



"""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 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					_a       =				['image_processor', 'tokenizer']
					_a       =				'LayoutLMv2ImageProcessor'
					_a       =				('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')



					def __init__(			self				:    Optional[Any],    lowerCamelCase				:    Any=None,    lowerCamelCase				:    int=None,    **lowerCamelCase				:    Any	)->   Optional[int]:
								lowerCamelCase__						:						List[str]						      =None
								if "feature_extractor" in kwargs:
											warnings.warn(
											    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
											    ''' instead.''',    lowerCamelCase,    )
											lowerCamelCase__						:						Dict						      =kwargs.pop('''feature_extractor'''	)

								lowerCamelCase__						:						Tuple						      =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],    lowerCamelCase				:    Tuple,    lowerCamelCase				:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,    lowerCamelCase				:    Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,    lowerCamelCase				:    Union[List[List[int]], List[List[List[int]]]] = None,    lowerCamelCase				:    Optional[Union[List[int], List[List[int]]]] = None,    lowerCamelCase				:    bool = True,    lowerCamelCase				:    Union[bool, str, PaddingStrategy] = False,    lowerCamelCase				:    Union[bool, str, TruncationStrategy] = False,    lowerCamelCase				:    Optional[int] = None,    lowerCamelCase				:    int = 0,    lowerCamelCase				:    Optional[int] = None,    lowerCamelCase				:    Optional[bool] = None,    lowerCamelCase				:    Optional[bool] = None,    lowerCamelCase				:    bool = False,    lowerCamelCase				:    bool = False,    lowerCamelCase				:    bool = False,    lowerCamelCase				:    bool = False,    lowerCamelCase				:    bool = True,    lowerCamelCase				:    Optional[Union[str, TensorType]] = None,    **lowerCamelCase				:    Optional[Any],    )->   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
								lowerCamelCase__						:						Dict						      =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	):
														lowerCamelCase__						:						List[str]						      =[text]  # add batch dimension (as the image processor always adds a batch dimension)
											lowerCamelCase__						:						int						      =features['''words''']

								lowerCamelCase__						:						Tuple						      =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
								lowerCamelCase__						:						List[Any]						      =features.pop('''pixel_values'''	)
								if return_overflowing_tokens is True:
											lowerCamelCase__						:						Union[str, Any]						      =self.get_overflowing_images(lowerCamelCase,    encoded_inputs['''overflow_to_sample_mapping''']	)
								lowerCamelCase__						:						Optional[int]						      =images

								return encoded_inputs



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



					def 						snake_case			(			self				:    Tuple,    *lowerCamelCase				:    Dict,    **lowerCamelCase				:    Tuple	)->   Any:
								return self.tokenizer.decode(*lowerCamelCase,    **lowerCamelCase	)



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



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









import numpy as np

def 			a		( _UpperCAmelCase				:   str					,  _UpperCAmelCase				:   Any="utf-8"					,  _UpperCAmelCase				:   List[Any]="surrogatepass"		):







		'''simple docstring'''





		__UpperCAmelCase       :	Union[str, Any]						=      bin(int.from_bytes(text.encode(_UpperCAmelCase					,  _UpperCAmelCase		)					,  '''big'''		)		)[2:]
		return bits.zfill(8 * ((len(_UpperCAmelCase		) + 7) // 8)		)

def 			a		( _UpperCAmelCase				:   Dict					,  _UpperCAmelCase				:   Any="utf-8"					,  _UpperCAmelCase				:   str="surrogatepass"		):







		'''simple docstring'''





		__UpperCAmelCase       :	List[str]						=      int(_UpperCAmelCase					,  2		)
		return n.to_bytes((n.bit_length() + 7) // 8					,  '''big'''		).decode(_UpperCAmelCase					,  _UpperCAmelCase		) or "\0"

def 			a		( _UpperCAmelCase				:   List[Any]					,  _UpperCAmelCase				:   List[str]		):







		'''simple docstring'''





		if size_par + len(_UpperCAmelCase		) <= 2**size_par - (len(_UpperCAmelCase		) - 1):
				raise ValueError('''size of parity don\'t match with size of data'''		)

		__UpperCAmelCase       :	str						=      []
		__UpperCAmelCase       :	Any						=      []
		__UpperCAmelCase       :	Any						=      [bin(_UpperCAmelCase		)[2:] for x in range(1					,  size_par + len(_UpperCAmelCase		) + 1		)]

		# sorted information data for the size of the output data
		__UpperCAmelCase       :	Optional[Any]						=      []
		# data position template + parity
		__UpperCAmelCase       :	Any						=      []
		# parity bit counter
		__UpperCAmelCase       :	Tuple						=      0
		# counter position of data bits
		__UpperCAmelCase       :	Optional[int]						=      0

		for x in range(1					,  size_par + len(_UpperCAmelCase		) + 1		):
				# Performs a template of bit positions - who should be given,
				# and who should be parity
				if qtd_bp < size_par:
						if (np.log(_UpperCAmelCase		) / np.log(2		)).is_integer():
								data_out_gab.append('''P'''		)
								__UpperCAmelCase       :	Union[str, Any]						=      qtd_bp + 1
						else:
								data_out_gab.append('''D'''		)
				else:
						data_out_gab.append('''D'''		)

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_ord.append(data[cont_data]		)
						cont_data += 1
				else:
						data_ord.append(_UpperCAmelCase		)

    # Calculates parity
		__UpperCAmelCase       :	Union[str, Any]						=      0  # parity bit counter
		for bp in range(1					,  size_par + 1		):
				# Bit counter one for a given parity
				__UpperCAmelCase       :	List[str]						=      0
				# counter to control the loop reading
				__UpperCAmelCase       :	str						=      0
				for x in data_ord:
						if x is not None:
								try:
										__UpperCAmelCase       :	Tuple						=      (bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										__UpperCAmelCase       :	List[str]						=      '''0'''
								if aux == "1" and x == "1":
										cont_bo += 1
						cont_loop += 1
				parity.append(cont_bo % 2		)

				qtd_bp += 1

		# Mount the message
		__UpperCAmelCase       :	int						=      0  # parity bit counter
		for x in range(0					,  size_par + len(_UpperCAmelCase		)		):
				if data_ord[x] is None:
						data_out.append(str(parity[cont_bp]		)		)
						cont_bp += 1
				else:
						data_out.append(data_ord[x]		)

		return data_out

def 			a		( _UpperCAmelCase				:   List[str]					,  _UpperCAmelCase				:   str		):







		'''simple docstring'''





		__UpperCAmelCase       :	str						=      []
		# Parity bit counter
		__UpperCAmelCase       :	List[str]						=      0
		# Counter p data bit reading
		__UpperCAmelCase       :	int						=      0
		# list of parity received
		__UpperCAmelCase       :	int						=      []
		__UpperCAmelCase       :	List[Any]						=      []

		for x in range(1					,  len(_UpperCAmelCase		) + 1		):
				# Performs a template of bit positions - who should be given,
				#  and who should be parity
				if qtd_bp < size_par and (np.log(_UpperCAmelCase		) / np.log(2		)).is_integer():
						data_out_gab.append('''P'''		)
						__UpperCAmelCase       :	Union[str, Any]						=      qtd_bp + 1
				else:
						data_out_gab.append('''D'''		)

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_output.append(data[cont_data]		)
				else:
						parity_received.append(data[cont_data]		)
				cont_data += 1

		# -----------calculates the parity with the data
		__UpperCAmelCase       :	Optional[int]						=      []
		__UpperCAmelCase       :	Dict						=      []
		__UpperCAmelCase       :	Union[str, Any]						=      [bin(_UpperCAmelCase		)[2:] for x in range(1					,  size_par + len(_UpperCAmelCase		) + 1		)]

		#  sorted information data for the size of the output data
		__UpperCAmelCase       :	str						=      []
		# Data position feedback + parity
		__UpperCAmelCase       :	Any						=      []
		# Parity bit counter
		__UpperCAmelCase       :	int						=      0
		# Counter p data bit reading
		__UpperCAmelCase       :	List[Any]						=      0

		for x in range(1					,  size_par + len(_UpperCAmelCase		) + 1		):
				# Performs a template position of bits - who should be given,
				# and who should be parity
				if qtd_bp < size_par and (np.log(_UpperCAmelCase		) / np.log(2		)).is_integer():
						data_out_gab.append('''P'''		)
						__UpperCAmelCase       :	Dict						=      qtd_bp + 1
				else:
						data_out_gab.append('''D'''		)

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_ord.append(data_output[cont_data]		)
						cont_data += 1
				else:
						data_ord.append(_UpperCAmelCase		)

    # Calculates parity
		__UpperCAmelCase       :	Union[str, Any]						=      0  # parity bit counter
		for bp in range(1					,  size_par + 1		):
				# Bit counter one for a certain parity
				__UpperCAmelCase       :	Union[str, Any]						=      0
				# Counter to control loop reading
				__UpperCAmelCase       :	Optional[Any]						=      0
				for x in data_ord:
						if x is not None:
								try:
										__UpperCAmelCase       :	int						=      (bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										__UpperCAmelCase       :	int						=      '''0'''
								if aux == "1" and x == "1":
										cont_bo += 1
						cont_loop += 1
				parity.append(str(cont_bo % 2		)		)

				qtd_bp += 1

		# Mount the message
		__UpperCAmelCase       :	Any						=      0  # Parity bit counter
		for x in range(0					,  size_par + len(_UpperCAmelCase		)		):
				if data_ord[x] is None:
						data_out.append(str(parity[cont_bp]		)		)
						cont_bp += 1
				else:
						data_out.append(data_ord[x]		)

		__UpperCAmelCase       :	Optional[Any]						=      parity_received == parity
		return data_output, ack





