

import torch
from torch import nn


class 				SCREAMING_SNAKE_CASE__ (nn.Module					):





    def __init__(				self						,       a						,       a						,       a						,       a						,       a=1						,       a=False):
         super().__init__()

         lowercase__   :       Optional[int]				=	n_token
         lowercase__   :       Optional[Any]				=	d_embed
         lowercase__   :       Union[str, Any]				=	d_proj

         lowercase__   :       Optional[Any]				=	cutoffs + [n_token]
         lowercase__   :       Any				=	[0] + self.cutoffs
         lowercase__   :       Optional[Any]				=	div_val

         lowercase__   :       str				=	self.cutoffs[0]
         lowercase__   :       Union[str, Any]				=	len(self.cutoffs) - 1
         lowercase__   :       Tuple				=	self.shortlist_size + self.n_clusters

         if self.n_clusters > 0:
              lowercase__   :       Optional[int]				=	nn.Parameter(torch.zeros(self.n_clusters						,       self.d_embed))
              lowercase__   :       Union[str, Any]				=	nn.Parameter(torch.zeros(self.n_clusters))

         lowercase__   :       Optional[Any]				=	nn.ModuleList()
         lowercase__   :       Optional[Any]				=	nn.ParameterList()

         if div_val == 1:
              for i in range(len(self.cutoffs)):
                   if d_proj != d_embed:
                        self.out_projs.append(nn.Parameter(torch.FloatTensor(a						,       a)))
                   else:
                        self.out_projs.append(a)

              self.out_layers.append(nn.Linear(a						,       a))
         else:
              for i in range(len(self.cutoffs)):
                   lowercase__   :       Union[str, Any]				=	self.cutoff_ends[i], self.cutoff_ends[i + 1]
                   lowercase__   :       List[str]				=	d_embed // (div_val**i)

                   self.out_projs.append(nn.Parameter(torch.FloatTensor(a						,       a)))

                   self.out_layers.append(nn.Linear(a						,       r_idx - l_idx))

         lowercase__   :       Dict				=	keep_order





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a):
         if proj is None:
              lowercase__   :       int				=	nn.functional.linear(a						,       a						,       bias=a)
         else:
              # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
              lowercase__   :       Optional[int]				=	nn.functional.linear(a						,       proj.t().contiguous())
              lowercase__   :       Any				=	nn.functional.linear(a						,       a						,       bias=a)
              # else:
              #     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
              #     if bias is not None:
              #         logit = logit + bias

         return logit





    def 				snake_case_    (				self						,       a						,       a=None						,       a=False):

         if labels is not None:
              # Shift so that tokens < n predict n
              lowercase__   :       Tuple				=	hidden[..., :-1, :].contiguous()
              lowercase__   :       Optional[int]				=	labels[..., 1:].contiguous()
              lowercase__   :       Optional[int]				=	hidden.view(-1						,       hidden.size(-1))
              lowercase__   :       List[Any]				=	labels.view(-1)
              if hidden.size(0) != labels.size(0):
                   raise RuntimeError('Input and labels should have the same size in the batch dimension.')
         else:
              lowercase__   :       Dict				=	hidden.view(-1						,       hidden.size(-1))

         if self.n_clusters == 0:
              lowercase__   :       int				=	self._compute_logit(a						,       self.out_layers[0].weight						,       self.out_layers[0].bias						,       self.out_projs[0])
              if labels is not None:
                   lowercase__   :       Union[str, Any]				=	labels != -100
                   lowercase__   :       Dict				=	torch.zeros_like(a						,       dtype=hidden.dtype						,       device=hidden.device)
                   lowercase__   :       Any				=	(
                       -nn.functional.log_softmax(a						,       dim=-1)[mask].gather(1						,       labels[mask].unsqueeze(1)).squeeze(1)
                   )
              else:
                   lowercase__   :       Optional[int]				=	nn.functional.log_softmax(a						,       dim=-1)
         else:
              # construct weights and biases
              lowercase__   :       Union[str, Any]				=	[], []
              for i in range(len(self.cutoffs)):
                   if self.div_val == 1:
                        lowercase__   :       Tuple				=	self.cutoff_ends[i], self.cutoff_ends[i + 1]
                        lowercase__   :       Any				=	self.out_layers[0].weight[l_idx:r_idx]
                        lowercase__   :       Optional[Any]				=	self.out_layers[0].bias[l_idx:r_idx]
                   else:
                        lowercase__   :       Dict				=	self.out_layers[i].weight
                        lowercase__   :       Union[str, Any]				=	self.out_layers[i].bias

                   if i == 0:
                        lowercase__   :       Optional[int]				=	torch.cat([weight_i, self.cluster_weight]						,       dim=0)
                        lowercase__   :       Dict				=	torch.cat([bias_i, self.cluster_bias]						,       dim=0)

                   weights.append(a)
                   biases.append(a)

              lowercase__   :       Tuple				=	weights[0], biases[0], self.out_projs[0]

              lowercase__   :       Tuple				=	self._compute_logit(a						,       a						,       a						,       a)
              lowercase__   :       Dict				=	nn.functional.log_softmax(a						,       dim=1)

              if labels is None:
                   lowercase__   :       Tuple				=	hidden.new_empty((head_logit.size(0), self.n_token))
              else:
                   lowercase__   :       Optional[Any]				=	torch.zeros_like(a						,       dtype=hidden.dtype						,       device=hidden.device)

              lowercase__   :       Dict				=	0
              lowercase__   :       List[str]				=	[0] + self.cutoffs
              for i in range(len(a) - 1):
                   lowercase__   :       Any				=	cutoff_values[i], cutoff_values[i + 1]

                   if labels is not None:
                        lowercase__   :       Optional[Any]				=	(labels >= l_idx) & (labels < r_idx)
                        lowercase__   :       Union[str, Any]				=	mask_i.nonzero().squeeze()

                        if indices_i.numel() == 0:
                             continue

                        lowercase__   :       Any				=	labels.index_select(0						,       a) - l_idx
                        lowercase__   :       Union[str, Any]				=	head_logprob.index_select(0						,       a)
                        lowercase__   :       List[Any]				=	hidden.index_select(0						,       a)
                   else:
                        lowercase__   :       Any				=	hidden

                   if i == 0:
                        if labels is not None:
                             lowercase__   :       List[Any]				=	head_logprob_i.gather(1						,       target_i[:, None]).squeeze(1)
                        else:
                             lowercase__   :       Any				=	head_logprob[:, : self.cutoffs[0]]
                   else:
                        lowercase__   :       Any				=	weights[i], biases[i], self.out_projs[i]

                        lowercase__   :       Tuple				=	self._compute_logit(a						,       a						,       a						,       a)
                        lowercase__   :       Tuple				=	nn.functional.log_softmax(a						,       dim=1)
                        lowercase__   :       Tuple				=	self.cutoffs[0] + i - 1  # No probability for the head cluster
                        if labels is not None:
                             lowercase__   :       List[Any]				=	head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
                                 1						,       target_i[:, None]).squeeze(1)
                        else:
                             lowercase__   :       List[Any]				=	head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
                             lowercase__   :       Tuple				=	logprob_i

                   if labels is not None:
                        if (hasattr(self						,       'keep_order') and self.keep_order) or keep_order:
                             out.index_copy_(0						,       a						,       -logprob_i)
                        else:
                             out[offset : offset + logprob_i.size(0)].copy_(-logprob_i)
                        offset += logprob_i.size(0)

         return out





    def 				snake_case_    (				self						,       a):
         if self.n_clusters == 0:
              lowercase__   :       List[Any]				=	self._compute_logit(a						,       self.out_layers[0].weight						,       self.out_layers[0].bias						,       self.out_projs[0])
              return nn.functional.log_softmax(a						,       dim=-1)
         else:
              # construct weights and biases
              lowercase__   :       Optional[Any]				=	[], []
              for i in range(len(self.cutoffs)):
                   if self.div_val == 1:
                        lowercase__   :       str				=	self.cutoff_ends[i], self.cutoff_ends[i + 1]
                        lowercase__   :       Dict				=	self.out_layers[0].weight[l_idx:r_idx]
                        lowercase__   :       Optional[Any]				=	self.out_layers[0].bias[l_idx:r_idx]
                   else:
                        lowercase__   :       Any				=	self.out_layers[i].weight
                        lowercase__   :       Optional[Any]				=	self.out_layers[i].bias

                   if i == 0:
                        lowercase__   :       Any				=	torch.cat([weight_i, self.cluster_weight]						,       dim=0)
                        lowercase__   :       Any				=	torch.cat([bias_i, self.cluster_bias]						,       dim=0)

                   weights.append(a)
                   biases.append(a)

              lowercase__   :       Optional[Any]				=	weights[0], biases[0], self.out_projs[0]
              lowercase__   :       List[Any]				=	self._compute_logit(a						,       a						,       a						,       a)

              lowercase__   :       int				=	hidden.new_empty((head_logit.size(0), self.n_token))
              lowercase__   :       str				=	nn.functional.log_softmax(a						,       dim=1)

              lowercase__   :       Optional[Any]				=	[0] + self.cutoffs
              for i in range(len(a) - 1):
                   lowercase__   :       str				=	cutoff_values[i], cutoff_values[i + 1]

                   if i == 0:
                        lowercase__   :       Optional[int]				=	head_logprob[:, : self.cutoffs[0]]
                   else:
                        lowercase__   :       List[str]				=	weights[i], biases[i], self.out_projs[i]

                        lowercase__   :       Union[str, Any]				=	self._compute_logit(a						,       a						,       a						,       a)
                        lowercase__   :       Dict				=	nn.functional.log_softmax(a						,       dim=1)

                        lowercase__   :       Dict				=	head_logprob[:, -i] + tail_logprob_i
                        lowercase__   :       Union[str, Any]				=	logprob_i

              return out









import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    '''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
    ''' StableDiffusionInpaintPipeline` instead.'''
)

