

'''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      lowerCAmelCase   (					__a      ):
       lowerCAmelCase_				=       ['''image_processor''', '''tokenizer''']
       lowerCAmelCase_				=       '''LayoutLMv2ImageProcessor'''
       lowerCAmelCase_				=       ('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')







       def __init__(							self	:       int  ,					__lowercase	:       List[str]=None  ,					__lowercase	:       Dict=None  ,					**__lowercase	:       Tuple					):







            """simple docstring"""







            if "feature_extractor" in kwargs:
                 warnings.warn(
                     'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                     ' instead.'  ,					lowerCAmelCase__  ,					)
                 __lowercase							=kwargs.pop('feature_extractor'					)

            __lowercase							=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__(lowerCAmelCase__  ,					lowerCAmelCase__					)







       def __call__(							self	:       Union[str, Any]  ,					__lowercase	:       Optional[int]  ,					__lowercase	:       List[str] = None  ,					__lowercase	:       Any = None  ,					__lowercase	:       Optional[int] = None  ,					__lowercase	:       int = None  ,					__lowercase	:       str = True  ,					__lowercase	:       Union[str, Any] = False  ,					__lowercase	:       Optional[int] = None  ,					__lowercase	:       List[Any] = None  ,					__lowercase	:       Tuple = 0  ,					__lowercase	:       Any = None  ,					__lowercase	:       Any = None  ,					__lowercase	:       List[str] = None  ,					__lowercase	:       List[str] = False  ,					__lowercase	:       List[str] = False  ,					__lowercase	:       Tuple = False  ,					__lowercase	:       Tuple = False  ,					__lowercase	:       int = True  ,					__lowercase	:       Any = None  ,					**__lowercase	:       Union[str, 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
            __lowercase							=self.image_processor(images=lowerCAmelCase__  ,					return_tensors=lowerCAmelCase__					)

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

            __lowercase							=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=lowerCAmelCase__  ,					add_special_tokens=lowerCAmelCase__  ,					padding=lowerCAmelCase__  ,					truncation=lowerCAmelCase__  ,					max_length=lowerCAmelCase__  ,					stride=lowerCAmelCase__  ,					pad_to_multiple_of=lowerCAmelCase__  ,					return_token_type_ids=lowerCAmelCase__  ,					return_attention_mask=lowerCAmelCase__  ,					return_overflowing_tokens=lowerCAmelCase__  ,					return_special_tokens_mask=lowerCAmelCase__  ,					return_offsets_mapping=lowerCAmelCase__  ,					return_length=lowerCAmelCase__  ,					verbose=lowerCAmelCase__  ,					return_tensors=lowerCAmelCase__  ,					**lowerCAmelCase__  ,					)

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

            return encoded_inputs







       def 	snake_case      (							self	:       Optional[Any]  ,					__lowercase	:       Tuple  ,					__lowercase	:       Any					):







            """simple docstring"""







            __lowercase							=[]
            for sample_idx in overflow_to_sample_mapping:
                 images_with_overflow.append(images[sample_idx]					)

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

            return images_with_overflow







       def 	snake_case      (							self	:       Tuple  ,					*__lowercase	:       Dict  ,					**__lowercase	:       Optional[Any]					):







            """simple docstring"""







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







       def 	snake_case      (							self	:       List[str]  ,					*__lowercase	:       Optional[int]  ,					**__lowercase	:       List[Any]					):







            """simple docstring"""







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







       @property
       def 	snake_case      (							self	:       List[str]					):







            """simple docstring"""







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







       @property
       def 	snake_case      (							self	:       Optional[int]					):







            """simple docstring"""







            warnings.warn(
                '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'  ,					lowerCAmelCase__  ,					)
            return self.image_processor_class







       @property
       def 	snake_case      (							self	:       Optional[int]					):







            """simple docstring"""







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




"""simple docstring"""





from __future__ import annotations

__magic_name__       =			[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__magic_name__       =			[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = []
		__SCREAMING_SNAKE_CASE            = len(UpperCamelCase_				)

		for i in range(UpperCamelCase_				):
				__SCREAMING_SNAKE_CASE            = -1
				for j in range(i + 1  ,				UpperCamelCase_				):
						if arr[i] < arr[j]:
								__SCREAMING_SNAKE_CASE            = arr[j]
								break
				result.append(UpperCamelCase_				)
		return result



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = []
		for i, outer in enumerate(UpperCamelCase_				):
				__SCREAMING_SNAKE_CASE            = -1
				for inner in arr[i + 1 :]:
						if outer < inner:
								__SCREAMING_SNAKE_CASE            = inner
								break
				result.append(UpperCamelCase_				)
		return result



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = len(UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = []
		__SCREAMING_SNAKE_CASE            = [-1] * arr_size

		for index in reversed(range(UpperCamelCase_				)				):
				if stack:
						while stack[-1] <= arr[index]:
								stack.pop()
								if not stack:
										break
				if stack:
						__SCREAMING_SNAKE_CASE            = stack[-1]
				stack.append(arr[index]				)
		return result


if __name__ == "__main__":
							from doctest import testmod
							from timeit import timeit

							testmod()
							print(next_greatest_element_slow(arr))
							print(next_greatest_element_fast(arr))
							print(next_greatest_element(arr))

							__magic_name__       =			(
							    "from __main__ import arr, next_greatest_element_slow, "
							    "next_greatest_element_fast, next_greatest_element"
							)
							print(
							    "next_greatest_element_slow():",
							    timeit("next_greatest_element_slow(arr)", setup=setup),
							)
							print(
							    "next_greatest_element_fast():",
							    timeit("next_greatest_element_fast(arr)", setup=setup),
							)
							print(
							    "     next_greatest_element():",
							    timeit("next_greatest_element(arr)", setup=setup),
							)




