import torch
from torch import nn




class  __magic_name__  (		nn.Module    ):





   """simple docstring"""




   def __init__(	self					:  Optional[Any]		,  _lowercase					:  Dict		,  _lowercase					:  Optional[int]		,  _lowercase					:  Any		,  _lowercase					:  Optional[int]		,  _lowercase					:  Any=1		,  _lowercase					:  Tuple=False							):



    """simple docstring"""



    super().__init__()

    _UpperCamelCase:     Tuple							= n_token
    _UpperCamelCase:     Dict							= d_embed
    _UpperCamelCase:     List[str]							= d_proj

    _UpperCamelCase:     Any							= cutoffs + [n_token]
    _UpperCamelCase:     str							= [0] + self.cutoffs
    _UpperCamelCase:     str							= div_val

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

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

    _UpperCamelCase:     Dict							= nn.ModuleList()
    _UpperCamelCase:     Optional[int]							= 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							)							):
      _UpperCamelCase	,							_UpperCamelCase:     List[Any]							= self.cutoff_ends[i], self.cutoff_ends[i + 1]
      _UpperCamelCase:     Optional[int]							= 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							)							)

    _UpperCamelCase:     Union[str, Any]							= keep_order



   def   lowerCAmelCase						(	self					:  Union[str, Any]		,  _lowercase					:  Optional[int]		,  _lowercase					:  Optional[int]		,  _lowercase					:  int		,  _lowercase					:  List[Any]							):



    """simple docstring"""



    if proj is None:
     _UpperCamelCase:     List[Any]							= nn.functional.linear(_lowercase		,  _lowercase		,  bias=_lowercase							)
    else:
     # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
     _UpperCamelCase:     Tuple							= nn.functional.linear(_lowercase		,  proj.t().contiguous()							)
     _UpperCamelCase:     List[str]							= 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   lowerCAmelCase						(	self					:  Optional[Any]		,  _lowercase					:  Any		,  _lowercase					:  List[str]=None		,  _lowercase					:  str=False							):



    """simple docstring"""



    if labels is not None:
     # Shift so that tokens < n predict n
     _UpperCamelCase:     Optional[Any]							= hidden[..., :-1, :].contiguous()
     _UpperCamelCase:     Tuple							= labels[..., 1:].contiguous()
     _UpperCamelCase:     Dict							= hidden.view(-1		,  hidden.size(-1							)							)
     _UpperCamelCase:     List[str]							= 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:
     _UpperCamelCase:     str							= hidden.view(-1		,  hidden.size(-1							)							)

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

      if i == 0:
       _UpperCamelCase:     int							= torch.cat([weight_i, self.cluster_weight]		,  dim=0							)
       _UpperCamelCase:     List[str]							= torch.cat([bias_i, self.cluster_bias]		,  dim=0							)

      weights.append(_lowercase							)
      biases.append(_lowercase							)

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

     _UpperCamelCase:     str							= self._compute_logit(_lowercase		,  _lowercase		,  _lowercase		,  _lowercase							)
     _UpperCamelCase:     Optional[int]							= nn.functional.log_softmax(_lowercase		,  dim=1							)

     if labels is None:
      _UpperCamelCase:     str							= hidden.new_empty((head_logit.size(0							), self.n_token)							)
     else:
      _UpperCamelCase:     List[Any]							= torch.zeros_like(_lowercase		,  dtype=hidden.dtype		,  device=hidden.device							)

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

      if labels is not None:
       _UpperCamelCase:     List[str]							= (labels >= l_idx) & (labels < r_idx)
       _UpperCamelCase:     Optional[int]							= mask_i.nonzero().squeeze()

       if indices_i.numel() == 0:
        continue

       _UpperCamelCase:     Tuple							= labels.index_select(0		,  _lowercase							) - l_idx
       _UpperCamelCase:     Optional[Any]							= head_logprob.index_select(0		,  _lowercase							)
       _UpperCamelCase:     Optional[Any]							= hidden.index_select(0		,  _lowercase							)
      else:
       _UpperCamelCase:     Dict							= hidden

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

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



    """simple docstring"""



    if self.n_clusters == 0:
     _UpperCamelCase:     List[str]							= 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
     _UpperCamelCase	,							_UpperCamelCase:     List[Any]							= [], []
     for i in range(len(self.cutoffs							)							):
      if self.div_val == 1:
       _UpperCamelCase	,							_UpperCamelCase:     List[str]							= self.cutoff_ends[i], self.cutoff_ends[i + 1]
       _UpperCamelCase:     List[str]							= self.out_layers[0].weight[l_idx:r_idx]
       _UpperCamelCase:     Union[str, Any]							= self.out_layers[0].bias[l_idx:r_idx]
      else:
       _UpperCamelCase:     Optional[int]							= self.out_layers[i].weight
       _UpperCamelCase:     Union[str, Any]							= self.out_layers[i].bias

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

      weights.append(_lowercase							)
      biases.append(_lowercase							)

     _UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase:     Dict							= weights[0], biases[0], self.out_projs[0]
     _UpperCamelCase:     Optional[int]							= self._compute_logit(_lowercase		,  _lowercase		,  _lowercase		,  _lowercase							)

     _UpperCamelCase:     Dict							= hidden.new_empty((head_logit.size(0							), self.n_token)							)
     _UpperCamelCase:     List[str]							= nn.functional.log_softmax(_lowercase		,  dim=1							)

     _UpperCamelCase:     Any							= [0] + self.cutoffs
     for i in range(len(_lowercase							) - 1							):
      _UpperCamelCase	,							_UpperCamelCase:     List[Any]							= cutoff_values[i], cutoff_values[i + 1]

      if i == 0:
       _UpperCamelCase:     List[Any]							= head_logprob[:, : self.cutoffs[0]]
      else:
       _UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase:     str							= weights[i], biases[i], self.out_projs[i]

       _UpperCamelCase:     Any							= self._compute_logit(_lowercase		,  _lowercase		,  _lowercase		,  _lowercase							)
       _UpperCamelCase:     Optional[Any]							= nn.functional.log_softmax(_lowercase		,  dim=1							)

       _UpperCamelCase:     str							= head_logprob[:, -i] + tail_logprob_i
       _UpperCamelCase:     int							= logprob_i

     return out
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool




class  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    Optional[Any]  			=   '''philschmid/bart-large-cnn-samsum'''
   lowerCAmelCase      :    Any  			=   (
       '''This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '''
       '''and returns a summary of the text.'''
   )
   lowerCAmelCase      :    Any  			=   '''summarizer'''
   lowerCAmelCase      :    Tuple  			=   AutoTokenizer
   lowerCAmelCase      :    Optional[Any]  			=   AutoModelForSeqaSeqLM

   lowerCAmelCase      :    Union[str, Any]  			=   ['''text''']
   lowerCAmelCase      :    Dict  			=   ['''text''']



   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  Union[str, Any]							):



    """simple docstring"""



    return self.pre_processor(_lowercase		,  return_tensors='''pt'''		,  truncation=_lowercase							)



   def   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  Optional[Any]							):



    """simple docstring"""



    return self.model.generate(**_lowercase							)[0]



   def   lowerCAmelCase						(	self					:  int		,  _lowercase					:  List[str]							):



    """simple docstring"""



    return self.pre_processor.decode(_lowercase		,  skip_special_tokens=_lowercase		,  clean_up_tokenization_spaces=_lowercase							)