import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

def 			lowercase_				(			_lowerCamelCase							:       Features):
    lowercase__ :   List[Any]    					=				np.inf

    def set_batch_size(_lowerCamelCase							:       FeatureType) -> None:
        nonlocal batch_size
        if isinstance(_lowerCamelCase		,	_lowerCamelCase):
            lowercase__ :   Any    					=				min(_lowerCamelCase		,	config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS)
        elif isinstance(_lowerCamelCase		,	_lowerCamelCase):
            lowercase__ :   Union[str, Any]    					=				min(_lowerCamelCase		,	config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS)
        elif isinstance(_lowerCamelCase		,	_lowerCamelCase) and feature.dtype == "binary":
            lowercase__ :   Dict    					=				min(_lowerCamelCase		,	config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS)

    _visit(_lowerCamelCase		,	_lowerCamelCase)

    return None if batch_size is np.inf else batch_size



class   snake_case_       (		__A       ):






   def __init__(      self				:     List[str]	,						lowercase_				:     NestedDataStructureLike[PathLike]	,						lowercase_				:     Optional[NamedSplit] = None	,						lowercase_				:     Optional[Features] = None	,						lowercase_				:     str = None	,						lowercase_				:     bool = False	,						lowercase_				:     bool = False	,						lowercase_				:     Optional[int] = None	,						**lowercase_				:     Union[str, Any]	,						)					->						Union[str, Any]:
       super().__init__(
           lowercase_	,						split=lowercase_	,						features=lowercase_	,						cache_dir=lowercase_	,						keep_in_memory=lowercase_	,						streaming=lowercase_	,						num_proc=lowercase_	,						**lowercase_	,						)
       lowercase__ :   List[str]    					=				path_or_paths if isinstance(lowercase_	,						lowercase_  ) else {self.split: path_or_paths}
       lowercase__ :   Optional[int]    					=				_PACKAGED_DATASETS_MODULES["parquet"][1]
       lowercase__ :   Union[str, Any]    					=				Parquet(
           cache_dir=lowercase_	,						data_files=lowercase_	,						features=lowercase_	,						hash=lowercase_	,						**lowercase_	,						)







   def       __UpperCamelCase     (      self				:     Tuple  )					->						Dict:
       # Build iterable dataset
       if self.streaming:
           lowercase__ :   List[Any]    					=				self.builder.as_streaming_dataset(split=self.split  )
       # Build regular (map-style) dataset
       else:
           lowercase__ :   Any    					=				None
           lowercase__ :   int    					=				None
           lowercase__ :   Dict    					=				None
           lowercase__ :   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	,						)
           lowercase__ :   Optional[Any]    					=				self.builder.as_dataset(
               split=self.split	,						verification_mode=lowercase_	,						in_memory=self.keep_in_memory  )
       return dataset



class   snake_case_       :






   def __init__(      self				:     str	,						lowercase_				:     Dataset	,						lowercase_				:     Union[PathLike, BinaryIO]	,						lowercase_				:     Optional[int] = None	,						**lowercase_				:     Optional[Any]	,						)					->						int:
       lowercase__ :   Dict    					=				dataset
       lowercase__ :   List[Any]    					=				path_or_buf
       lowercase__ :   Any    					=				batch_size or get_writer_batch_size(dataset.features  )
       lowercase__ :   Any    					=				parquet_writer_kwargs






   def       __UpperCamelCase     (      self				:     List[Any]  )					->						int:
       lowercase__ :   Tuple    					=				self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

       if isinstance(self.path_or_buf	,						(str, bytes, os.PathLike)  ):
           with open(self.path_or_buf	,						"wb+"  ) as buffer:
               lowercase__ :   str    					=				self._write(file_obj=lowercase_	,						batch_size=lowercase_	,						**self.parquet_writer_kwargs  )
       else:
           lowercase__ :   Tuple    					=				self._write(file_obj=self.path_or_buf	,						batch_size=lowercase_	,						**self.parquet_writer_kwargs  )
       return written







   def       __UpperCamelCase     (      self				:     Union[str, Any]	,						lowercase_				:     BinaryIO	,						lowercase_				:     int	,						**lowercase_				:     Optional[int]  )					->						int:
       lowercase__ :   List[Any]    					=				0
       lowercase__ :   List[Any]    					=				parquet_writer_kwargs.pop("path_or_buf"	,						lowercase_  )
       lowercase__ :   List[Any]    					=				self.dataset.features.arrow_schema

       lowercase__ :   Optional[Any]    					=				pq.ParquetWriter(lowercase_	,						schema=lowercase_	,						**lowercase_  )

       for offset in logging.tqdm(
           range(0	,						len(self.dataset  )	,						lowercase_  )	,						unit="ba"	,						disable=not logging.is_progress_bar_enabled()	,						desc="Creating parquet from Arrow format"	,						):
           lowercase__ :   Any    					=				query_table(
               table=self.dataset._data	,						key=slice(lowercase_	,						offset + batch_size  )	,						indices=self.dataset._indices if self.dataset._indices is not None else None	,						)
           writer.write_table(lowercase_  )
           written += batch.nbytes
       writer.close()
       return written










'''simple docstring'''
from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
    if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import (
        VersatileDiffusionDualGuidedPipeline,
        VersatileDiffusionImageVariationPipeline,
        VersatileDiffusionPipeline,
        VersatileDiffusionTextToImagePipeline,
    )
else:
    from .modeling_text_unet import UNetFlatConditionModel
    from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
    from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
    from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
    from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
