
from __future__ import annotations

from collections import Counter
from random import random





class    __A:

					def __init__(						self       :  Union[str, Any] ):
												lowerCamelCase_					=       {}

					def  lowercase__   (						self       :  Optional[int] ,       __UpperCamelCase       :  Optional[int] ):
												lowerCamelCase_					=       {}

					def  lowercase__   (						self       :  int ,       __UpperCamelCase       :  Optional[int] ,       __UpperCamelCase       :  List[str] ,       __UpperCamelCase       :  Optional[int] ):
												if nodea not in self.connections:
																			self.add_node(a_ )
												if nodea not in self.connections:
																			self.add_node(a_ )
												lowerCamelCase_					=       probability

					def  lowercase__   (						self       :  Union[str, Any] ):
												return list(self.connections )



					def  lowercase__   (						self       :  Any ,       __UpperCamelCase       :  Union[str, Any] ):
												lowerCamelCase_					=       0
												lowerCamelCase_					=       random()

												for dest in self.connections[node]:
																			current_probability += self.connections[node][dest]
																			if current_probability > random_value:
																										return dest
												return ""



def   __lowerCAmelCase    (				UpperCAmelCase__					:   List[Any]      ,					UpperCAmelCase__					:   Optional[int]      ,					UpperCAmelCase__					:   Dict							)	->     dict[str, int]:
							lowerCamelCase_					=       MarkovChainGraphUndirectedUnweighted()

							for nodea, nodea, probability in transitions:
														graph.add_transition_probability(snake_case__      ,					snake_case__      ,					snake_case__							)

							lowerCamelCase_					=       Counter(graph.get_nodes()							)
							lowerCamelCase_					=       start

							for _ in range(snake_case__							):
														lowerCamelCase_					=       graph.transition(snake_case__							)
														visited[node] += 1

							return visited


if __name__ == "__main__":
		import doctest

		doctest.testmod()










from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin


@dataclass
class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	42



class 	__snake_case   ( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE ):
		@register_to_config
		def __init__(							self    ,a_ = 16    ,a_ = 88    ,a_ = None    ,a_ = None    ,a_ = 1    ,a_ = 0.0    ,a_ = 32    ,a_ = None    ,a_ = False    ,a_ = None    ,a_ = "geglu"    ,a_ = True    ,a_ = True    ,):







						"""simple docstring"""
						super().__init__()
						lowerCAmelCase__		  =  num_attention_heads
						lowerCAmelCase__		  =  attention_head_dim
						lowerCAmelCase__		  =  num_attention_heads * attention_head_dim

						lowerCAmelCase__		  =  in_channels

						lowerCAmelCase__		  =  torch.nn.GroupNorm(num_groups=a_    ,num_channels=a_    ,eps=1e-6    ,affine=a_						)
						lowerCAmelCase__		  =  nn.Linear(a_    ,a_						)

						# 3. Define transformers blocks
						lowerCAmelCase__		  =  nn.ModuleList(
						    [
						        BasicTransformerBlock(
						            a_    ,a_    ,a_    ,dropout=a_    ,cross_attention_dim=a_    ,activation_fn=a_    ,attention_bias=a_    ,double_self_attention=a_    ,norm_elementwise_affine=a_    ,)
						        for d in range(a_						)
						    ]						)

						lowerCAmelCase__		  =  nn.Linear(a_    ,a_						)



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_=None    ,a_=None    ,a_=None    ,a_=1    ,a_=None    ,a_ = True    ,):







						"""simple docstring"""
						lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__		  =  hidden_states.shape
						lowerCAmelCase__		  =  batch_frames // num_frames

						lowerCAmelCase__		  =  hidden_states

						lowerCAmelCase__		  =  hidden_states[None, :].reshape(a_    ,a_    ,a_    ,a_    ,a_						)
						lowerCAmelCase__		  =  hidden_states.permute(0    ,2    ,1    ,3    ,4						)

						lowerCAmelCase__		  =  self.norm(a_						)
						lowerCAmelCase__		  =  hidden_states.permute(0    ,3    ,4    ,2    ,1						).reshape(batch_size * height * width    ,a_    ,a_						)

						lowerCAmelCase__		  =  self.proj_in(a_						)

						# 2. Blocks
						for block in self.transformer_blocks:
										lowerCAmelCase__		  =  block(
										    a_    ,encoder_hidden_states=a_    ,timestep=a_    ,cross_attention_kwargs=a_    ,class_labels=a_    ,)

						# 3. Output
						lowerCAmelCase__		  =  self.proj_out(a_						)
						lowerCAmelCase__		  =  (
						    hidden_states[None, None, :]
						    .reshape(a_    ,a_    ,a_    ,a_    ,a_						)
						    .permute(0    ,3    ,4    ,1    ,2						)
						    .contiguous()
						)
						lowerCAmelCase__		  =  hidden_states.reshape(a_    ,a_    ,a_    ,a_						)

						lowerCAmelCase__		  =  hidden_states + residual

						if not return_dict:
										return (output,)

						return TransformerTemporalModelOutput(sample=a_						)
