





"""simple docstring"""


import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation




def        lowercase__     (       lowercase_ = 8      )       ->		str:




      """simple docstring"""

      _UpperCamelCase   :						Any    =							ascii_letters + digits + punctuation
      return "".join(secrets.choice(lowercase_      ) for _ in range(lowercase_      )      )




def        lowercase__     (       lowercase_		,lowercase_      )       ->		str:




      """simple docstring"""

      i -= len(lowercase_      )
      _UpperCamelCase   :						Tuple    =							i // 3
      _UpperCamelCase   :						Optional[Any]    =							i % 3
      # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
      #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
      _UpperCamelCase   :						Any    =							(
          chars_incl
          + random(lowercase_		,quotient + remainder      )
          + random(lowercase_		,lowercase_      )
          + random(lowercase_		,lowercase_      )
      )
      _UpperCamelCase   :						int    =							list(lowercase_      )
      shuffle(lowercase_      )
      return "".join(lowercase_      )

      # random is a generalised function for letters, characters and numbers




def        lowercase__     (       lowercase_		,lowercase_      )       ->		str:




      """simple docstring"""

      return "".join(secrets.choice(lowercase_      ) for _ in range(lowercase_      )      )




def        lowercase__     (       lowercase_		,lowercase_      )       ->		str:




      """simple docstring"""

      pass  # Put your code here...




def        lowercase__     (       lowercase_		,lowercase_      )       ->		Any:




      """simple docstring"""

      pass  # Put your code here...




def        lowercase__     (       lowercase_		,lowercase_      )       ->		List[str]:




      """simple docstring"""

      pass  # Put your code here...




def        lowercase__     (       lowercase_		,lowercase_ = 8      )       ->		bool:




      """simple docstring"""

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

      _UpperCamelCase   :						List[Any]    =							any(char in ascii_uppercase for char in password      )
      _UpperCamelCase   :						List[Any]    =							any(char in ascii_lowercase for char in password      )
      _UpperCamelCase   :						Dict    =							any(char in digits for char in password      )
      _UpperCamelCase   :						List[Any]    =							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        lowercase__     (       )       ->		Tuple:




      """simple docstring"""

      _UpperCamelCase   :						Optional[Any]    =							int(input("Please indicate the max length of your password: "      ).strip()      )
      _UpperCamelCase   :						Union[str, Any]    =							input(
          "Please indicate the characters that must be in your password: "      ).strip()
      print("Password generated:"		,password_generator(lowercase_      )      )
      print(
          "Alternative Password generated:"		,alternative_password_generator(lowercase_		,lowercase_      )		,)
      print("[If you are thinking of using this passsword, You better save it.]"      )


if __name__ == "__main__":
  main()







"""simple docstring"""


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase__						= logging.get_logger(__name__)


if is_vision_available():
  import PIL



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Optional[int]						  =       ["pixel_values"]

  def __init__(		self :							Tuple       , __a :							bool = True       , __a :							Dict[str, int] = None       , __a :							PILImageResampling = PILImageResampling.BICUBIC       , __a :							bool = True       , __a :							Dict[str, int] = None       , __a :							bool = True       , __a :							Union[int, float] = 1 / 255       , __a :							bool = True       , __a :							Optional[Union[float, List[float]]] = None       , __a :							Optional[Union[float, List[float]]] = None       , __a :							bool = True       , **__a :							List[Any]       , )		->					None:
        super().__init__(**__a						)
        _UpperCamelCase   :						Optional[int]    =							size if size is not None else {"shortest_edge": 224}
        _UpperCamelCase   :						Optional[int]    =							get_size_dict(__a       , default_to_square=__a						)
        _UpperCamelCase   :						Union[str, Any]    =							crop_size if crop_size is not None else {"height": 224, "width": 224}
        _UpperCamelCase   :						Tuple    =							get_size_dict(__a       , default_to_square=__a       , param_name="crop_size"						)

        _UpperCamelCase   :						Optional[Any]    =							do_resize
        _UpperCamelCase   :						Dict    =							size
        _UpperCamelCase   :						Any    =							resample
        _UpperCamelCase   :						Tuple    =							do_center_crop
        _UpperCamelCase   :						str    =							crop_size
        _UpperCamelCase   :						Any    =							do_rescale
        _UpperCamelCase   :						Dict    =							rescale_factor
        _UpperCamelCase   :						int    =							do_normalize
        _UpperCamelCase   :						Dict    =							image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        _UpperCamelCase   :						Tuple    =							image_std if image_std is not None else OPENAI_CLIP_STD
        _UpperCamelCase   :						List[Any]    =							do_convert_rgb

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , __a :							np.ndarray       , __a :							Dict[str, int]       , __a :							PILImageResampling = PILImageResampling.BICUBIC       , __a :							Optional[Union[str, ChannelDimension]] = None       , **__a :							str       , )		->					np.ndarray:
        _UpperCamelCase   :						Any    =							get_size_dict(__a       , default_to_square=__a						)
        if "shortest_edge" not in size:
              raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''						)
        _UpperCamelCase   :						Dict    =							get_resize_output_image_size(__a       , size=size["shortest_edge"]       , default_to_square=__a						)
        return resize(__a       , size=__a       , resample=__a       , data_format=__a       , **__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]       , __a :							np.ndarray       , __a :							Dict[str, int]       , __a :							Optional[Union[str, ChannelDimension]] = None       , **__a :							Tuple       , )		->					np.ndarray:
        _UpperCamelCase   :						List[str]    =							get_size_dict(__a						)
        if "height" not in size or "width" not in size:
              raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''						)
        return center_crop(__a       , size=(size["height"], size["width"])       , data_format=__a       , **__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]       , __a :							np.ndarray       , __a :							Union[int, float]       , __a :							Optional[Union[str, ChannelDimension]] = None       , **__a :							Optional[Any]       , )		->					List[str]:
        return rescale(__a       , scale=__a       , data_format=__a       , **__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple       , __a :							np.ndarray       , __a :							Union[float, List[float]]       , __a :							Union[float, List[float]]       , __a :							Optional[Union[str, ChannelDimension]] = None       , **__a :							Tuple       , )		->					np.ndarray:
        return normalize(__a       , mean=__a       , std=__a       , data_format=__a       , **__a						)


  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , __a :							ImageInput       , __a :							bool = None       , __a :							Dict[str, int] = None       , __a :							PILImageResampling = None       , __a :							bool = None       , __a :							int = None       , __a :							bool = None       , __a :							float = None       , __a :							bool = None       , __a :							Optional[Union[float, List[float]]] = None       , __a :							Optional[Union[float, List[float]]] = None       , __a :							bool = None       , __a :							Optional[Union[str, TensorType]] = None       , __a :							Optional[ChannelDimension] = ChannelDimension.FIRST       , **__a :							Union[str, Any]       , )		->					PIL.Image.Image:
        _UpperCamelCase   :						Tuple    =							do_resize if do_resize is not None else self.do_resize
        _UpperCamelCase   :						int    =							size if size is not None else self.size
        _UpperCamelCase   :						int    =							get_size_dict(__a       , param_name="size"       , default_to_square=__a						)
        _UpperCamelCase   :						List[Any]    =							resample if resample is not None else self.resample
        _UpperCamelCase   :						List[Any]    =							do_center_crop if do_center_crop is not None else self.do_center_crop
        _UpperCamelCase   :						Union[str, Any]    =							crop_size if crop_size is not None else self.crop_size
        _UpperCamelCase   :						List[Any]    =							get_size_dict(__a       , param_name="crop_size"       , default_to_square=__a						)
        _UpperCamelCase   :						Dict    =							do_rescale if do_rescale is not None else self.do_rescale
        _UpperCamelCase   :						Union[str, Any]    =							rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCamelCase   :						Union[str, Any]    =							do_normalize if do_normalize is not None else self.do_normalize
        _UpperCamelCase   :						List[Any]    =							image_mean if image_mean is not None else self.image_mean
        _UpperCamelCase   :						List[Any]    =							image_std if image_std is not None else self.image_std
        _UpperCamelCase   :						List[str]    =							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        _UpperCamelCase   :						Any    =							make_list_of_images(__a						)

        if not valid_images(__a						):
              raise ValueError(
                  "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                  "torch.Tensor, tf.Tensor or jax.ndarray."						)

        if do_resize and size is None:
              raise ValueError("Size must be specified if do_resize is True."						)

        if do_center_crop and crop_size is None:
              raise ValueError("Crop size must be specified if do_center_crop is True."						)

        if do_rescale and rescale_factor is None:
              raise ValueError("Rescale factor must be specified if do_rescale is True."						)

        if do_normalize and (image_mean is None or image_std is None):
              raise ValueError("Image mean and std must be specified if do_normalize is True."						)

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
              _UpperCamelCase   :						int    =							[convert_to_rgb(__a						) for image in images]

        # All transformations expect numpy arrays.
        _UpperCamelCase   :						List[Any]    =							[to_numpy_array(__a						) for image in images]

        if do_resize:
              _UpperCamelCase   :						Union[str, Any]    =							[self.resize(image=__a       , size=__a       , resample=__a						) for image in images]

        if do_center_crop:
              _UpperCamelCase   :						Optional[Any]    =							[self.center_crop(image=__a       , size=__a						) for image in images]

        if do_rescale:
              _UpperCamelCase   :						Dict    =							[self.rescale(image=__a       , scale=__a						) for image in images]

        if do_normalize:
              _UpperCamelCase   :						List[Any]    =							[self.normalize(image=__a       , mean=__a       , std=__a						) for image in images]

        _UpperCamelCase   :						List[Any]    =							[to_channel_dimension_format(__a       , __a						) for image in images]

        _UpperCamelCase   :						str    =							{"pixel_values": images}
        return BatchFeature(data=__a       , tensor_type=__a						)
