


"""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  (snake_case__):





							'''simple docstring'''

							__lowercase:		str				   =     ["""image_processor""", """tokenizer"""]
							__lowercase:		Optional[int]				   =     """LayoutLMv2ImageProcessor"""
							__lowercase:		Tuple				   =     ("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")



							def __init__(					self				:   Any		,							UpperCAmelCase_				:   Optional[int]=None		,							UpperCAmelCase_				:   str=None		,							**UpperCAmelCase_				:   Union[str, Any]							)       ->Any:



														"""simple docstring"""



														snake_case_     							=		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_		,							)
																					snake_case_     							=		kwargs.pop("""feature_extractor"""							)

														snake_case_     							=		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				:   Tuple		,							UpperCAmelCase_				:   List[str]		,							UpperCAmelCase_				:   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,							UpperCAmelCase_				:   Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None		,							UpperCAmelCase_				:   Union[List[List[int]], List[List[List[int]]]] = None		,							UpperCAmelCase_				:   Optional[Union[List[int], List[List[int]]]] = None		,							UpperCAmelCase_				:   bool = True		,							UpperCAmelCase_				:   Union[bool, str, PaddingStrategy] = False		,							UpperCAmelCase_				:   Union[bool, str, TruncationStrategy] = False		,							UpperCAmelCase_				:   Optional[int] = None		,							UpperCAmelCase_				:   int = 0		,							UpperCAmelCase_				:   Optional[int] = None		,							UpperCAmelCase_				:   Optional[bool] = None		,							UpperCAmelCase_				:   Optional[bool] = None		,							UpperCAmelCase_				:   bool = False		,							UpperCAmelCase_				:   bool = False		,							UpperCAmelCase_				:   bool = False		,							UpperCAmelCase_				:   bool = False		,							UpperCAmelCase_				:   bool = True		,							UpperCAmelCase_				:   Optional[Union[str, TensorType]] = None		,							**UpperCAmelCase_				:   List[Any]		,							)       ->BatchEncoding:



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

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

														return encoded_inputs



							def 		lowerCAmelCase							(					self				:   int		,							UpperCAmelCase_				:   Union[str, Any]		,							UpperCAmelCase_				:   int							)       ->int:



														"""simple docstring"""



														snake_case_     							=		[]
														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				:   str		,							*UpperCAmelCase_				:   Tuple		,							**UpperCAmelCase_				:   Dict							)       ->Tuple:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   List[str]		,							*UpperCAmelCase_				:   int		,							**UpperCAmelCase_				:   Dict							)       ->Optional[Any]:



														"""simple docstring"""



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



							@property
							def 		lowerCAmelCase							(					self				:   str							)       ->Tuple:



														"""simple docstring"""



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



							@property
							def 		lowerCAmelCase							(					self				:   Optional[int]							)       ->List[str]:



														"""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				:   Tuple							)       ->int:



														"""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"""
from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize





class   __A  (snake_case__):





							'''simple docstring'''

							__lowercase:		Dict				   =     1
							__lowercase:		List[str]				   =     2
							__lowercase:		int				   =     3



							def __repr__(					self				:   Optional[Any]							)       ->Optional[int]:



														"""simple docstring"""



														return F"""{self.__class__.__name__}.{self.name}"""







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					float:
							snake_case_							,    snake_case_     							=		point
							return degrees(atana(y - miny					,			x - minx					)					)







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Direction:
							snake_case_							,    snake_case_     							=		starting
							snake_case_							,    snake_case_     							=		via
							snake_case_							,    snake_case_     							=		target
							snake_case_     							=		degrees(atana(ya - ya					,			xa - xa					)					)
							via_angle %= 360
							snake_case_     							=		degrees(atana(ya - ya					,			xa - xa					)					)
							target_angle %= 360
							# t-
							#  \ \
							#   \ v
							#    \|
							#     s
							# via_angle is always lower than target_angle, if direction is left.
							# If they are same, it means they are on a same line of convex hull.
							if target_angle > via_angle:
														return Direction.left
							elif target_angle == via_angle:
														return Direction.straight
							else:
														return Direction.right







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					list[tuple[int, int]]:

							if len(_SCREAMING_SNAKE_CASE					) <= 2:
														# There is no convex hull
														raise ValueError("""graham_scan: argument must contain more than 3 points."""					)
							if len(_SCREAMING_SNAKE_CASE					) == 3:
														return points
							# find the lowest and the most left point
							snake_case_     							=		0
							snake_case_							,    snake_case_     							=		maxsize, maxsize
							for i, point in enumerate(_SCREAMING_SNAKE_CASE					):
														snake_case_     							=		point[0]
														snake_case_     							=		point[1]
														if y < miny:
																					snake_case_     							=		y
																					snake_case_     							=		x
																					snake_case_     							=		i
														if y == miny and x < minx:
																					snake_case_     							=		x
																					snake_case_     							=		i

    # remove the lowest and the most left point from points for preparing for sort
							points.pop(_SCREAMING_SNAKE_CASE					)

							snake_case_     							=		sorted(_SCREAMING_SNAKE_CASE					,			key=lambda _SCREAMING_SNAKE_CASE					: angle_comparer(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)					)
							# This insert actually costs complexity,
							# and you should instead add (minx, miny) into stack later.
							# I'm using insert just for easy understanding.
							sorted_points.insert(0					,			(minx, miny)					)

							snake_case_     							=		deque()
							stack.append(sorted_points[0]					)
							stack.append(sorted_points[1]					)
							stack.append(sorted_points[2]					)
							# In any ways, the first 3 points line are towards left.
							# Because we sort them the angle from minx, miny.
							snake_case_     							=		Direction.left

							for i in range(3					,			len(_SCREAMING_SNAKE_CASE					)					):
														while True:
																					snake_case_     							=		stack[-2]
																					snake_case_     							=		stack[-1]
																					snake_case_     							=		sorted_points[i]
																					snake_case_     							=		check_direction(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)

																					if next_direction == Direction.left:
																												snake_case_     							=		Direction.left
																												break
																					if next_direction == Direction.straight:
																												if current_direction == Direction.left:
																																			# We keep current_direction as left.
																																			# Because if the straight line keeps as straight,
																																			# we want to know if this straight line is towards left.
																																			break
																												elif current_direction == Direction.right:
																																			# If the straight line is towards right,
																																			# every previous points on those straigh line is not convex hull.
																																			stack.pop()
																					if next_direction == Direction.right:
																												stack.pop()
														stack.append(sorted_points[i]					)
							return list(_SCREAMING_SNAKE_CASE					)



