
_SCREAMING_SNAKE_CASE     :   str							 =      [0.33, 0.44, 0.55, 0.44, 0.33]
_SCREAMING_SNAKE_CASE     :   List[str]							 =      3
_SCREAMING_SNAKE_CASE     :   List[str]							 =      5

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

# Machine Time
_SCREAMING_SNAKE_CASE     :   Dict							 =      0

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Any							)			->							Tuple:

				"""simple docstring"""







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

				# Choosing Dynamical Systems (All)
				for key, value in enumerate(__UpperCamelCase							):
								# Evolution Parameter
								A__	:						Optional[int]								=				float(seed / value							)

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

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

								# Modification (Transition Function) - Jumps
								A__	:						List[str]								=				round(float(r * value * (1 - value)							)				,    10							)
								A__	:						int								=				r  # Saving to Parameters Space

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

				# Machine Time
				machine_time += 1

def 					SCREAMING_SNAKE_CASE  (   )			->							List[str]:

				"""simple docstring"""







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

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

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

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

								# Modification (Transition Function) - Flow
								A__	:						List[str]								=				round(float(r * value * (1 - value)							)				,    10							)
								A__	:						Dict								=				(machine_time * 0.0_1 + r * 1.0_1) % 1 + 3

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

				# Machine Time
				machine_time += 1

				return xorshift(__UpperCamelCase				,    __UpperCamelCase							) % 0Xffffffff

def 					SCREAMING_SNAKE_CASE  (   )			->							Any:

				"""simple docstring"""







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

				A__	:						str								=				K
				A__	:						Tuple								=				[0] * m
				A__	:						Optional[Any]								=				0


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

					# Pushing Data (Input)
					import random

					_SCREAMING_SNAKE_CASE     :   str							 =      random.sample(range(0xFFFF_FFFF), 1_0_0)
					for chunk in message:
										push(chunk)

					# for controlling
					_SCREAMING_SNAKE_CASE     :   Optional[Any]							 =      ''

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

import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
					import torch

					from transformers import GPTaLMHeadModel







@require_torch
@require_sentencepiece
@require_tokenizers
class 			UpperCamelCase__		(     unittest.TestCase ):







	'''simple docstring'''





	@slow
	@unittest.skip('''Model is not available.'''		)
	def 	__snake_case					(      self		):
					A__	:						int								=				'''nvidia/megatron-gpt2-345m/'''
					if "MYDIR" in os.environ:
									A__	:						Union[str, Any]								=				os.path.join(os.environ['''MYDIR''']					,							UpperCamelCase__		)
					A__	:						Union[str, Any]								=				GPTaLMHeadModel.from_pretrained(UpperCamelCase__		)
					model.to(UpperCamelCase__		)
					model.half()

					A__	:						Dict								=				torch.tensor(
					    [[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]					,							device=UpperCamelCase__					,							dtype=torch.long					,							)

					with torch.no_grad():
									A__	:						str								=				model(UpperCamelCase__		).logits

					A__	:						int								=				torch.Size((1, 9, 5_0257)		)
					self.assertEqual(output.shape					,							UpperCamelCase__		)

					A__	:						Dict								=				torch.tensor(
					    [
					        4.9_4_1_4,
					        -0.2_9_2_0,
					        -1.2_1_4_8,
					        -4.0_2_7_3,
					        -0.5_1_6_1,
					        -5.2_1_0_9,
					        -1.2_4_1_2,
					        -1.8_3_0_1,
					        -1.7_7_3_4,
					        -4.7_1_4_8,
					        -0.2_3_1_7,
					        -1.0_8_1_1,
					        -2.1_7_7_7,
					        0.4_1_4_1,
					        -3.7_9_6_9,
					        -4.0_5_8_6,
					        -2.5_3_3_2,
					        -3.3_8_0_9,
					        4.3_8_6_7,
					    ]					,							device=UpperCamelCase__					,							dtype=torch.half					,							)

					for i in range(19		):
									A__							,				A__	:						int								=				8 * i // 17, 2792 * i  # along the diagonal
									A__							,				A__	:						Optional[Any]								=				output[0, r, c], expected_diag[i]
									A__	:						Dict								=				F"row={r} col={c} computed={computed} expected={expected}"
									self.assertAlmostEqual(UpperCamelCase__					,							UpperCamelCase__					,							delta=1e-4					,							msg=UpperCamelCase__		)