





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   lowerCAmelCase_				( __a	)  ->	List[str]:







  """simple docstring"""





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






class      _SCREAMING_SNAKE_CASE		(			__UpperCAmelCase					):







 '''simple docstring'''

 def __init__(self       :     Any ,				UpperCAmelCase_       :     VQModel ,				UpperCAmelCase_       :     UNetaDModel ,				UpperCAmelCase_       :     Union[
         DDIMScheduler,
         PNDMScheduler,
         LMSDiscreteScheduler,
         EulerDiscreteScheduler,
         EulerAncestralDiscreteScheduler,
         DPMSolverMultistepScheduler,
     ] ,				) ->Dict:



   '''simple docstring'''



   super().__init__()
   self.register_modules(vqvae=__SCREAMING_SNAKE_CASE ,				unet=__SCREAMING_SNAKE_CASE ,				scheduler=__SCREAMING_SNAKE_CASE)







 @torch.no_grad()
 def __call__(self       :     List[Any] ,				UpperCAmelCase_       :     Union[torch.Tensor, PIL.Image.Image] = None ,				UpperCAmelCase_       :     Optional[int] = 1 ,				UpperCAmelCase_       :     Optional[int] = 100 ,				UpperCAmelCase_       :     Optional[float] = 0.0 ,				UpperCAmelCase_       :     Optional[Union[torch.Generator, List[torch.Generator]]] = None ,				UpperCAmelCase_       :     Optional[str] = "pil" ,				UpperCAmelCase_       :     bool = True ,				) ->Tuple:



   '''simple docstring'''



   if isinstance(__SCREAMING_SNAKE_CASE ,				PIL.Image.Image):
     lowerCamelCase__:   Optional[int]    						=1
   elif isinstance(__SCREAMING_SNAKE_CASE ,				torch.Tensor):
     lowerCamelCase__:   Any    						=image.shape[0]
   else:
     raise ValueError(F"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__SCREAMING_SNAKE_CASE)}""")

   if isinstance(__SCREAMING_SNAKE_CASE ,				PIL.Image.Image):
     lowerCamelCase__:   str    						=preprocess(__SCREAMING_SNAKE_CASE)

   lowerCamelCase__						,      lowerCamelCase__:   Any    						=image.shape[-2:]

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

   lowerCamelCase__:   List[str]    						=randn_tensor(__SCREAMING_SNAKE_CASE ,				generator=__SCREAMING_SNAKE_CASE ,				device=self.device ,				dtype=__SCREAMING_SNAKE_CASE)

   lowerCamelCase__:   List[str]    						=image.to(device=self.device ,				dtype=__SCREAMING_SNAKE_CASE)

   # set timesteps and move to the correct device
   self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE ,				device=self.device)
   lowerCamelCase__:   int    						=self.scheduler.timesteps

   # scale the initial noise by the standard deviation required by the scheduler
   lowerCamelCase__:   Dict    						=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]
   lowerCamelCase__:   str    						="eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
   lowerCamelCase__:   int    						={}
   if accepts_eta:
     lowerCamelCase__:   int    						=eta

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

   # decode the image latents with the VQVAE
   lowerCamelCase__:   Optional[Any]    						=self.vqvae.decode(__SCREAMING_SNAKE_CASE).sample
   lowerCamelCase__:   List[Any]    						=torch.clamp(__SCREAMING_SNAKE_CASE ,				-1.0 ,				1.0)
   lowerCamelCase__:   List[str]    						=image / 2 + 0.5
   lowerCamelCase__:   Optional[int]    						=image.cpu().permute(0 ,				2 ,				3 ,				1).numpy()

   if output_type == "pil":
     lowerCamelCase__:   List[str]    						=self.numpy_to_pil(__SCREAMING_SNAKE_CASE)

   if not return_dict:
     return (image,)

   return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE)








import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


__A             =       datasets.utils.logging.get_logger(__name__)



@dataclass
class      _SCREAMING_SNAKE_CASE		(			datasets.BuilderConfig					):







 '''simple docstring'''
 lowercase_	=    1_0000
 lowercase_	=    None
 lowercase_	=    None



class      _SCREAMING_SNAKE_CASE		(			datasets.ArrowBasedBuilder					):







 '''simple docstring'''
 lowercase_	=    ParquetConfig

 def 							SCREAMING_SNAKE_CASE_      (self       :     List[Any]) ->Any:



   '''simple docstring'''



   return datasets.DatasetInfo(features=self.config.features)

 def 							SCREAMING_SNAKE_CASE_      (self       :     Any ,				UpperCAmelCase_       :     Any) ->Any:



   '''simple docstring'''



   if not self.config.data_files:
     raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""")
   lowerCamelCase__:   Optional[Any]    						=dl_manager.download_and_extract(self.config.data_files)
   if isinstance(UpperCAmelCase_ ,				(str, list, tuple)):
     lowerCamelCase__:   Any    						=data_files
     if isinstance(UpperCAmelCase_ ,				UpperCAmelCase_):
       lowerCamelCase__:   Union[str, Any]    						=[files]
     # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
     lowerCamelCase__:   Optional[int]    						=[dl_manager.iter_files(UpperCAmelCase_) for file in files]
     return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,				gen_kwargs={"files": files})]
   lowerCamelCase__:   int    						=[]
   for split_name, files in data_files.items():
     if isinstance(UpperCAmelCase_ ,				UpperCAmelCase_):
       lowerCamelCase__:   List[Any]    						=[files]
     # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
     lowerCamelCase__:   str    						=[dl_manager.iter_files(UpperCAmelCase_) for file in files]
     # Infer features is they are stoed in the arrow schema
     if self.info.features is None:
       for file in itertools.chain.from_iterable(UpperCAmelCase_):
         with open(UpperCAmelCase_ ,				"rb") as f:
           lowerCamelCase__:   Union[str, Any]    						=datasets.Features.from_arrow_schema(pq.read_schema(UpperCAmelCase_))
         break
     splits.append(datasets.SplitGenerator(name=UpperCAmelCase_ ,				gen_kwargs={"files": files}))
   return splits

 def 							SCREAMING_SNAKE_CASE_      (self       :     Any ,				UpperCAmelCase_       :     pa.Table) ->pa.Table:



   '''simple docstring'''



   if self.info.features is not None:
     # more expensive cast to support nested features with keys in a different order
     # allows str <-> int/float or str to Audio for example
     lowerCamelCase__:   str    						=table_cast(UpperCAmelCase_ ,				self.info.features.arrow_schema)
   return pa_table







 def 							SCREAMING_SNAKE_CASE_      (self       :     str ,				UpperCAmelCase_       :     Optional[Any]) ->int:



   '''simple docstring'''



   lowerCamelCase__:   str    						=self.info.features.arrow_schema if self.info.features is not None else None
   if self.info.features is not None and self.config.columns is not None:
     if sorted(field.name for field in schema) != sorted(self.config.columns):
       raise ValueError(
           F"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'""")
   for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase_)):
     with open(UpperCAmelCase_ ,				"rb") as f:
       lowerCamelCase__:   Optional[Any]    						=pq.ParquetFile(UpperCAmelCase_)
       try:
         for batch_idx, record_batch in enumerate(
             parquet_file.iter_batches(batch_size=self.config.batch_size ,				columns=self.config.columns)):
           lowerCamelCase__:   Optional[Any]    						=pa.Table.from_batches([record_batch])
           # Uncomment for debugging (will print the Arrow table size and elements)
           # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
           # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
           yield F"""{file_idx}_{batch_idx}""", self._cast_table(UpperCAmelCase_)
       except ValueError as e:
         logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase_)}: {e}""")
         raise

