



'''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			(    _lowerCamelCase							):


 """simple docstring"""


 UpperCAmelCase		      =    ["""image_processor""", """tokenizer"""]
 UpperCAmelCase		      =    """LayoutLMv2ImageProcessor"""
 UpperCAmelCase		      =    ("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")







 def __init__(				self    ,a_=None    ,a_=None    ,**a_				)						->  Tuple:
        _UpperCAmelCase    :		List[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_    ,)
               _UpperCAmelCase    :		Tuple     	=					kwargs.pop("""feature_extractor"""				)

        _UpperCAmelCase    :		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
        _UpperCAmelCase    :		Tuple     	=					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_				):
                      _UpperCAmelCase    :		Union[str, Any]     	=					[text]  # add batch dimension (as the image processor always adds a batch dimension)
               _UpperCAmelCase    :		List[str]     	=					features["""words"""]

        _UpperCAmelCase    :		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
        _UpperCAmelCase    :		int     	=					features.pop("""pixel_values"""				)
        if return_overflowing_tokens is True:
               _UpperCAmelCase    :		Any     	=					self.get_overflowing_images(a_    ,encoded_inputs["""overflow_to_sample_mapping"""]				)
        _UpperCAmelCase    :		List[str]     	=					images

        return encoded_inputs







 def _snake_case					(				self    ,a_    ,a_				)						->  str:
        # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
        _UpperCAmelCase    :		Optional[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 _snake_case					(				self    ,*a_    ,**a_				)						->  Union[str, Any]:
        return self.tokenizer.batch_decode(*a_    ,**a_				)







 def _snake_case					(				self    ,*a_    ,**a_				)						->  Dict:
        return self.tokenizer.decode(*a_    ,**a_				)







 @property
 def _snake_case					(				self				)						->  Optional[int]:
        return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]







 @property
 def _snake_case					(				self				)						->  Union[str, Any]:
        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 _snake_case					(				self				)						->  List[str]:
        warnings.warn(
            """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""    ,a_    ,)
        return self.image_processor








'''simple docstring'''






from unittest.mock import patch

import datasets
from datasets import Dataset




def        snake_case_   (	)->						str:





       '''simple docstring'''


       _UpperCAmelCase    :		Tuple     	=					Dataset.from_dict({"""col_1""": [3, 2, 0, 1]}						)

       with patch("""tqdm.auto.tqdm"""						) as mock_tqdm:
              datasets.disable_progress_bar()
              dset.map(lambda lowerCAmelCase_						: {"col_2": x["col_1"] + 1}						)
              mock_tqdm.assert_not_called()

              mock_tqdm.reset_mock()

              datasets.enable_progress_bar()
              dset.map(lambda lowerCAmelCase_						: {"col_2": x["col_1"] + 1}						)
              mock_tqdm.assert_called()



