
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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   Any					=				np.inf

      def set_batch_size(UpperCamelCase__		) -> None:
            nonlocal batch_size
            if isinstance(UpperCamelCase__ ,		UpperCamelCase__		):
                  UpperCamelCase__    :   List[str]					=				min(UpperCamelCase__ ,		config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS		)
            elif isinstance(UpperCamelCase__ ,		UpperCamelCase__		):
                  UpperCamelCase__    :   Tuple					=				min(UpperCamelCase__ ,		config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS		)
            elif isinstance(UpperCamelCase__ ,		UpperCamelCase__		) and feature.dtype == "binary":
                  UpperCamelCase__    :   Any					=				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 (      UpperCamelCase_ ):





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = False						,						__SCREAMING_SNAKE_CASE = False						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   Tuple:




           """simple docstring"""







           super().__init__(
               __SCREAMING_SNAKE_CASE						,						split=__SCREAMING_SNAKE_CASE						,						features=__SCREAMING_SNAKE_CASE						,						cache_dir=__SCREAMING_SNAKE_CASE						,						keep_in_memory=__SCREAMING_SNAKE_CASE						,						streaming=__SCREAMING_SNAKE_CASE						,						num_proc=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE						,						)
           UpperCamelCase__    :   List[Any]					=				path_or_paths if isinstance(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ) else {self.split: path_or_paths}
           UpperCamelCase__    :   Optional[Any]					=				_PACKAGED_DATASETS_MODULES['''parquet'''][1]
           UpperCamelCase__    :   Optional[Any]					=				Parquet(
               cache_dir=__SCREAMING_SNAKE_CASE						,						data_files=__SCREAMING_SNAKE_CASE						,						features=__SCREAMING_SNAKE_CASE						,						hash=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE						,						)






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




           """simple docstring"""







           if self.streaming:
                 UpperCamelCase__    :   int					=				self.builder.as_streaming_dataset(split=self.split  )
           # Build regular (map-style) dataset
           else:
                 UpperCamelCase__    :   Optional[Any]					=				None
                 UpperCamelCase__    :   List[Any]					=				None
                 UpperCamelCase__    :   Tuple					=				None
                 UpperCamelCase__    :   List[str]					=				None

                 self.builder.download_and_prepare(
                     download_config=__SCREAMING_SNAKE_CASE						,						download_mode=__SCREAMING_SNAKE_CASE						,						verification_mode=__SCREAMING_SNAKE_CASE						,						base_path=__SCREAMING_SNAKE_CASE						,						num_proc=self.num_proc						,						)
                 UpperCamelCase__    :   Dict					=				self.builder.as_dataset(
                     split=self.split						,						verification_mode=__SCREAMING_SNAKE_CASE						,						in_memory=self.keep_in_memory  )
           return dataset




class 				_lowerCamelCase :





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   Optional[int]:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				dataset
           UpperCamelCase__    :   int					=				path_or_buf
           UpperCamelCase__    :   str					=				batch_size or get_writer_batch_size(dataset.features  )
           UpperCamelCase__    :   Tuple					=				parquet_writer_kwargs






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				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:
                       UpperCamelCase__    :   int					=				self._write(file_obj=__SCREAMING_SNAKE_CASE						,						batch_size=__SCREAMING_SNAKE_CASE						,						**self.parquet_writer_kwargs  )
           else:
                 UpperCamelCase__    :   str					=				self._write(file_obj=self.path_or_buf						,						batch_size=__SCREAMING_SNAKE_CASE						,						**self.parquet_writer_kwargs  )
           return written






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   int					=				0
           UpperCamelCase__    :   Tuple					=				parquet_writer_kwargs.pop('''path_or_buf'''						,						__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[str]					=				self.dataset.features.arrow_schema

           UpperCamelCase__    :   List[str]					=				pq.ParquetWriter(__SCREAMING_SNAKE_CASE						,						schema=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )

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




lowerCamelCase      ={
    "Pillow": "Pillow",
    "accelerate": "accelerate>=0.11.0",
    "compel": "compel==0.1.8",
    "black": "black~=23.1",
    "datasets": "datasets",
    "filelock": "filelock",
    "flax": "flax>=0.4.1",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.13.2",
    "requests-mock": "requests-mock==1.10.0",
    "importlib_metadata": "importlib_metadata",
    "invisible-watermark": "invisible-watermark",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2",
    "jaxlib": "jaxlib>=0.1.65",
    "Jinja2": "Jinja2",
    "k-diffusion": "k-diffusion>=0.0.12",
    "torchsde": "torchsde",
    "note_seq": "note_seq",
    "librosa": "librosa",
    "numpy": "numpy",
    "omegaconf": "omegaconf",
    "parameterized": "parameterized",
    "protobuf": "protobuf>=3.20.3,<4",
    "pytest": "pytest",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "ruff": "ruff>=0.0.241",
    "safetensors": "safetensors",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "scipy": "scipy",
    "onnx": "onnx",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "tensorboard": "tensorboard",
    "torch": "torch>=1.4",
    "torchvision": "torchvision",
    "transformers": "transformers>=4.25.1",
    "urllib3": "urllib3<=2.0.0",
}


