

from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings





@maybe_allow_in_graph
class       snake_case_  (						nn.Module   ):







			'''simple docstring'''

			def __init__(	self					:							str				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							List[Any]=0.0				,  _UpperCamelCase					:							Optional[int] = None				,  _UpperCamelCase					:							str = "geglu"				,  _UpperCamelCase					:							Optional[int] = None				,  _UpperCamelCase					:							bool = False				,  _UpperCamelCase					:							bool = False				,  _UpperCamelCase					:							bool = False				,  _UpperCamelCase					:							bool = False				,  _UpperCamelCase					:							bool = True				,  _UpperCamelCase					:							str = "layer_norm"				,  _UpperCamelCase					:							bool = False				,  ) ->Optional[int]:
									super().__init__()
									snake_case_  =      only_cross_attention

									snake_case_  =      (num_embeds_ada_norm is not None) and norm_type == '''ada_norm_zero'''
									snake_case_  =      (num_embeds_ada_norm is not None) and norm_type == '''ada_norm'''

									if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
															raise ValueError(
															    f'''`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'''
															    f''' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.'''				)

									# Define 3 blocks. Each block has its own normalization layer.
									# 1. Self-Attn
									if self.use_ada_layer_norm:
															snake_case_  =      AdaLayerNorm(_UpperCamelCase				,  _UpperCamelCase				)
									elif self.use_ada_layer_norm_zero:
															snake_case_  =      AdaLayerNormZero(_UpperCamelCase				,  _UpperCamelCase				)
									else:
															snake_case_  =      nn.LayerNorm(_UpperCamelCase				,  elementwise_affine=_UpperCamelCase				)
									snake_case_  =      Attention(
									    query_dim=_UpperCamelCase				,  heads=_UpperCamelCase				,  dim_head=_UpperCamelCase				,  dropout=_UpperCamelCase				,  bias=_UpperCamelCase				,  cross_attention_dim=cross_attention_dim if only_cross_attention else None				,  upcast_attention=_UpperCamelCase				,  )

									# 2. Cross-Attn
									if cross_attention_dim is not None or double_self_attention:
															# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
															# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
															# the second cross attention block.
															snake_case_  =      (
															    AdaLayerNorm(_UpperCamelCase				,  _UpperCamelCase				)
															    if self.use_ada_layer_norm
															    else nn.LayerNorm(_UpperCamelCase				,  elementwise_affine=_UpperCamelCase				)
															)
															snake_case_  =      Attention(
															    query_dim=_UpperCamelCase				,  cross_attention_dim=cross_attention_dim if not double_self_attention else None				,  heads=_UpperCamelCase				,  dim_head=_UpperCamelCase				,  dropout=_UpperCamelCase				,  bias=_UpperCamelCase				,  upcast_attention=_UpperCamelCase				,  )  # is self-attn if encoder_hidden_states is none
									else:
															snake_case_  =      None
															snake_case_  =      None

									# 3. Feed-forward
									snake_case_  =      nn.LayerNorm(_UpperCamelCase				,  elementwise_affine=_UpperCamelCase				)
									snake_case_  =      FeedForward(_UpperCamelCase				,  dropout=_UpperCamelCase				,  activation_fn=_UpperCamelCase				,  final_dropout=_UpperCamelCase				)

									# let chunk size default to None
									snake_case_  =      None
									snake_case_  =      0







			def 							snake_case__(	self					:							str				,  _UpperCamelCase					:							Optional[int]				,  _UpperCamelCase					:							int				) ->str:
									# Sets chunk feed-forward
									snake_case_  =      chunk_size
									snake_case_  =      dim







			def 							snake_case__(	self					:							List[str]				,  _UpperCamelCase					:							torch.FloatTensor				,  _UpperCamelCase					:							Optional[torch.FloatTensor] = None				,  _UpperCamelCase					:							Optional[torch.FloatTensor] = None				,  _UpperCamelCase					:							Optional[torch.FloatTensor] = None				,  _UpperCamelCase					:							Optional[torch.LongTensor] = None				,  _UpperCamelCase					:							Dict[str, Any] = None				,  _UpperCamelCase					:							Optional[torch.LongTensor] = None				,  ) ->str:
									# Notice that normalization is always applied before the real computation in the following blocks.
									# 1. Self-Attention
									if self.use_ada_layer_norm:
															snake_case_  =      self.norma(_UpperCamelCase				,  _UpperCamelCase				)
									elif self.use_ada_layer_norm_zero:
															snake_case_,       snake_case_,       snake_case_,       snake_case_,       snake_case_  =      self.norma(
															    _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  hidden_dtype=hidden_states.dtype				)
									else:
															snake_case_  =      self.norma(_UpperCamelCase				)

									snake_case_  =      cross_attention_kwargs if cross_attention_kwargs is not None else {}

									snake_case_  =      self.attna(
									    _UpperCamelCase				,  encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None				,  attention_mask=_UpperCamelCase				,  **_UpperCamelCase				,  )
									if self.use_ada_layer_norm_zero:
															snake_case_  =      gate_msa.unsqueeze(1				) * attn_output
									snake_case_  =      attn_output + hidden_states

									# 2. Cross-Attention
									if self.attna is not None:
															snake_case_  =      (
															    self.norma(_UpperCamelCase				,  _UpperCamelCase				) if self.use_ada_layer_norm else self.norma(_UpperCamelCase				)
															)

															snake_case_  =      self.attna(
															    _UpperCamelCase				,  encoder_hidden_states=_UpperCamelCase				,  attention_mask=_UpperCamelCase				,  **_UpperCamelCase				,  )
															snake_case_  =      attn_output + hidden_states

									# 3. Feed-forward
									snake_case_  =      self.norma(_UpperCamelCase				)

									if self.use_ada_layer_norm_zero:
															snake_case_  =      norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

									if self._chunk_size is not None:
															# "feed_forward_chunk_size" can be used to save memory
															if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
																					raise ValueError(
																					    f'''`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.'''				)

															snake_case_  =      norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
															snake_case_  =      torch.cat(
															    [self.ff(_UpperCamelCase				) for hid_slice in norm_hidden_states.chunk(_UpperCamelCase				,  dim=self._chunk_dim				)]				,  dim=self._chunk_dim				,  )
									else:
															snake_case_  =      self.ff(_UpperCamelCase				)

									if self.use_ada_layer_norm_zero:
															snake_case_  =      gate_mlp.unsqueeze(1				) * ff_output

									snake_case_  =      ff_output + hidden_states

									return hidden_states






class       snake_case_  (						nn.Module   ):







			'''simple docstring'''

			def __init__(	self					:							str				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							Optional[int] = None				,  _UpperCamelCase					:							int = 4				,  _UpperCamelCase					:							float = 0.0				,  _UpperCamelCase					:							str = "geglu"				,  _UpperCamelCase					:							bool = False				,  ) ->str:
									super().__init__()
									snake_case_  =      int(dim * mult				)
									snake_case_  =      dim_out if dim_out is not None else dim

									if activation_fn == "gelu":
															snake_case_  =      GELU(_UpperCamelCase				,  _UpperCamelCase				)
									if activation_fn == "gelu-approximate":
															snake_case_  =      GELU(_UpperCamelCase				,  _UpperCamelCase				,  approximate='''tanh'''				)
									elif activation_fn == "geglu":
															snake_case_  =      GEGLU(_UpperCamelCase				,  _UpperCamelCase				)
									elif activation_fn == "geglu-approximate":
															snake_case_  =      ApproximateGELU(_UpperCamelCase				,  _UpperCamelCase				)

									snake_case_  =      nn.ModuleList([]				)
									# project in
									self.net.append(_UpperCamelCase				)
									# project dropout
									self.net.append(nn.Dropout(_UpperCamelCase				)				)
									# project out
									self.net.append(nn.Linear(_UpperCamelCase				,  _UpperCamelCase				)				)
									# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
									if final_dropout:
															self.net.append(nn.Dropout(_UpperCamelCase				)				)







			def 							snake_case__(	self					:							Any				,  _UpperCamelCase					:							Optional[Any]				) ->int:
									for module in self.net:
															snake_case_  =      module(_UpperCamelCase				)
									return hidden_states






class       snake_case_  (						nn.Module   ):







			'''simple docstring'''

			def __init__(	self					:							Tuple				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							str = "none"				) ->Any:
									super().__init__()
									snake_case_  =      nn.Linear(_UpperCamelCase				,  _UpperCamelCase				)
									snake_case_  =      approximate







			def 							snake_case__(	self					:							List[str]				,  _UpperCamelCase					:							Tuple				) ->List[Any]:
									if gate.device.type != "mps":
															return F.gelu(_UpperCamelCase				,  approximate=self.approximate				)
									# mps: gelu is not implemented for float16
									return F.gelu(gate.to(dtype=torch.floataa				)				,  approximate=self.approximate				).to(dtype=gate.dtype				)







			def 							snake_case__(	self					:							List[Any]				,  _UpperCamelCase					:							Optional[int]				) ->List[str]:
									snake_case_  =      self.proj(_UpperCamelCase				)
									snake_case_  =      self.gelu(_UpperCamelCase				)
									return hidden_states






class       snake_case_  (						nn.Module   ):







			'''simple docstring'''

			def __init__(	self					:							Union[str, Any]				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							int				) ->Union[str, Any]:
									super().__init__()
									snake_case_  =      nn.Linear(_UpperCamelCase				,  dim_out * 2				)







			def 							snake_case__(	self					:							Union[str, Any]				,  _UpperCamelCase					:							List[str]				) ->str:
									if gate.device.type != "mps":
															return F.gelu(_UpperCamelCase				)
									# mps: gelu is not implemented for float16
									return F.gelu(gate.to(dtype=torch.floataa				)				).to(dtype=gate.dtype				)







			def 							snake_case__(	self					:							str				,  _UpperCamelCase					:							List[str]				) ->Optional[int]:
									snake_case_,       snake_case_  =      self.proj(_UpperCamelCase				).chunk(2				,  dim=-1				)
									return hidden_states * self.gelu(_UpperCamelCase				)






class       snake_case_  (						nn.Module   ):







			'''simple docstring'''

			def __init__(	self					:							Optional[Any]				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							int				) ->Optional[Any]:
									super().__init__()
									snake_case_  =      nn.Linear(_UpperCamelCase				,  _UpperCamelCase				)







			def 							snake_case__(	self					:							List[str]				,  _UpperCamelCase					:							int				) ->Any:
									snake_case_  =      self.proj(_UpperCamelCase				)
									return x * torch.sigmoid(1.702 * x				)






class       snake_case_  (						nn.Module   ):







			'''simple docstring'''

			def __init__(	self					:							Optional[Any]				,  _UpperCamelCase					:							List[str]				,  _UpperCamelCase					:							List[Any]				) ->List[Any]:
									super().__init__()
									snake_case_  =      nn.Embedding(_UpperCamelCase				,  _UpperCamelCase				)
									snake_case_  =      nn.SiLU()
									snake_case_  =      nn.Linear(_UpperCamelCase				,  embedding_dim * 2				)
									snake_case_  =      nn.LayerNorm(_UpperCamelCase				,  elementwise_affine=_UpperCamelCase				)







			def 							snake_case__(	self					:							Dict				,  _UpperCamelCase					:							Any				,  _UpperCamelCase					:							Optional[Any]				) ->Union[str, Any]:
									snake_case_  =      self.linear(self.silu(self.emb(_UpperCamelCase				)				)				)
									snake_case_,       snake_case_  =      torch.chunk(_UpperCamelCase				,  2				)
									snake_case_  =      self.norm(_UpperCamelCase				) * (1 + scale) + shift
									return x






class       snake_case_  (						nn.Module   ):







			'''simple docstring'''

			def __init__(	self					:							Any				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							int				) ->str:
									super().__init__()

									snake_case_  =      CombinedTimestepLabelEmbeddings(_UpperCamelCase				,  _UpperCamelCase				)

									snake_case_  =      nn.SiLU()
									snake_case_  =      nn.Linear(_UpperCamelCase				,  6 * embedding_dim				,  bias=_UpperCamelCase				)
									snake_case_  =      nn.LayerNorm(_UpperCamelCase				,  elementwise_affine=_UpperCamelCase				,  eps=1e-6				)







			def 							snake_case__(	self					:							List[str]				,  _UpperCamelCase					:							List[Any]				,  _UpperCamelCase					:							List[str]				,  _UpperCamelCase					:							Dict				,  _UpperCamelCase					:							Dict=None				) ->Optional[int]:
									snake_case_  =      self.linear(self.silu(self.emb(_UpperCamelCase				,  _UpperCamelCase				,  hidden_dtype=_UpperCamelCase				)				)				)
									snake_case_,       snake_case_,       snake_case_,       snake_case_,       snake_case_,       snake_case_  =      emb.chunk(6				,  dim=1				)
									snake_case_  =      self.norm(_UpperCamelCase				) * (1 + scale_msa[:, None]) + shift_msa[:, None]
									return x, gate_msa, shift_mlp, scale_mlp, gate_mlp






class       snake_case_  (						nn.Module   ):







			'''simple docstring'''

			def __init__(	self					:							List[str]				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							Optional[str] = None				,  _UpperCamelCase					:							float = 1e-5				) ->Optional[Any]:
									super().__init__()
									snake_case_  =      num_groups
									snake_case_  =      eps

									if act_fn is None:
															snake_case_  =      None
									else:
															snake_case_  =      get_activation(_UpperCamelCase				)

									snake_case_  =      nn.Linear(_UpperCamelCase				,  out_dim * 2				)







			def 							snake_case__(	self					:							List[str]				,  _UpperCamelCase					:							Any				,  _UpperCamelCase					:							Tuple				) ->List[Any]:
									if self.act:
															snake_case_  =      self.act(_UpperCamelCase				)
									snake_case_  =      self.linear(_UpperCamelCase				)
									snake_case_  =      emb[:, :, None, None]
									snake_case_,       snake_case_  =      emb.chunk(2				,  dim=1				)

									snake_case_  =      F.group_norm(_UpperCamelCase				,  self.num_groups				,  eps=self.eps				)
									snake_case_  =      x * (1 + scale) + shift
									return x



from __future__ import annotations

import requests

lowerCAmelCase       		=    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     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_ = 1      ,       lowercase_ = "new"      ,       lowercase_ = None      )		->     dict:




						'''simple docstring'''







						__UpperCAmelCase			:							Tuple	    =	wanted_data or []
						if invalid_search_terms := ", ".join(sorted(set(lowercase_      ) - valid_terms      )      ):
												__UpperCAmelCase			:							List[Any]	    =	f"Invalid search term: {invalid_search_terms}"
												raise ValueError(lowercase_      )
						__UpperCAmelCase			:							Optional[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

						__UpperCAmelCase			:							List[str]	    =	response.json()
						if not wanted_data:
												return {id_: data["data"]["children"][id_] for id_ in range(lowercase_      )}

						__UpperCAmelCase			:							List[Any]	    =	{}
						for id_ in range(lowercase_      ):
												__UpperCAmelCase			:							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"""]))

