"""simple docstring"""



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 					_lowerCamelCase(					a							):
       __a     				=  np.inf

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

       _visit(A__ ,  A__							)

       return None if batch_size is np.inf else batch_size






class 				snake_case__     (   __lowerCamelCase   ):


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = False  ,						lowerCamelCase = False  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        super().__init__(
            UpperCamelCase_  ,						split=UpperCamelCase_  ,						features=UpperCamelCase_  ,						cache_dir=UpperCamelCase_  ,						keep_in_memory=UpperCamelCase_  ,						streaming=UpperCamelCase_  ,						num_proc=UpperCamelCase_  ,						**UpperCamelCase_  ,						)
        __a     				=  path_or_paths if isinstance(UpperCamelCase_  ,						UpperCamelCase_		) else {self.split: path_or_paths}
        __a     				=  _PACKAGED_DATASETS_MODULES["parquet"][1]
        __a     				=  Parquet(
            cache_dir=UpperCamelCase_  ,						data_files=UpperCamelCase_  ,						features=UpperCamelCase_  ,						hash=UpperCamelCase_  ,						**UpperCamelCase_  ,						)






 def 		a__ (				self		):
        # Build iterable dataset
        if self.streaming:
               __a     				=  self.builder.as_streaming_dataset(split=self.split		)
        # Build regular (map-style) dataset
        else:
               __a     				=  None
               __a     				=  None
               __a     				=  None
               __a     				=  None

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






class 				snake_case__     :


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        __a     				=  dataset
        __a     				=  path_or_buf
        __a     				=  batch_size or get_writer_batch_size(dataset.features		)
        __a     				=  parquet_writer_kwargs


 def 		a__ (				self		):
        __a     				=  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:
                      __a     				=  self._write(file_obj=UpperCamelCase_  ,						batch_size=UpperCamelCase_  ,						**self.parquet_writer_kwargs		)
        else:
               __a     				=  self._write(file_obj=self.path_or_buf  ,						batch_size=UpperCamelCase_  ,						**self.parquet_writer_kwargs		)
        return written






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						**lowerCamelCase		):
        __a     				=  0
        __a     				=  parquet_writer_kwargs.pop("path_or_buf"  ,						UpperCamelCase_		)
        __a     				=  self.dataset.features.arrow_schema

        __a     				=  pq.ParquetWriter(UpperCamelCase_  ,						schema=UpperCamelCase_  ,						**UpperCamelCase_		)

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

"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE__:List[str]			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:int			=	{
    """microsoft/focalnet-tiny""": """https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json""",
}






class 				snake_case__     (   snake_case_,		snake_case_   ):
 _snake_case   :			List[Any]           =		"""focalnet"""


 def __init__(				self  ,						lowerCamelCase=224  ,						lowerCamelCase=4  ,						lowerCamelCase=3  ,						lowerCamelCase=96  ,						lowerCamelCase=False  ,						lowerCamelCase=[192, 384, 768, 768]  ,						lowerCamelCase=[2, 2, 6, 2]  ,						lowerCamelCase=[2, 2, 2, 2]  ,						lowerCamelCase=[3, 3, 3, 3]  ,						lowerCamelCase="gelu"  ,						lowerCamelCase=4.0  ,						lowerCamelCase=0.0  ,						lowerCamelCase=0.1  ,						lowerCamelCase=False  ,						lowerCamelCase=1E-4  ,						lowerCamelCase=False  ,						lowerCamelCase=False  ,						lowerCamelCase=False  ,						lowerCamelCase=0.02  ,						lowerCamelCase=1E-5  ,						lowerCamelCase=32  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						**lowerCamelCase  ,						):
        super().__init__(**lowerCamelCase		)

        __a     				=  image_size
        __a     				=  patch_size
        __a     				=  num_channels
        __a     				=  embed_dim
        __a     				=  use_conv_embed
        __a     				=  hidden_sizes
        __a     				=  depths
        __a     				=  focal_levels
        __a     				=  focal_windows
        __a     				=  hidden_act
        __a     				=  mlp_ratio
        __a     				=  hidden_dropout_prob
        __a     				=  drop_path_rate
        __a     				=  use_layerscale
        __a     				=  layerscale_value
        __a     				=  use_post_layernorm
        __a     				=  use_post_layernorm_in_modulation
        __a     				=  normalize_modulator
        __a     				=  initializer_range
        __a     				=  layer_norm_eps
        __a     				=  encoder_stride
        __a     				=  ["stem"] + [F"stage{idx}" for idx in range(1  ,						len(self.depths		) + 1		)]
        __a				, __a     				=  get_aligned_output_features_output_indices(
            out_features=lowerCamelCase  ,						out_indices=lowerCamelCase  ,						stage_names=self.stage_names		)
