




import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class  UpperCAmelCase						(			A_   ):






   def __init__(	self   :       List[Any]						,					UpperCamelCase_   :       Union[str, Any]						,					UpperCamelCase_   :       str = None						,					UpperCamelCase_   :       Any = None						,					UpperCamelCase_   :       List[str] = None						,					UpperCamelCase_   :       Optional[Any] = False						,					UpperCamelCase_   :       str = False						,					UpperCamelCase_   :       Dict = None						,					**UpperCamelCase_   :       Optional[int]						,					)->			Optional[Any]:
     super().__init__(
         UpperCamelCase_						,					split=UpperCamelCase_						,					features=UpperCamelCase_						,					cache_dir=UpperCamelCase_						,					keep_in_memory=UpperCamelCase_						,					streaming=UpperCamelCase_						,					num_proc=UpperCamelCase_						,					**UpperCamelCase_						,					)
     _lowercase: Optional[int]						     =    path_or_paths if isinstance(UpperCamelCase_						,					UpperCamelCase_     ) else {self.split: path_or_paths}
     _lowercase: List[Any]						     =    Csv(
         cache_dir=UpperCamelCase_						,					data_files=UpperCamelCase_						,					features=UpperCamelCase_						,					**UpperCamelCase_						,					)






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

       self.builder.download_and_prepare(
           download_config=UpperCamelCase_						,					download_mode=UpperCamelCase_						,					verification_mode=UpperCamelCase_						,					base_path=UpperCamelCase_						,					num_proc=self.num_proc						,					)
       _lowercase: List[str]						     =    self.builder.as_dataset(
           split=self.split						,					verification_mode=UpperCamelCase_						,					in_memory=self.keep_in_memory     )
     return dataset

class  UpperCAmelCase						:






   def __init__(	self   :       Optional[int]						,					UpperCamelCase_   :       List[Any]						,					UpperCamelCase_   :       int						,					UpperCamelCase_   :       Optional[Any] = None						,					UpperCamelCase_   :       Optional[Any] = None						,					**UpperCamelCase_   :       Optional[Any]						,					)->			Optional[Any]:
     if num_proc is not None and num_proc <= 0:
       raise ValueError(f'num_proc {num_proc} must be an integer > 0.'     )

     _lowercase: List[Any]						     =    dataset
     _lowercase: List[str]						     =    path_or_buf
     _lowercase: Any						     =    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
     _lowercase: Optional[Any]						     =    num_proc
     _lowercase: int						     =    'utf-8'
     _lowercase: Optional[int]						     =    to_csv_kwargs






   def  _a						(	self   :       List[Any]     )->			str:
     _lowercase: Tuple						     =    self.to_csv_kwargs.pop('path_or_buf'						,					UpperCamelCase_     )
     _lowercase: str						     =    self.to_csv_kwargs.pop('header'						,					UpperCamelCase_     )
     _lowercase: Tuple						     =    self.to_csv_kwargs.pop('index'						,					UpperCamelCase_     )

     if isinstance(self.path_or_buf						,					(str, bytes, os.PathLike)     ):
       with open(self.path_or_buf						,					'wb+'     ) as buffer:
         _lowercase: Dict						     =    self._write(file_obj=UpperCamelCase_						,					header=UpperCamelCase_						,					index=UpperCamelCase_						,					**self.to_csv_kwargs     )
     else:
       _lowercase: Optional[Any]						     =    self._write(file_obj=self.path_or_buf						,					header=UpperCamelCase_						,					index=UpperCamelCase_						,					**self.to_csv_kwargs     )
     return written






   def  _a						(	self   :       Dict						,					UpperCamelCase_   :       str     )->			int:
     _lowercase     ,					_lowercase     ,					_lowercase     ,					_lowercase: Optional[int]						     =    args

     _lowercase: List[Any]						     =    query_table(
         table=self.dataset.data						,					key=slice(UpperCamelCase_						,					offset + self.batch_size     )						,					indices=self.dataset._indices						,					)
     _lowercase: str						     =    batch.to_pandas().to_csv(
         path_or_buf=UpperCamelCase_						,					header=header if (offset == 0) else False						,					index=UpperCamelCase_						,					**UpperCamelCase_     )
     return csv_str.encode(self.encoding     )






   def  _a						(	self   :       Tuple						,					UpperCamelCase_   :       Union[str, Any]						,					UpperCamelCase_   :       List[str]						,					UpperCamelCase_   :       str						,					**UpperCamelCase_   :       int     )->			Dict:
     _lowercase: Optional[int]						     =    0

     if self.num_proc is None or self.num_proc == 1:
       for offset in logging.tqdm(
           range(0						,					len(self.dataset     )						,					self.batch_size     )						,					unit='ba'						,					disable=not logging.is_progress_bar_enabled()						,					desc='Creating CSV from Arrow format'						,					):
         _lowercase: List[Any]						     =    self._batch_csv((offset, header, index, to_csv_kwargs)     )
         written += file_obj.write(UpperCamelCase_     )

     else:
       _lowercase     ,					_lowercase: Dict						     =    len(self.dataset     ), self.batch_size
       with multiprocessing.Pool(self.num_proc     ) as pool:
         for csv_str in logging.tqdm(
             pool.imap(
                 self._batch_csv						,					[(offset, header, index, to_csv_kwargs) for offset in range(0						,					UpperCamelCase_						,					UpperCamelCase_     )]						,					)						,					total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size						,					unit='ba'						,					disable=not logging.is_progress_bar_enabled()						,					desc='Creating CSV from Arrow format'						,					):
           written += file_obj.write(UpperCamelCase_     )

     return written






import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


UpperCAmelCase__						:       Dict							=    logging.get_logger(__name__)

UpperCAmelCase__						:       Optional[Any]							=    {
    'google/vit-hybrid-base-bit-384': 'https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}

class      UpperCAmelCase						(			A   ):
   __SCREAMING_SNAKE_CASE		:							int							=					"vit-hybrid"






   def __init__(	self   :       Optional[int]						,					UpperCamelCase_   :       Any=None						,					UpperCamelCase_   :       str=7_68						,					UpperCamelCase_   :       Optional[int]=12						,					UpperCamelCase_   :       str=12						,					UpperCamelCase_   :       Dict=30_72						,					UpperCamelCase_   :       Dict="gelu"						,					UpperCamelCase_   :       str=0.0						,					UpperCamelCase_   :       List[Any]=0.0						,					UpperCamelCase_   :       List[str]=0.02						,					UpperCamelCase_   :       Tuple=1E-12						,					UpperCamelCase_   :       Union[str, Any]=2_24						,					UpperCamelCase_   :       Any=1						,					UpperCamelCase_   :       Optional[Any]=3						,					UpperCamelCase_   :       Optional[Any]=[1, 10_24, 24, 24]						,					UpperCamelCase_   :       Any=True						,					**UpperCamelCase_   :       int						,					)->			Any:
     super().__init__(**UpperCamelCase_     )

     if backbone_config is None:
       logger.info('`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'     )
       _lowercase: Dict						     =    {
           'global_padding': 'same',
           'layer_type': 'bottleneck',
           'depths': [3, 4, 9],
           'out_features': ['stage3'],
           'embedding_dynamic_padding': True,
       }

     if isinstance(UpperCamelCase_						,					UpperCamelCase_     ):
       if "model_type" in backbone_config:
         _lowercase: int						     =    CONFIG_MAPPING[backbone_config['model_type']]
       else:
         logger.info(
             '`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'     )
         _lowercase: Optional[int]						     =    BitConfig
       _lowercase: List[Any]						     =    backbone_config_class(**UpperCamelCase_     )

     _lowercase: List[Any]						     =    backbone_featmap_shape
     _lowercase: Dict						     =    backbone_config
     _lowercase: Optional[int]						     =    hidden_size
     _lowercase: List[Any]						     =    num_hidden_layers
     _lowercase: Union[str, Any]						     =    num_attention_heads
     _lowercase: str						     =    intermediate_size
     _lowercase: Optional[int]						     =    hidden_act
     _lowercase: int						     =    hidden_dropout_prob
     _lowercase: List[str]						     =    attention_probs_dropout_prob
     _lowercase: Tuple						     =    initializer_range
     _lowercase: Any						     =    layer_norm_eps
     _lowercase: Any						     =    image_size
     _lowercase: List[Any]						     =    patch_size
     _lowercase: Dict						     =    num_channels
     _lowercase: Tuple						     =    qkv_bias






   def  _a						(	self   :       Optional[int]     )->			Dict[str, any]:
     _lowercase: List[str]						     =    copy.deepcopy(self.__dict__     )
     _lowercase: Tuple						     =    self.backbone_config.to_dict()
     _lowercase: int						     =    self.__class__.model_type
     return output
