




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 __snake_case     (     __UpperCAmelCase						):
   UpperCAmelCase__    :       Dict		=    ['''image_processor''', '''tokenizer''']
   UpperCAmelCase__    :       List[str]		=    '''LayoutLMv2ImageProcessor'''
   UpperCAmelCase__    :       Union[str, Any]		=    ('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')







   def __init__( self						:		Optional[Any]   ,		_snake_case						:		Optional[Any]=None   ,		_snake_case						:		List[Any]=None   ,		**_snake_case						:		List[str]):

        """simple docstring"""


        UpperCAmelCase_		=				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_		=				kwargs.pop('''feature_extractor''')

        UpperCAmelCase_		=				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						:		List[Any]   ,		_snake_case						:		Dict   ,		_snake_case						:		Any = None   ,		_snake_case						:		Dict = None   ,		_snake_case						:		Tuple = None   ,		_snake_case						:		int = None   ,		_snake_case						:		List[Any] = True   ,		_snake_case						:		Optional[Any] = False   ,		_snake_case						:		Any = False   ,		_snake_case						:		Tuple = None   ,		_snake_case						:		Tuple = 0   ,		_snake_case						:		List[str] = None   ,		_snake_case						:		Optional[int] = None   ,		_snake_case						:		int = None   ,		_snake_case						:		Union[str, Any] = False   ,		_snake_case						:		Tuple = False   ,		_snake_case						:		Optional[int] = False   ,		_snake_case						:		Union[str, Any] = False   ,		_snake_case						:		Tuple = True   ,		_snake_case						:		str = None   ,		**_snake_case						:		List[Any]   ,		):

        """simple docstring"""


        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
        UpperCAmelCase_		=				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_):
                  UpperCAmelCase_		=				[text]  # add batch dimension (as the image processor always adds a batch dimension)
             UpperCAmelCase_		=				features['words']

        UpperCAmelCase_		=				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
        UpperCAmelCase_		=				features.pop('''pixel_values''')
        if return_overflowing_tokens is True:
             UpperCAmelCase_		=				self.get_overflowing_images(UpperCAmelCase_   ,		encoded_inputs['''overflow_to_sample_mapping'''])
        UpperCAmelCase_		=				images

        return encoded_inputs







   def lowerCamelCase		( self						:		Any   ,		_snake_case						:		Any   ,		_snake_case						:		Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_		=				[]
        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						:		Optional[Any]   ,		*_snake_case						:		Union[str, Any]   ,		**_snake_case						:		Optional[int]):

        """simple docstring"""


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







   def lowerCamelCase		( self						:		Optional[Any]   ,		*_snake_case						:		Optional[int]   ,		**_snake_case						:		Optional[int]):

        """simple docstring"""


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







   @property
   def lowerCamelCase		( self						:		List[Any]):

        """simple docstring"""


        return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]







   @property
   def lowerCamelCase		( self						:		Optional[Any]):

        """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 lowerCamelCase		( self						:		Any):

        """simple docstring"""


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








"""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 						UpperCAmelCase   (						a_,		a_     ):





		'''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 						UpperCAmelCase   (						a_,		a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     List[Any]            =       tmp_path / 'cache'
		lowerCamelCase	:     Optional[int]            =       {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				lowerCamelCase	:     Tuple            =       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 						UpperCAmelCase   (						a_,		a_,		a_     ):





		'''simple docstring'''


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





		'''simple docstring'''


		lowerCamelCase	:     Any            =       tmp_path / 'cache'
		lowerCamelCase	:     Dict            =       {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
		lowerCamelCase	:     Dict            =       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 						UpperCAmelCase   (						a_,		a_,		a_     ):





		'''simple docstring'''


		if issubclass(a_,		a_     ):
				lowerCamelCase	:     Dict            =       csv_path
		elif issubclass(a_,		a_     ):
				lowerCamelCase	:     Tuple            =       [csv_path]
		lowerCamelCase	:     Optional[Any]            =       tmp_path / 'cache'
		lowerCamelCase	:     Tuple            =       {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
		lowerCamelCase	:     List[Any]            =       CsvDatasetReader(a_,		cache_dir=a_     ).read()
		_check_csv_dataset(a_,		a_     )
def 						UpperCAmelCase   (						a_,		a_,		a_=("train",)     ):





		'''simple docstring'''


		assert isinstance(a_,		a_     )
		for split in splits:
				lowerCamelCase	:     Any            =       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 						UpperCAmelCase   (						a_,		a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     Any            =       tmp_path / 'cache'
		lowerCamelCase	:     int            =       {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				lowerCamelCase	:     int            =       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 						UpperCAmelCase   (						a_,		a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     str            =       tmp_path / 'cache'
		# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
		lowerCamelCase	:     Dict            =       {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
		lowerCamelCase	:     str            =       features.copy() if features else default_expected_features
		lowerCamelCase	:     Any            =       (
		    Features({feature: Value(a_     ) for feature, dtype in features.items()}     ) if features is not None else None
		)
		lowerCamelCase	:     Optional[Any]            =       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 						UpperCAmelCase   (						a_,		a_,		a_     ):





		'''simple docstring'''


		if split:
				lowerCamelCase	:     List[Any]            =       {split: csv_path}
		else:
				lowerCamelCase	:     Any            =       {'train': csv_path, 'test': csv_path}
		lowerCamelCase	:     List[Any]            =       tmp_path / 'cache'
		lowerCamelCase	:     Union[str, Any]            =       {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
		lowerCamelCase	:     Union[str, Any]            =       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 						UpperCAmelCase   (						a_     ):





		'''simple docstring'''


		with open(a_,		encoding='utf-8'     ) as csvfile:
				yield from csv.reader(a_     )
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     Optional[int]            =       tmp_path / 'cache'
		lowerCamelCase	:     int            =       os.path.join(a_,		'tmp.csv'     )
		lowerCamelCase	:     List[str]            =       CsvDatasetReader({'train': csv_path},		cache_dir=a_     ).read()
		CsvDatasetWriter(dataset['train'],		a_,		num_proc=1     ).write()

		lowerCamelCase	:     Tuple            =       iter_csv_file(a_     )
		lowerCamelCase	:     Tuple            =       iter_csv_file(a_     )

		for rowa, rowa in zip(a_,		a_     ):
				assert rowa == rowa
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     Optional[Any]            =       tmp_path / 'cache'
		lowerCamelCase	:     Optional[Any]            =       os.path.join(a_,		'tmp.csv'     )
		lowerCamelCase	:     Dict            =       CsvDatasetReader({'train': csv_path},		cache_dir=a_     ).read()
		CsvDatasetWriter(dataset['train'],		a_,		num_proc=2     ).write()

		lowerCamelCase	:     int            =       iter_csv_file(a_     )
		lowerCamelCase	:     Optional[int]            =       iter_csv_file(a_     )

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





def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     Tuple            =       tmp_path / 'cache'
		lowerCamelCase	:     Optional[Any]            =       os.path.join(a_,		'tmp.csv'     )
		lowerCamelCase	:     Optional[int]            =       CsvDatasetReader({'train': csv_path},		cache_dir=a_     ).read()
		with pytest.raises(a_     ):
				CsvDatasetWriter(dataset['train'],		a_,		num_proc=0     )






