





'''simple docstring'''


_lowerCamelCase					:						List[Any]					     =							[0.3_3, 0.4_4, 0.5_5, 0.4_4, 0.3_3]
_lowerCamelCase					:						List[Any]					     =							3
_lowerCamelCase					:						Tuple					     =							5

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

# Machine Time
_lowerCamelCase					:						List[str]					     =							0




def  __a			(  UpperCAmelCase     )   ->Union[str, 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
		A					=   float(seed / value     )

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

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

		# Modification (Transition Function) - Jumps
		A					=   round(float(r * value * (1 - value)     )							,      10     )
		A					=   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							,      UpperCAmelCase     ):
		x ^= y >> 13
		y ^= x << 17
		x ^= y >> 5
		return x

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

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

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

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

	# Machine Time
	machine_time += 1

	return xorshift(UpperCAmelCase							,      UpperCAmelCase     ) % 0XFF_FF_FF_FF




def  __a			(  )   ->Tuple:



	"""simple docstring"""







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

	A					=   K
	A					=   [0] * m
	A					=   0


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

				# Pushing Data (Input)
				import random

				_lowerCamelCase					:						Dict					     =							random.sample(range(0Xff_fff_fff), 100)
				for chunk in message:
								push(chunk)

				# for controlling
				_lowerCamelCase					:						Optional[int]					     =							""""""

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





"""simple docstring"""







import argparse
import json
import re
import string
import sys
from collections import Counter
def _A		(		lowercase						):







    """simple docstring"""

    def remove_articles(lowercase						):
        return re.sub(R'''\b(a|an|the)\b'''			,       ''' '''			,       lowercase						)

    def white_space_fix(lowercase						):
        return " ".join(text.split()						)

    def remove_punc(lowercase						):
        a											=set(string.punctuation						)
        return "".join(ch for ch in text if ch not in exclude						)

    def lower(lowercase						):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(lowercase						)						)						)						)
def _A		(		lowercase			,       lowercase						):







    """simple docstring"""
    a											=normalize_answer(lowercase						).split()
    a											=normalize_answer(lowercase						).split()
    a											=Counter(lowercase						) & Counter(lowercase						)
    a											=sum(common.values()						)
    if num_same == 0:
        return 0
    a											=1.0 * num_same / len(lowercase						)
    a											=1.0 * num_same / len(lowercase						)
    a											=(2 * precision * recall) / (precision + recall)
    return fa
def _A		(		lowercase			,       lowercase						):







    """simple docstring"""
    return normalize_answer(lowercase						) == normalize_answer(lowercase						)
def _A		(		lowercase			,       lowercase			,       lowercase						):







    """simple docstring"""
    a											=[]
    for ground_truth in ground_truths:
        a											=metric_fn(lowercase			,       lowercase						)
        scores_for_ground_truths.append(lowercase						)
    return max(lowercase						)
def _A		(		lowercase			,       lowercase						):







    """simple docstring"""
    a											=a											=a											=0
    for article in dataset:
        for paragraph in article["paragraphs"]:
            for qa in paragraph["qas"]:
                total += 1
                if qa["id"] not in predictions:
                    a											='''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
                    print(lowercase			,       file=sys.stderr						)
                    continue
                a											=[x['''text'''] for x in qa['''answers''']]
                a											=predictions[qa['''id''']]
                exact_match += metric_max_over_ground_truths(lowercase			,       lowercase			,       lowercase						)
                fa += metric_max_over_ground_truths(lowercase			,       lowercase			,       lowercase						)

    a											=100.0 * exact_match / total
    a											=100.0 * fa / total

    return {"exact_match": exact_match, "f1": fa}


if __name__ == "__main__":
       lowerCamelCase_  :  List[Any]     		=    """1.1"""
       lowerCamelCase_  :  Optional[Any]     		=    argparse.ArgumentParser(description="""Evaluation for SQuAD """ + expected_version)
       parser.add_argument("""dataset_file""", help="""Dataset file""")
       parser.add_argument("""prediction_file""", help="""Prediction File""")
       lowerCamelCase_  :  Optional[Any]     		=    parser.parse_args()
       with open(args.dataset_file) as dataset_file:
              lowerCamelCase_  :  List[str]     		=    json.load(dataset_file)
              if dataset_json["version"] != expected_version:
                     print(
                         """Evaluation expects v-""" + expected_version + """, but got dataset with v-""" + dataset_json["""version"""],
                         file=sys.stderr,
                     )
              lowerCamelCase_  :  Dict     		=    dataset_json["""data"""]
       with open(args.prediction_file) as prediction_file:
              lowerCamelCase_  :  Optional[Any]     		=    json.load(prediction_file)
       print(json.dumps(evaluate(dataset, predictions)))