'''simple docstring'''






_UpperCAmelCase						:    str     					=					[0.33, 0.44, 0.55, 0.44, 0.33]
_UpperCAmelCase						:    Tuple     					=					3
_UpperCAmelCase						:    Union[str, Any]     					=					5

# Buffer Space (with Parameters Space)
_UpperCAmelCase						:    list[float]     					=					[]
_UpperCAmelCase						:    list[float]     					=					[]

# Machine Time
_UpperCAmelCase						:    List[Any]     					=					0





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[str]							):
     global buffer_space, params_space, machine_time, K, m, t

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

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

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

          # Modification (Transition Function) - Jumps
          _A								=     round(float(r * value * (1 - value)							)      ,		1_0							)
          _A								=     r  # Saving to Parameters Space

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

     # Machine Time
     machine_time += 1





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

     # PRNG (Xorshift by George Marsaglia)
     def xorshift(__snake_case       : Optional[int]      ,		__snake_case       : str							):
          x ^= y >> 1_3
          y ^= x << 1_7
          x ^= y >> 5
          return x

     # Choosing Dynamical Systems (Increment)
     _A								=     machine_time % m

     # Evolution (Time Length)
     for _ in range(0      ,		__snake_case							):
          # Variables (Position + Parameters)
          _A								=     params_space[key]
          _A								=     buffer_space[key]

          # Modification (Transition Function) - Flow
          _A								=     round(float(r * value * (1 - value)							)      ,		1_0							)
          _A								=     (machine_time * 0.01 + r * 1.01) % 1 + 3

     # Choosing Chaotic Data
     _A								=     int(buffer_space[(key + 2) % m] * (1_0**1_0)							)
     _A								=     int(buffer_space[(key - 2) % m] * (1_0**1_0)							)

     # Machine Time
     machine_time += 1

     return xorshift(__snake_case      ,		__snake_case							) % 0XFF_FFF_FFF





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

     _A								=     K
     _A								=     [0] * m
     _A								=     0


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

   # Pushing Data (Input)
   import random

   _UpperCAmelCase						:    Optional[int]     					=					random.sample(range(0xFF_FF_FF_FF), 1_00)
   for chunk in message:
      push(chunk)

   # for controlling
   _UpperCAmelCase						:    int     					=					''''''

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




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_UpperCAmelCase						:    Optional[Any]     					=					{
    '''configuration_unispeech_sat''': ['''UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechSatConfig'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase						:    Dict     					=					[
       '''UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''UniSpeechSatForAudioFrameClassification''',
       '''UniSpeechSatForCTC''',
       '''UniSpeechSatForPreTraining''',
       '''UniSpeechSatForSequenceClassification''',
       '''UniSpeechSatForXVector''',
       '''UniSpeechSatModel''',
       '''UniSpeechSatPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_unispeech_sat import (
          UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
          UniSpeechSatForAudioFrameClassification,
          UniSpeechSatForCTC,
          UniSpeechSatForPreTraining,
          UniSpeechSatForSequenceClassification,
          UniSpeechSatForXVector,
          UniSpeechSatModel,
          UniSpeechSatPreTrainedModel,
      )

else:
   import sys

   _UpperCAmelCase						:    int     					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



