



import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
							import sqlitea

							import sqlalchemy

class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       str				,					_snake_case					:       Union[str, "sqlalchemy.sql.Selectable"]				,					_snake_case					:       Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]				,					_snake_case					:       Optional[Features] = None				,					_snake_case					:       str = None				,					_snake_case					:       bool = False				,					**_snake_case					:       Optional[Any]				,					):






								"""simple docstring"""


								super().__init__(features=_snake_case				,					cache_dir=_snake_case				,					keep_in_memory=_snake_case				,					**_snake_case					)
								A__       =    Sql(
								    cache_dir=_snake_case				,					features=_snake_case				,					sql=_snake_case				,					con=_snake_case				,					**_snake_case				,					)



					def     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


								A__       =    None
								A__       =    None
								A__       =    None
								A__       =    None

								self.builder.download_and_prepare(
								    download_config=_snake_case				,					download_mode=_snake_case				,					verification_mode=_snake_case				,					base_path=_snake_case				,					)

								# Build dataset for splits
								A__       =    self.builder.as_dataset(
								    split='train'				,					verification_mode=_snake_case				,					in_memory=self.keep_in_memory					)
								return dataset







class      __lowerCAmelCase     :
					"""simple docstring"""






					def __init__(     self					:       List[Any]				,					_snake_case					:       Dataset				,					_snake_case					:       str				,					_snake_case					:       Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]				,					_snake_case					:       Optional[int] = None				,					_snake_case					:       Optional[int] = None				,					**_snake_case					:       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.'''					)

								A__       =    dataset
								A__       =    name
								A__       =    con
								A__       =    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
								A__       =    num_proc
								A__       =    to_sql_kwargs



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__       =    self.to_sql_kwargs.pop('sql'				,					_snake_case					)
								A__       =    self.to_sql_kwargs.pop('con'				,					_snake_case					)
								A__       =    self.to_sql_kwargs.pop('index'				,					_snake_case					)

								A__       =    self._write(index=_snake_case				,					**self.to_sql_kwargs					)
								return written



					def     _a							(     self					:       Any				,					_snake_case					:       Union[str, Any]					):






								"""simple docstring"""


								A__     , A__     , A__       =    args
								A__       =    {**to_sql_kwargs, 'if_exists': 'append'} if offset > 0 else to_sql_kwargs
								A__       =    query_table(
								    table=self.dataset.data				,					key=slice(_snake_case				,					offset + self.batch_size					)				,					indices=self.dataset._indices				,					)
								A__       =    batch.to_pandas()
								A__       =    df.to_sql(self.name				,					self.con				,					index=_snake_case				,					**_snake_case					)
								return num_rows or len(_snake_case					)



					def     _a							(     self					:       Optional[int]				,					_snake_case					:       Dict				,					**_snake_case					:       Dict					):






								"""simple docstring"""


								A__       =    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 SQL from Arrow format'				,					):
														written += self._batch_sql((offset, index, to_sql_kwargs)					)
								else:
											A__     , A__       =    len(self.dataset					), self.batch_size
											with multiprocessing.Pool(self.num_proc					) as pool:
														for num_rows in logging.tqdm(
														    pool.imap(
														        self._batch_sql				,					[(offset, index, to_sql_kwargs) for offset in range(0				,					_snake_case				,					_snake_case					)]				,					)				,					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 SQL from Arrow format'				,					):
																	written += num_rows

								return written









from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		Union[str, Any]			       =    ["image_processor", "tokenizer"]
					A__				:		Optional[Any]			       =    "BridgeTowerImageProcessor"
					A__				:		List[Any]			       =    ("RobertaTokenizer", "RobertaTokenizerFast")



					def __init__(     self					:       List[Any]				,					_snake_case					:       Optional[Any]				,					_snake_case					:       Optional[int]					):






								"""simple docstring"""


								super().__init__(_snake_case				,					_snake_case					)



					def __call__(     self					:       List[Any]				,					_snake_case					:       int				,					_snake_case					:       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,					_snake_case					:       bool = True				,					_snake_case					:       Union[bool, str, PaddingStrategy] = False				,					_snake_case					:       Union[bool, str, TruncationStrategy] = None				,					_snake_case					:       Optional[int] = None				,					_snake_case					:       int = 0				,					_snake_case					:       Optional[int] = None				,					_snake_case					:       Optional[bool] = None				,					_snake_case					:       Optional[bool] = None				,					_snake_case					:       bool = False				,					_snake_case					:       bool = False				,					_snake_case					:       bool = False				,					_snake_case					:       bool = False				,					_snake_case					:       bool = True				,					_snake_case					:       Optional[Union[str, TensorType]] = None				,					**_snake_case					:       Optional[int]				,					):






								"""simple docstring"""


								A__       =    self.tokenizer(
								    text=_snake_case				,					add_special_tokens=_snake_case				,					padding=_snake_case				,					truncation=_snake_case				,					max_length=_snake_case				,					stride=_snake_case				,					pad_to_multiple_of=_snake_case				,					return_token_type_ids=_snake_case				,					return_attention_mask=_snake_case				,					return_overflowing_tokens=_snake_case				,					return_special_tokens_mask=_snake_case				,					return_offsets_mapping=_snake_case				,					return_length=_snake_case				,					verbose=_snake_case				,					return_tensors=_snake_case				,					**_snake_case				,					)
								# add pixel_values + pixel_mask
								A__       =    self.image_processor(
								    _snake_case				,					return_tensors=_snake_case				,					do_normalize=_snake_case				,					do_center_crop=_snake_case				,					**_snake_case					)
								encoding.update(_snake_case					)

								return encoding



					def     _a							(     self					:       Any				,					*_snake_case					:       Tuple				,					**_snake_case					:       List[Any]					):






								"""simple docstring"""


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



					def     _a							(     self					:       Dict				,					*_snake_case					:       Dict				,					**_snake_case					:       List[str]					):






								"""simple docstring"""


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



					@property
					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								A__       =    self.tokenizer.model_input_names
								A__       =    self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)




