






"""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       snake_case  (A_				:Features				):







		'''simple docstring'''

		a					:      List[str]    =       np.inf

		def set_batch_size(A_				:FeatureType				) -> None:
				nonlocal batch_size
				if isinstance(A_     ,      A_				):
						a					:      Dict    =       min(A_     ,      config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS				)
				elif isinstance(A_     ,      A_				):
						a					:      Any    =       min(A_     ,      config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS				)
				elif isinstance(A_     ,      A_				) and feature.dtype == "binary":
						a					:      Tuple    =       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  (  UpperCAmelCase	):





				def __init__(							self     : Union[str, Any]    ,		A     : NestedDataStructureLike[PathLike]    ,		A     : Optional[NamedSplit] = None    ,		A     : Optional[Features] = None    ,		A     : str = None    ,		A     : bool = False    ,		A     : bool = False    ,		A     : Optional[int] = None    ,		**A     : int    ,		):


						'''simple docstring'''




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





				def     lowerCamelCase__				(							self     : List[str]      ):


						'''simple docstring'''




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

								self.builder.download_and_prepare(
								    download_config=A    ,		download_mode=A    ,		verification_mode=A    ,		base_path=A    ,		num_proc=self.num_proc    ,		)
								a					:      List[Any]    =       self.builder.as_dataset(
								    split=self.split    ,		verification_mode=A    ,		in_memory=self.keep_in_memory      )
						return dataset






class 						snake_case  :





				def __init__(							self     : List[str]    ,		A     : Dataset    ,		A     : Union[PathLike, BinaryIO]    ,		A     : Optional[int] = None    ,		**A     : Any    ,		):


						'''simple docstring'''




						a					:      Union[str, Any]    =       dataset
						a					:      Any    =       path_or_buf
						a					:      List[Any]    =       batch_size or get_writer_batch_size(dataset.features      )
						a					:      str    =       parquet_writer_kwargs





				def     lowerCamelCase__				(							self     : Tuple      ):


						'''simple docstring'''




						a					:      Dict    =       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					:      str    =       self._write(file_obj=A    ,		batch_size=A    ,		**self.parquet_writer_kwargs      )
						else:
								a					:      str    =       self._write(file_obj=self.path_or_buf    ,		batch_size=A    ,		**self.parquet_writer_kwargs      )
						return written





				def     lowerCamelCase__				(							self     : List[str]    ,		A     : BinaryIO    ,		A     : int    ,		**A     : int      ):


						'''simple docstring'''




						a					:      List[str]    =       0
						a					:      int    =       parquet_writer_kwargs.pop('path_or_buf'    ,		A      )
						a					:      Any    =       self.dataset.features.arrow_schema

						a					:      Dict    =       pq.ParquetWriter(A    ,		schema=A    ,		**A      )

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












"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCamelCase    :   Union[str, Any]   			=						logging.get_logger(__name__)

_UpperCamelCase    :   List[Any]   			=						{
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}






class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					'''beit'''





    def __init__(							self     : int    ,		A     : int=8_1_9_2    ,		A     : List[Any]=7_6_8    ,		A     : str=1_2    ,		A     : str=1_2    ,		A     : Dict=3_0_7_2    ,		A     : Optional[int]="gelu"    ,		A     : List[Any]=0.0    ,		A     : Union[str, Any]=0.0    ,		A     : Optional[Any]=0.02    ,		A     : Optional[int]=1E-12    ,		A     : Dict=2_2_4    ,		A     : str=1_6    ,		A     : Optional[Any]=3    ,		A     : List[Any]=False    ,		A     : Union[str, Any]=False    ,		A     : Optional[Any]=False    ,		A     : int=False    ,		A     : List[str]=0.1    ,		A     : Union[str, Any]=0.1    ,		A     : str=True    ,		A     : Tuple=[3, 5, 7, 1_1]    ,		A     : List[str]=[1, 2, 3, 6]    ,		A     : Optional[Any]=True    ,		A     : Union[str, Any]=0.4    ,		A     : Any=2_5_6    ,		A     : List[Any]=1    ,		A     : Optional[Any]=False    ,		A     : Any=2_5_5    ,		**A     : List[Any]    ,		):


      '''simple docstring'''




      super().__init__(**A      )

      a					:      Optional[int]    =       vocab_size
      a					:      Dict    =       hidden_size
      a					:      Optional[int]    =       num_hidden_layers
      a					:      Tuple    =       num_attention_heads
      a					:      Optional[int]    =       intermediate_size
      a					:      Optional[Any]    =       hidden_act
      a					:      Optional[int]    =       hidden_dropout_prob
      a					:      Optional[int]    =       attention_probs_dropout_prob
      a					:      Optional[Any]    =       initializer_range
      a					:      Union[str, Any]    =       layer_norm_eps

      a					:      Union[str, Any]    =       image_size
      a					:      str    =       patch_size
      a					:      Optional[Any]    =       num_channels
      a					:      List[str]    =       use_mask_token
      a					:      Optional[Any]    =       use_absolute_position_embeddings
      a					:      Any    =       use_relative_position_bias
      a					:      Any    =       use_shared_relative_position_bias
      a					:      Dict    =       layer_scale_init_value
      a					:      Optional[int]    =       drop_path_rate
      a					:      Dict    =       use_mean_pooling
      # decode head attributes (semantic segmentation)
      a					:      Optional[Any]    =       out_indices
      a					:      List[str]    =       pool_scales
      # auxiliary head attributes (semantic segmentation)
      a					:      Tuple    =       use_auxiliary_head
      a					:      Dict    =       auxiliary_loss_weight
      a					:      Any    =       auxiliary_channels
      a					:      Dict    =       auxiliary_num_convs
      a					:      List[str]    =       auxiliary_concat_input
      a					:      List[Any]    =       semantic_loss_ignore_index






class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					version.parse('''1.11'''	)





    @property
    def     lowerCamelCase__				(							self     : List[str]      ):


      '''simple docstring'''




      return OrderedDict(
          [
              ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
          ]      )





    @property
    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      return 1E-4




