


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class  UpperCAmelCase_						(				_UpperCAmelCase):
       '''simple docstring'''
       __UpperCamelCase   :  int				=       ["image_processor", "tokenizer"]
       __UpperCamelCase   :  Dict				=       "VideoMAEImageProcessor"
       __UpperCamelCase   :  List[Any]				=       ("CLIPTokenizer", "CLIPTokenizerFast")






       def __init__(  self			,       __SCREAMING_SNAKE_CASE=None			,       __SCREAMING_SNAKE_CASE=None			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						int 						=		None
              if "feature_extractor" in kwargs:
                     warnings.warn(
                         '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                         ''' instead.'''			,       UpperCAmelCase_			,       )
                     UpperCamelCase     :						int 						=		kwargs.pop('''feature_extractor'''    )

              UpperCamelCase     :						Any 						=		image_processor if image_processor is not None else feature_extractor
              if image_processor is None:
                     raise ValueError('''You need to specify an `image_processor`.'''    )
              if tokenizer is None:
                     raise ValueError('''You need to specify a `tokenizer`.'''    )

              super().__init__(UpperCAmelCase_			,       UpperCAmelCase_    )
              UpperCamelCase     :						Tuple 						=		self.image_processor






       def __call__(  self			,       __SCREAMING_SNAKE_CASE=None			,       __SCREAMING_SNAKE_CASE=None			,       __SCREAMING_SNAKE_CASE=None			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              if text is None and videos is None:
                     raise ValueError('''You have to specify either text or videos. Both cannot be none.'''    )

              if text is not None:
                     UpperCamelCase     :						List[str] 						=		self.tokenizer(UpperCAmelCase_			,       return_tensors=UpperCAmelCase_			,       **UpperCAmelCase_    )

              if videos is not None:
                     UpperCamelCase     :						Union[str, Any] 						=		self.image_processor(UpperCAmelCase_			,       return_tensors=UpperCAmelCase_			,       **UpperCAmelCase_    )

              if text is not None and videos is not None:
                     UpperCamelCase     :						Any 						=		image_features.pixel_values
                     return encoding
              elif text is not None:
                     return encoding
              else:
                     return BatchEncoding(data=dict(**UpperCAmelCase_    )			,       tensor_type=UpperCAmelCase_    )






       def 			_lowercase (  self			,       *__SCREAMING_SNAKE_CASE			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              return self.tokenizer.batch_decode(*UpperCAmelCase_			,       **UpperCAmelCase_    )






       def 			_lowercase (  self			,       *__SCREAMING_SNAKE_CASE			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              return self.tokenizer.decode(*UpperCAmelCase_			,       **UpperCAmelCase_    )






       @property
       def 			_lowercase (  self    ):




              """simple docstring"""




              return ["input_ids", "attention_mask", "position_ids", "pixel_values"]






       @property
       def 			_lowercase (  self    ):




              """simple docstring"""




              warnings.warn(
                  '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''			,       UpperCAmelCase_			,       )
              return self.image_processor_class






       @property
       def 			_lowercase (  self    ):




              """simple docstring"""




              warnings.warn(
                  '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,       UpperCAmelCase_			,       )
              return self.image_processor









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						(_UpperCAmelCase       ):







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







									'''simple docstring'''




									super().__init__(
									    UpperCAmelCase_ ,      split=UpperCAmelCase_ ,      features=UpperCAmelCase_ ,      cache_dir=UpperCAmelCase_ ,      keep_in_memory=UpperCAmelCase_ ,      streaming=UpperCAmelCase_ ,      num_proc=UpperCAmelCase_ ,      **UpperCAmelCase_ ,      )
									_SCREAMING_SNAKE_CASE       =    path_or_paths if isinstance(UpperCAmelCase_ ,      UpperCAmelCase_	) else {self.split: path_or_paths}
									_SCREAMING_SNAKE_CASE       =    Csv(
									    cache_dir=UpperCAmelCase_ ,      data_files=UpperCAmelCase_ ,      features=UpperCAmelCase_ ,      **UpperCAmelCase_ ,      )







			def 				UpperCamelCase (      self:    int	):







									'''simple docstring'''




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

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



class  __UpperCAmelCase						:







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







									'''simple docstring'''




									if num_proc is not None and num_proc <= 0:
															raise ValueError(F'num_proc {num_proc} must be an integer > 0.'	)

									_SCREAMING_SNAKE_CASE       =    dataset
									_SCREAMING_SNAKE_CASE       =    path_or_buf
									_SCREAMING_SNAKE_CASE       =    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
									_SCREAMING_SNAKE_CASE       =    num_proc
									_SCREAMING_SNAKE_CASE       =    """utf-8"""
									_SCREAMING_SNAKE_CASE       =    to_csv_kwargs







			def 				UpperCamelCase (      self:    Optional[int]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.to_csv_kwargs.pop("""path_or_buf""" ,      UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    self.to_csv_kwargs.pop("""header""" ,      UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    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:
																					_SCREAMING_SNAKE_CASE       =    self._write(file_obj=UpperCAmelCase_ ,      header=UpperCAmelCase_ ,      index=UpperCAmelCase_ ,      **self.to_csv_kwargs	)
									else:
															_SCREAMING_SNAKE_CASE       =    self._write(file_obj=self.path_or_buf ,      header=UpperCAmelCase_ ,      index=UpperCAmelCase_ ,      **self.to_csv_kwargs	)
									return written







			def 				UpperCamelCase (      self:    str ,      UpperCAmelCase_:    Tuple	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    args

									_SCREAMING_SNAKE_CASE       =    query_table(
									    table=self.dataset.data ,      key=slice(UpperCAmelCase_ ,      offset + self.batch_size	) ,      indices=self.dataset._indices ,      )
									_SCREAMING_SNAKE_CASE       =    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 				UpperCamelCase (      self:    Optional[Any] ,      UpperCAmelCase_:    BinaryIO ,      UpperCAmelCase_:    Tuple ,      UpperCAmelCase_:    Any ,      **UpperCAmelCase_:    Union[str, Any]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    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""" ,      ):
																					_SCREAMING_SNAKE_CASE       =    self._batch_csv((offset, header, index, to_csv_kwargs)	)
																					written += file_obj.write(UpperCAmelCase_	)

									else:
															_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    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

