





'''simple docstring'''


import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase_   :   Union[str, Any]       =					get_tests_dir("fixtures/test_sentencepiece_bpe.model")






class       UpperCamelCase__  (		__lowerCAmelCase	,unittest.TestCase     ):
	lowerCAmelCase__					:			Tuple								=						BartphoTokenizer
	lowerCAmelCase__					:			Union[str, Any]								=						False
	lowerCAmelCase__					:			Optional[Any]								=						True







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				super().setUp()

				a__     = ["▁This", "▁is", "▁a", "▁t", "est"]
				a__     = dict(zip(lowerCamelCase , range(len(lowerCamelCase		)		)		)		)
				a__     = {"unk_token": "<unk>"}

				a__     = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["monolingual_vocab_file"]		)
				with open(self.monolingual_vocab_file , "w" , encoding="utf-8"		) as fp:
							for token in vocab_tokens:
										fp.write(F'''{token} {vocab_tokens[token]}\n'''		)

				a__     = BartphoTokenizer(lowerCamelCase , self.monolingual_vocab_file , **self.special_tokens_map		)
				tokenizer.save_pretrained(self.tmpdirname		)







	def 						__a   (				self			:   Tuple , **lowerCamelCase			:   Tuple		):




				'''simple docstring'''
				kwargs.update(self.special_tokens_map		)
				return BartphoTokenizer.from_pretrained(self.tmpdirname , **lowerCamelCase		)







	def 						__a   (				self			:   Tuple , lowerCamelCase			:   Any		):




				'''simple docstring'''
				a__     = "This is a là test"
				a__     = "This is a<unk><unk> test"
				return input_text, output_text







	def 						__a   (				self			:   Union[str, Any]		):




				'''simple docstring'''
				a__     = BartphoTokenizer(lowerCamelCase , self.monolingual_vocab_file , **self.special_tokens_map		)
				a__     = "This is a là test"
				a__     = "▁This ▁is ▁a ▁l à ▁t est".split()
				a__     = tokenizer.tokenize(lowerCamelCase		)
				self.assertListEqual(lowerCamelCase , lowerCamelCase		)

				a__     = tokens + [tokenizer.unk_token]
				a__     = [4, 5, 6, 3, 3, 7, 8, 3]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase		) , lowerCamelCase		)










'''simple docstring'''


def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]     )   ->    list[list[float]]:
			a__     = []
			for data in source_data:
						for i, el in enumerate(__lowerCamelCase     ):
									if len(__lowerCamelCase     ) < i + 1:
												data_lists.append([]     )
									data_lists[i].append(float(__lowerCamelCase     )     )
			return data_lists







def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]					,  __lowerCamelCase  :     list[int]     )   ->    list[list[float]]:
			a__     = []
			for dlist, weight in zip(__lowerCamelCase					,  __lowerCamelCase     ):
						a__     = min(__lowerCamelCase     )
						a__     = max(__lowerCamelCase     )

						a__     = []
						# for weight 0 score is 1 - actual score
						if weight == 0:
									for item in dlist:
												try:
															score.append(1 - ((item - mind) / (maxd - mind))     )
												except ZeroDivisionError:
															score.append(1     )

						elif weight == 1:
									for item in dlist:
												try:
															score.append((item - mind) / (maxd - mind)     )
												except ZeroDivisionError:
															score.append(0     )

        # weight not 0 or 1
						else:
									a__     = f'''Invalid weight of {weight:f} provided'''
									raise ValueError(__lowerCamelCase     )

						score_lists.append(__lowerCamelCase     )

			return score_lists







def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]     )   ->    list[float]:
			a__     = [0 for i in range(len(score_lists[0]     )     )]

			for slist in score_lists:
						for j, ele in enumerate(__lowerCamelCase     ):
									a__     = final_scores[j] + ele

			return final_scores







def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]					,  __lowerCamelCase  :     list[int]     )   ->    list[list[float]]:
			a__     = get_data(__lowerCamelCase     )
			a__     = calculate_each_score(__lowerCamelCase					,  __lowerCamelCase     )
			a__     = generate_final_scores(__lowerCamelCase     )

			# append scores to source data
			for i, ele in enumerate(__lowerCamelCase     ):
						source_data[i].append(__lowerCamelCase     )

			return source_data



