
'''simple docstring'''

UpperCAmelCase__   : Optional[Any]					=		[0.3_3, 0.4_4, 0.5_5, 0.4_4, 0.3_3]
UpperCAmelCase__   : str					=		3
UpperCAmelCase__   : Optional[int]					=		5

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

# Machine Time
UpperCAmelCase__   : Tuple					=		0







def   A     (      UpperCamelCase_					:     Optional[int]      )					->       List[Any]:







			'''simple docstring'''



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

			# Choosing Dynamical Systems (All)
			for key, value in enumerate(UpperCamelCase_      ):
						# 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)      )						,	10      )
						lowerCAmelCase__								=							r  # Saving to Parameters Space

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

			# Machine Time
			machine_time += 1







def   A     (      )					->       Any:







			'''simple docstring'''



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

			# PRNG (Xorshift by George Marsaglia)
			def xorshift(UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     List[Any]      ):
						x ^= y >> 13
						y ^= x << 17
						x ^= y >> 5
						return x

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

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

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

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

			# Machine Time
			machine_time += 1

			return xorshift(UpperCamelCase_						,	UpperCamelCase_      ) % 0Xff_fff_fff







def   A     (      )					->       Dict:







			'''simple docstring'''



			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

		UpperCAmelCase__   : Optional[Any]					=		random.sample(range(0xffffffff), 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__   : Any					=		input("(e)exit? ").strip()







'''simple docstring'''

import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def   A     (      UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Union[str, Any]						,	UpperCamelCase_					:     Optional[Any]      )					->       List[str]:







			'''simple docstring'''



			lowerCAmelCase__								=							TaConfig.from_json_file(UpperCamelCase_      )
			print(F"""Building PyTorch model from configuration: {config}"""      )
			lowerCAmelCase__								=							TaForConditionalGeneration(UpperCamelCase_      )

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_      )

			# Save pytorch-model
			print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
			model.save_pretrained(UpperCamelCase_      )


if __name__ == "__main__":
		UpperCAmelCase__   : List[str]					=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
		)
		parser.add_argument(
		    "--config_file",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        "The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."
		    ),
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		UpperCAmelCase__   : Tuple					=		parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





