


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_mbart import MBartTokenizer
else:
	A							:					List[Any]     						=     None


A							:					Optional[int]     						=     logging.get_logger(__name__)


A							:					str     						=     {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

A							:					str     						=     {
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

A							:					str     						=     {
    'facebook/mbart-large-en-ro': 1024,
    'facebook/mbart-large-cc25': 1024,
}

# fmt: off
A							:					int     						=     ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']






class     UpperCamelCase(  _a   ):
					snake_case_		:				int										=	VOCAB_FILES_NAMES
					snake_case_		:				str										=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					snake_case_		:				Any										=	PRETRAINED_VOCAB_FILES_MAP
					snake_case_		:				Tuple										=	["""input_ids""", """attention_mask"""]
					snake_case_		:				Optional[Any]										=	MBartTokenizer

					snake_case_		:				List[int]										=	[]
					snake_case_		:				List[int]										=	[]


					def __init__(						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=None     ,    SCREAMING_SNAKE_CASE	:		Tuple=None     ,    SCREAMING_SNAKE_CASE	:		List[Any]="<s>"     ,    SCREAMING_SNAKE_CASE	:		Optional[int]="</s>"     ,    SCREAMING_SNAKE_CASE	:		Tuple="</s>"     ,    SCREAMING_SNAKE_CASE	:		List[Any]="<s>"     ,    SCREAMING_SNAKE_CASE	:		str="<unk>"     ,    SCREAMING_SNAKE_CASE	:		Any="<pad>"     ,    SCREAMING_SNAKE_CASE	:		List[Any]="<mask>"     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=None     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=None     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=None     ,    **SCREAMING_SNAKE_CASE	:		List[Any]     ,    )			->	List[Any]:




											'''simple docstring'''

											__snake_case				=		AddedToken(SCREAMING_SNAKE_CASE     ,    lstrip=SCREAMING_SNAKE_CASE     ,    rstrip=SCREAMING_SNAKE_CASE						) if isinstance(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						) else mask_token

											super().__init__(
											    vocab_file=SCREAMING_SNAKE_CASE     ,    tokenizer_file=SCREAMING_SNAKE_CASE     ,    bos_token=SCREAMING_SNAKE_CASE     ,    eos_token=SCREAMING_SNAKE_CASE     ,    sep_token=SCREAMING_SNAKE_CASE     ,    cls_token=SCREAMING_SNAKE_CASE     ,    unk_token=SCREAMING_SNAKE_CASE     ,    pad_token=SCREAMING_SNAKE_CASE     ,    mask_token=SCREAMING_SNAKE_CASE     ,    src_lang=SCREAMING_SNAKE_CASE     ,    tgt_lang=SCREAMING_SNAKE_CASE     ,    additional_special_tokens=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE     ,    )

											__snake_case				=		vocab_file
											__snake_case				=		False if not self.vocab_file else True

											__snake_case				=		FAIRSEQ_LANGUAGE_CODES.copy()

											if additional_special_tokens is not None:
																	# Only add those special tokens if they are not already there.
																	_additional_special_tokens.extend(
																	    [t for t in additional_special_tokens if t not in _additional_special_tokens]						)

											self.add_special_tokens({"additional_special_tokens": _additional_special_tokens}						)
											__snake_case				=		{
											    lang_code: self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE						) for lang_code in FAIRSEQ_LANGUAGE_CODES
											}

											__snake_case				=		src_lang if src_lang is not None else "en_XX"
											__snake_case				=		self.convert_tokens_to_ids(self._src_lang						)
											__snake_case				=		tgt_lang
											self.set_src_lang_special_tokens(self._src_lang						)


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	str:




											'''simple docstring'''

											return self._src_lang


					@src_lang.setter
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		str						)			->	None:




											'''simple docstring'''

											__snake_case				=		new_src_lang
											self.set_src_lang_special_tokens(self._src_lang						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		List[int]     ,    SCREAMING_SNAKE_CASE	:		Optional[List[int]] = None						)			->	List[int]:




											'''simple docstring'''

											if token_ids_a is None:
																	return self.prefix_tokens + token_ids_a + self.suffix_tokens
											# We don't expect to process pairs, but leave the pair logic for API consistency
											return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		List[int]     ,    SCREAMING_SNAKE_CASE	:		Optional[List[int]] = None						)			->	List[int]:




											'''simple docstring'''

											__snake_case				=		[self.sep_token_id]
											__snake_case				=		[self.cls_token_id]

											if token_ids_a is None:
																	return len(cls + token_ids_a + sep						) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		Optional[str]     ,    SCREAMING_SNAKE_CASE	:		Optional[str]     ,    **SCREAMING_SNAKE_CASE	:		Dict						)			->	Dict:




											'''simple docstring'''

											if src_lang is None or tgt_lang is None:
																	raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"						)
											__snake_case				=		src_lang
											__snake_case				=		self(SCREAMING_SNAKE_CASE     ,    add_special_tokens=SCREAMING_SNAKE_CASE     ,    return_tensors=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)
											__snake_case				=		self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE						)
											__snake_case				=		tgt_lang_id
											return inputs


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		str = "en_XX"     ,    SCREAMING_SNAKE_CASE	:		Optional[List[str]] = None     ,    SCREAMING_SNAKE_CASE	:		str = "ro_RO"     ,    **SCREAMING_SNAKE_CASE	:		Any     ,    )			->	BatchEncoding:




											'''simple docstring'''

											__snake_case				=		src_lang
											__snake_case				=		tgt_lang
											return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Optional[Any]:




											'''simple docstring'''

											return self.set_src_lang_special_tokens(self.src_lang						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	int:




											'''simple docstring'''

											return self.set_tgt_lang_special_tokens(self.tgt_lang						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		str						)			->	None:




											'''simple docstring'''

											__snake_case				=		self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE						)
											__snake_case				=		[]
											__snake_case				=		[self.eos_token_id, self.cur_lang_code]

											__snake_case				=		self.convert_ids_to_tokens(self.prefix_tokens						)
											__snake_case				=		self.convert_ids_to_tokens(self.suffix_tokens						)

											__snake_case				=		processors.TemplateProcessing(
											    single=prefix_tokens_str + ["$A"] + suffix_tokens_str     ,    pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str     ,    special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str     ,    self.prefix_tokens + self.suffix_tokens						)						)     ,    )


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		str						)			->	None:




											'''simple docstring'''

											__snake_case				=		self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE						)
											__snake_case				=		[]
											__snake_case				=		[self.eos_token_id, self.cur_lang_code]

											__snake_case				=		self.convert_ids_to_tokens(self.prefix_tokens						)
											__snake_case				=		self.convert_ids_to_tokens(self.suffix_tokens						)

											__snake_case				=		processors.TemplateProcessing(
											    single=prefix_tokens_str + ["$A"] + suffix_tokens_str     ,    pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str     ,    special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str     ,    self.prefix_tokens + self.suffix_tokens						)						)     ,    )






					def 							SCREAMING_SNAKE_CASE_  (						self	:		str     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		Optional[str] = None						)			->	Tuple[str]:




											'''simple docstring'''

											if not self.can_save_slow_tokenizer:
																	raise ValueError(
																	    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
																	    "tokenizer."						)

											if not os.path.isdir(SCREAMING_SNAKE_CASE						):
																	logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''						)
																	return
											__snake_case				=		os.path.join(
											    SCREAMING_SNAKE_CASE     ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

											if os.path.abspath(self.vocab_file						) != os.path.abspath(SCREAMING_SNAKE_CASE						):
																	copyfile(self.vocab_file     ,    SCREAMING_SNAKE_CASE						)

											return (out_vocab_file,)



from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup





def 	_lowerCAmelCase       (    _lowerCAmelCase = "" )						->	dict[str, float]:


						'''simple docstring'''






						__snake_case				=		url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
						__snake_case				=		BeautifulSoup(requests.get(_lowerCAmelCase ).text      ,	"html.parser" )
						__snake_case				=		soup.find_all("td"      ,	attrs="titleColumn" )
						__snake_case				=		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 = "IMDb_Top_250_Movies.csv" )						->	None:


						'''simple docstring'''






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


if __name__ == "__main__":
	write_movies()





