





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

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class     lowercase__       (	SCREAMING_SNAKE_CASE_				):

   '''simple docstring'''


   def __init__(    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__ = None,		) ->      Tuple:






       """simple docstring"""
       super().__init__()
       self.register_modules(transformer=a__,		vae=a__,		scheduler=a__							)

       # create a imagenet -> id dictionary for easier use
       UpperCamelCase__							:				Optional[int]          =      {}
       if idalabel is not None:
           for key, value in idalabel.items():
               for label in value.split(''','''							):
                   UpperCamelCase__							:				Any          =      int(a__							)
           UpperCamelCase__							:				List[Any]          =      dict(sorted(self.labels.items()							)							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Tuple:






       """simple docstring"""

       if not isinstance(a__,		a__							):
           UpperCamelCase__							:				Union[str, Any]          =      list(a__							)

       for l in label:
           if l not in self.labels:
               raise ValueError(
                   f"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."							)

       return [self.labels[l] for l in label]




   @torch.no_grad()
   def __call__(    self,		__magic_name__,		__magic_name__ = 4.0,		__magic_name__ = None,		__magic_name__ = 50,		__magic_name__ = "pil",		__magic_name__ = True,		) ->      List[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      len(a__							)
       UpperCamelCase__							:				Union[str, Any]          =      self.transformer.config.sample_size
       UpperCamelCase__							:				str          =      self.transformer.config.in_channels

       UpperCamelCase__							:				Any          =      randn_tensor(
           shape=(batch_size, latent_channels, latent_size, latent_size),		generator=a__,		device=self.device,		dtype=self.transformer.dtype,		)
       UpperCamelCase__							:				Optional[int]          =      torch.cat([latents] * 2							) if guidance_scale > 1 else latents

       UpperCamelCase__							:				Any          =      torch.tensor(a__,		device=self.device							).reshape(-1							)
       UpperCamelCase__							:				List[str]          =      torch.tensor([1000] * batch_size,		device=self.device							)
       UpperCamelCase__							:				int          =      torch.cat([class_labels, class_null],		0							) if guidance_scale > 1 else class_labels

       # set step values
       self.scheduler.set_timesteps(a__							)

       for t in self.progress_bar(self.scheduler.timesteps							):
           if guidance_scale > 1:
               UpperCamelCase__							:				Dict          =      latent_model_input[: len(a__							) // 2]
               UpperCamelCase__							:				Any          =      torch.cat([half, half],		dim=0							)
           UpperCamelCase__							:				Dict          =      self.scheduler.scale_model_input(a__,		a__							)

           UpperCamelCase__							:				List[str]          =      t
           if not torch.is_tensor(a__							):
               # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
               # This would be a good case for the `match` statement (Python 3.10+)
               UpperCamelCase__							:				List[Any]          =      latent_model_input.device.type == """mps"""
               if isinstance(a__,		a__							):
                   UpperCamelCase__							:				List[Any]          =      torch.floataa if is_mps else torch.floataa
               else:
                   UpperCamelCase__							:				Tuple          =      torch.intaa if is_mps else torch.intaa
               UpperCamelCase__							:				Union[str, Any]          =      torch.tensor([timesteps],		dtype=a__,		device=latent_model_input.device							)
           elif len(timesteps.shape							) == 0:
               UpperCamelCase__							:				Any          =      timesteps[None].to(latent_model_input.device							)
           # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
           UpperCamelCase__							:				Union[str, Any]          =      timesteps.expand(latent_model_input.shape[0]							)
           # predict noise model_output
           UpperCamelCase__							:				str          =      self.transformer(
               a__,		timestep=a__,		class_labels=a__							).sample

           # perform guidance
           if guidance_scale > 1:
               UpperCamelCase__							:				Any          =      noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
               UpperCamelCase__							:				List[str]          =      torch.split(a__,		len(a__							) // 2,		dim=0							)

               UpperCamelCase__							:				Union[str, Any]          =      uncond_eps + guidance_scale * (cond_eps - uncond_eps)
               UpperCamelCase__							:				Any          =      torch.cat([half_eps, half_eps],		dim=0							)

               UpperCamelCase__							:				List[Any]          =      torch.cat([eps, rest],		dim=1							)

           # learned sigma
           if self.transformer.config.out_channels // 2 == latent_channels:
               UpperCamelCase__							:				str          =      torch.split(a__,		a__,		dim=1							)
           else:
               UpperCamelCase__							:				Any          =      noise_pred

           # compute previous image: x_t -> x_t-1
           UpperCamelCase__							:				str          =      self.scheduler.step(a__,		a__,		a__							).prev_sample

       if guidance_scale > 1:
           UpperCamelCase__							:				Any          =      latent_model_input.chunk(2,		dim=0							)
       else:
           UpperCamelCase__							:				List[Any]          =      latent_model_input

       UpperCamelCase__							:				Union[str, Any]          =      1 / self.vae.config.scaling_factor * latents
       UpperCamelCase__							:				Optional[Any]          =      self.vae.decode(a__							).sample

       UpperCamelCase__							:				Optional[int]          =      (samples / 2 + 0.5).clamp(0,		1							)

       # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
       UpperCamelCase__							:				List[str]          =      samples.cpu().permute(0,		2,		3,		1							).float().numpy()

       if output_type == "pil":
           UpperCamelCase__							:				Union[str, Any]          =      self.numpy_to_pil(a__							)

       if not return_dict:
           return (samples,)

       return ImagePipelineOutput(images=a__							)


"""simple docstring"""




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      :		Dict           =   datasets.utils.logging.get_logger(__name__)
@dataclass
class 						__A							( datasets.BuilderConfig		):
     _UpperCamelCase							:			int							=		10_000
     _UpperCamelCase							:			Optional[List[str]]							=		None
     _UpperCamelCase							:			Optional[datasets.Features]							=		None







class 						__A							( datasets.ArrowBasedBuilder		):
     _UpperCamelCase							:			List[str]							=		ParquetConfig






     def     __A  (		self  ):
       return datasets.DatasetInfo(features=self.config.features  )






     def     __A  (		self				,     a__  ):
       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(a__				,     (str, list, tuple)  ):
         _lowerCAmelCase						:       Any  			=     data_files
         if isinstance(a__				,     a__  ):
           _lowerCAmelCase						:       Tuple  			=     [files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _lowerCAmelCase						:       Any  			=     [dl_manager.iter_files(a__  ) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,     gen_kwargs={"""files""": files}  )]
       _lowerCAmelCase						:       Optional[Any]  			=     []
       for split_name, files in data_files.items():
         if isinstance(a__				,     a__  ):
           _lowerCAmelCase						:       Dict  			=     [files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _lowerCAmelCase						:       Tuple  			=     [dl_manager.iter_files(a__  ) 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(a__  ):
             with open(a__				,     """rb"""  ) as f:
               _lowerCAmelCase						:       Optional[Any]  			=     datasets.Features.from_arrow_schema(pq.read_schema(a__  )  )
             break
         splits.append(datasets.SplitGenerator(name=a__				,     gen_kwargs={"""files""": files}  )  )
       return splits






     def     __A  (		self				,     a__  ):
       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						:       Optional[int]  			=     table_cast(a__				,     self.info.features.arrow_schema  )
       return pa_table






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     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(a__  )  ):
         with open(a__				,     """rb"""  ) as f:
           _lowerCAmelCase						:       Tuple  			=     pq.ParquetFile(a__  )
           try:
             for batch_idx, record_batch in enumerate(
                 parquet_file.iter_batches(batch_size=self.config.batch_size				,     columns=self.config.columns  )  ):
               _lowerCAmelCase						:       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(a__  )
           except ValueError as e:
             logger.error(F"Failed to read file '{file}' with error {type(a__  )}: {e}"  )
             raise




