






import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


A__			:    int         =				logging.getLogger(__name__)
@dataclass
class    _UpperCAmelCase      :

				"""simple docstring"""




				lowercase__       							= field(
				    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}			)
				lowercase__       							= field(
				    default=UpperCamelCase_	,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}			)
				lowercase__       							= field(
				    default="""NER"""	,metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}			)
				lowercase__       							= field(
				    default=UpperCamelCase_	,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}			)
				lowercase__       							= field(default=UpperCamelCase_	,metadata={"""help""": """Set this flag to use fast tokenization."""}			)
				# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
				# or just modify its tokenizer_config.json.
				lowercase__       							= field(
				    default=UpperCamelCase_	,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}	,)



@dataclass
class    _UpperCAmelCase      :

				"""simple docstring"""




				lowercase__       							= field(
				    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}			)
				lowercase__       							= field(
				    default=UpperCamelCase_	,metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}	,)
				lowercase__       							= field(
				    default=128	,metadata={
				        """help""": (
				            """The maximum total input sequence length after tokenization. Sequences longer """
				            """than this will be truncated, sequences shorter will be padded."""
				        )
				    }	,)
				lowercase__       							= field(
				    default=UpperCamelCase_	,metadata={"""help""": """Overwrite the cached training and evaluation sets"""}			)






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
				if len(sys.argv       ) == 2 and sys.argv[1].endswith('''.json'''       ):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								lowercase__     				=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
				else:
								lowercase__     				=    parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir       )
				    and os.listdir(training_args.output_dir       )
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
								    ''' --overwrite_output_dir to overcome.'''       )

				lowercase__     				=    import_module('''tasks'''       )
				try:
								lowercase__     				=    getattr(lowerCamelCase_	,     model_args.task_type       )
								lowercase__     				=    token_classification_task_clazz()
				except AttributeError:
								raise ValueError(
								    F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
								    F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""       )

				# Setup logging
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''	,     datefmt='''%m/%d/%Y %H:%M:%S'''	,     level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN	,     )
				logger.warning(
				    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''	,     training_args.local_rank	,     training_args.device	,     training_args.n_gpu	,     bool(training_args.local_rank != -1       )	,     training_args.fpaa	,     )
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank       ):
								transformers.utils.logging.set_verbosity_info()
								transformers.utils.logging.enable_default_handler()
								transformers.utils.logging.enable_explicit_format()
				logger.info('''Training/evaluation parameters %s'''	,     lowerCamelCase_       )

				# Set seed
				set_seed(training_args.seed       )

				# Prepare CONLL-2003 task
				lowercase__     				=    token_classification_task.get_labels(data_args.labels       )
				lowercase__     				=    dict(enumerate(lowerCamelCase_       )       )
				lowercase__     				=    len(lowerCamelCase_       )

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				lowercase__     				=    AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,     num_labels=lowerCamelCase_	,     idalabel=lowerCamelCase_	,     labelaid={label: i for i, label in enumerate(lowerCamelCase_       )}	,     cache_dir=model_args.cache_dir	,     )
				lowercase__     				=    AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path	,     cache_dir=model_args.cache_dir	,     use_fast=model_args.use_fast	,     )
				lowercase__     				=    AutoModelForTokenClassification.from_pretrained(
				    model_args.model_name_or_path	,     from_tf=bool('''.ckpt''' in model_args.model_name_or_path       )	,     config=lowerCamelCase_	,     cache_dir=model_args.cache_dir	,     )

				# Get datasets
				lowercase__     				=    (
				    TokenClassificationDataset(
				        token_classification_task=lowerCamelCase_	,     data_dir=data_args.data_dir	,     tokenizer=lowerCamelCase_	,     labels=lowerCamelCase_	,     model_type=config.model_type	,     max_seq_length=data_args.max_seq_length	,     overwrite_cache=data_args.overwrite_cache	,     mode=Split.train	,     )
				    if training_args.do_train
				    else None
				)
				lowercase__     				=    (
				    TokenClassificationDataset(
				        token_classification_task=lowerCamelCase_	,     data_dir=data_args.data_dir	,     tokenizer=lowerCamelCase_	,     labels=lowerCamelCase_	,     model_type=config.model_type	,     max_seq_length=data_args.max_seq_length	,     overwrite_cache=data_args.overwrite_cache	,     mode=Split.dev	,     )
				    if training_args.do_eval
				    else None
				)

				def align_predictions(lowerCamelCase_	,     lowerCamelCase_       ) -> Tuple[List[int], List[int]]:
								lowercase__     				=    np.argmax(lowerCamelCase_	,     axis=2       )

								lowercase__     				=    preds.shape

								lowercase__     				=    [[] for _ in range(lowerCamelCase_       )]
								lowercase__     				=    [[] for _ in range(lowerCamelCase_       )]

								for i in range(lowerCamelCase_       ):
												for j in range(lowerCamelCase_       ):
																if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																				out_label_list[i].append(label_map[label_ids[i][j]]       )
																				preds_list[i].append(label_map[preds[i][j]]       )

								return preds_list, out_label_list

				def compute_metrics(lowerCamelCase_       ) -> Dict:
								lowercase__     				=    align_predictions(p.predictions	,     p.label_ids       )
								return {
								    "accuracy_score": accuracy_score(lowerCamelCase_	,     lowerCamelCase_       ),
								    "precision": precision_score(lowerCamelCase_	,     lowerCamelCase_       ),
								    "recall": recall_score(lowerCamelCase_	,     lowerCamelCase_       ),
								    "f1": fa_score(lowerCamelCase_	,     lowerCamelCase_       ),
								}

				# Data collator
				lowercase__     				=    DataCollatorWithPadding(lowerCamelCase_	,     pad_to_multiple_of=8       ) if training_args.fpaa else None

				# Initialize our Trainer
				lowercase__     				=    Trainer(
				    model=lowerCamelCase_	,     args=lowerCamelCase_	,     train_dataset=lowerCamelCase_	,     eval_dataset=lowerCamelCase_	,     compute_metrics=lowerCamelCase_	,     data_collator=lowerCamelCase_	,     )

				# Training
				if training_args.do_train:
								trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
								trainer.save_model()
								# For convenience, we also re-save the tokenizer to the same directory,
								# so that you can share your model easily on huggingface.co/models =)
								if trainer.is_world_process_zero():
												tokenizer.save_pretrained(training_args.output_dir       )

    # Evaluation
				lowercase__     				=    {}
				if training_args.do_eval:
								logger.info('''*** Evaluate ***'''       )

								lowercase__     				=    trainer.evaluate()

								lowercase__     				=    os.path.join(training_args.output_dir	,     '''eval_results.txt'''       )
								if trainer.is_world_process_zero():
												with open(lowerCamelCase_	,     '''w'''       ) as writer:
																logger.info('''***** Eval results *****'''       )
																for key, value in result.items():
																				logger.info('''  %s = %s'''	,     lowerCamelCase_	,     lowerCamelCase_       )
																				writer.write('''%s = %s\n''' % (key, value)       )

												results.update(lowerCamelCase_       )

    # Predict
				if training_args.do_predict:
								lowercase__     				=    TokenClassificationDataset(
								    token_classification_task=lowerCamelCase_	,     data_dir=data_args.data_dir	,     tokenizer=lowerCamelCase_	,     labels=lowerCamelCase_	,     model_type=config.model_type	,     max_seq_length=data_args.max_seq_length	,     overwrite_cache=data_args.overwrite_cache	,     mode=Split.test	,     )

								lowercase__     				=    trainer.predict(lowerCamelCase_       )
								lowercase__     				=    align_predictions(lowerCamelCase_	,     lowerCamelCase_       )

								lowercase__     				=    os.path.join(training_args.output_dir	,     '''test_results.txt'''       )
								if trainer.is_world_process_zero():
												with open(lowerCamelCase_	,     '''w'''       ) as writer:
																for key, value in metrics.items():
																				logger.info('''  %s = %s'''	,     lowerCamelCase_	,     lowerCamelCase_       )
																				writer.write('''%s = %s\n''' % (key, value)       )

        # Save predictions
								lowercase__     				=    os.path.join(training_args.output_dir	,     '''test_predictions.txt'''       )
								if trainer.is_world_process_zero():
												with open(lowerCamelCase_	,     '''w'''       ) as writer:
																with open(os.path.join(data_args.data_dir	,     '''test.txt'''       )	,     '''r'''       ) as f:
																				token_classification_task.write_predictions_to_file(lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_       )

				return results






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				main()


if __name__ == "__main__":
						main()












from math import sqrt






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				assert isinstance(lowerCamelCase_	,     lowerCamelCase_       ) and (
				    number >= 0
				), "'number' must been an int and positive"

				lowercase__     				=    True

				# 0 and 1 are none primes.
				if number <= 1:
								lowercase__     				=    False

				for divisor in range(2	,     int(round(sqrt(lowerCamelCase_       )       )       ) + 1       ):
								# if 'number' divisible by 'divisor' then sets 'status'
								# of false and break up the loop.
								if number % divisor == 0:
												lowercase__     				=    False
												break

    # precondition
				assert isinstance(lowerCamelCase_	,     lowerCamelCase_       ), "'status' must been from type bool"

				return status






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				assert isinstance(lowerCamelCase_	,     lowerCamelCase_       ) and (n > 2), "'N' must been an int and > 2"

				# beginList: contains all natural numbers from 2 up to N
				lowercase__     				=    list(range(2	,     n + 1       )       )

				lowercase__     				=    []  # this list will be returns.

				# actual sieve of erathostenes
				for i in range(len(lowerCamelCase_       )       ):
								for j in range(i + 1	,     len(lowerCamelCase_       )       ):
												if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
																lowercase__     				=    0

    # filters actual prime numbers.
				lowercase__     				=    [x for x in begin_list if x != 0]

				# precondition
				assert isinstance(lowerCamelCase_	,     lowerCamelCase_       ), "'ans' must been from type list"

				return ans






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				assert isinstance(lowerCamelCase_	,     lowerCamelCase_       ) and (n > 2), "'N' must been an int and > 2"

				lowercase__     				=    []

				# iterates over all numbers between 2 up to N+1
				# if a number is prime then appends to list 'ans'
				for number in range(2	,     n + 1       ):
								if is_prime(lowerCamelCase_       ):
												ans.append(lowerCamelCase_       )

    # precondition
				assert isinstance(lowerCamelCase_	,     lowerCamelCase_       ), "'ans' must been from type list"

				return ans






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				assert isinstance(lowerCamelCase_	,     lowerCamelCase_       ) and number >= 0, "'number' must been an int and >= 0"

				lowercase__     				=    []  # this list will be returns of the function.

				# potential prime number factors.

				lowercase__     				=    2

				lowercase__     				=    number

				if number == 0 or number == 1:
								ans.append(lowerCamelCase_       )

				# if 'number' not prime then builds the prime factorization of 'number'
				elif not is_prime(lowerCamelCase_       ):
								while quotient != 1:
												if is_prime(lowerCamelCase_       ) and (quotient % factor == 0):
																ans.append(lowerCamelCase_       )
																quotient /= factor
												else:
																factor += 1

				else:
								ans.append(lowerCamelCase_       )

				# precondition
				assert isinstance(lowerCamelCase_	,     lowerCamelCase_       ), "'ans' must been from type list"

				return ans






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				assert isinstance(lowerCamelCase_	,     lowerCamelCase_       ) and (
				    number >= 0
				), "'number' bust been an int and >= 0"

				lowercase__     				=    0

				# prime factorization of 'number'
				lowercase__     				=    prime_factorization(lowerCamelCase_       )

				lowercase__     				=    max(lowerCamelCase_       )

				# precondition
				assert isinstance(lowerCamelCase_	,     lowerCamelCase_       ), "'ans' must been from type int"

				return ans






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				assert isinstance(lowerCamelCase_	,     lowerCamelCase_       ) and (
				    number >= 0
				), "'number' bust been an int and >= 0"

				lowercase__     				=    0

				# prime factorization of 'number'
				lowercase__     				=    prime_factorization(lowerCamelCase_       )

				lowercase__     				=    min(lowerCamelCase_       )

				# precondition
				assert isinstance(lowerCamelCase_	,     lowerCamelCase_       ), "'ans' must been from type int"

				return ans






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				assert isinstance(lowerCamelCase_	,     lowerCamelCase_       ), "'number' must been an int"
				assert isinstance(number % 2 == 0	,     lowerCamelCase_       ), "compare bust been from type bool"

				return number % 2 == 0






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				assert isinstance(lowerCamelCase_	,     lowerCamelCase_       ), "'number' must been an int"
				assert isinstance(number % 2 != 0	,     lowerCamelCase_       ), "compare bust been from type bool"

				return number % 2 != 0






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				assert (
				    isinstance(lowerCamelCase_	,     lowerCamelCase_       ) and (number > 2) and is_even(lowerCamelCase_       )
				), "'number' must been an int, even and > 2"

				lowercase__     				=    []  # this list will returned

				# creates a list of prime numbers between 2 up to 'number'
				lowercase__     				=    get_prime_numbers(lowerCamelCase_       )
				lowercase__     				=    len(lowerCamelCase_       )

				# run variable for while-loops.
				lowercase__     				=    0
				lowercase__     				=    None

				# exit variable. for break up the loops
				lowercase__     				=    True

				while i < len_pn and loop:
								lowercase__     				=    i + 1

								while j < len_pn and loop:
												if prime_numbers[i] + prime_numbers[j] == number:
																lowercase__     				=    False
																ans.append(prime_numbers[i]       )
																ans.append(prime_numbers[j]       )

												j += 1

								i += 1

				# precondition
				assert (
				    isinstance(lowerCamelCase_	,     lowerCamelCase_       )
				    and (len(lowerCamelCase_       ) == 2)
				    and (ans[0] + ans[1] == number)
				    and is_prime(ans[0]       )
				    and is_prime(ans[1]       )
				), "'ans' must contains two primes. And sum of elements must been eq 'number'"

				return ans






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				assert (
				    isinstance(lowerCamelCase_	,     lowerCamelCase_       )
				    and isinstance(lowerCamelCase_	,     lowerCamelCase_       )
				    and (numbera >= 0)
				    and (numbera >= 0)
				), "'number1' and 'number2' must been positive integer."

				lowercase__     				=    0

				while numbera != 0:
								lowercase__     				=    numbera % numbera
								lowercase__     				=    numbera
								lowercase__     				=    rest

				# precondition
				assert isinstance(lowerCamelCase_	,     lowerCamelCase_       ) and (
				    numbera >= 0
				), "'number' must been from type int and positive"

				return numbera






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				assert (
				    isinstance(lowerCamelCase_	,     lowerCamelCase_       )
				    and isinstance(lowerCamelCase_	,     lowerCamelCase_       )
				    and (numbera >= 1)
				    and (numbera >= 1)
				), "'number1' and 'number2' must been positive integer."

				lowercase__     				=    1  # actual answer that will be return.

				# for kgV (x,1)
				if numbera > 1 and numbera > 1:
								# builds the prime factorization of 'number1' and 'number2'
								lowercase__     				=    prime_factorization(lowerCamelCase_       )
								lowercase__     				=    prime_factorization(lowerCamelCase_       )

				elif numbera == 1 or numbera == 1:
								lowercase__     				=    []
								lowercase__     				=    []
								lowercase__     				=    max(lowerCamelCase_	,     lowerCamelCase_       )

				lowercase__     				=    0
				lowercase__     				=    0

				lowercase__     				=    []  # captured numbers int both 'primeFac1' and 'primeFac2'

				# iterates through primeFac1
				for n in prime_fac_a:
								if n not in done:
												if n in prime_fac_a:
																lowercase__     				=    prime_fac_a.count(lowerCamelCase_       )
																lowercase__     				=    prime_fac_a.count(lowerCamelCase_       )

																for _ in range(max(lowerCamelCase_	,     lowerCamelCase_       )       ):
																				ans *= n

												else:
																lowercase__     				=    prime_fac_a.count(lowerCamelCase_       )

																for _ in range(lowerCamelCase_       ):
																				ans *= n

												done.append(lowerCamelCase_       )

    # iterates through primeFac2
				for n in prime_fac_a:
								if n not in done:
												lowercase__     				=    prime_fac_a.count(lowerCamelCase_       )

												for _ in range(lowerCamelCase_       ):
																ans *= n

												done.append(lowerCamelCase_       )

    # precondition
				assert isinstance(lowerCamelCase_	,     lowerCamelCase_       ) and (
				    ans >= 0
				), "'ans' must been from type int and positive"

				return ans






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				assert isinstance(lowerCamelCase_	,     lowerCamelCase_       ) and (n >= 0), "'number' must been a positive int"

				lowercase__     				=    0
				lowercase__     				=    2  # this variable holds the answer

				while index < n:
								index += 1

								ans += 1  # counts to the next number

								# if ans not prime then
								# runs to the next prime number.
								while not is_prime(lowerCamelCase_       ):
												ans += 1

    # precondition
				assert isinstance(lowerCamelCase_	,     lowerCamelCase_       ) and is_prime(
				    lowerCamelCase_       ), "'ans' must been a prime number and from type int"

				return ans






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				assert (
				    is_prime(lowerCamelCase_       ) and is_prime(lowerCamelCase_       ) and (p_number_a < p_number_a)
				), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"

				lowercase__     				=    p_number_a + 1  # jump to the next number

				lowercase__     				=    []  # this list will be returns.

				# if number is not prime then
				# fetch the next prime number.
				while not is_prime(lowerCamelCase_       ):
								number += 1

				while number < p_number_a:
								ans.append(lowerCamelCase_       )

								number += 1

								# fetch the next prime number.
								while not is_prime(lowerCamelCase_       ):
												number += 1

    # precondition
				assert (
				    isinstance(lowerCamelCase_	,     lowerCamelCase_       )
				    and ans[0] != p_number_a
				    and ans[len(lowerCamelCase_       ) - 1] != p_number_a
				), "'ans' must been a list without the arguments"

				# 'ans' contains not 'pNumber1' and 'pNumber2' !
				return ans






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				assert isinstance(lowerCamelCase_	,     lowerCamelCase_       ) and (n >= 1), "'n' must been int and >= 1"

				lowercase__     				=    []  # will be returned.

				for divisor in range(1	,     n + 1       ):
								if n % divisor == 0:
												ans.append(lowerCamelCase_       )

    # precondition
				assert ans[0] == 1 and ans[len(lowerCamelCase_       ) - 1] == n, "Error in function getDivisiors(...)"

				return ans






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				assert isinstance(lowerCamelCase_	,     lowerCamelCase_       ) and (
				    number > 1
				), "'number' must been an int and >= 1"

				lowercase__     				=    get_divisors(lowerCamelCase_       )

				# precondition
				assert (
				    isinstance(lowerCamelCase_	,     lowerCamelCase_       )
				    and (divisors[0] == 1)
				    and (divisors[len(lowerCamelCase_       ) - 1] == number)
				), "Error in help-function getDivisiors(...)"

				# summed all divisors up to 'number' (exclusive), hence [:-1]
				return sum(divisors[:-1]       ) == number






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				assert (
				    isinstance(lowerCamelCase_	,     lowerCamelCase_       )
				    and isinstance(lowerCamelCase_	,     lowerCamelCase_       )
				    and (denominator != 0)
				), "The arguments must been from type int and 'denominator' != 0"

				# build the greatest common divisor of numerator and denominator.
				lowercase__     				=    gcd(abs(lowerCamelCase_       )	,     abs(lowerCamelCase_       )       )

				# precondition
				assert (
				    isinstance(lowerCamelCase_	,     lowerCamelCase_       )
				    and (numerator % gcd_of_fraction == 0)
				    and (denominator % gcd_of_fraction == 0)
				), "Error in function gcd(...,...)"

				return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				assert isinstance(lowerCamelCase_	,     lowerCamelCase_       ) and (n >= 0), "'n' must been a int and >= 0"

				lowercase__     				=    1  # this will be return.

				for factor in range(1	,     n + 1       ):
								ans *= factor

				return ans






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				assert isinstance(lowerCamelCase_	,     lowerCamelCase_       ) and (n >= 0), "'n' must been an int and >= 0"

				lowercase__     				=    0
				lowercase__     				=    1
				lowercase__     				=    1  # this will be return

				for _ in range(n - 1       ):
								lowercase__     				=    ans
								ans += fiba
								lowercase__     				=    tmp

				return ans




