
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


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





@dataclass
class     __UpperCamelCase (							datasets.BuilderConfig       ):






     """simple docstring"""







     lowerCAmelCase_							   =			1_00_00
     lowerCAmelCase_							   =			None
     lowerCAmelCase_							   =			None






class     __UpperCamelCase (							datasets.ArrowBasedBuilder       ):






     """simple docstring"""







     lowerCAmelCase_							   =			ParquetConfig



     def  UpperCAmelCase__							(				self							:							Any ):







       """simple docstring"""




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



     def  UpperCAmelCase__							(				self							:							Any     ,						_A							:							Optional[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}''' )
       __SCREAMING_SNAKE_CASE      :	List[str]					=		dl_manager.download_and_extract(self.config.data_files )
       if isinstance(_A     ,						(str, list, tuple) ):
         __SCREAMING_SNAKE_CASE      :	Tuple					=		data_files
         if isinstance(_A     ,						_A ):
           __SCREAMING_SNAKE_CASE      :	Optional[int]					=		[files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		[dl_manager.iter_files(_A ) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,						gen_kwargs={'''files''': files} )]
       __SCREAMING_SNAKE_CASE      :	int					=		[]
       for split_name, files in data_files.items():
         if isinstance(_A     ,						_A ):
           __SCREAMING_SNAKE_CASE      :	Any					=		[files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		[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:
               __SCREAMING_SNAKE_CASE      :	Dict					=		datasets.Features.from_arrow_schema(pq.read_schema(_A ) )
             break
         splits.append(datasets.SplitGenerator(name=_A     ,						gen_kwargs={'''files''': files} ) )
       return splits



     def  UpperCAmelCase__							(				self							:							str     ,						_A							:							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
         __SCREAMING_SNAKE_CASE      :	str					=		table_cast(_A     ,						self.info.features.arrow_schema )
       return pa_table




     def  UpperCAmelCase__							(				self							:							Tuple     ,						_A							:							str ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	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:
           __SCREAMING_SNAKE_CASE      :	str					=		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 ) ):
               __SCREAMING_SNAKE_CASE      :	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(_A )
           except ValueError as e:
             logger.error(F'''Failed to read file \'{file}\' with error {type(_A )}: {e}''' )
             raise


"""simple docstring"""





from ..utils import is_flax_available, is_torch_available


if is_torch_available():
		from .autoencoder_kl import AutoencoderKL
		from .controlnet import ControlNetModel
		from .dual_transformer_ad import DualTransformeraDModel
		from .modeling_utils import ModelMixin
		from .prior_transformer import PriorTransformer
		from .ta_film_transformer import TaFilmDecoder
		from .transformer_ad import TransformeraDModel
		from .unet_ad import UNetaDModel
		from .unet_ad import UNetaDModel
		from .unet_ad_condition import UNetaDConditionModel
		from .unet_ad_condition import UNetaDConditionModel
		from .vq_model import VQModel

if is_flax_available():
		from .controlnet_flax import FlaxControlNetModel
		from .unet_ad_condition_flax import FlaxUNetaDConditionModel
		from .vae_flax import FlaxAutoencoderKL




