






"""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 (		_snake_case							):







      """simple docstring"""






      global buffer_space, params_space, machine_time, K, m, t

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

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

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

            # Modification (Transition Function) - Jumps
            UpperCAmelCase							=       round(float(r * value * (1 - value)							)      ,       10							)
            UpperCAmelCase							=       r  # Saving to Parameters Space

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

      # Machine Time
      machine_time += 1

def _a (		):







      """simple docstring"""






      global buffer_space, params_space, machine_time, K, m, t

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

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

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

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

      # Choosing Chaotic Data
      UpperCAmelCase							=       int(buffer_space[(key + 2) % m] * (10**10)							)
      UpperCAmelCase							=       int(buffer_space[(key - 2) % m] * (10**10)							)

      # Machine Time
      machine_time += 1

      return xorshift(SCREAMING_SNAKE_CASE_      ,       SCREAMING_SNAKE_CASE_							) % 0xFF_FFF_FFF

def _a (		):







      """simple docstring"""






      global buffer_space, params_space, machine_time, K, m, t

      UpperCAmelCase							=       K
      UpperCAmelCase							=       [0] * m
      UpperCAmelCase							=       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'''



from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCamelCase__					:						Any											=     logging.get_logger(__name__)

class       _a (_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     'mt5'
       SCREAMING_SNAKE_CASE					=     ['past_key_values']






       def __init__(							self				,							A__=25_01_12				,							A__=5_12				,							A__=64				,							A__=10_24				,							A__=8				,							A__=None				,							A__=6				,							A__=32				,							A__=1_28				,							A__=0.1				,							A__=1E-6				,							A__=1.0				,							A__="gated-gelu"				,							A__=True				,							A__=True				,							A__="T5Tokenizer"				,							A__=False				,							A__=0				,							A__=1				,							A__=0				,							**A__				,							)			->	Union[str, Any]:
          super().__init__(
              is_encoder_decoder=A__				,							tokenizer_class=A__				,							tie_word_embeddings=A__				,							pad_token_id=A__				,							eos_token_id=A__				,							decoder_start_token_id=A__				,							**A__				,							)
          _SCREAMING_SNAKE_CASE          = vocab_size
          _SCREAMING_SNAKE_CASE          = d_model
          _SCREAMING_SNAKE_CASE          = d_kv
          _SCREAMING_SNAKE_CASE          = d_ff
          _SCREAMING_SNAKE_CASE          = num_layers
          _SCREAMING_SNAKE_CASE          = (
              num_decoder_layers if num_decoder_layers is not None else self.num_layers
          )  # default = symmetry
          _SCREAMING_SNAKE_CASE          = num_heads
          _SCREAMING_SNAKE_CASE          = relative_attention_num_buckets
          _SCREAMING_SNAKE_CASE          = relative_attention_max_distance
          _SCREAMING_SNAKE_CASE          = dropout_rate
          _SCREAMING_SNAKE_CASE          = layer_norm_epsilon
          _SCREAMING_SNAKE_CASE          = initializer_factor
          _SCREAMING_SNAKE_CASE          = feed_forward_proj
          _SCREAMING_SNAKE_CASE          = use_cache

          _SCREAMING_SNAKE_CASE          = self.feed_forward_proj.split("""-"""					)
          _SCREAMING_SNAKE_CASE          = act_info[-1]
          _SCREAMING_SNAKE_CASE          = act_info[0] == """gated"""

          if len(A__					) > 1 and act_info[0] != "gated" or len(A__					) > 2:
             raise ValueError(
                 F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
                 """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
                 """'gated-gelu' or 'relu'"""					)

          # for backwards compatibility
          if feed_forward_proj == "gated-gelu":
             _SCREAMING_SNAKE_CASE          = """gelu_new"""






       @property
       def 				UpperCamelCase (							self					)			->	int:
          return self.d_model






       @property
       def 				UpperCamelCase (							self					)			->	Optional[Any]:
          return self.num_heads






       @property
       def 				UpperCamelCase (							self					)			->	Dict:
          return self.num_layers






class       _a (_lowerCamelCase):





       """simple docstring"""




       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
       def 				UpperCamelCase (							self					)			->	Mapping[str, Mapping[int, str]]:
          _SCREAMING_SNAKE_CASE          = {
              """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
              """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
          }
          if self.use_past:
             _SCREAMING_SNAKE_CASE          = """past_encoder_sequence + sequence"""
             _SCREAMING_SNAKE_CASE          = {0: """batch"""}
             _SCREAMING_SNAKE_CASE          = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
          else:
             _SCREAMING_SNAKE_CASE          = {0: """batch""", 1: """decoder_sequence"""}
             _SCREAMING_SNAKE_CASE          = {0: """batch""", 1: """decoder_sequence"""}

          if self.use_past:
             self.fill_with_past_key_values_(A__				,							direction="""inputs"""					)

          return common_inputs






       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
       def 				UpperCamelCase (							self					)			->	int:
          return 13






       @property
       def 				UpperCamelCase (							self					)			->	float:
          return 5E-4






