
"""simple docstring"""



import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def 					lowerCamelCase   (     _snake_case ):
    def wrapper(*_snake_case    ,**_snake_case ):
        UpperCAmelCase__	:      Any     							=					timeit.default_timer()
        UpperCAmelCase__	:      Optional[int]     							=					func(*_snake_case    ,**_snake_case )
        UpperCAmelCase__	:      int     							=					timeit.default_timer() - starttime
        return delta

    UpperCAmelCase__	:      Any     							=					func.__name__

    return wrapper
def 					lowerCamelCase   (     _snake_case    ,_snake_case=100    ,_snake_case=None ):
    UpperCAmelCase__	:      int     							=					[]
    UpperCAmelCase__	:      str     							=					seq_shapes or {}
    for i in range(_snake_case ):
        UpperCAmelCase__	:      Optional[Any]     							=					{}
        for col_id, (k, v) in enumerate(features.items() ):
            if isinstance(_snake_case    ,_ArrayXD ):
                UpperCAmelCase__	:      Union[str, Any]     							=					np.random.rand(*v.shape ).astype(v.dtype )
            elif isinstance(_snake_case    ,datasets.Value ):
                if v.dtype == "string":
                    UpperCAmelCase__	:      str     							=					'The small grey turtle was surprisingly fast when challenged.'
                else:
                    UpperCAmelCase__	:      Optional[Any]     							=					np.random.randint(10    ,size=1 ).astype(v.dtype ).item()
            elif isinstance(_snake_case    ,datasets.Sequence ):
                while isinstance(_snake_case    ,datasets.Sequence ):
                    UpperCAmelCase__	:      Optional[Any]     							=					v.feature
                UpperCAmelCase__	:      Tuple     							=					seq_shapes[k]
                UpperCAmelCase__	:      int     							=					np.random.rand(*_snake_case ).astype(v.dtype )
            UpperCAmelCase__	:      Optional[int]     							=					data

        dummy_data.append((i, example) )

    return dummy_data

def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case=100    ,_snake_case=None ):
    UpperCAmelCase__	:      List[str]     							=					generate_examples(_snake_case    ,num_examples=_snake_case    ,seq_shapes=_snake_case )

    with ArrowWriter(features=_snake_case    ,path=_snake_case ) as writer:
        for key, record in dummy_data:
            UpperCAmelCase__	:      Dict     							=					features.encode_example(_snake_case )
            writer.write(_snake_case )

        UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[int]     							=					writer.finalize()

    if not num_final_examples == num_examples:
        raise ValueError(
            F'''Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.''' )

    UpperCAmelCase__	:      int     							=					datasets.Dataset.from_file(filename=_snake_case    ,info=datasets.DatasetInfo(features=_snake_case ) )

    return dataset



"""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 				a			(					lowercase      ):
      UpperCamelCase							:   Tuple           =     ["""image_processor""", """tokenizer"""]
      UpperCamelCase							:   List[str]           =     """LayoutLMv2ImageProcessor"""
      UpperCamelCase							:   Optional[int]           =     ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")



      def __init__(   self     ,	UpperCamelCase_=None     ,	UpperCamelCase_=None     ,	**UpperCamelCase_      ):
          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__	:      Tuple     							=					kwargs.pop('feature_extractor'      )

          UpperCAmelCase__	:      Optional[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__(UpperCamelCase_     ,	UpperCamelCase_      )



      def __call__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = True     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = 0     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = True     ,	UpperCamelCase_ = None     ,	**UpperCamelCase_     ,	):
          # 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__	:      List[Any]     							=					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__	:      Any     							=					[text]  # add batch dimension (as the image processor always adds a batch dimension)
              UpperCAmelCase__	:      Union[str, Any]     							=					features['words']

          UpperCAmelCase__	:      List[Any]     							=					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__	:      List[Any]     							=					features.pop('pixel_values'      )
          if return_overflowing_tokens is True:
              UpperCAmelCase__	:      int     							=					self.get_overflowing_images(UpperCamelCase_     ,	encoded_inputs['overflow_to_sample_mapping']      )
          UpperCAmelCase__	:      Dict     							=					images

          return encoded_inputs



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          UpperCAmelCase__	:      Dict     							=					[]
          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  __snake_case							(   self     ,	*UpperCamelCase_     ,	**UpperCamelCase_      ):
          return self.tokenizer.batch_decode(*UpperCamelCase_     ,	**UpperCamelCase_      )



      def  __snake_case							(   self     ,	*UpperCamelCase_     ,	**UpperCamelCase_      ):
          return self.tokenizer.decode(*UpperCamelCase_     ,	**UpperCamelCase_      )



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



      @property
      def  __snake_case							(   self      ):
          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  __snake_case							(   self      ):
          warnings.warn(
              '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'     ,	UpperCamelCase_     ,	)
          return self.image_processor

