
'''simple docstring'''





UpperCAmelCase            = [0.33, 0.44, 0.55, 0.44, 0.33]
UpperCAmelCase            = 3
UpperCAmelCase            = 5

# Buffer Space (with Parameters Space)
UpperCAmelCase            = []
UpperCAmelCase            = []

# Machine Time
UpperCAmelCase            = 0

def   A			(							A_				:       Optional[int]							):
						global buffer_space, params_space, machine_time, K, m, t

						# Choosing Dynamical Systems (All)
						for key, value in enumerate(A_							):
												# Evolution Parameter
												snake_case							:  str			=     float(seed / value							)

												# Control Theory: Orbit Change
												snake_case							:  Any			=     (buffer_space[(key + 1) % m] + e) % 1

												# Control Theory: Trajectory Change
												snake_case							:  Optional[int]			=     (params_space[key] + e) % 1 + 3

												# Modification (Transition Function) - Jumps
												snake_case							:  Tuple			=     round(float(r * value * (1 - value)							)     ,				10							)
												snake_case							:  Optional[Any]			=     r  # Saving to Parameters Space

						# Logistic Map
						assert max(A_							) < 1
						assert max(A_							) < 4

						# Machine Time
						machine_time += 1

def   A			(							):
						global buffer_space, params_space, machine_time, K, m, t

						# PRNG (Xorshift by George Marsaglia)
						def xorshift(A_				:       str     ,				A_				:       Dict							):
												x ^= y >> 13
												y ^= x << 17
												x ^= y >> 5
												return x

						# Choosing Dynamical Systems (Increment)
						snake_case							:  Tuple			=     machine_time % m

						# Evolution (Time Length)
						for _ in range(0     ,				A_							):
												# Variables (Position + Parameters)
												snake_case							:  int			=     params_space[key]
												snake_case							:  List[Any]			=     buffer_space[key]

												# Modification (Transition Function) - Flow
												snake_case							:  Tuple			=     round(float(r * value * (1 - value)							)     ,				10							)
												snake_case							:  Any			=     (machine_time * 0.01 + r * 1.01) % 1 + 3

						# Choosing Chaotic Data
						snake_case							:  List[Any]			=     int(buffer_space[(key + 2) % m] * (10**10)							)
						snake_case							:  Optional[Any]			=     int(buffer_space[(key - 2) % m] * (10**10)							)

						# Machine Time
						machine_time += 1

						return xorshift(A_     ,				A_							) % 0XF_F_F_F_F_F_F_F

def   A			(							):
						global buffer_space, params_space, machine_time, K, m, t

						snake_case							:  Optional[int]			=     K
						snake_case							:  str			=     [0] * m
						snake_case							:  Union[str, Any]			=     0


if __name__ == "__main__":
						# Initialization
						reset()

						# Pushing Data (Input)
						import random

						UpperCAmelCase            = random.sample(range(0xFF_FFF_FFF), 100)
						for chunk in message:
												push(chunk)

						# for controlling
						UpperCAmelCase            = ""

						# Pulling Data (Output)
						while inp in ("e", "E"):
												print(f'''{format(pull(), '#04x')}''')
												print(buffer_space)
												print(params_space)
												UpperCAmelCase            = input("(e)exit? ").strip()









"""simple docstring"""





import functools
import operator

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


A__       :			int=						logging.get_logger(__name__)

A__       :			Optional[int]=						{
    """facebook/hubert-base-ls960""": """https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json""",
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}
class  __lowerCamelCase     (	_a	):
   a   :					Dict			    ="""hubert"""

   def __init__(       self			,					snake_case_=32			,					snake_case_=768			,					snake_case_=12			,					snake_case_=12			,					snake_case_=3072			,					snake_case_="gelu"			,					snake_case_=0.1			,					snake_case_=0.1			,					snake_case_=0.1			,					snake_case_=True			,					snake_case_=0.0			,					snake_case_=0.1			,					snake_case_=0.1			,					snake_case_=0.02			,					snake_case_=1E-5			,					snake_case_="group"			,					snake_case_="gelu"			,					snake_case_=(512, 512, 512, 512, 512, 512, 512)			,					snake_case_=(5, 2, 2, 2, 2, 2, 2)			,					snake_case_=(10, 3, 3, 3, 3, 2, 2)			,					snake_case_=False			,					snake_case_=128			,					snake_case_=16			,					snake_case_=False			,					snake_case_=True			,					snake_case_=0.05			,					snake_case_=10			,					snake_case_=2			,					snake_case_=0.0			,					snake_case_=10			,					snake_case_=0			,					snake_case_="sum"			,					snake_case_=False			,					snake_case_=False			,					snake_case_=256			,					snake_case_=0			,					snake_case_=1			,					snake_case_=2			,					**snake_case_			,					)		->							Any:
       super().__init__(**snake_case_			,					pad_token_id=snake_case_			,					bos_token_id=snake_case_			,					eos_token_id=snake_case_   )
       UpperCamelCase__								=     hidden_size
       UpperCamelCase__								=     feat_extract_norm
       UpperCamelCase__								=     feat_extract_activation
       UpperCamelCase__								=     list(snake_case_   )
       UpperCamelCase__								=     list(snake_case_   )
       UpperCamelCase__								=     list(snake_case_   )
       UpperCamelCase__								=     conv_bias
       UpperCamelCase__								=     num_conv_pos_embeddings
       UpperCamelCase__								=     num_conv_pos_embedding_groups
       UpperCamelCase__								=     len(self.conv_dim   )
       UpperCamelCase__								=     num_hidden_layers
       UpperCamelCase__								=     intermediate_size
       UpperCamelCase__								=     hidden_act
       UpperCamelCase__								=     num_attention_heads
       UpperCamelCase__								=     hidden_dropout
       UpperCamelCase__								=     attention_dropout
       UpperCamelCase__								=     activation_dropout
       UpperCamelCase__								=     feat_proj_layer_norm
       UpperCamelCase__								=     feat_proj_dropout
       UpperCamelCase__								=     final_dropout
       UpperCamelCase__								=     layerdrop
       UpperCamelCase__								=     layer_norm_eps
       UpperCamelCase__								=     initializer_range
       UpperCamelCase__								=     vocab_size
       UpperCamelCase__								=     do_stable_layer_norm
       UpperCamelCase__								=     use_weighted_layer_sum
       UpperCamelCase__								=     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
       UpperCamelCase__								=     apply_spec_augment
       UpperCamelCase__								=     mask_time_prob
       UpperCamelCase__								=     mask_time_length
       UpperCamelCase__								=     mask_time_min_masks
       UpperCamelCase__								=     mask_feature_prob
       UpperCamelCase__								=     mask_feature_length
       UpperCamelCase__								=     mask_feature_min_masks

       # ctc loss
       UpperCamelCase__								=     ctc_loss_reduction
       UpperCamelCase__								=     ctc_zero_infinity







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




