



"""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_     (	UpperCAmelCase_							):



						"""simple docstring"""
						__UpperCamelCase													=				["""image_processor""", """tokenizer"""]
						__UpperCamelCase													=				"""LayoutLMv2ImageProcessor"""
						__UpperCamelCase													=				("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")






						def __init__(   self					:List[Any]  ,    lowercase_					:List[Any]=None  ,    lowercase_					:Tuple=None  ,    **lowercase_					:Union[str, Any]   )							->     Any:
												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.'  ,    a__  ,    )
																		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__(a__  ,    a__   )






						def __call__(   self					:int  ,    lowercase_					:int  ,    lowercase_					:Any = None  ,    lowercase_					:str = None  ,    lowercase_					:Optional[int] = None  ,    lowercase_					:List[str] = None  ,    lowercase_					:Dict = True  ,    lowercase_					:int = False  ,    lowercase_					:Any = False  ,    lowercase_					:Union[str, Any] = None  ,    lowercase_					:Tuple = 0  ,    lowercase_					:Any = None  ,    lowercase_					:List[Any] = None  ,    lowercase_					:str = None  ,    lowercase_					:Dict = False  ,    lowercase_					:List[str] = False  ,    lowercase_					:Union[str, Any] = False  ,    lowercase_					:Union[str, Any] = False  ,    lowercase_					:List[Any] = True  ,    lowercase_					:Dict = None  ,    **lowercase_					:Dict  ,    )							->     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			 =					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			 =					[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=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			 =					features.pop('pixel_values'   )
												if return_overflowing_tokens is True:
																		UpperCAmelCase			 =					self.get_overflowing_images(a__  ,    encoded_inputs['overflow_to_sample_mapping']   )
												UpperCAmelCase			 =					images

												return encoded_inputs






						def       UpperCAmelCase__	(   self					:Tuple  ,    lowercase_					:List[str]  ,    lowercase_					:Union[str, Any]   )							->     str:
												# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
												UpperCAmelCase			 =					[]
												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       UpperCAmelCase__	(   self					:Dict  ,    *lowercase_					:int  ,    **lowercase_					:Tuple   )							->     List[Any]:
												return self.tokenizer.batch_decode(*a__  ,    **a__   )






						def       UpperCAmelCase__	(   self					:Union[str, Any]  ,    *lowercase_					:Optional[int]  ,    **lowercase_					:Dict   )							->     Optional[Any]:
												return self.tokenizer.decode(*a__  ,    **a__   )






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






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











'''simple docstring'''





import math
from decimal import Decimal, getcontext


def 					UpperCamelCase						(	snake_case = 4_00_00_00 ):




   '''simple docstring'''

   try:
      SCREAMING_SNAKE_CASE__      :					List[Any]							    =			int(snake_case )
   except (TypeError, ValueError):
      raise TypeError('''Parameter n must be int or castable to int.''' )
   if n <= 0:
      raise ValueError('''Parameter n must be greater than or equal to one.''' )
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			1_00
   SCREAMING_SNAKE_CASE__      :					int							    =			(Decimal(5 ) ** Decimal(0.5 ) + 1) / Decimal(2 )

   SCREAMING_SNAKE_CASE__      :					int							    =			(math.floor(math.log(n * (phi + 2)						,  snake_case ) - 1 ) // 3) * 3 + 2
   SCREAMING_SNAKE_CASE__      :					int							    =			Decimal(round(phi ** Decimal(index + 1 ) ) ) / (phi + 2)
   SCREAMING_SNAKE_CASE__      :					Any							    =			num // 2
   return int(snake_case )


if __name__ == "__main__":
       print(F"{solution() = }")

