



import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
		from ... import PreTrainedTokenizerBase, TensorType

_lowercase		:				Optional[int]      		=logging.get_logger(__name__)

class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:Dict      =					"vision-encoder-decoder"
						__lowerCAmelCase					:int      =					True

						def __init__(       self ,       **__lowercase  ) ->				List[Any]:
									"""simple docstring"""




									super().__init__(**__lowercase  )
									if "encoder" not in kwargs or "decoder" not in kwargs:
												raise ValueError(
												    F'''A configuraton of type {self.model_type} cannot be instantiated because '''
												    F'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'''  )

									a__       :						Optional[Any]							=  kwargs.pop("""encoder"""  )
									a__       :						int							=  encoder_config.pop("""model_type"""  )
									a__       :						List[Any]							=  kwargs.pop("""decoder"""  )
									a__       :						str							=  decoder_config.pop("""model_type"""  )

									a__       :						Union[str, Any]							=  AutoConfig.for_model(__lowercase ,       **__lowercase  )
									a__       :						Optional[Any]							=  AutoConfig.for_model(__lowercase ,       **__lowercase  )
									a__       :						str							=  True

						@classmethod
						def 							SCREAMING_SNAKE_CASE__(       cls ,       __lowercase ,       __lowercase ,       **__lowercase  ) ->				PretrainedConfig:
									"""simple docstring"""




									logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""  )
									a__       :						str							=  True
									a__       :						Optional[Any]							=  True

									return cls(encoder=encoder_config.to_dict() ,       decoder=decoder_config.to_dict() ,       **__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Any:
									"""simple docstring"""




									a__       :						str							=  copy.deepcopy(self.__dict__  )
									a__       :						Tuple							=  self.encoder.to_dict()
									a__       :						Union[str, Any]							=  self.decoder.to_dict()
									a__       :						str							=  self.__class__.model_type
									return output




class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:int      =					version.parse("1.11"		)

						@property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Mapping[str, Mapping[int, str]]:
									"""simple docstring"""




									return OrderedDict(
									    [
									        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
									    ]  )

						@property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				float:
									"""simple docstring"""




									return 1E-4

						@property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Mapping[str, Mapping[int, str]]:
									"""simple docstring"""




									return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}}  )




class        snake_case__    (A__		):







						"""simple docstring"""




						@property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Mapping[str, Mapping[int, str]]:
									"""simple docstring"""




									a__       :						Any							=  OrderedDict()
									a__       :						Optional[int]							=  {0: """batch""", 1: """past_decoder_sequence + sequence"""}
									a__       :						int							=  {0: """batch""", 1: """past_decoder_sequence + sequence"""}
									a__       :						Tuple							=  {0: """batch""", 1: """encoder_sequence"""}

									return common_inputs

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = -1 ,       __lowercase = -1 ,       __lowercase = False ,       __lowercase = None ,       ) ->				Mapping[str, Any]:
									"""simple docstring"""




									import torch

									a__       :						Dict							=  OrderedDict()

									a__       :						int							=  super().generate_dummy_inputs(
									    __lowercase ,       batch_size=__lowercase ,       seq_length=__lowercase ,       is_pair=__lowercase ,       framework=__lowercase  )

									a__	,						a__       :						List[str]							=  dummy_input["""input_ids"""].shape
									a__       :						List[str]							=  (batch, encoder_sequence, self._config.encoder_hidden_size)
									a__       :						Union[str, Any]							=  dummy_input.pop("""input_ids"""  )
									a__       :						str							=  dummy_input.pop("""attention_mask"""  )
									a__       :						str							=  torch.zeros(__lowercase  )

									return common_inputs




class        snake_case__    (A__		):







						"""simple docstring"""




						@property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				None:
									"""simple docstring"""




									pass

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				OnnxConfig:
									"""simple docstring"""




									return VisionEncoderDecoderEncoderOnnxConfig(__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase = "default"  ) ->				OnnxConfig:
									"""simple docstring"""




									a__       :						Tuple							=  encoder_config.hidden_size
									return VisionEncoderDecoderDecoderOnnxConfig(__lowercase ,       __lowercase  )









from __future__ import annotations

import requests

_lowercase		:				Dict      		=set(
    "approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories created_utc downs\nedited gilded gildings hidden hide_score is_created_from_ads_ui is_meta\nis_original_content is_reddit_media_domain is_video link_flair_css_class\nlink_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title\nname permalink pwls quarantine saved score secure_media secure_media_embed selftext\nsubreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type\ntotal_awards_received ups upvote_ratio url user_reports".split()
)

def      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       int = 1						,       _lowercase    :       str = "new"						,       _lowercase    :       list | None = None)							->	dict:







			"""simple docstring"""
			a__       :						Optional[Any]							=  wanted_data or []
			if invalid_search_terms := ", ".join(sorted(set(_lowercase) - valid_terms)):
						a__       :						Any							=  F'''Invalid search term: {invalid_search_terms}'''
						raise ValueError(_lowercase)
			a__       :						int							=  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

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

			a__       :						int							=  {}
			for id_ in range(_lowercase):
						a__       :						List[Any]							=  {
						    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"]))




