




"""simple docstring"""

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 			lowerCamelCase__    (  lowerCamelCase_			):


			def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = False				, SCREAMING_SNAKE_CASE = False				, SCREAMING_SNAKE_CASE = None				, **SCREAMING_SNAKE_CASE				, ):



					"""simple docstring"""







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



			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







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

							self.builder.download_and_prepare(
							    download_config=SCREAMING_SNAKE_CASE				, download_mode=SCREAMING_SNAKE_CASE				, verification_mode=SCREAMING_SNAKE_CASE				, base_path=SCREAMING_SNAKE_CASE				, num_proc=self.num_proc				, )
							snake_case		:						int								=				self.builder.as_dataset(
							    split=self.split				, verification_mode=SCREAMING_SNAKE_CASE				, in_memory=self.keep_in_memory							)
					return dataset







class 			lowerCamelCase__    :


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



					"""simple docstring"""







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

					snake_case		:						List[str]								=				dataset
					snake_case		:						str								=				path_or_buf
					snake_case		:						Union[str, Any]								=				batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
					snake_case		:						List[str]								=				num_proc
					snake_case		:						Dict								=				"utf-8"
					snake_case		:						Optional[int]								=				to_csv_kwargs


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						List[str]								=				self.to_csv_kwargs.pop("path_or_buf"				, SCREAMING_SNAKE_CASE							)
					snake_case		:						str								=				self.to_csv_kwargs.pop("header"				, SCREAMING_SNAKE_CASE							)
					snake_case		:						Union[str, Any]								=				self.to_csv_kwargs.pop("index"				, SCREAMING_SNAKE_CASE							)

					if isinstance(self.path_or_buf				, (str, bytes, os.PathLike)							):
							with open(self.path_or_buf				, "wb+"							) as buffer:
									snake_case		:						str								=				self._write(file_obj=SCREAMING_SNAKE_CASE				, header=SCREAMING_SNAKE_CASE				, index=SCREAMING_SNAKE_CASE				, **self.to_csv_kwargs							)
					else:
							snake_case		:						int								=				self._write(file_obj=self.path_or_buf				, header=SCREAMING_SNAKE_CASE				, index=SCREAMING_SNAKE_CASE				, **self.to_csv_kwargs							)
					return written


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case		:						str								=				args

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



			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case		:						str								=				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"				, ):
									snake_case		:						List[str]								=				self._batch_csv((offset, header, index, to_csv_kwargs)							)
									written += file_obj.write(SCREAMING_SNAKE_CASE							)

					else:
							snake_case		:						Optional[Any]								=				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				, SCREAMING_SNAKE_CASE				, SCREAMING_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 CSV from Arrow format"				, ):
											written += file_obj.write(SCREAMING_SNAKE_CASE							)

					return written










"""simple docstring"""

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 			lowerCamelCase__    (  lowerCamelCase_			):
			a__ :      Tuple								=     ["""image_processor""", """tokenizer"""]
			a__ :      List[Any]								=     """EfficientNetImageProcessor"""
			a__ :      str								=     ("""BertTokenizer""", """BertTokenizerFast""")


			def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					super().__init__(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)


			def __call__(     self				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE="max_length"				, SCREAMING_SNAKE_CASE=64				, SCREAMING_SNAKE_CASE=None				, **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







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

					if text is not None:
							snake_case		:						Dict								=				self.tokenizer(
							    SCREAMING_SNAKE_CASE				, padding=SCREAMING_SNAKE_CASE				, max_length=SCREAMING_SNAKE_CASE				, return_tensors=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)

					if images is not None:
							snake_case		:						Tuple								=				self.image_processor(SCREAMING_SNAKE_CASE				, return_tensors=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)

					if text is not None and images is not None:
							snake_case		:						Dict								=				image_features.pixel_values
							return encoding
					elif text is not None:
							return encoding
					else:
							return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE							)				, tensor_type=SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_				(     self				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







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


			def 							lowerCamelCase_				(     self				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







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



			@property
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[Any]								=				self.tokenizer.model_input_names
					snake_case		:						Any								=				self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)

