import torch
from torch import nn





class      lowerCAmelCase						(					nn.Module			):



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

            _A=	n_token
            _A=	d_embed
            _A=	d_proj

            _A=	cutoffs + [n_token]
            _A=	[0] + self.cutoffs
            _A=	div_val

            _A=	self.cutoffs[0]
            _A=	len(self.cutoffs       ) - 1
            _A=	self.shortlist_size + self.n_clusters

            if self.n_clusters > 0:
                   _A=	nn.Parameter(torch.zeros(self.n_clusters				,      self.d_embed       )       )
                   _A=	nn.Parameter(torch.zeros(self.n_clusters       )       )

            _A=	nn.ModuleList()
            _A=	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(lowerCAmelCase__				,      lowerCAmelCase__       )       )       )
                          else:
                                 self.out_projs.append(lowerCAmelCase__       )

                   self.out_layers.append(nn.Linear(lowerCAmelCase__				,      lowerCAmelCase__       )       )
            else:
                   for i in range(len(self.cutoffs       )       ):
                          _A,			_A=	self.cutoff_ends[i], self.cutoff_ends[i + 1]
                          _A=	d_embed // (div_val**i)

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

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

            _A=	keep_order



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            if proj is None:
                   _A=	nn.functional.linear(lowerCAmelCase__				,      lowerCAmelCase__				,      bias=lowerCAmelCase__       )
            else:
                   # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
                   _A=	nn.functional.linear(lowerCAmelCase__				,      proj.t().contiguous()       )
                   _A=	nn.functional.linear(lowerCAmelCase__				,      lowerCAmelCase__				,      bias=lowerCAmelCase__       )
                   # else:
                   #     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
                   #     if bias is not None:
                   #         logit = logit + bias

            return logit



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__=None				,      lowerCAmelCase__=False       ):

            if labels is not None:
                   # Shift so that tokens < n predict n
                   _A=	hidden[..., :-1, :].contiguous()
                   _A=	labels[..., 1:].contiguous()
                   _A=	hidden.view(-1				,      hidden.size(-1       )       )
                   _A=	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:
                   _A=	hidden.view(-1				,      hidden.size(-1       )       )

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

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

                          weights.append(lowerCAmelCase__       )
                          biases.append(lowerCAmelCase__       )

                   _A,			_A,			_A=	weights[0], biases[0], self.out_projs[0]

                   _A=	self._compute_logit(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       )
                   _A=	nn.functional.log_softmax(lowerCAmelCase__				,      dim=1       )

                   if labels is None:
                          _A=	hidden.new_empty((head_logit.size(0       ), self.n_token)       )
                   else:
                          _A=	torch.zeros_like(lowerCAmelCase__				,      dtype=hidden.dtype				,      device=hidden.device       )

                   _A=	0
                   _A=	[0] + self.cutoffs
                   for i in range(len(lowerCAmelCase__       ) - 1       ):
                          _A,			_A=	cutoff_values[i], cutoff_values[i + 1]

                          if labels is not None:
                                 _A=	(labels >= l_idx) & (labels < r_idx)
                                 _A=	mask_i.nonzero().squeeze()

                                 if indices_i.numel() == 0:
                                        continue

                                 _A=	labels.index_select(0				,      lowerCAmelCase__       ) - l_idx
                                 _A=	head_logprob.index_select(0				,      lowerCAmelCase__       )
                                 _A=	hidden.index_select(0				,      lowerCAmelCase__       )
                          else:
                                 _A=	hidden

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

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

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

            return out





     def 			a__  (					self				,      lowerCAmelCase__       ):
            if self.n_clusters == 0:
                   _A=	self._compute_logit(lowerCAmelCase__				,      self.out_layers[0].weight				,      self.out_layers[0].bias				,      self.out_projs[0]       )
                   return nn.functional.log_softmax(lowerCAmelCase__				,      dim=-1       )
            else:
                   # construct weights and biases
                   _A,			_A=	[], []
                   for i in range(len(self.cutoffs       )       ):
                          if self.div_val == 1:
                                 _A,			_A=	self.cutoff_ends[i], self.cutoff_ends[i + 1]
                                 _A=	self.out_layers[0].weight[l_idx:r_idx]
                                 _A=	self.out_layers[0].bias[l_idx:r_idx]
                          else:
                                 _A=	self.out_layers[i].weight
                                 _A=	self.out_layers[i].bias

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

                          weights.append(lowerCAmelCase__       )
                          biases.append(lowerCAmelCase__       )

                   _A,			_A,			_A=	weights[0], biases[0], self.out_projs[0]
                   _A=	self._compute_logit(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       )

                   _A=	hidden.new_empty((head_logit.size(0       ), self.n_token)       )
                   _A=	nn.functional.log_softmax(lowerCAmelCase__				,      dim=1       )

                   _A=	[0] + self.cutoffs
                   for i in range(len(lowerCAmelCase__       ) - 1       ):
                          _A,			_A=	cutoff_values[i], cutoff_values[i + 1]

                          if i == 0:
                                 _A=	head_logprob[:, : self.cutoffs[0]]
                          else:
                                 _A,			_A,			_A=	weights[i], biases[i], self.out_projs[i]

                                 _A=	self._compute_logit(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       )
                                 _A=	nn.functional.log_softmax(lowerCAmelCase__				,      dim=1       )

                                 _A=	head_logprob[:, -i] + tail_logprob_i
                                 _A=	logprob_i

                   return out
import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


UpperCAmelCase_								=							logging.get_logger(__name__)





class      lowerCAmelCase						(					_a			):



     def __init__(					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            warnings.warn(
                'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
                ' Please use PerceiverImageProcessor instead.'				,      lowerCAmelCase__				,      )
            super().__init__(*lowerCAmelCase__				,      **lowerCAmelCase__       )