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      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  np.inf

      def set_batch_size(__UpperCamelCase				) -> None:
            nonlocal batch_size
            if isinstance(__UpperCamelCase     ,      __UpperCamelCase				):
                  SCREAMING_SNAKE_CASE_          =  min(__UpperCamelCase     ,      config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS				)
            elif isinstance(__UpperCamelCase     ,      __UpperCamelCase				):
                  SCREAMING_SNAKE_CASE_          =  min(__UpperCamelCase     ,      config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS				)
            elif isinstance(__UpperCamelCase     ,      __UpperCamelCase				) and feature.dtype == "binary":
                  SCREAMING_SNAKE_CASE_          =  min(__UpperCamelCase     ,      config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS				)

      _visit(__UpperCamelCase     ,      __UpperCamelCase				)

      return None if batch_size is np.inf else batch_size


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







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







  def    __A     (							self :       List[str]   )  ->	Optional[int]:
        # Build iterable dataset
        if self.streaming:
              SCREAMING_SNAKE_CASE_          =  self.builder.as_streaming_dataset(split=self.split   )
        # Build regular (map-style) dataset
        else:
              SCREAMING_SNAKE_CASE_          =  None
              SCREAMING_SNAKE_CASE_          =  None
              SCREAMING_SNAKE_CASE_          =  None
              SCREAMING_SNAKE_CASE_          =  None

              self.builder.download_and_prepare(
                  download_config=__magic_name__  ,     download_mode=__magic_name__  ,     verification_mode=__magic_name__  ,     base_path=__magic_name__  ,     num_proc=self.num_proc  ,     )
              SCREAMING_SNAKE_CASE_          =  self.builder.as_dataset(
                  split=self.split  ,     verification_mode=__magic_name__  ,     in_memory=self.keep_in_memory   )
        return dataset



class   lowerCamelCase					:
  """simple docstring"""







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







  def    __A     (							self :       List[Any]   )  ->	int:
        SCREAMING_SNAKE_CASE_          =  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:
                    SCREAMING_SNAKE_CASE_          =  self._write(file_obj=__magic_name__  ,     batch_size=__magic_name__  ,     **self.parquet_writer_kwargs   )
        else:
              SCREAMING_SNAKE_CASE_          =  self._write(file_obj=self.path_or_buf  ,     batch_size=__magic_name__  ,     **self.parquet_writer_kwargs   )
        return written







  def    __A     (							self :       Any  ,     __magic_name__ :       BinaryIO  ,     __magic_name__ :       int  ,     **__magic_name__ :       List[str]   )  ->	int:
        SCREAMING_SNAKE_CASE_          =  0
        SCREAMING_SNAKE_CASE_          =  parquet_writer_kwargs.pop("path_or_buf"  ,     __magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.dataset.features.arrow_schema

        SCREAMING_SNAKE_CASE_          =  pq.ParquetWriter(__magic_name__  ,     schema=__magic_name__  ,     **__magic_name__   )

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



# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
      import jax
      import jaxlib

A     :  Optional[Any]					     =       get_logger()

A     :  Optional[dict]					     =       None


class   lowerCamelCase					(TensorFormatter[Mapping, '''jax.Array''', Mapping]					):
  """simple docstring"""







  def __init__(							self :       int  ,     __magic_name__ :       int=None  ,     __magic_name__ :       Union[str, Any]=None  ,     **__magic_name__ :       str   )  ->	Tuple:
        super().__init__(features=__magic_name__   )
        import jax
        from jaxlib.xla_client import Device

        if isinstance(__magic_name__  ,     __magic_name__   ):
              raise ValueError(
                  F'''Expected {device} to be a `str` not {type(__magic_name__   )}, as `jaxlib.xla_extension.Device` '''
                  "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
                  "the device with `str()` to get its string identifier that will be internally mapped "
                  "to the actual `jaxlib.xla_extension.Device`."   )
        SCREAMING_SNAKE_CASE_          =  device if isinstance(__magic_name__  ,     __magic_name__   ) else str(jax.devices()[0]   )
        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
        global DEVICE_MAPPING
        if DEVICE_MAPPING is None:
              SCREAMING_SNAKE_CASE_          =  self._map_devices_to_str()
        if self.device not in list(DEVICE_MAPPING.keys()   ):
              logger.warning(
                  F'''Device with string identifier {self.device} not listed among the available '''
                  F'''devices: {list(DEVICE_MAPPING.keys()   )}, so falling back to the default '''
                  F'''device: {str(jax.devices()[0]   )}.'''   )
              SCREAMING_SNAKE_CASE_          =  str(jax.devices()[0]   )
        SCREAMING_SNAKE_CASE_          =  jnp_array_kwargs







  @staticmethod
  def    __A     (							)  ->	Dict[str, "jaxlib.xla_extension.Device"]:
        import jax

        return {str(__magic_name__   ): device for device in jax.devices()}







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       Union[str, Any]   )  ->	List[str]:
        import jax
        import jax.numpy as jnp

        if isinstance(__magic_name__  ,     __magic_name__   ) and column:
              if all(
                  isinstance(__magic_name__  ,     jax.Array   ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column   ):
                    return jnp.stack(__magic_name__  ,     axis=0   )
        return column







  def    __A     (							self :       Tuple  ,     __magic_name__ :       int   )  ->	Optional[Any]:
        import jax
        import jax.numpy as jnp

        if isinstance(__magic_name__  ,     (str, bytes, type(__magic_name__   ))   ):
              return value
        elif isinstance(__magic_name__  ,     (np.character, np.ndarray)   ) and np.issubdtype(value.dtype  ,     np.character   ):
              return value.tolist()

        SCREAMING_SNAKE_CASE_          =  {}

        if isinstance(__magic_name__  ,     (np.number, np.ndarray)   ) and np.issubdtype(value.dtype  ,     np.integer   ):
              # the default int precision depends on the jax config
              # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
              if jax.config.jax_enable_xaa:
                    SCREAMING_SNAKE_CASE_          =  {"dtype": jnp.intaa}
              else:
                    SCREAMING_SNAKE_CASE_          =  {"dtype": jnp.intaa}
        elif isinstance(__magic_name__  ,     (np.number, np.ndarray)   ) and np.issubdtype(value.dtype  ,     np.floating   ):
              SCREAMING_SNAKE_CASE_          =  {"dtype": jnp.floataa}
        elif config.PIL_AVAILABLE and "PIL" in sys.modules:
              import PIL.Image

              if isinstance(__magic_name__  ,     PIL.Image.Image   ):
                    SCREAMING_SNAKE_CASE_          =  np.asarray(__magic_name__   )

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
        global DEVICE_MAPPING
        if DEVICE_MAPPING is None:
              SCREAMING_SNAKE_CASE_          =  self._map_devices_to_str()

        with jax.default_device(DEVICE_MAPPING[self.device]   ):
              # calling jnp.array on a np.ndarray does copy the data
              # see https://github.com/google/jax/issues/4486
              return jnp.array(__magic_name__  ,     **{**default_dtype, **self.jnp_array_kwargs}   )







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       Optional[Any]   )  ->	Union[str, Any]:
        import jax

        # support for torch, tf, jax etc.
        if config.TORCH_AVAILABLE and "torch" in sys.modules:
              import torch

              if isinstance(__magic_name__  ,     torch.Tensor   ):
                    return self._tensorize(data_struct.detach().cpu().numpy()[()]   )
        if hasattr(__magic_name__  ,     "__array__"   ) and not isinstance(__magic_name__  ,     jax.Array   ):
              SCREAMING_SNAKE_CASE_          =  data_struct.__array__()
        # support for nested types like struct of list of struct
        if isinstance(__magic_name__  ,     np.ndarray   ):
              if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
                    return self._consolidate([self.recursive_tensorize(__magic_name__   ) for substruct in data_struct]   )
        elif isinstance(__magic_name__  ,     (list, tuple)   ):
              return self._consolidate([self.recursive_tensorize(__magic_name__   ) for substruct in data_struct]   )
        return self._tensorize(__magic_name__   )







  def    __A     (							self :       int  ,     __magic_name__ :       dict   )  ->	Any:
        return map_nested(self._recursive_tensorize  ,     __magic_name__  ,     map_list=__magic_name__   )







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       pa.Table   )  ->	Mapping:
        SCREAMING_SNAKE_CASE_          =  self.numpy_arrow_extractor().extract_row(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.python_features_decoder.decode_row(__magic_name__   )
        return self.recursive_tensorize(__magic_name__   )







  def    __A     (							self :       Dict  ,     __magic_name__ :       pa.Table   )  ->	"jax.Array":
        SCREAMING_SNAKE_CASE_          =  self.numpy_arrow_extractor().extract_column(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.python_features_decoder.decode_column(__magic_name__  ,     pa_table.column_names[0]   )
        SCREAMING_SNAKE_CASE_          =  self.recursive_tensorize(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self._consolidate(__magic_name__   )
        return column







  def    __A     (							self :       Dict  ,     __magic_name__ :       pa.Table   )  ->	Mapping:
        SCREAMING_SNAKE_CASE_          =  self.numpy_arrow_extractor().extract_batch(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.python_features_decoder.decode_batch(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.recursive_tensorize(__magic_name__   )
        for column_name in batch:
              SCREAMING_SNAKE_CASE_          =  self._consolidate(batch[column_name]   )
        return batch


