake_case_	,			cache_dir=snake_case_	,			keep_in_memory=snake_case_	,			**snake_case_    )
      _UpperCAmelCase     							=   Sql(
          cache_dir=snake_case_	,			features=snake_case_	,			sql=snake_case_	,			con=snake_case_	,			**snake_case_	,			)




 def      lowercase	(					self       :    Tuple    ):
      _UpperCAmelCase     							=   None
      _UpperCAmelCase     							=   None
      _UpperCAmelCase     							=   None
      _UpperCAmelCase     							=   None

      self.builder.download_and_prepare(
          download_config=snake_case_	,			download_mode=snake_case_	,			verification_mode=snake_case_	,			base_path=snake_case_	,			)

      # Build dataset for splits
      _UpperCAmelCase     							=   self.builder.as_dataset(
          split="train"	,			verification_mode=snake_case_	,			in_memory=self.keep_in_memory    )
      return dataset







class   A_	:




 def __init__(					self       :    List[Any]	,			snake_case_       :    Dataset	,			snake_case_       :    str	,			snake_case_       :    Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]	,			snake_case_       :    Optional[int] = None	,			snake_case_       :    Optional[int] = None	,			**snake_case_       :    Union[str, Any]	,			):
      if num_proc is not None and num_proc <= 0:
           raise ValueError(f'num_proc {num_proc} must be an integer > 0.'    )

      _UpperCAmelCase     							=   dataset
      _UpperCAmelCase     							=   name
      _UpperCAmelCase     							=   con
      _UpperCAmelCase     							=   batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
      _UpperCAmelCase     							=   num_proc
      _UpperCAmelCase     							=   to_sql_kwargs




 def      lowercase	(					self       :    Dict    ):
      _UpperCAmelCase     							=   self.to_sql_kwargs.pop("sql"	,			snake_case_    )
      _UpperCAmelCase     							=   self.to_sql_kwargs.pop("con"	,			snake_case_    )
      _UpperCAmelCase     							=   self.to_sql_kwargs.pop("index"	,			snake_case_    )

      _UpperCAmelCase     							=   self._write(index=snake_case_	,			**self.to_sql_kwargs    )
      return written




 def      lowercase	(					self       :    Tuple	,			snake_case_       :    Any    ):
      _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase     							=   args
      _UpperCAmelCase     							=   {**to_sql_kwargs, "if_exists": "append"} if offset > 0 else to_sql_kwargs
      _UpperCAmelCase     							=   query_table(
          table=self.dataset.data	,			key=slice(snake_case_	,			offset + self.batch_size    )	,			indices=self.dataset._indices	,			)
      _UpperCAmelCase     							=   batch.to_pandas()
      _UpperCAmelCase     							=   df.to_sql(self.name	,			self.con	,			index=snake_case_	,			**snake_case_    )
      return num_rows or len(snake_case_    )




 def      lowercase	(					self       :    Optional[int]	,			snake_case_       :    Optional[Any]	,			**snake_case_       :    str    ):
      _UpperCAmelCase     							=   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 SQL from Arrow format"	,			):
                written += self._batch_sql((offset, index, to_sql_kwargs)    )
      else:
           _UpperCAmelCase							,  _UpperCAmelCase     							=   len(self.dataset    ), self.batch_size
           with multiprocessing.Pool(self.num_proc    ) as pool:
                for num_rows in logging.tqdm(
                    pool.imap(
                        self._batch_sql	,			[(offset, index, to_sql_kwargs) for offset in range(0	,			snake_case_	,			snake_case_    )]	,			)	,			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 SQL from Arrow format"	,			):
                     written += num_rows

      return written











'''simple docstring'''




import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def 	UpperCAmelCase_							(					__lowercase							:      str       )		->       List[str]:


     '''simple docstring'''



     _UpperCAmelCase							,  _UpperCAmelCase     							=   image.size
     _UpperCAmelCase							,  _UpperCAmelCase     							=   (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
     _UpperCAmelCase     							=   image.resize((w, h)			,     resample=PIL_INTERPOLATION["lanczos"]       )
     _UpperCAmelCase     							=   np.array(__lowercase       ).astype(np.floataa       ) / 255.0
     _UpperCAmelCase     							=   image[None].transpose(0			,     3			,     1			,     2       )
     _UpperCAmelCase     							=   torch.from_numpy(__lowercase       )
     return 2.0 * image - 1.0







class   A_	(  lowerCAmelCase_			):




 def __init__(					self       :    Optional[Any]	,			snake_case_       :    VQModel	,			snake_case_       :    UNetaDModel	,			snake_case_       :    Union[
         DDIMScheduler,
         PNDMScheduler,
         LMSDiscreteScheduler,
         EulerDiscreteScheduler,
         EulerAncestralDiscreteScheduler,
         DPMSolverMultistepScheduler,
     ]	,			):
      super().__init__()
      self.register_modules(vqvae=snake_case_	,			unet=snake_case_	,			scheduler=snake_case_    )




 @torch.no_grad()
 def __call__(					self       :    Any	,			snake_case_       :    Union[torch.Tensor, PIL.Image.Image] = None	,			snake_case_       :    Optional[int] = 1	,			snake_case_       :    Optional[int] = 1_0_0	,			snake_case_       :    Optional[float] = 0.0	,			snake_case_       :    Optional[Union[torch.Generator, List[torch.Generator]]] = None	,			snake_case_       :    Optional[str] = "pil"	,			snake_case_       :    bool = True	,			):
      if isinstance(snake_case_	,			PIL.Image.Image    ):
           _UpperCAmelCase     							=   1
      elif isinstance(snake_case_	,			torch.Tensor    ):
           _UpperCAmelCase     							=   image.shape[0]
      else:
           raise ValueError(f'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(snake_case_    )}'    )

      if isinstance(snake_case_	,			PIL.Image.Image    ):
           _UpperCAmelCase     							=   preprocess(snake_case_    )

      _UpperCAmelCase							,  _UpperCAmelCase     							=   image.shape[-2:]

      # in_channels should be 6: 3 for latents, 3 for low resolution image
      _UpperCAmelCase     							=   (batch_size, self.unet.config.in_channels // 2, height, width)
      _UpperCAmelCase     							=   next(self.unet.parameters()    ).dtype

      _UpperCAmelCase     							=   randn_tensor(snake_case_	,			generator=snake_case_	,			device=self.device	,			dtype=snake_case_    )

      _UpperCAmelCase     							=   image.to(device=self.device	,			dtype=snake_case_    )

      # set timesteps and move to the correct device
      self.scheduler.set_timesteps(snake_case_	,			device=self.device    )
      _UpperCAmelCase     							=   self.scheduler.timesteps

      # scale the initial noise by the standard deviation required by the scheduler
      _UpperCAmelCase     							=   latents * self.scheduler.init_noise_sigma

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
      # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
      # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
      # and should be between [0, 1]
      _UpperCAmelCase     							=   "eta" in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
      _UpperCAmelCase     							=   {}
      if accepts_eta:
           _UpperCAmelCase     							=   eta

      for t in self.progress_bar(snake_case_    ):
           # concat latents and low resolution image in the channel dimension.
           _UpperCAmelCase     							=   torch.cat([latents, image]	,			dim=1    )
           _UpperCAmelCase     							=   self.scheduler.scale_model_input(snake_case_	,			snake_case_    )
           # predict the noise residual
           _UpperCAmelCase     							=   self.unet(snake_case_	,			snake_case_    ).sample
           # compute the previous noisy sample x_t -> x_t-1
           _UpperCAmelCase     							=   self.scheduler.step(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    ).prev_sample

      # decode the image latents with the VQVAE
      _UpperCAmelCase     							=   self.vqvae.decode(snake_case_    ).sample
      _UpperCAmelCase     							=   torch.clamp(snake_case_	,			-1.0	,			1.0    )
      _UpperCAmelCase     							=   image / 2 + 0.5
      _UpperCAmelCase     							=   image.cpu().permute(0	,			2	,			3	,			1    ).numpy()

      if output_type == "pil":
           _UpperCAmelCase     							=   self.numpy_to_pil(snake_case_    )

      if not return_dict:
           return (image,)

      return ImagePipelineOutput(images=snake_case_    )




