





__A    :   List[Any]          =       [0.33, 0.44, 0.55, 0.44, 0.33]
__A    :   Dict          =       3
__A    :   str          =       5

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

# Machine Time
__A    :   Dict          =       0





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    str:







			'''simple docstring'''

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

			# Choosing Dynamical Systems (All)
			for key, value in enumerate(UpperCamelCase__			):
						# 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(UpperCamelCase__			) < 1
			assert max(UpperCamelCase__			) < 4

			# Machine Time
			machine_time += 1





def 						__SCREAMING_SNAKE_CASE  (    )  ->    Any:







			'''simple docstring'''

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

			# PRNG (Xorshift by George Marsaglia)
			def xorshift(UpperCamelCase__      ,      UpperCamelCase__			):
						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      ,      UpperCamelCase__			):
						# 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(UpperCamelCase__      ,      UpperCamelCase__			) % 0XFFFFFFFF





def 						__SCREAMING_SNAKE_CASE  (    )  ->    Any:







			'''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

				__A    :   Tuple          =       random.sample(range(0Xffffffff), 100)
				for chunk in message:
								push(chunk)

				# for controlling
				__A    :   List[Any]          =       ""

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










import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard

class      A_	(unittest.TestCase      ):
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    {
				    '''model_details''': {
				        '''Organization''': '''testing''',
				        '''Model date''': '''today''',
				        '''Model version''': '''v2.1, Developed by Test Corp in 2019.''',
				        '''Architecture''': '''Convolutional Neural Network.''',
				    },
				    '''metrics''': '''BLEU and ROUGE-1''',
				    '''evaluation_data''': {
				        '''Datasets''': {'''BLEU''': '''My-great-dataset-v1''', '''ROUGE-1''': '''My-short-dataset-v2.1'''},
				        '''Preprocessing''': '''See details on https://arxiv.org/pdf/1810.03993.pdf''',
				    },
				    '''training_data''': {
				        '''Dataset''': '''English Wikipedia dump dated 2018-12-01''',
				        '''Preprocessing''': (
				            '''Using SentencePiece vocabulary of size 52k tokens. See details on'''
				            ''' https://arxiv.org/pdf/1810.03993.pdf'''
				        ),
				    },
				    '''quantitative_analyses''': {'''BLEU''': 55.1, '''ROUGE-1''': 7_6},
				}
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    ModelCard.from_dict(self.inputs_dict		)
				self.assertTrue(hasattr(_A							, '''model_details'''		)		)
				self.assertTrue(hasattr(_A							, '''intended_use'''		)		)
				self.assertTrue(hasattr(_A							, '''factors'''		)		)
				self.assertTrue(hasattr(_A							, '''metrics'''		)		)
				self.assertTrue(hasattr(_A							, '''evaluation_data'''		)		)
				self.assertTrue(hasattr(_A							, '''training_data'''		)		)
				self.assertTrue(hasattr(_A							, '''quantitative_analyses'''		)		)
				self.assertTrue(hasattr(_A							, '''ethical_considerations'''		)		)
				self.assertTrue(hasattr(_A							, '''caveats_and_recommendations'''		)		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    ModelCard.from_dict(self.inputs_dict		)
				UpperCAmelCase       		=    json.loads(modelcard.to_json_string()		)
				for key, value in self.inputs_dict.items():
							self.assertEqual(obj[key]							, _A		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    ModelCard.from_dict(self.inputs_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase       		=    os.path.join(_A							, '''modelcard.json'''		)
							model_card_first.to_json_file(_A		)
							UpperCAmelCase       		=    ModelCard.from_json_file(_A		)

				self.assertEqual(model_card_second.to_dict()							, model_card_first.to_dict()		)







	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    ModelCard.from_dict(self.inputs_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
							model_card_first.save_pretrained(_A		)
							UpperCAmelCase       		=    ModelCard.from_pretrained(_A		)

				self.assertEqual(model_card_second.to_dict()							, model_card_first.to_dict()		)



