import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase            =		logging.get_logger(__name__)

_UpperCAmelCase            =		"""▁"""

_UpperCAmelCase            =		{"""vocab_file""": """prophetnet.tokenizer"""}

_UpperCAmelCase            =		{
    """vocab_file""": {
        """microsoft/xprophetnet-large-wiki100-cased""": (
            """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"""
        ),
    }
}

_UpperCAmelCase            =		{
    """microsoft/xprophetnet-large-wiki100-cased""": {"""do_lower_case""": False},
}

_UpperCAmelCase            =		{
    """microsoft/xprophetnet-large-wiki100-cased""": 512,
}




def     UpperCamelCase						(	__lowercase						:			List[Any]     ):
		'''simple docstring'''




		A_		:    Optional[int]         =				collections.OrderedDict()
		with open(__lowercase						,'r'						,encoding='utf-8'     ) as reader:
				A_		:    int         =				reader.readlines()
		for index, token in enumerate(__lowercase     ):
				A_		:    Optional[int]         =				token.rstrip('\n'     )
				A_		:    Dict         =				index
		return vocab





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = VOCAB_FILES_NAMES
				lowerCamelCase_    = PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase_    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase_    = ['''input_ids''', '''attention_mask''']
				def __init__(						self		,     lowercase		,     lowercase="[SEP]"		,     lowercase="[SEP]"		,     lowercase="[SEP]"		,     lowercase="[UNK]"		,     lowercase="[PAD]"		,     lowercase="[CLS]"		,     lowercase="[MASK]"		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				{} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    bos_token=lowercase		,     eos_token=lowercase		,     sep_token=lowercase		,     unk_token=lowercase		,     pad_token=lowercase		,     cls_token=lowercase		,     mask_token=lowercase		,     sp_model_kwargs=self.sp_model_kwargs		,     **lowercase		,     )

						try:
								import sentencepiece as spm
						except ImportError:
								logger.warning(
								    'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
								    ' pip install sentencepiece' )
								raise

						A_		:    Optional[Any]         =				spm.SentencePieceProcessor(**self.sp_model_kwargs )
						self.sp_model.Load(str(lowercase ) )
						A_		:    Any         =				vocab_file

						# Original fairseq vocab and spm vocab must be "aligned":
						# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
						# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
						# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
						# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

						# put special tokens and [unused] tokens into the vocab
						A_		:    Any         =				{'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[UNK]': 3, '[MASK]': 4}

						for i in range(1_0 ):
								A_		:    List[Any]         =				F'''[unused{i}]'''
								A_		:    Tuple         =				5 + i

						# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
						A_		:    Optional[Any]         =				1_2
						A_		:    List[str]         =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}
						for k in self.fairseq_tokens_to_ids.keys():
								self.unique_no_split_tokens.append(lowercase )
				def __getstate__(						self ):



						"""simple docstring"""





						A_		:    str         =				self.__dict__.copy()
						A_		:    Optional[Any]         =				None
						return state
				def __setstate__(						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				d
						try:
								import sentencepiece as spm
						except ImportError:
								logger.warning(
								    'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
								    ' pip install sentencepiece' )
								raise

						# for backward compatibility
						if not hasattr(self		,     'sp_model_kwargs' ):
								A_		:    List[str]         =				{}

						A_		:    Tuple         =				spm.SentencePieceProcessor(**self.sp_model_kwargs )
						self.sp_model.Load(self.vocab_file )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None		,     lowercase = False ):



						"""simple docstring"""





						if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=lowercase		,     token_ids_a=lowercase		,     already_has_special_tokens=lowercase )

						if token_ids_a is None:
								return ([0] * len(lowercase )) + [1]
						return ([0] * len(lowercase )) + [1] + ([0] * len(lowercase )) + [1]
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						A_		:    Tuple         =				[self.sep_token_id]

						if token_ids_a is None:
								return len(token_ids_a + sep ) * [0]
						return len(token_ids_a + sep + sep + token_ids_a + sep ) * [0]
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return len(self.sp_model ) + self.fairseq_offset
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Dict         =				{self.convert_ids_to_tokens(lowercase ): i for i in range(self.vocab_size )}
						vocab.update(self.added_tokens_encoder )
						return vocab
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						return self.sp_model.encode(lowercase		,     out_type=lowercase )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						if token in self.fairseq_tokens_to_ids:
								return self.fairseq_tokens_to_ids[token]
						A_		:    Tuple         =				self.sp_model.PieceToId(lowercase )

						# Need to return unknown token if the SP model returned 0
						return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						if index in self.fairseq_ids_to_tokens:
								return self.fairseq_ids_to_tokens[index]
						return self.sp_model.IdToPiece(index - self.fairseq_offset )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Optional[int]         =				''.join(lowercase ).replace(lowercase		,     ' ' ).strip()
						return out_string
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





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

						if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase ) and os.path.isfile(self.vocab_file ):
								copyfile(self.vocab_file		,     lowercase )
						elif not os.path.isfile(self.vocab_file ):
								with open(lowercase		,     'wb' ) as fi:
										A_		:    int         =				self.sp_model.serialized_model_proto()
										fi.write(lowercase )

						return (out_vocab_file,)







				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						if token_ids_a is None:
								return token_ids_a + [self.sep_token_id]
						A_		:    Tuple         =				[self.sep_token_id]
						return token_ids_a + sep + token_ids_a + sep







from __future__ import annotations

import requests

_UpperCAmelCase            =		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     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			int = 1						,__lowercase						:			str = "new"						,__lowercase						:			list | None = None     ):
		'''simple docstring'''




		A_		:    Tuple         =				wanted_data or []
		if invalid_search_terms := ", ".join(sorted(set(__lowercase     ) - valid_terms     )     ):
				A_		:    int         =				f'''Invalid search term: {invalid_search_terms}'''
				raise ValueError(__lowercase     )
		A_		:    Optional[int]         =				requests.get(
		    f'''https://reddit.com/r/{subreddit}/{age}.json?limit={limit}'''						,headers={'User-agent': 'A random string'}						,)
		if response.status_code == 4_29:
				raise requests.HTTPError

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

		A_		:    Union[str, Any]         =				{}
		for id_ in range(__lowercase     ):
				A_		:    List[str]         =				{
				    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"""]))






