
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:
			_lowerCamelCase	:    Union[str, Any]          =      None


_lowerCamelCase	:    Union[str, Any]          =      logging.get_logger(__name__)

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

_lowerCamelCase	:    Optional[int]          =      {
    '''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
_lowerCamelCase	:    Any          =      {
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}




class        SCREAMING_SNAKE_CASE__					(     __lowerCamelCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Union[str, Any]          = VOCAB_FILES_NAMES
	_UpperCAmelCase	:		Optional[Any]          = PRETRAINED_VOCAB_FILES_MAP
	_UpperCAmelCase	:		Optional[Any]          = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_UpperCAmelCase	:		Union[str, Any]          = ['input_ids', 'attention_mask']
	_UpperCAmelCase	:		List[str]          = TaTokenizer

	_UpperCAmelCase	:		List[int]          = []



	def __init__(  self     :   int      ,  lowercase     :   str=None      ,  lowercase     :   Tuple=None      ,  lowercase     :   int="</s>"      ,  lowercase     :   Tuple="<unk>"      ,  lowercase     :   Any="<pad>"      ,  lowercase     :   str=100      ,  lowercase     :   str=None      ,  **lowercase     :   int      ,  ):


				'''simple docstring'''


				if extra_ids > 0 and additional_special_tokens is None:
							_snake_case          =      [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
							_snake_case          =      len(set(filter(lambda lowercase     : 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_      ,  )

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



	@staticmethod
	def 			A							(  lowercase     :   List[Any]      ,  lowercase     :   str      ,  lowercase     :   Optional[int]     ):


				'''simple docstring'''


				if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
							_snake_case          =      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 			A							(  self     :   int      ,  lowercase     :   str      ,  lowercase     :   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(UpperCamelCase_     ):
							logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
							return
				_snake_case          =      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 			A							(  self     :   Dict      ,  lowercase     :   List[int]      ,  lowercase     :   Optional[List[int]] = None     ):


				'''simple docstring'''


				_snake_case          =      token_ids_a + [self.eos_token_id]
				if token_ids_a is None:
							return self.prefix_tokens + token_ids_a
				else:
							_snake_case          =      token_ids_a + [self.eos_token_id]
							return self.prefix_tokens + token_ids_a + token_ids_a



	def 			A							(  self     :   Optional[int]      ,  lowercase     :   List[int]      ,  lowercase     :   Optional[List[int]] = None     ):


				'''simple docstring'''


				_snake_case          =      [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 			A							(  self     :   Any     ):


				'''simple docstring'''


				return list(
				    set(filter(lambda lowercase     : bool(re.search(R'<extra_id_\d+>'      ,  UpperCamelCase_     )     ) is not None      ,  self.additional_special_tokens     )     )     )





	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				return [self.convert_tokens_to_ids(UpperCamelCase_     ) for token in self.get_sentinel_tokens()]

from __future__ import annotations

import requests

_lowerCamelCase	:    List[str]          =      set(
    '''approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports'''.split()
)






def 	a_		(	__lowercase       :     str	, __lowercase       :     int = 1	, __lowercase       :     str = "new"	, __lowercase       :     list | None = None			)			->  dict:
			_snake_case          =      wanted_data or []
			if invalid_search_terms := ", ".join(sorted(set(__lowercase			) - valid_terms			)			):
						_snake_case          =      f'''Invalid search term: {invalid_search_terms}'''
						raise ValueError(__lowercase			)
			_snake_case          =      requests.get(
			    f'''https://reddit.com/r/{subreddit}/{age}.json?limit={limit}'''	, headers={'User-agent': 'A random string'}	, )
			if response.status_code == 429:
						raise requests.HTTPError

			_snake_case          =      response.json()
			if not wanted_data:
						return {id_: data["data"]["children"][id_] for id_ in range(__lowercase			)}

			_snake_case          =      {}
			for id_ in range(__lowercase			):
						_snake_case          =      {
						    item: data['data']['children'][id_]['data'][item] for item in wanted_data
						}
			return data_dict


if __name__ == "__main__":
			# If you get Error 429, that means you are rate limited.Try after some time
			print(get_subreddit_data('''learnpython''', wanted_data=['''title''', '''url''', '''selftext''']))