


"""simple docstring"""
import functools
import operator

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


__SCREAMING_SNAKE_CASE							:    List[Any]		=    logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    {
    'microsoft/unispeech-sat-base-100h-libri-ft': (
        'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}





class      __A     (snake_case__):





							'''simple docstring'''

							__lowercase:		int							      =    """unispeech-sat"""



							def __init__(				self							:      int		,       UpperCAmelCase_							:      Union[str, Any]=32		,       UpperCAmelCase_							:      Optional[Any]=768		,       UpperCAmelCase_							:      Dict=12		,       UpperCAmelCase_							:      Tuple=12		,       UpperCAmelCase_							:      Optional[int]=3_072		,       UpperCAmelCase_							:      str="gelu"		,       UpperCAmelCase_							:      List[Any]=0.1		,       UpperCAmelCase_							:      Tuple=0.1		,       UpperCAmelCase_							:      Optional[int]=0.1		,       UpperCAmelCase_							:      Optional[Any]=0.0		,       UpperCAmelCase_							:      str=0.0		,       UpperCAmelCase_							:      Tuple=0.1		,       UpperCAmelCase_							:      List[Any]=0.1		,       UpperCAmelCase_							:      Dict=0.02		,       UpperCAmelCase_							:      Union[str, Any]=1E-5		,       UpperCAmelCase_							:      Any="group"		,       UpperCAmelCase_							:      Tuple="gelu"		,       UpperCAmelCase_							:      List[str]=(512, 512, 512, 512, 512, 512, 512)		,       UpperCAmelCase_							:      List[str]=(5, 2, 2, 2, 2, 2, 2)		,       UpperCAmelCase_							:      List[str]=(10, 3, 3, 3, 3, 2, 2)		,       UpperCAmelCase_							:      Union[str, Any]=False		,       UpperCAmelCase_							:      Dict=128		,       UpperCAmelCase_							:      Union[str, Any]=16		,       UpperCAmelCase_							:      Any=False		,       UpperCAmelCase_							:      int=True		,       UpperCAmelCase_							:      Dict=0.05		,       UpperCAmelCase_							:      List[Any]=10		,       UpperCAmelCase_							:      List[Any]=2		,       UpperCAmelCase_							:      Any=0.0		,       UpperCAmelCase_							:      List[Any]=10		,       UpperCAmelCase_							:      Dict=0		,       UpperCAmelCase_							:      str=320		,       UpperCAmelCase_							:      Optional[Any]=2		,       UpperCAmelCase_							:      Dict=0.1		,       UpperCAmelCase_							:      List[str]=100		,       UpperCAmelCase_							:      Any=256		,       UpperCAmelCase_							:      List[Any]=256		,       UpperCAmelCase_							:      Dict=0.1		,       UpperCAmelCase_							:      Any="mean"		,       UpperCAmelCase_							:      List[str]=False		,       UpperCAmelCase_							:      Dict=False		,       UpperCAmelCase_							:      Optional[Any]=256		,       UpperCAmelCase_							:      Optional[Any]=(512, 512, 512, 512, 1_500)		,       UpperCAmelCase_							:      Optional[Any]=(5, 3, 3, 1, 1)		,       UpperCAmelCase_							:      Dict=(1, 2, 3, 1, 1)		,       UpperCAmelCase_							:      str=512		,       UpperCAmelCase_							:      int=0		,       UpperCAmelCase_							:      Any=1		,       UpperCAmelCase_							:      Any=2		,       UpperCAmelCase_							:      Tuple=504		,       **UpperCAmelCase_							:      str		,       )					->Any:



														"""simple docstring"""



														super().__init__(**UpperCAmelCase_		,       pad_token_id=UpperCAmelCase_		,       bos_token_id=UpperCAmelCase_		,       eos_token_id=UpperCAmelCase_       )
														snake_case_           =		hidden_size
														snake_case_           =		feat_extract_norm
														snake_case_           =		feat_extract_activation
														snake_case_           =		list(UpperCAmelCase_       )
														snake_case_           =		list(UpperCAmelCase_       )
														snake_case_           =		list(UpperCAmelCase_       )
														snake_case_           =		conv_bias
														snake_case_           =		num_conv_pos_embeddings
														snake_case_           =		num_conv_pos_embedding_groups
														snake_case_           =		len(self.conv_dim       )
														snake_case_           =		num_hidden_layers
														snake_case_           =		intermediate_size
														snake_case_           =		hidden_act
														snake_case_           =		num_attention_heads
														snake_case_           =		hidden_dropout
														snake_case_           =		attention_dropout
														snake_case_           =		activation_dropout
														snake_case_           =		feat_proj_dropout
														snake_case_           =		final_dropout
														snake_case_           =		layerdrop
														snake_case_           =		layer_norm_eps
														snake_case_           =		initializer_range
														snake_case_           =		vocab_size
														snake_case_           =		num_clusters
														snake_case_           =		do_stable_layer_norm
														snake_case_           =		use_weighted_layer_sum

														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
														snake_case_           =		apply_spec_augment
														snake_case_           =		mask_time_prob
														snake_case_           =		mask_time_length
														snake_case_           =		mask_time_min_masks
														snake_case_           =		mask_feature_prob
														snake_case_           =		mask_feature_length
														snake_case_           =		mask_feature_min_masks

														# parameters for pretraining with codevector quantized representations
														snake_case_           =		num_codevectors_per_group
														snake_case_           =		num_codevector_groups
														snake_case_           =		contrastive_logits_temperature
														snake_case_           =		feat_quantizer_dropout
														snake_case_           =		num_negatives
														snake_case_           =		codevector_dim
														snake_case_           =		proj_codevector_dim
														snake_case_           =		diversity_loss_weight

														# ctc loss
														snake_case_           =		ctc_loss_reduction
														snake_case_           =		ctc_zero_infinity

														# SequenceClassification-specific parameter. Feel free to ignore for other classes.
														snake_case_           =		classifier_proj_size

														# XVector-specific parameters. Feel free to ignore for other classes.
														snake_case_           =		list(UpperCAmelCase_       )
														snake_case_           =		list(UpperCAmelCase_       )
														snake_case_           =		list(UpperCAmelCase_       )
														snake_case_           =		xvector_output_dim



							@property
							def 		lowerCAmelCase       (				self							:      Tuple       )					->List[Any]:



														"""simple docstring"""



														return functools.reduce(operator.mul		,       self.conv_stride		,       1       )







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







def 						_a				(      _SCREAMING_SNAKE_CASE = 8				)    ->				str:
							snake_case_           =		ascii_letters + digits + punctuation
							return "".join(secrets.choice(_SCREAMING_SNAKE_CASE				) for _ in range(_SCREAMING_SNAKE_CASE				)				)







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				str:
							# Password Generator = full boot with random_number, random_letters, and
							# random_character FUNCTIONS
							# Put your code here...
							i -= len(_SCREAMING_SNAKE_CASE				)
							snake_case_           =		i // 3
							snake_case_           =		i % 3
							# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
							#     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
							snake_case_           =		(
							    chars_incl
							    + random(_SCREAMING_SNAKE_CASE				, quotient + remainder				)
							    + random(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)
							    + random(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)
							)
							snake_case_           =		list(_SCREAMING_SNAKE_CASE				)
							shuffle(_SCREAMING_SNAKE_CASE				)
							return "".join(_SCREAMING_SNAKE_CASE				)

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







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				str:
							return "".join(secrets.choice(_SCREAMING_SNAKE_CASE				) for _ in range(_SCREAMING_SNAKE_CASE				)				)







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Any:
							pass  # Put your code here...







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				List[str]:
							pass  # Put your code here...







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Optional[int]:
							pass  # Put your code here...







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE = 8				)    ->				bool:

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

							snake_case_           =		any(char in ascii_uppercase for char in password				)
							snake_case_           =		any(char in ascii_lowercase for char in password				)
							snake_case_           =		any(char in digits for char in password				)
							snake_case_           =		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 						_a				(      )    ->				str:
							snake_case_           =		int(input("""Please indicate the max length of your password: """				).strip()				)
							snake_case_           =		input(
							    """Please indicate the characters that must be in your password: """				).strip()
							print("""Password generated:"""				, password_generator(_SCREAMING_SNAKE_CASE				)				)
							print(
							    """Alternative Password generated:"""				, alternative_password_generator(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)				, )
							print("""[If you are thinking of using this passsword, You better save it.]"""				)


if __name__ == "__main__":
				main()



