


import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def 							__lowerCamelCase		(					UpperCAmelCase_		:						int = 8		):




      """simple docstring"""





      a		:Optional[int]									=			ascii_letters + digits + punctuation
      return "".join(secrets.choice(UpperCAmelCase_		) for _ in range(UpperCAmelCase_		)		)
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





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

      # random is a generalised function for letters, characters and numbers
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      return "".join(secrets.choice(UpperCAmelCase_		) for _ in range(UpperCAmelCase_		)		)
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Union[str, Any]					,    UpperCAmelCase_		:						Tuple		):




      """simple docstring"""





      pass  # Put your code here...
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Union[str, Any]					,    UpperCAmelCase_		:						str		):




      """simple docstring"""





      pass  # Put your code here...
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Optional[int]		):




      """simple docstring"""





      pass  # Put your code here...
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						int = 8		):




      """simple docstring"""





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

      a		:Dict									=			any(char in ascii_uppercase for char in password		)
      a		:Optional[int]									=			any(char in ascii_lowercase for char in password		)
      a		:Tuple									=			any(char in digits for char in password		)
      a		: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 							__lowerCamelCase		(					):




      """simple docstring"""





      a		:int									=			int(input('''Please indicate the max length of your password: '''		).strip()		)
      a		:Union[str, 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()









import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case							:       Optional[int]										=	logging.get_logger(__name__)

snake_case							:       Optional[int]										=	{
    '''microsoft/unispeech-large-1500h-cv''': (
        '''https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'''
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			'unispeech'




 def __init__(						self   ,    _lowerCamelCase=32   ,    _lowerCamelCase=768   ,    _lowerCamelCase=12   ,    _lowerCamelCase=12   ,    _lowerCamelCase=3072   ,    _lowerCamelCase="gelu"   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.0   ,    _lowerCamelCase=0.0   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.02   ,    _lowerCamelCase=1e-5   ,    _lowerCamelCase="group"   ,    _lowerCamelCase="gelu"   ,    _lowerCamelCase=(512, 512, 512, 512, 512, 512, 512)   ,    _lowerCamelCase=(5, 2, 2, 2, 2, 2, 2)   ,    _lowerCamelCase=(10, 3, 3, 3, 3, 2, 2)   ,    _lowerCamelCase=False   ,    _lowerCamelCase=128   ,    _lowerCamelCase=16   ,    _lowerCamelCase=False   ,    _lowerCamelCase=True   ,    _lowerCamelCase=0.05   ,    _lowerCamelCase=10   ,    _lowerCamelCase=2   ,    _lowerCamelCase=0.0   ,    _lowerCamelCase=10   ,    _lowerCamelCase=0   ,    _lowerCamelCase=320   ,    _lowerCamelCase=2   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=100   ,    _lowerCamelCase=256   ,    _lowerCamelCase=256   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase="mean"   ,    _lowerCamelCase=False   ,    _lowerCamelCase=False   ,    _lowerCamelCase=256   ,    _lowerCamelCase=80   ,    _lowerCamelCase=0   ,    _lowerCamelCase=1   ,    _lowerCamelCase=2   ,    _lowerCamelCase=0.5   ,    **_lowerCamelCase   ,    ):
       super().__init__(**_lowerCamelCase   ,    pad_token_id=_lowerCamelCase   ,    bos_token_id=_lowerCamelCase   ,    eos_token_id=_lowerCamelCase       )
       a		:Any									=			hidden_size
       a		:str									=			feat_extract_norm
       a		:List[Any]									=			feat_extract_activation
       a		:Tuple									=			list(_lowerCamelCase       )
       a		:Any									=			list(_lowerCamelCase       )
       a		:List[Any]									=			list(_lowerCamelCase       )
       a		:Union[str, Any]									=			conv_bias
       a		:str									=			num_conv_pos_embeddings
       a		:str									=			num_conv_pos_embedding_groups
       a		:Tuple									=			len(self.conv_dim       )
       a		:int									=			num_hidden_layers
       a		:Any									=			intermediate_size
       a		:Optional[Any]									=			hidden_act
       a		:Tuple									=			num_attention_heads
       a		:Any									=			hidden_dropout
       a		:Any									=			attention_dropout
       a		:Optional[Any]									=			activation_dropout
       a		:Optional[Any]									=			feat_proj_dropout
       a		:Any									=			final_dropout
       a		:int									=			layerdrop
       a		:int									=			layer_norm_eps
       a		:Dict									=			initializer_range
       a		:Dict									=			num_ctc_classes
       a		:Optional[Any]									=			vocab_size
       a		:str									=			do_stable_layer_norm
       a		:Tuple									=			use_weighted_layer_sum
       a		:Any									=			classifier_proj_size

       if (
           (len(self.conv_stride       ) != self.num_feat_extract_layers)
           or (len(self.conv_kernel       ) != self.num_feat_extract_layers)
           or (len(self.conv_dim       ) != self.num_feat_extract_layers)
       ):
             raise ValueError(
                 '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                 ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                 F''' {len(self.conv_dim       )}`, `len(config.conv_stride) = {len(self.conv_stride       )}`,'''
                 F''' `len(config.conv_kernel) = {len(self.conv_kernel       )}`.'''       )

       # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
       a		:List[Any]									=			apply_spec_augment
       a		:Any									=			mask_time_prob
       a		:Union[str, Any]									=			mask_time_length
       a		:str									=			mask_time_min_masks
       a		:Tuple									=			mask_feature_prob
       a		:Dict									=			mask_feature_length
       a		:int									=			mask_feature_min_masks

       # parameters for pretraining with codevector quantized representations
       a		:Union[str, Any]									=			num_codevectors_per_group
       a		:Dict									=			num_codevector_groups
       a		:List[Any]									=			contrastive_logits_temperature
       a		:Union[str, Any]									=			feat_quantizer_dropout
       a		:Optional[Any]									=			num_negatives
       a		:Tuple									=			codevector_dim
       a		:Optional[Any]									=			proj_codevector_dim
       a		:Union[str, Any]									=			diversity_loss_weight

       # ctc loss
       a		:List[Any]									=			ctc_loss_reduction
       a		:Union[str, Any]									=			ctc_zero_infinity

       # pretraining loss
       a		:int									=			replace_prob




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return functools.reduce(operator.mul   ,    self.conv_stride   ,    1       )





