
"""simple docstring"""
import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def 					a__ ( a :					Optional[Any]							,  a :					Any		):

  """simple docstring"""







  assert isinstance(a							,  a		)
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory"							,  [False, True]		)
def 					a__ ( a :					int							,  a :					Union[str, Any]							,  a :					Dict		):

  """simple docstring"""







  _snake_case  :    List[Any]            = tmp_path / "cache"
  _snake_case  :    Tuple            = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    _snake_case  :    Optional[int]            = CsvDatasetReader(a							,  cache_dir=a							,  keep_in_memory=a		).read()
  _check_csv_dataset(a							,  a		)
@pytest.mark.parametrize(
    "features"							,  [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]							,  )
def 					a__ ( a :					List[Any]							,  a :					str							,  a :					Any		):

  """simple docstring"""







  _snake_case  :    Optional[int]            = tmp_path / "cache"
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  _snake_case  :    str            = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  _snake_case  :    str            = features.copy() if features else default_expected_features
  _snake_case  :    List[Any]            = (
      Features({feature: Value(a		) for feature, dtype in features.items()}		) if features is not None else None
  )
  _snake_case  :    List[str]            = CsvDatasetReader(a							,  features=a							,  cache_dir=a		).read()
  _check_csv_dataset(a							,  a		)
@pytest.mark.parametrize("split"							,  [None, NamedSplit("train"		), "train", "test"]		)
def 					a__ ( a :					int							,  a :					List[str]							,  a :					Any		):

  """simple docstring"""







  _snake_case  :    Dict            = tmp_path / "cache"
  _snake_case  :    Optional[int]            = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  _snake_case  :    Any            = CsvDatasetReader(a							,  cache_dir=a							,  split=a		).read()
  _check_csv_dataset(a							,  a		)
  assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type"							,  [str, list]		)
def 					a__ ( a :					int							,  a :					Union[str, Any]							,  a :					Optional[Any]		):

  """simple docstring"""







  if issubclass(a							,  a		):
    _snake_case  :    Any            = csv_path
  elif issubclass(a							,  a		):
    _snake_case  :    Optional[int]            = [csv_path]
  _snake_case  :    Optional[Any]            = tmp_path / "cache"
  _snake_case  :    Tuple            = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  _snake_case  :    int            = CsvDatasetReader(a							,  cache_dir=a		).read()
  _check_csv_dataset(a							,  a		)
def 					a__ ( a :					List[str]							,  a :					int							,  a :					int=("train",)		):

  """simple docstring"""







  assert isinstance(a							,  a		)
  for split in splits:
    _snake_case  :    Tuple            = dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory"							,  [False, True]		)
def 					a__ ( a :					Optional[int]							,  a :					str							,  a :					str		):

  """simple docstring"""







  _snake_case  :    int            = tmp_path / "cache"
  _snake_case  :    Optional[Any]            = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    _snake_case  :    Any            = CsvDatasetReader({"train": csv_path}							,  cache_dir=a							,  keep_in_memory=a		).read()
  _check_csv_datasetdict(a							,  a		)
@pytest.mark.parametrize(
    "features"							,  [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]							,  )
def 					a__ ( a :					Any							,  a :					int							,  a :					Optional[int]		):

  """simple docstring"""







  _snake_case  :    Dict            = tmp_path / "cache"
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  _snake_case  :    Any            = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  _snake_case  :    str            = features.copy() if features else default_expected_features
  _snake_case  :    str            = (
      Features({feature: Value(a		) for feature, dtype in features.items()}		) if features is not None else None
  )
  _snake_case  :    Dict            = CsvDatasetReader({"train": csv_path}							,  features=a							,  cache_dir=a		).read()
  _check_csv_datasetdict(a							,  a		)
@pytest.mark.parametrize("split"							,  [None, NamedSplit("train"		), "train", "test"]		)
def 					a__ ( a :					Optional[int]							,  a :					Dict							,  a :					List[str]		):

  """simple docstring"""







  if split:
    _snake_case  :    str            = {split: csv_path}
  else:
    _snake_case  :    List[Any]            = {"train": csv_path, "test": csv_path}
  _snake_case  :    Dict            = tmp_path / "cache"
  _snake_case  :    Tuple            = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  _snake_case  :    str            = CsvDatasetReader(a							,  cache_dir=a		).read()
  _check_csv_datasetdict(a							,  a							,  splits=list(path.keys()		)		)
  assert all(dataset[split].split == split for split in path.keys()		)
def 					a__ ( a :					int		):

  """simple docstring"""







  with open(a							,  encoding="utf-8"		) as csvfile:
    yield from csv.reader(a		)
def 					a__ ( a :					List[str]							,  a :					Optional[Any]		):

  """simple docstring"""







  _snake_case  :    str            = tmp_path / "cache"
  _snake_case  :    List[Any]            = os.path.join(a							,  "tmp.csv"		)
  _snake_case  :    int            = CsvDatasetReader({"train": csv_path}							,  cache_dir=a		).read()
  CsvDatasetWriter(dataset["train"]							,  a							,  num_proc=1		).write()

  _snake_case  :    List[Any]            = iter_csv_file(a		)
  _snake_case  :    Any            = iter_csv_file(a		)

  for rowa, rowa in zip(a							,  a		):
    assert rowa == rowa
def 					a__ ( a :					Optional[int]							,  a :					Optional[int]		):

  """simple docstring"""







  _snake_case  :    Dict            = tmp_path / "cache"
  _snake_case  :    str            = os.path.join(a							,  "tmp.csv"		)
  _snake_case  :    List[str]            = CsvDatasetReader({"train": csv_path}							,  cache_dir=a		).read()
  CsvDatasetWriter(dataset["train"]							,  a							,  num_proc=2		).write()

  _snake_case  :    int            = iter_csv_file(a		)
  _snake_case  :    Optional[Any]            = iter_csv_file(a		)

  for rowa, rowa in zip(a							,  a		):
    assert rowa == rowa



def 					a__ ( a :					Tuple							,  a :					Tuple		):

  """simple docstring"""







  _snake_case  :    str            = tmp_path / "cache"
  _snake_case  :    Any            = os.path.join(a							,  "tmp.csv"		)
  _snake_case  :    Optional[Any]            = CsvDatasetReader({"train": csv_path}							,  cache_dir=a		).read()
  with pytest.raises(a		):
    CsvDatasetWriter(dataset["train"]							,  a							,  num_proc=0		)







'''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     lowercase  ( UpperCAmelCase_):
      __lowercase					=	['''image_processor''', '''tokenizer''']
      __lowercase					=	'''LayoutLMv2ImageProcessor'''
      __lowercase					=	('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')






      def __init__(self			,	a__=None			,	a__=None			,	**a__       )							-> Any:
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			None
         if "feature_extractor" in kwargs:
            warnings.warn(
                '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                ''' instead.'''			,	a__			,	)
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			kwargs.pop('''feature_extractor'''       )

         SCREAMING_SNAKE_CASE__      :					List[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__(a__			,	a__       )






      def __call__(self			,	a__			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = True			,	a__ = False			,	a__ = False			,	a__ = None			,	a__ = 0			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = False			,	a__ = False			,	a__ = False			,	a__ = False			,	a__ = True			,	a__ = None			,	**a__			,	)							-> BatchEncoding:
         # 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
         SCREAMING_SNAKE_CASE__      :					str							    =			self.image_processor(images=a__			,	return_tensors=a__       )

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

         SCREAMING_SNAKE_CASE__      :					int							    =			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=a__			,	add_special_tokens=a__			,	padding=a__			,	truncation=a__			,	max_length=a__			,	stride=a__			,	pad_to_multiple_of=a__			,	return_token_type_ids=a__			,	return_attention_mask=a__			,	return_overflowing_tokens=a__			,	return_special_tokens_mask=a__			,	return_offsets_mapping=a__			,	return_length=a__			,	verbose=a__			,	return_tensors=a__			,	**a__			,	)

         # add pixel values
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			features.pop('''pixel_values'''       )
         if return_overflowing_tokens is True:
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			self.get_overflowing_images(a__			,	encoded_inputs['''overflow_to_sample_mapping''']       )
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			images

         return encoded_inputs






      def  A__(self			,	a__			,	a__       )							-> str:
         # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
         SCREAMING_SNAKE_CASE__      :					int							    =			[]
         for sample_idx in overflow_to_sample_mapping:
            images_with_overflow.append(images[sample_idx]       )

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

         return images_with_overflow






      def  A__(self			,	*a__			,	**a__       )							-> List[Any]:
         return self.tokenizer.batch_decode(*a__			,	**a__       )






      def  A__(self			,	*a__			,	**a__       )							-> Optional[Any]:
         return self.tokenizer.decode(*a__			,	**a__       )






      @property
      def  A__(self       )							-> Tuple:
         return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]






      @property
      def  A__(self       )							-> str:
         warnings.warn(
             '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''			,	a__			,	)
         return self.image_processor_class






      @property
      def  A__(self       )							-> Dict:
         warnings.warn(
             '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,	a__			,	)
         return self.image_processor

