






"""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        _UpperCamelCase		(			__snake_case):
    __lowerCamelCase    =	["image_processor", "tokenizer"]
    __lowerCamelCase    =	"LayoutLMv2ImageProcessor"
    __lowerCamelCase    =	("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")






    def __init__(self    ,   lowerCamelCase__=None    ,   lowerCamelCase__=None    ,   **lowerCamelCase__ ):


      """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."""    ,   __A    ,   )
        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__(__A    ,   __A )






    def __call__(self    ,   lowerCamelCase__    ,   lowerCamelCase__ = None    ,   lowerCamelCase__ = None    ,   lowerCamelCase__ = None    ,   lowerCamelCase__ = None    ,   lowerCamelCase__ = True    ,   lowerCamelCase__ = False    ,   lowerCamelCase__ = None    ,   lowerCamelCase__ = None    ,   lowerCamelCase__ = 0    ,   lowerCamelCase__ = None    ,   lowerCamelCase__ = None    ,   lowerCamelCase__ = None    ,   lowerCamelCase__ = False    ,   lowerCamelCase__ = False    ,   lowerCamelCase__ = False    ,   lowerCamelCase__ = False    ,   lowerCamelCase__ = True    ,   lowerCamelCase__ = None    ,   **lowerCamelCase__    ,   ):


      """simple docstring"""
      # 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=__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 ):
          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=__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
      A__           =				features.pop("""pixel_values""" )
      if return_overflowing_tokens is True:
        A__           =				self.get_overflowing_images(__A    ,   encoded_inputs["""overflow_to_sample_mapping"""] )
      A__           =				images

      return encoded_inputs






    def 			A     (self    ,   lowerCamelCase__    ,   lowerCamelCase__ ):


      """simple docstring"""
      # 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(__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 			A     (self    ,   *lowerCamelCase__    ,   **lowerCamelCase__ ):


      """simple docstring"""
      return self.tokenizer.batch_decode(*__A    ,   **__A )






    def 			A     (self    ,   *lowerCamelCase__    ,   **lowerCamelCase__ ):


      """simple docstring"""
      return self.tokenizer.decode(*__A    ,   **__A )






    @property
    def 			A     (self ):


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






    @property
    def 			A     (self ):


      """simple docstring"""
      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 			A     (self ):


      """simple docstring"""
      warnings.warn(
          """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""    ,   __A    ,   )
      return self.image_processor






import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int = 8				)			->						str:
       SCREAMING_SNAKE_CASE_							:				str		=ascii_letters + digits + punctuation
       return "".join(secrets.choice(UpperCAmelCase_				) for _ in range(UpperCAmelCase_				)				)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     int				)			->						str:
       # Password Generator = full boot with random_number, random_letters, and
       # random_character FUNCTIONS
       # Put your code here...
       i -= len(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				Any		=i // 3
       SCREAMING_SNAKE_CASE_							:				Optional[Any]		=i % 3
       # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
       #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
       SCREAMING_SNAKE_CASE_							:				int		=(
           chars_incl
           + random(UpperCAmelCase_			,							quotient + remainder				)
           + random(UpperCAmelCase_			,							UpperCAmelCase_				)
           + random(UpperCAmelCase_			,							UpperCAmelCase_				)
       )
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=list(UpperCAmelCase_				)
       shuffle(UpperCAmelCase_				)
       return "".join(UpperCAmelCase_				)

       # random is a generalised function for letters, characters and numbers
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     int				)			->						str:
       return "".join(secrets.choice(UpperCAmelCase_				) for _ in range(UpperCAmelCase_				)				)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Tuple			,							UpperCAmelCase_	:     Tuple				)			->						Optional[int]:
       pass  # Put your code here...
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Optional[Any]			,							UpperCAmelCase_	:     Tuple				)			->						Dict:
       pass  # Put your code here...
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Dict			,							UpperCAmelCase_	:     List[str]				)			->						Optional[int]:
       pass  # Put your code here...
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     int = 8				)			->						bool:

       if len(UpperCAmelCase_				) < min_length:
              # Your Password must be at least 8 characters long
              return False

       SCREAMING_SNAKE_CASE_							:				List[str]		=any(char in ascii_uppercase for char in password				)
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=any(char in ascii_lowercase for char in password				)
       SCREAMING_SNAKE_CASE_							:				List[Any]		=any(char in digits for char in password				)
       SCREAMING_SNAKE_CASE_							:				str		=any(char in punctuation for char in password				)

       return upper and lower and num and spec_char
       # Passwords should contain UPPERCASE, lowerase
       # numbers, and special characters
def 							SCREAMING_SNAKE_CASE_	(					)			->						Optional[int]:
       SCREAMING_SNAKE_CASE_							:				Any		=int(input('''Please indicate the max length of your password: '''				).strip()				)
       SCREAMING_SNAKE_CASE_							:				Any		=input(
           '''Please indicate the characters that must be in your password: '''				).strip()
       print('''Password generated:'''			,							password_generator(UpperCAmelCase_				)				)
       print(
           '''Alternative Password generated:'''			,							alternative_password_generator(UpperCAmelCase_			,							UpperCAmelCase_				)			,							)
       print('''[If you are thinking of using this passsword, You better save it.]'''				)


if __name__ == "__main__":
 main()
