i{0 lddlZddlmZddlmZmZmZmZmZddl Z ddl m Z m Z ddl mZddlmZdd e j"d ee j$d e j"fd Z dd e j"de j"ded ee j$d e j"f dZeGddeZGddee Zy)N) dataclass)ListLiteralOptionalTupleUnion) ConfigMixinregister_to_config) BaseOutput)SchedulerMixint generatorreturncJ| |jn |j}tj||jdd|j |j}tj tj |j d j d S)a Generate Gumbel noise for sampling. Args: t (`torch.Tensor`): Input tensor to match the shape and dtype of the output noise. generator (`torch.Generator`, *optional*): A random number generator for reproducible sampling. Returns: `torch.Tensor`: Gumbel-distributed noise with the same shape, dtype, and device as the input tensor. devicerr r#B ;)rtorch zeros_likeuniform_tologclamp)rrrnoises p/home/obispo/Crisostomo_bridge/mision_env/lib/python3.12/site-packages/diffusers/schedulers/scheduling_amused.py gumbel_noiser s"+!6Y  AHHF   Qv . 7 71 7 R U UVWV^V^ _E II %++e"455<r?)selfr@rAs r__init__zAmusedScheduler.__init__fs !r Nnum_inference_stepsr#rrc tj||jd|_t |t t fr%tj|d|d|||_ytj|d|||_y)a Set the discrete timesteps used for the diffusion chain (to be run before inference). Args: num_inference_steps (`int`): The number of diffusion steps used when generating samples with a pre-trained model. temperature (`Union[float, Tuple[float, float], List[float]]`, *optional*, defaults to `(2, 0)`): Temperature parameter(s) for controlling the randomness of sampling. If a tuple or list is provided, temperatures will be linearly interpolated between the first and second values across all timesteps. If a single value is provided, temperatures will be linearly interpolated from that value to 0.01. device (`str` or `torch.device`, *optional*): The device to which the timesteps and temperatures should be moved to. If `None`, the timesteps are not moved. rrr g{Gz?N) rarangeflipr? isinstancetuplelistlinspacer>)rFrHr#rs r set_timestepszAmusedScheduler.set_timestepsosj(&9&INNqQ kE4= 1 %{1~{1~Obkq rD  %{DBU^d eD r model_outputtimestepsamplestarting_mask_ratior return_dictc|jdk(xr|jdk(}|rM|j\}} } } |j|| | z}|j|| | | zjddd}||jj k(} |j d} | j}|| j|jn| }|jjd k(r-|jtjk7r|j}|jd| jd}tj|d| j| }|dddfj | jdd}tj"| ||}|dk(r|}n|jd}|j$|k(j'}|dzt)|j$z }|jj*d k(r*tj,|t.j0zdz }nA|jj*d k(rd|z }n"t3d|jj*||z}||zj5}tj6| j9dddz |}tj:tj<dg|j |}tj>| d|dddddfdddddf}tj"| |tj@|jj:}tC|||jD||}tj"||jj |}|r&|j  }|j|| | }|s||fStG||S)ad Predict the sample at the previous timestep by masking tokens based on confidence scores. Args: model_output (`torch.Tensor`): The direct output from the learned diffusion model. Typically of shape `(batch_size, num_tokens, codebook_size)` or `(batch_size, codebook_size, height, width)` for 2D inputs. timestep (`int`): The current discrete timestep in the diffusion chain. sample (`torch.LongTensor`): A current instance of a sample created by the diffusion process. Contains token IDs, with masked positions indicated by `mask_token_id`. starting_mask_ratio (`float`, *optional*, defaults to 1.0): A multiplier applied to the mask ratio schedule. Values less than 1.0 will result in fewer tokens being masked at each step. generator (`torch.Generator`, *optional*): A random number generator for reproducible sampling. return_dict (`bool`, *optional*, defaults to `True`): Whether to return an [`~schedulers.scheduling_amused.AmusedSchedulerOutput`] or a plain tuple. Returns: [`~schedulers.scheduling_amused.AmusedSchedulerOutput`] or `tuple`: If `return_dict` is `True`, [`~schedulers.scheduling_amused.AmusedSchedulerOutput`] is returned, otherwise a tuple is returned where the first element is the sample tensor (`prev_sample`) and the second element is the predicted original sample tensor (`pred_original_sample`). rr r r%r&NcpurrrBrCunknown masking schedule T)r'keepdim)$ndimshapereshapepermuteconfigr@softmaxrrtypedtyperfloat32floatsize multinomialviewwherer?nonzerolenrAcosmathpi ValueErrorfloorminsummaxtensorr*finfor0r>r2)rFrQrRrSrTrrU two_dim_input batch_size codebook_sizeheightwidth unknown_mapr"rprobs_r4r3seq_lenstep_idxratio mask_ratior!selected_probsr/s rstepzAmusedScheduler.stepsTF q(C\->->!-C 7C7I7I 4J vu^^J?F'// M6TY>ZbbcdfgijkL 9 99 $$$,/8/D)**+% ==   &6<<5==+H\\^FEJJrN3$00iPSS[aSb>3AqD9>> CR@PQ${{;8LfU q=.Kll1oG(2;;=H\S%88E{{++x7"YYutww':; --9Y  #rs !88 A,EELLEXeoo-FERWR^R^E,+/ ll <<(   \\ < 8J8 8"DnkDr