
import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class 	_lowerCAmelCase					(	__a					,      unittest.TestCase       ):
				_lowercase					=PhobertTokenizer
				_lowercase					=False




				def      __a						(					self     )				->      Union[str, Any]:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							lowerCAmelCase_						=	["T@@", "i", "I", "R@@", "r", "e@@"]
							lowerCAmelCase_						=	dict(zip(_UpperCamelCase				,				range(len(_UpperCamelCase     )     )     )     )
							lowerCAmelCase_						=	["#version: 0.2", "l à</w>"]
							lowerCAmelCase_						=	{"unk_token": "<unk>"}

							lowerCAmelCase_						=	os.path.join(self.tmpdirname				,				VOCAB_FILES_NAMES["vocab_file"]     )
							lowerCAmelCase_						=	os.path.join(self.tmpdirname				,				VOCAB_FILES_NAMES["merges_file"]     )

							with open(self.vocab_file				,				"w"				,				encoding="utf-8"     ) as fp:
										for token in vocab_tokens:
													fp.write(f"""{token} {vocab_tokens[token]}\n"""     )
							with open(self.merges_file				,				"w"				,				encoding="utf-8"     ) as fp:
										fp.write("\n".join(_UpperCamelCase     )     )




				def      __a						(					self				,				**_UpperCamelCase     )				->      List[Any]:
							kwargs.update(self.special_tokens_map     )
							return PhobertTokenizer.from_pretrained(self.tmpdirname				,				**_UpperCamelCase     )




				def      __a						(					self				,				_UpperCamelCase     )				->      List[str]:
							lowerCAmelCase_						=	"Tôi là VinAI Research"
							lowerCAmelCase_						=	"T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
							return input_text, output_text




				def      __a						(					self     )				->      Dict:
							lowerCAmelCase_						=	PhobertTokenizer(self.vocab_file				,				self.merges_file				,				**self.special_tokens_map     )
							lowerCAmelCase_						=	"Tôi là VinAI Research"
							lowerCAmelCase_						=	"T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
							lowerCAmelCase_						=	tokenizer.tokenize(_UpperCamelCase     )
							print(_UpperCamelCase     )
							self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )

							lowerCAmelCase_						=	tokens + [tokenizer.unk_token]

							lowerCAmelCase_						=	[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase     )				,				_UpperCamelCase     )





from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup






def    lowerCamelCase__				(				__lowerCAmelCase  :  str = ""				):




			"""simple docstring"""


			lowerCAmelCase_						=	url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
			lowerCAmelCase_						=	BeautifulSoup(requests.get(__lowerCAmelCase				).text    ,							"html.parser"				)
			lowerCAmelCase_						=	soup.find_all("td"    ,							attrs="titleColumn"				)
			lowerCAmelCase_						=	soup.find_all("td"    ,							class_="ratingColumn imdbRating"				)
			return {
			    title.a.text: float(rating.strong.text				)
			    for title, rating in zip(__lowerCAmelCase    ,							__lowerCAmelCase				)
			}






def    lowerCamelCase__				(				__lowerCAmelCase  :  str = "IMDb_Top_250_Movies.csv"				):




			"""simple docstring"""


			lowerCAmelCase_						=	get_imdb_top_aaa_movies()
			with open(__lowerCAmelCase    ,							"w"    ,							newline=""				) as out_file:
						lowerCAmelCase_						=	csv.writer(__lowerCAmelCase				)
						writer.writerow(["Movie title", "IMDb rating"]				)
						for title, rating in movies.items():
									writer.writerow([title, rating]				)


if __name__ == "__main__":
			write_movies()



