


import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


_snake_case					:		Tuple      =    datasets.utils.logging.get_logger(__name__)

@dataclass
class     a		(datasets.BuilderConfig				):




							"""simple docstring"""


							__UpperCAmelCase   :							int					  =  1_0000
							__UpperCAmelCase   :							Optional[List[str]]					  =  None
							__UpperCAmelCase   :							Optional[datasets.Features]					  =  None

class     a		(datasets.ArrowBasedBuilder				):




							"""simple docstring"""


							__UpperCAmelCase   :							Tuple					  =  ParquetConfig

							def    __snake_case (							self	:  Union[str, Any]    )     ->   Optional[Any]:
														return datasets.DatasetInfo(features=self.config.features    )

							def    __snake_case (							self	:  int   ,     lowerCamelCase	:  Tuple    )     ->   Dict:
														if not self.config.data_files:
																					raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}'    )
														__snake_case		:      Any										=       dl_manager.download_and_extract(self.config.data_files    )
														if isinstance(lowerCamelCase   ,     (str, list, tuple)    ):
																					__snake_case		:      Dict										=       data_files
																					if isinstance(lowerCamelCase   ,     lowerCamelCase    ):
																												__snake_case		:      Union[str, Any]										=       [files]
																					# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																					__snake_case		:      str										=       [dl_manager.iter_files(lowerCamelCase    ) for file in files]
																					return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,     gen_kwargs={"files": files}    )]
														__snake_case		:      Union[str, Any]										=       []
														for split_name, files in data_files.items():
																					if isinstance(lowerCamelCase   ,     lowerCamelCase    ):
																												__snake_case		:      List[str]										=       [files]
																					# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																					__snake_case		:      Any										=       [dl_manager.iter_files(lowerCamelCase    ) for file in files]
																					# Infer features is they are stoed in the arrow schema
																					if self.info.features is None:
																												for file in itertools.chain.from_iterable(lowerCamelCase    ):
																																			with open(lowerCamelCase   ,     "rb"    ) as f:
																																										__snake_case		:      Dict										=       datasets.Features.from_arrow_schema(pq.read_schema(lowerCamelCase    )    )
																																			break
																					splits.append(datasets.SplitGenerator(name=lowerCamelCase   ,     gen_kwargs={"files": files}    )    )
														return splits

							def    __snake_case (							self	:  List[Any]   ,     lowerCamelCase	:  pa.Table    )     ->   pa.Table:
														if self.info.features is not None:
																					# more expensive cast to support nested features with keys in a different order
																					# allows str <-> int/float or str to Audio for example
																					__snake_case		:      Optional[int]										=       table_cast(lowerCamelCase   ,     self.info.features.arrow_schema    )
														return pa_table






							def    __snake_case (							self	:  Optional[int]   ,     lowerCamelCase	:  Dict    )     ->   List[str]:
														__snake_case		:      List[str]										=       self.info.features.arrow_schema if self.info.features is not None else None
														if self.info.features is not None and self.config.columns is not None:
																					if sorted(field.name for field in schema    ) != sorted(self.config.columns    ):
																												raise ValueError(
																												    F'Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\''    )
														for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCamelCase    )    ):
																					with open(lowerCamelCase   ,     "rb"    ) as f:
																												__snake_case		:      int										=       pq.ParquetFile(lowerCamelCase    )
																												try:
																																			for batch_idx, record_batch in enumerate(
																																			    parquet_file.iter_batches(batch_size=self.config.batch_size   ,     columns=self.config.columns    )    ):
																																										__snake_case		:      int										=       pa.Table.from_batches([record_batch]    )
																																										# Uncomment for debugging (will print the Arrow table size and elements)
																																										# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																										# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																										yield F'{file_idx}_{batch_idx}', self._cast_table(lowerCamelCase    )
																												except ValueError as e:
																																			logger.error(F'Failed to read file \'{file}\' with error {type(lowerCamelCase    )}: {e}'    )
																																			raise





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


_snake_case					:		int      =    logging.get_logger(__name__)

_snake_case					:		int      =    {
    "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     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  "beit"

							def __init__(							self	:  Union[str, Any]   ,     lowerCamelCase	:  Any=8192   ,     lowerCamelCase	:  Dict=768   ,     lowerCamelCase	:  int=12   ,     lowerCamelCase	:  Optional[Any]=12   ,     lowerCamelCase	:  List[str]=3072   ,     lowerCamelCase	:  Tuple="gelu"   ,     lowerCamelCase	:  Union[str, Any]=0.0   ,     lowerCamelCase	:  int=0.0   ,     lowerCamelCase	:  Dict=0.02   ,     lowerCamelCase	:  List[str]=1E-12   ,     lowerCamelCase	:  Optional[Any]=224   ,     lowerCamelCase	:  Optional[int]=16   ,     lowerCamelCase	:  Any=3   ,     lowerCamelCase	:  Optional[int]=False   ,     lowerCamelCase	:  Any=False   ,     lowerCamelCase	:  Optional[Any]=False   ,     lowerCamelCase	:  int=False   ,     lowerCamelCase	:  Any=0.1   ,     lowerCamelCase	:  Tuple=0.1   ,     lowerCamelCase	:  Optional[int]=True   ,     lowerCamelCase	:  int=[3, 5, 7, 11]   ,     lowerCamelCase	:  str=[1, 2, 3, 6]   ,     lowerCamelCase	:  int=True   ,     lowerCamelCase	:  List[Any]=0.4   ,     lowerCamelCase	:  int=256   ,     lowerCamelCase	:  str=1   ,     lowerCamelCase	:  List[str]=False   ,     lowerCamelCase	:  List[str]=255   ,     **lowerCamelCase	:  Dict   ,     )     ->   int:
														super().__init__(**lowerCamelCase    )

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

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

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  version.parse("1.11"				)

							@property
							def    __snake_case (							self	:  Dict    )     ->   Mapping[str, Mapping[int, str]]:
														return OrderedDict(
														    [
														        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
														    ]    )






							@property
							def    __snake_case (							self	:  str    )     ->   float:
														return 1E-4

