



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__					(  _lowerCamelCase       ):
				A_   :     Optional[Any]								=     ['image_processor', 'tokenizer']
				A_   :     int								=     'LayoutLMv2ImageProcessor'
				A_   :     Optional[Any]								=     ('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')


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

											A      						=    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      :  List[Any]						, __UpperCamelCase      :  List[Any]						, __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] = None						, __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[str]						, )		->     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
											A      						=    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       ):
																									A      						=    [text]  # add batch dimension (as the image processor always adds a batch dimension)
																		A      						=    features['words']

											A      						=    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
											A      						=    features.pop('pixel_values'       )
											if return_overflowing_tokens is True:
																		A      						=    self.get_overflowing_images(__UpperCamelCase						, encoded_inputs['overflow_to_sample_mapping']       )
											A      						=    images

											return encoded_inputs


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


				def __UpperCamelCase						(							self      :  List[str]						, *__UpperCamelCase      :  List[Any]						, **__UpperCamelCase      :  Optional[int]       )		->     Dict:
											return self.tokenizer.decode(*__UpperCamelCase						, **__UpperCamelCase       )


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


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




import math
import sys






def  lowerCamelCase_	(      lowerCAmelCase__	:	str				)       ->							str:

							'''simple docstring'''






							A      						=    ''
							try:
														with open(lowerCAmelCase__			,	'rb'				) as binary_file:
																					A      						=    binary_file.read()
														for dat in data:
																					A      						=    F'''{dat:08b}'''
																					result += curr_byte
														return result
							except OSError:
														print('File not accessible'				)
														sys.exit()






def  lowerCamelCase_	(      lowerCAmelCase__	:	str				)       ->							str:

							'''simple docstring'''






							A      						=    {'0': '0', '1': '1'}
							A						,   A      						=    '', ''
							A      						=    len(lowerCAmelCase__				)

							for i in range(len(lowerCAmelCase__				)				):
														curr_string += data_bits[i]
														if curr_string not in lexicon:
																					continue

														A      						=    lexicon[curr_string]
														result += last_match_id
														A      						=    last_match_id + '0'

														if math.loga(lowerCAmelCase__				).is_integer():
																					A      						=    {}
																					for curr_key in list(lowerCAmelCase__				):
																												A      						=    lexicon.pop(lowerCAmelCase__				)
																					A      						=    new_lex

														A      						=    last_match_id + '1'
														index += 1
														A      						=    ''
							return result






def  lowerCamelCase_	(      lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	str				)       ->							None:

							'''simple docstring'''






							A      						=    8
							try:
														with open(lowerCAmelCase__			,	'wb'				) as opened_file:
																					A      						=    [
																					    to_write[i : i + byte_length]
																					    for i in range(0			,	len(lowerCAmelCase__				)			,	lowerCAmelCase__				)
																					]

																					if len(result_byte_array[-1]				) % byte_length == 0:
																												result_byte_array.append('10000000'				)
																					else:
																												result_byte_array[-1] += "1" + "0" * (
																												    byte_length - len(result_byte_array[-1]				) - 1
																												)

																					for elem in result_byte_array[:-1]:
																												opened_file.write(int(lowerCAmelCase__			,	2				).to_bytes(1			,	byteorder='big'				)				)
							except OSError:
														print('File not accessible'				)
														sys.exit()






def  lowerCamelCase_	(      lowerCAmelCase__	:	str				)       ->							str:

							'''simple docstring'''






							A      						=    0
							for letter in data_bits:
														if letter == "1":
																					break
														counter += 1

							A      						=    data_bits[counter:]
							A      						=    data_bits[counter + 1 :]
							return data_bits






def  lowerCamelCase_	(      lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	str				)       ->							None:

							'''simple docstring'''






							A      						=    read_file_binary(lowerCAmelCase__				)
							A      						=    remove_prefix(lowerCAmelCase__				)
							A      						=    decompress_data(lowerCAmelCase__				)
							write_file_binary(lowerCAmelCase__			,	lowerCAmelCase__				)


if __name__ == "__main__":
				compress(sys.argv[1], sys.argv[2])