





"""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  _UpperCAmelCase       (     lowercase_			):


     def __init__( self      :Union[str, Any]					,	__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      :Tuple					,	):
            super().__init__(
                __UpperCamelCase					,	split=__UpperCamelCase					,	features=__UpperCamelCase					,	cache_dir=__UpperCamelCase					,	keep_in_memory=__UpperCamelCase					,	streaming=__UpperCamelCase					,	num_proc=__UpperCamelCase					,	**__UpperCamelCase					,	)
            A		=  path_or_paths if isinstance(__UpperCamelCase					,	__UpperCamelCase		) else {self.split: path_or_paths}
            A		=  Csv(
                cache_dir=__UpperCamelCase					,	data_files=__UpperCamelCase					,	features=__UpperCamelCase					,	**__UpperCamelCase					,	)


     def      lowerCamelCase	( self      :Optional[int]		):
            # Build iterable dataset
            if self.streaming:
                   A		=  self.builder.as_streaming_dataset(split=self.split		)
            # Build regular (map-style) dataset
            else:
                   A		=  None
                   A		=  None
                   A		=  None
                   A		=  None

                   self.builder.download_and_prepare(
                       download_config=__UpperCamelCase					,	download_mode=__UpperCamelCase					,	verification_mode=__UpperCamelCase					,	base_path=__UpperCamelCase					,	num_proc=self.num_proc					,	)
                   A		=  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      :Dataset					,	__UpperCamelCase      :Union[PathLike, BinaryIO]					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :Optional[int] = None					,	**__UpperCamelCase      :str					,	):
            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		=  path_or_buf
            A		=  batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
            A		=  num_proc
            A		=  "utf-8"
            A		=  to_csv_kwargs


     def      lowerCamelCase	( self      :Optional[int]		):
            A		=  self.to_csv_kwargs.pop("path_or_buf"					,	__UpperCamelCase		)
            A		=  self.to_csv_kwargs.pop("header"					,	__UpperCamelCase		)
            A		=  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:
                          A		=  self._write(file_obj=__UpperCamelCase					,	header=__UpperCamelCase					,	index=__UpperCamelCase					,	**self.to_csv_kwargs		)
            else:
                   A		=  self._write(file_obj=self.path_or_buf					,	header=__UpperCamelCase					,	index=__UpperCamelCase					,	**self.to_csv_kwargs		)
            return written


     def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :str		):
            A,     A,     A,     A		=  args

            A		=  query_table(
                table=self.dataset.data					,	key=slice(__UpperCamelCase					,	offset + self.batch_size		)					,	indices=self.dataset._indices					,	)
            A		=  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      lowerCamelCase	( self      :str					,	__UpperCamelCase      :BinaryIO					,	__UpperCamelCase      :Tuple					,	__UpperCamelCase      :Dict					,	**__UpperCamelCase      :Optional[int]		):
            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 CSV from Arrow format"					,	):
                          A		=  self._batch_csv((offset, header, index, to_csv_kwargs)		)
                          written += file_obj.write(__UpperCamelCase		)

            else:
                   A,     A		=  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









"""simple docstring"""
import warnings
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  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							['''image_processor''', '''tokenizer''']
					UpperCamelCase								=							'''LayoutLMv2ImageProcessor'''
					UpperCamelCase								=							('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')


					def __init__( self      :List[Any]					,	__UpperCamelCase      :List[Any]=None					,	__UpperCamelCase      :Tuple=None					,	**__UpperCamelCase      :int		):
												A		=  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					,	)
																			A		=  kwargs.pop("feature_extractor"		)

												A		=  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		)


					def __call__( self      :Optional[Any]					,	__UpperCamelCase      :int					,	__UpperCamelCase      :Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None					,	__UpperCamelCase      :Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None					,	__UpperCamelCase      :Union[List[List[int]], List[List[List[int]]]] = None					,	__UpperCamelCase      :Optional[Union[List[int], List[List[int]]]] = None					,	__UpperCamelCase      :bool = True					,	__UpperCamelCase      :Union[bool, str, PaddingStrategy] = False					,	__UpperCamelCase      :Union[bool, str, TruncationStrategy] = False					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :int = 0					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :Optional[bool] = None					,	__UpperCamelCase      :Optional[bool] = None					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = True					,	__UpperCamelCase      :Optional[Union[str, TensorType]] = None					,	**__UpperCamelCase      :int					,	):
												# verify input
												if self.image_processor.apply_ocr and (boxes is not None):
																			raise ValueError(
																			    "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."		)

												if self.image_processor.apply_ocr and (word_labels is not None):
																			raise ValueError(
																			    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."		)

												if return_overflowing_tokens is True and return_offsets_mapping is False:
																			raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."		)

												# first, apply the image processor
												A		=  self.image_processor(images=__UpperCamelCase					,	return_tensors=__UpperCamelCase		)

												# second, apply the tokenizer
												if text is not None and self.image_processor.apply_ocr and text_pair is None:
																			if isinstance(__UpperCamelCase					,	__UpperCamelCase		):
																										A		=  [text]  # add batch dimension (as the image processor always adds a batch dimension)
																			A		=  features["words"]

												A		=  self.tokenizer(
												    text=text if text is not None else features["words"]					,	text_pair=text_pair if text_pair is not None else None					,	boxes=boxes if boxes is not None else features["boxes"]					,	word_labels=__UpperCamelCase					,	add_special_tokens=__UpperCamelCase					,	padding=__UpperCamelCase					,	truncation=__UpperCamelCase					,	max_length=__UpperCamelCase					,	stride=__UpperCamelCase					,	pad_to_multiple_of=__UpperCamelCase					,	return_token_type_ids=__UpperCamelCase					,	return_attention_mask=__UpperCamelCase					,	return_overflowing_tokens=__UpperCamelCase					,	return_special_tokens_mask=__UpperCamelCase					,	return_offsets_mapping=__UpperCamelCase					,	return_length=__UpperCamelCase					,	verbose=__UpperCamelCase					,	return_tensors=__UpperCamelCase					,	**__UpperCamelCase					,	)

												# add pixel values
												A		=  features.pop("pixel_values"		)
												if return_overflowing_tokens is True:
																			A		=  self.get_overflowing_images(__UpperCamelCase					,	encoded_inputs["overflow_to_sample_mapping"]		)
												A		=  images

												return encoded_inputs


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :List[str]					,	__UpperCamelCase      :List[str]		):
												# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
												A		=  []
												for sample_idx in overflow_to_sample_mapping:
																			images_with_overflow.append(images[sample_idx]		)

												if len(__UpperCamelCase		) != len(__UpperCamelCase		):
																			raise ValueError(
																			    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
																			    f" {len(__UpperCamelCase		)} and {len(__UpperCamelCase		)}"		)

												return images_with_overflow


					def      lowerCamelCase	( self      :Tuple					,	*__UpperCamelCase      :Union[str, Any]					,	**__UpperCamelCase      :Tuple		):
												return self.tokenizer.batch_decode(*__UpperCamelCase					,	**__UpperCamelCase		)


					def      lowerCamelCase	( self      :Union[str, Any]					,	*__UpperCamelCase      :str					,	**__UpperCamelCase      :List[str]		):
												return self.tokenizer.decode(*__UpperCamelCase					,	**__UpperCamelCase		)


					@property
					def      lowerCamelCase	( self      :str		):
												return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]


					@property
					def      lowerCamelCase	( self      :str		):
												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      lowerCamelCase	( self      :int		):
												warnings.warn(
												    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."					,	__UpperCamelCase					,	)
												return self.image_processor


