





'''simple docstring'''


from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

lowerCAmelCase_   :   Union[str, Any]       =					{"UserAgent": UserAgent().random}







def    _lowerCamelCase  (__lowerCamelCase  :     Optional[int]     )   ->    dict:
			a__     = script.contents[0]
			a__     = json.loads(data[data.find("{\"config\""     ) : -1]     )
			return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]






class       UpperCamelCase__  :







	def __init__(				self			:   Optional[int] , lowerCamelCase			:   Tuple		):




				'''simple docstring'''
				a__     = F'''https://www.instagram.com/{username}/'''
				a__     = self.get_json()







	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				a__     = requests.get(self.url , headers=_SCREAMING_SNAKE_CASE		).text
				a__     = BeautifulSoup(_SCREAMING_SNAKE_CASE , "html.parser"		).find_all("script"		)
				try:
							return extract_user_profile(scripts[4]		)
				except (json.decoder.JSONDecodeError, KeyError):
							return extract_user_profile(scripts[3]		)







	def __repr__(				self			:   Optional[Any]		):




				'''simple docstring'''
				return F'''{self.__class__.__name__}(\'{self.username}\')'''







	def __str__(				self			:   str		):




				'''simple docstring'''
				return F'''{self.fullname} ({self.username}) is {self.biography}'''







	@property
	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				return self.user_data["username"]







	@property
	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				return self.user_data["full_name"]







	@property
	def 						__a   (				self			:   str		):




				'''simple docstring'''
				return self.user_data["biography"]







	@property
	def 						__a   (				self			:   str		):




				'''simple docstring'''
				return self.user_data["business_email"]







	@property
	def 						__a   (				self			:   str		):




				'''simple docstring'''
				return self.user_data["external_url"]







	@property
	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				return self.user_data["edge_followed_by"]["count"]







	@property
	def 						__a   (				self			:   int		):




				'''simple docstring'''
				return self.user_data["edge_follow"]["count"]







	@property
	def 						__a   (				self			:   str		):




				'''simple docstring'''
				return self.user_data["edge_owner_to_timeline_media"]["count"]







	@property
	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				return self.user_data["profile_pic_url_hd"]







	@property
	def 						__a   (				self			:   str		):




				'''simple docstring'''
				return self.user_data["is_verified"]







	@property
	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				return self.user_data["is_private"]







def    _lowerCamelCase  (__lowerCamelCase  :     List[str] = "github"     )   ->    None:
			import os

			if os.environ.get("CI"     ):
						return  # test failing on GitHub Actions
			a__     = InstagramUser(__UpperCamelCase     )
			assert instagram_user.user_data
			assert isinstance(instagram_user.user_data					,  __UpperCamelCase     )
			assert instagram_user.username == username
			if username != "github":
						return
			assert instagram_user.fullname == "GitHub"
			assert instagram_user.biography == "Built for developers."
			assert instagram_user.number_of_posts > 150
			assert instagram_user.number_of_followers > 12_0000
			assert instagram_user.number_of_followings > 15
			assert instagram_user.email == "support@github.com"
			assert instagram_user.website == "https://github.com/readme"
			assert instagram_user.profile_picture_url.startswith("https://instagram."     )
			assert instagram_user.is_verified is True
			assert instagram_user.is_private is False


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		lowerCAmelCase_   :   str       =					InstagramUser("github")
		print(instagram_user)
		print(f"""{instagram_user.number_of_posts = }""")
		print(f"""{instagram_user.number_of_followers = }""")
		print(f"""{instagram_user.number_of_followings = }""")
		print(f"""{instagram_user.email = }""")
		print(f"""{instagram_user.website = }""")
		print(f"""{instagram_user.profile_picture_url = }""")
		print(f"""{instagram_user.is_verified = }""")
		print(f"""{instagram_user.is_private = }""")









'''simple docstring'''


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_nllb import NllbTokenizer
else:
		lowerCAmelCase_   :   Optional[Any]       =					None


lowerCAmelCase_   :   List[str]       =					logging.get_logger(__name__)


lowerCAmelCase_   :   Union[str, Any]       =					{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase_   :   List[Any]       =					{
    "vocab_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
        ),
    },
}

lowerCAmelCase_   :   List[str]       =					{
    "facebook/nllb-large-en-ro": 1024,
    "facebook/nllb-200-distilled-600M": 1024,
}

# fmt: off
lowerCAmelCase_   :   int       =					["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]






class       UpperCamelCase__  (		__lowerCAmelCase     ):
	lowerCAmelCase__					:			int								=						VOCAB_FILES_NAMES
	lowerCAmelCase__					:			str								=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__					:			int								=						PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__					:			int								=						["input_ids", "attention_mask"]
	lowerCAmelCase__					:			List[Any]								=						NllbTokenizer

	lowerCAmelCase__					:			List[int]								=						[]
	lowerCAmelCase__					:			List[int]								=						[]







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




				'''simple docstring'''
				# Mask token behave like a normal word, i.e. include the space before it
				a__     = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase		) if isinstance(lowerCamelCase , lowerCamelCase		) else mask_token
				a__     = legacy_behaviour
				super().__init__(
				    vocab_file=lowerCamelCase , tokenizer_file=lowerCamelCase , bos_token=lowerCamelCase , eos_token=lowerCamelCase , sep_token=lowerCamelCase , cls_token=lowerCamelCase , unk_token=lowerCamelCase , pad_token=lowerCamelCase , mask_token=lowerCamelCase , src_lang=lowerCamelCase , tgt_lang=lowerCamelCase , additional_special_tokens=lowerCamelCase , legacy_behaviour=lowerCamelCase , **lowerCamelCase , )

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

				a__     = 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}		)
				a__     = {
				    lang_code: self.convert_tokens_to_ids(lowerCamelCase		) for lang_code in FAIRSEQ_LANGUAGE_CODES
				}

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







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




				'''simple docstring'''
				return self._src_lang







	@src_lang.setter
	def 						__a   (				self			:   int , lowerCamelCase			:   str		):




				'''simple docstring'''
				a__     = new_src_lang
				self.set_src_lang_special_tokens(self._src_lang		)







	def 						__a   (				self			:   Any , lowerCamelCase			:   List[int] , lowerCamelCase			:   Optional[List[int]] = None		):




				'''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 						__a   (				self			:   Union[str, Any] , lowerCamelCase			:   List[int] , lowerCamelCase			:   Optional[List[int]] = None		):




				'''simple docstring'''
				a__     = [self.sep_token_id]
				a__     = [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 						__a   (				self			:   str , lowerCamelCase			:   str , lowerCamelCase			:   str , lowerCamelCase			:   Optional[str] , lowerCamelCase			:   Optional[str] , **lowerCamelCase			:   List[str]		):




				'''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"		)
				a__     = src_lang
				a__     = self(lowerCamelCase , add_special_tokens=lowerCamelCase , return_tensors=lowerCamelCase , **lowerCamelCase		)
				a__     = self.convert_tokens_to_ids(lowerCamelCase		)
				a__     = tgt_lang_id
				return inputs







	def 						__a   (				self			:   str , lowerCamelCase			:   List[str] , lowerCamelCase			:   str = "eng_Latn" , lowerCamelCase			:   Optional[List[str]] = None , lowerCamelCase			:   str = "fra_Latn" , **lowerCamelCase			:   Any , ):




				'''simple docstring'''
				a__     = src_lang
				a__     = tgt_lang
				return super().prepare_seqaseq_batch(lowerCamelCase , lowerCamelCase , **lowerCamelCase		)







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				return self.set_src_lang_special_tokens(self.src_lang		)







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				return self.set_tgt_lang_special_tokens(self.tgt_lang		)







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




				'''simple docstring'''
				a__     = self.convert_tokens_to_ids(lowerCamelCase		)

				if self.legacy_behaviour:
							a__     = []
							a__     = [self.eos_token_id, self.cur_lang_code]
				else:
							a__     = [self.cur_lang_code]
							a__     = [self.eos_token_id]

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

				a__     = 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 						__a   (				self			:   Tuple , lowerCamelCase			:   str		):




				'''simple docstring'''
				a__     = self.convert_tokens_to_ids(lowerCamelCase		)
				if self.legacy_behaviour:
							a__     = []
							a__     = [self.eos_token_id, self.cur_lang_code]
				else:
							a__     = [self.cur_lang_code]
							a__     = [self.eos_token_id]

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

				a__     = 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 						__a   (				self			:   Tuple , lowerCamelCase			:   str , lowerCamelCase			:   Optional[str] = None		):




				'''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(lowerCamelCase		):
							logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''		)
							return
				a__     = os.path.join(
				    lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

				return (out_vocab_file,)



