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

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

# Machine Time
lowerCAmelCase__							= 0






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

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

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

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

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

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

       # Machine Time
       machine_time += 1






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

       # PRNG (Xorshift by George Marsaglia)
       def xorshift(lowerCAmelCase__	,  lowerCAmelCase__	):
              x ^= y >> 1_3
              y ^= x << 1_7
              x ^= y >> 5
              return x

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

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

              # Modification (Transition Function) - Flow
              lowerCAmelCase__     	=					round(float(r * value * (1 - value)	)	,  1_0	)
              lowerCAmelCase__     	=					(machine_time * 0.0_1 + r * 1.0_1) % 1 + 3

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

       # Machine Time
       machine_time += 1

       return xorshift(lowerCAmelCase__	,  lowerCAmelCase__	) % 0xffffffff






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

       lowerCAmelCase__     	=					K
       lowerCAmelCase__     	=					[0] * m
       lowerCAmelCase__     	=					0


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

    # Pushing Data (Input)
    import random

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

    # for controlling
    lowerCAmelCase__							= ''

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

from typing import TYPE_CHECKING

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


lowerCAmelCase__							= {
    'configuration_deberta_v2': ['DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaV2Config', 'DebertaV2OnnxConfig'],
    'tokenization_deberta_v2': ['DebertaV2Tokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__							= ['DebertaV2TokenizerFast']

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__							= [
        'TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFDebertaV2ForMaskedLM',
        'TFDebertaV2ForQuestionAnswering',
        'TFDebertaV2ForSequenceClassification',
        'TFDebertaV2ForTokenClassification',
        'TFDebertaV2Model',
        'TFDebertaV2PreTrainedModel',
    ]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__							= [
        'DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
        'DebertaV2ForMaskedLM',
        'DebertaV2ForMultipleChoice',
        'DebertaV2ForQuestionAnswering',
        'DebertaV2ForSequenceClassification',
        'DebertaV2ForTokenClassification',
        'DebertaV2Model',
        'DebertaV2PreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_deberta_va import (
        DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
        DebertaVaConfig,
        DebertaVaOnnxConfig,
    )
    from .tokenization_deberta_va import DebertaVaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_deberta_va import (
            TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDebertaVaForMaskedLM,
            TFDebertaVaForQuestionAnswering,
            TFDebertaVaForSequenceClassification,
            TFDebertaVaForTokenClassification,
            TFDebertaVaModel,
            TFDebertaVaPreTrainedModel,
        )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deberta_va import (
            DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
            DebertaVaForMaskedLM,
            DebertaVaForMultipleChoice,
            DebertaVaForQuestionAnswering,
            DebertaVaForSequenceClassification,
            DebertaVaForTokenClassification,
            DebertaVaModel,
            DebertaVaPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase__							= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
