


import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
	import torch


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation


A							:					str     						=     logging.get_logger(__name__)
A							:					Optional[int]     						=     {'vocab_file': 'spiece.model'}

A							:					Any     						=     {
    'vocab_file': {
        'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
    }
}

A							:					str     						=     {
    'AI-Sweden/gpt-sw3-126m': 2048,
    'AI-Sweden/gpt-sw3-350m': 2048,
    'AI-Sweden/gpt-sw3-1.6b': 2048,
    'AI-Sweden/gpt-sw3-6.7b': 2048,
    'AI-Sweden/gpt-sw3-20b': 2048,
}






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


					def __init__(						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Any=False     ,    SCREAMING_SNAKE_CASE	:		Tuple=False     ,    SCREAMING_SNAKE_CASE	:		Dict=False     ,    SCREAMING_SNAKE_CASE	:		str=None     ,    SCREAMING_SNAKE_CASE	:		List[str]=None     ,    SCREAMING_SNAKE_CASE	:		Any=None     ,    SCREAMING_SNAKE_CASE	:		str=None     ,    SCREAMING_SNAKE_CASE	:		Optional[Dict[str, Any]] = None     ,    **SCREAMING_SNAKE_CASE	:		str     ,    )			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		{} if sp_model_kwargs is None else sp_model_kwargs

											__snake_case				=		kwargs.get("name_or_path"						)
											if name_or_path is None:
																	logger.warning(
																	    "name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
																	    " you are testing the model, this can safely be ignored"						)
																	__snake_case				=		"None"

											# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
											__snake_case				=		"<|endoftext|>" if eos_token is None else eos_token
											__snake_case				=		"<unk>" if unk_token is None else unk_token
											if "gpt-sw3-7b" in name_or_path:
																	__snake_case				=		unk_token if pad_token is None else pad_token
																	__snake_case				=		eos_token if bos_token is None else bos_token
											else:
																	__snake_case				=		"<pad>" if pad_token is None else pad_token
																	__snake_case				=		"<s>" if bos_token is None else bos_token

											super().__init__(
											    do_lower_case=__A     ,    remove_space=__A     ,    keep_accents=__A     ,    bos_token=__A     ,    eos_token=__A     ,    unk_token=__A     ,    pad_token=__A     ,    sp_model_kwargs=self.sp_model_kwargs     ,    **__A     ,    )

											__snake_case				=		do_lower_case
											__snake_case				=		remove_space
											__snake_case				=		keep_accents
											__snake_case				=		vocab_file

											__snake_case				=		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
											self.sp_model.Load(__A						)

											# Used for whitespace normalization in input texts
											# fmt : off
											__snake_case				=		{" ", " ", " ", " ", " ", "　", " ", " ", " ", " ", "￼", ""}
											# fmt : on

											# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
											__snake_case				=		re.compile(
											    f'''[{''.join(map(__A     ,    list(range(0     ,    9						)						) + list(range(1_1     ,    3_2						)						) + list(range(1_2_7     ,    1_6_0						)						) + [1_6_0, 1_7_3, 8_2_0_3]						)						)}]'''						)


					def __getstate__(						self	:		Dict						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		self.__dict__.copy()
											__snake_case				=		None
											return state


					def __setstate__(						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		int						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		d

											# for backward compatibility
											if not hasattr(self     ,    "sp_model_kwargs"						):
																	__snake_case				=		{}

											__snake_case				=		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
											self.sp_model.Load(self.vocab_file						)


					@property
					# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	int:




											'''simple docstring'''

											return len(self.sp_model						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		str						)			->	int:




											'''simple docstring'''

											__snake_case				=		self.non_printing_characters_re.sub(""     ,    __A						)

											# Normalize whitespaces
											__snake_case				=		"".join([char if char not in self.whitespaces else " " for char in text]						)

											# NFC Unicode normalization
											__snake_case				=		unicodedata.normalize("NFC"     ,    __A						)
											return text


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




											'''simple docstring'''

											__snake_case				=		self.preprocess_text(__A						)
											return self.sp_model.encode(__A     ,    out_type=__A						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int     ,    SCREAMING_SNAKE_CASE	:		str						)			->	List[str]:




											'''simple docstring'''

											return self.sp_model.PieceToId(__A						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		int						)			->	Dict:




											'''simple docstring'''

											return self.sp_model.IdToPiece(__A						)


					@staticmethod
					def 							SCREAMING_SNAKE_CASE_  (						SCREAMING_SNAKE_CASE	:		str						)			->	Dict:




											'''simple docstring'''

											return out_string


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str     ,    SCREAMING_SNAKE_CASE	:		List[str]						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		[]
											__snake_case				=		""
											__snake_case				=		False
											for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																							# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
																							if not prev_is_special:
																													out_string += " "

																							out_string += self.sp_model.decode(__A						) + token
																							__snake_case				=		True
																							__snake_case				=		[]
																	else:
																							current_sub_tokens.append(__A						)
																							__snake_case				=		False
											out_string += self.sp_model.decode(__A						)

											return out_string


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	str:




											'''simple docstring'''

											__snake_case				=		{self.convert_ids_to_tokens(__A						): i for i in range(self.vocab_size						)}
											vocab.update(self.added_tokens_encoder						)
											return vocab


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




											'''simple docstring'''

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

											if os.path.abspath(self.vocab_file						) != os.path.abspath(__A						) and os.path.isfile(self.vocab_file						):
																	copyfile(self.vocab_file     ,    __A						)
											elif not os.path.isfile(self.vocab_file						):
																	with open(__A     ,    "wb"						) as fi:
																							__snake_case				=		self.sp_model.serialized_model_proto()
																							fi.write(__A						)

											return (out_vocab_file,)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Union[str, List[str]]     ,    SCREAMING_SNAKE_CASE	:		Union[str, bool] = False						)			->	Dict:




											'''simple docstring'''

											if isinstance(__A     ,    __A						):
																	__snake_case				=		self.preprocess_text(__A						)
																	__snake_case				=		self.sp_model.encode(__A						)
											else:
																	__snake_case				=		[self.preprocess_text(__A						) for t in text]
																	__snake_case				=		self.sp_model.encode(__A						)

											if return_tensors is True or return_tensors == "pt":
																	__snake_case				=		torch.tensor(__A						)

											return token_ids


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Union[int, List[int]]						)			->	Any:




											'''simple docstring'''

											return self.sp_model.decode(__A						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		"Conversation"						)			->	int:




											'''simple docstring'''

											__snake_case				=		[f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
											__snake_case				=		(
											    f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(__A						) + f'''{self.bos_token}Bot:'''
											)
											return self.encode(text=__A						)



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()





