

'''simple docstring'''






import torch
from torch import nn






class 			lowercase_	(nn.Module      ):




  """simple docstring"""




  def __init__(      self   :     Optional[int]					,lowercase__   :     List[str]					,lowercase__   :     Any					,lowercase__   :     Union[str, Any]					,lowercase__   :     Optional[int]					,lowercase__   :     Optional[int]=1					,lowercase__   :     Optional[Any]=False			):
   super().__init__()

   __lowercase							  = n_token
   __lowercase							  = d_embed
   __lowercase							  = d_proj

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

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

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

   __lowercase							  = nn.ModuleList()
   __lowercase							  = 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(lowercase__					,lowercase__			)			)			)
     else:
      self.out_projs.append(lowercase__			)

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

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

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

   __lowercase							  = keep_order






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

   return logit






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     Optional[Any]					,lowercase__   :     Any=None					,lowercase__   :     List[str]=False			):

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

   if self.n_clusters == 0:
    __lowercase							  = self._compute_logit(lowercase__					,self.out_layers[0].weight					,self.out_layers[0].bias					,self.out_projs[0]			)
    if labels is not None:
     __lowercase							  = labels != -1_0_0
     __lowercase							  = torch.zeros_like(lowercase__					,dtype=hidden.dtype					,device=hidden.device			)
     __lowercase							  = (
         -nn.functional.log_softmax(lowercase__					,dim=-1			)[mask].gather(1					,labels[mask].unsqueeze(1			)			).squeeze(1			)
     )
    else:
     __lowercase							  = nn.functional.log_softmax(lowercase__					,dim=-1			)
   else:
    # construct weights and biases
    __lowercase  ,	__lowercase							  = [], []
    for i in range(len(self.cutoffs			)			):
     if self.div_val == 1:
      __lowercase  ,	__lowercase							  = self.cutoff_ends[i], self.cutoff_ends[i + 1]
      __lowercase							  = self.out_layers[0].weight[l_idx:r_idx]
      __lowercase							  = self.out_layers[0].bias[l_idx:r_idx]
     else:
      __lowercase							  = self.out_layers[i].weight
      __lowercase							  = self.out_layers[i].bias

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

     weights.append(lowercase__			)
     biases.append(lowercase__			)

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

    __lowercase							  = self._compute_logit(lowercase__					,lowercase__					,lowercase__					,lowercase__			)
    __lowercase							  = nn.functional.log_softmax(lowercase__					,dim=1			)

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

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

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

      if indices_i.numel() == 0:
       continue

      __lowercase							  = labels.index_select(0					,lowercase__			) - l_idx
      __lowercase							  = head_logprob.index_select(0					,lowercase__			)
      __lowercase							  = hidden.index_select(0					,lowercase__			)
     else:
      __lowercase							  = hidden

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

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

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

   return out






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

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

     weights.append(lowercase__			)
     biases.append(lowercase__			)

    __lowercase  ,	__lowercase  ,	__lowercase							  = weights[0], biases[0], self.out_projs[0]
    __lowercase							  = self._compute_logit(lowercase__					,lowercase__					,lowercase__					,lowercase__			)

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

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

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

      __lowercase							  = self._compute_logit(lowercase__					,lowercase__					,lowercase__					,lowercase__			)
      __lowercase							  = nn.functional.log_softmax(lowercase__					,dim=1			)

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

    return out




'''simple docstring'''






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Optional[Any]							=			['image_processor', 'tokenizer']
  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			'CLIPImageProcessor'
  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			('CLIPTokenizer', 'CLIPTokenizerFast')






  def __init__(      self   :     List[Any]					,lowercase__   :     Dict=None					,lowercase__   :     Union[str, Any]=None					,**lowercase__   :     Tuple			):
   __lowercase							  = None
   if "feature_extractor" in kwargs:
    warnings.warn(
        '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
        ''' instead.'''					,lowercase__					,)
    __lowercase							  = kwargs.pop('''feature_extractor'''			)

   __lowercase							  = image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
    raise ValueError('''You need to specify an `image_processor`.'''			)
   if tokenizer is None:
    raise ValueError('''You need to specify a `tokenizer`.'''			)

   super().__init__(lowercase__					,lowercase__			)






  def __call__(      self   :     List[Any]					,lowercase__   :     str=None					,lowercase__   :     List[Any]=None					,lowercase__   :     Optional[Any]=None					,**lowercase__   :     int			):

   if text is None and images is None:
    raise ValueError('''You have to specify either text or images. Both cannot be none.'''			)

   if text is not None:
    __lowercase							  = self.tokenizer(lowercase__					,return_tensors=lowercase__					,**lowercase__			)

   if images is not None:
    __lowercase							  = self.image_processor(lowercase__					,return_tensors=lowercase__					,**lowercase__			)

   if text is not None and images is not None:
    __lowercase							  = image_features.pixel_values
    return encoding
   elif text is not None:
    return encoding
   else:
    return BatchEncoding(data=dict(**lowercase__			)					,tensor_type=lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]					,*lowercase__   :     List[str]					,**lowercase__   :     int			):
   return self.tokenizer.batch_decode(*lowercase__					,**lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]					,*lowercase__   :     Optional[int]					,**lowercase__   :     Union[str, Any]			):
   return self.tokenizer.decode(*lowercase__					,**lowercase__			)






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.tokenizer.model_input_names
   __lowercase							  = self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   warnings.warn(
       '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''					,lowercase__					,)
   return self.image_processor_class






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   warnings.warn(
       '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''					,lowercase__					,)
   return self.image_processor

