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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_ta import TaTokenizer
else:
		lowercase   =	None


lowercase   =	logging.get_logger(__name__)

lowercase   =	{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase   =	{
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/tokenizer.json''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/tokenizer.json''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/tokenizer.json''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/tokenizer.json''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/tokenizer.json''',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
lowercase   =	{
    '''t5-small''': 5_1_2,
    '''t5-base''': 5_1_2,
    '''t5-large''': 5_1_2,
    '''t5-3b''': 5_1_2,
    '''t5-11b''': 5_1_2,
}





class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE           =     PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE           =     ['''input_ids''', '''attention_mask''']
					SCREAMING_SNAKE_CASE           =     TaTokenizer

					SCREAMING_SNAKE_CASE           =     []

					def __init__(						self       :  List[str] ,       __UpperCamelCase       :  List[Any]=None ,       __UpperCamelCase       :  Dict=None ,       __UpperCamelCase       :  Any="</s>" ,       __UpperCamelCase       :  Union[str, Any]="<unk>" ,       __UpperCamelCase       :  List[str]="<pad>" ,       __UpperCamelCase       :  Any=1_0_0 ,       __UpperCamelCase       :  List[str]=None ,       **__UpperCamelCase       :  Tuple ,       ):
												# Add extra_ids to the special token list
												if extra_ids > 0 and additional_special_tokens is None:
																			lowerCamelCase_					=       [F'''<extra_id_{i}>''' for i in range(__UpperCamelCase )]
												elif extra_ids > 0 and additional_special_tokens is not None:
																			# Check that we have the right number of extra special tokens
																			lowerCamelCase_					=       len(set(filter(lambda __UpperCamelCase : bool("""extra_id_""" in str(__UpperCamelCase ) ) ,       __UpperCamelCase ) ) )
																			if extra_tokens != extra_ids:
																										raise ValueError(
																										    F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
																										    """ provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
																										    """ tokens""" )

												super().__init__(
												    __UpperCamelCase ,       tokenizer_file=__UpperCamelCase ,       eos_token=__UpperCamelCase ,       unk_token=__UpperCamelCase ,       pad_token=__UpperCamelCase ,       extra_ids=__UpperCamelCase ,       additional_special_tokens=__UpperCamelCase ,       **__UpperCamelCase ,       )

												lowerCamelCase_					=       vocab_file
												lowerCamelCase_					=       False if not self.vocab_file else True
												lowerCamelCase_					=       extra_ids

					@staticmethod
					def  lowercase__   (						__UpperCamelCase       :  Optional[Any] ,       __UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  List[Any] ):
												if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
																			lowerCamelCase_					=       TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
																			if init_max_model_length is not None and init_max_model_length != max_model_length:
																										return init_max_model_length
																			elif init_max_model_length is None:
																										warnings.warn(
																										    """This tokenizer was incorrectly instantiated with a model max length of"""
																										    F''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
																										    """ behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
																										    """ `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
																										    F''' {pretrained_model_name_or_path} automatically truncating your input to'''
																										    F''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
																										    F''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
																										    """ `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
																										    """ instantiate this tokenizer with `model_max_length` set to your preferred value.""" ,       __UpperCamelCase ,       )

												return max_model_length

					def  lowercase__   (						self       :  str ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  Optional[str] = None ):
												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(__UpperCamelCase ):
																			logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
																			return
												lowerCamelCase_					=       os.path.join(
												    __UpperCamelCase ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

												if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCamelCase ):
																			copyfile(self.vocab_file ,       __UpperCamelCase )
																			logger.info(F'''Copy vocab file to {out_vocab_file}''' )

												return (out_vocab_file,)

					def  lowercase__   (						self       :  List[Any] ,       __UpperCamelCase       :  List[int] ,       __UpperCamelCase       :  Optional[List[int]] = None ):
												lowerCamelCase_					=       token_ids_a + [self.eos_token_id]
												if token_ids_a is None:
																			return self.prefix_tokens + token_ids_a
												else:
																			lowerCamelCase_					=       token_ids_a + [self.eos_token_id]
																			return self.prefix_tokens + token_ids_a + token_ids_a

					def  lowercase__   (						self       :  Dict ,       __UpperCamelCase       :  List[int] ,       __UpperCamelCase       :  Optional[List[int]] = None ):
												lowerCamelCase_					=       [self.eos_token_id]

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

					def  lowercase__   (						self       :  Any ):
												return list(
												    set(filter(lambda __UpperCamelCase : bool(re.search(R"""<extra_id_\d+>""" ,       __UpperCamelCase ) ) is not None ,       self.additional_special_tokens ) ) )



					def  lowercase__   (						self       :  List[str] ):
												return [self.convert_tokens_to_ids(__UpperCamelCase ) for token in self.get_sentinel_tokens()]







from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

lowercase   =	{'''UserAgent''': UserAgent().random}



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





class    __A:

					def __init__(						self       :  int ,       __UpperCamelCase       :  List[str] ):
												lowerCamelCase_					=       F'''https://www.instagram.com/{username}/'''
												lowerCamelCase_					=       self.get_json()

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       requests.get(self.url ,       headers=__UpperCamelCase ).text
												lowerCamelCase_					=       BeautifulSoup(__UpperCamelCase ,       """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       :  Dict ):
												return F'''{self.__class__.__name__}(\'{self.username}\')'''

					def __str__(						self       :  Optional[Any] ):
												return F'''{self.fullname} ({self.username}) is {self.biography}'''

					@property
					def  lowercase__   (						self       :  Dict ):
												return self.user_data["username"]

					@property
					def  lowercase__   (						self       :  List[Any] ):
												return self.user_data["full_name"]

					@property
					def  lowercase__   (						self       :  Any ):
												return self.user_data["biography"]

					@property
					def  lowercase__   (						self       :  int ):
												return self.user_data["business_email"]

					@property
					def  lowercase__   (						self       :  List[str] ):
												return self.user_data["external_url"]

					@property
					def  lowercase__   (						self       :  List[Any] ):
												return self.user_data["edge_followed_by"]["count"]

					@property
					def  lowercase__   (						self       :  str ):
												return self.user_data["edge_follow"]["count"]

					@property
					def  lowercase__   (						self       :  List[str] ):
												return self.user_data["edge_owner_to_timeline_media"]["count"]

					@property
					def  lowercase__   (						self       :  List[str] ):
												return self.user_data["profile_pic_url_hd"]

					@property
					def  lowercase__   (						self       :  Optional[Any] ):
												return self.user_data["is_verified"]



					@property
					def  lowercase__   (						self       :  int ):
												return self.user_data["is_private"]



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str = "github"							)	->     None:
							import os

							if os.environ.get("""CI"""							):
														return  # test failing on GitHub Actions
							lowerCamelCase_					=       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 > 1_5_0
							assert instagram_user.number_of_followers > 1_2_0_0_0_0
							assert instagram_user.number_of_followings > 1_5
							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()
		lowercase   =	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 = }""")





