





"""simple docstring"""






from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput



@dataclass
class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  None
       lowerCAmelCase__						 =  None
       lowerCAmelCase__						 =  None
       lowerCAmelCase__						 =  None



class 	lowerCAmelCase					(      snake_case							):






       def __init__(   self    ,  a__=1    ,  a__=0    ,  a__=2    ,  a__=5_12    ,  a__="cls"    ,  a__=False    ,  a__=True    ,  **a__    ,  ):
              super().__init__(pad_token_id=a__    ,  bos_token_id=a__    ,  eos_token_id=a__    ,  **a__     )
              _UpperCAmelCase     =     project_dim
              _UpperCAmelCase     =     pooler_fn
              _UpperCAmelCase     =     learn_encoder
              _UpperCAmelCase     =     use_attention_mask



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  [R"""pooler""", R"""logit_scale"""]
       lowerCAmelCase__						 =  [R"""position_ids""", R"""predictions.decoder.bias"""]
       lowerCAmelCase__						 =  """roberta"""
       lowerCAmelCase__						 =  RobertaSeriesConfig






       def __init__(   self    ,  a__     ):
              super().__init__(a__     )
              _UpperCAmelCase     =     XLMRobertaModel(a__     )
              _UpperCAmelCase     =     nn.Linear(config.hidden_size    ,  config.project_dim     )
              _UpperCAmelCase     =     getattr(a__    ,  'has_pre_transformation'    ,  a__     )
              if self.has_pre_transformation:
                     _UpperCAmelCase     =     nn.Linear(config.hidden_size    ,  config.project_dim     )
                     _UpperCAmelCase     =     nn.LayerNorm(config.hidden_size    ,  eps=config.layer_norm_eps     )
              self.post_init()






       def 							__A (   self    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  ):
              _UpperCAmelCase     =     return_dict if return_dict is not None else self.config.use_return_dict

              _UpperCAmelCase     =     self.base_model(
                  input_ids=a__    ,  attention_mask=a__    ,  token_type_ids=a__    ,  position_ids=a__    ,  head_mask=a__    ,  inputs_embeds=a__    ,  encoder_hidden_states=a__    ,  encoder_attention_mask=a__    ,  output_attentions=a__    ,  output_hidden_states=True if self.has_pre_transformation else output_hidden_states    ,  return_dict=a__    ,  )

              if self.has_pre_transformation:
                     _UpperCAmelCase     =     outputs['hidden_states'][-2]
                     _UpperCAmelCase     =     self.pre_LN(a__     )
                     _UpperCAmelCase     =     self.transformation_pre(a__     )

                     return TransformationModelOutput(
                         projection_state=a__    ,  last_hidden_state=outputs.last_hidden_state    ,  hidden_states=outputs.hidden_states    ,  attentions=outputs.attentions    ,  )
              else:
                     _UpperCAmelCase     =     self.transformation(outputs.last_hidden_state     )
                     return TransformationModelOutput(
                         projection_state=a__    ,  last_hidden_state=outputs.last_hidden_state    ,  hidden_states=outputs.hidden_states    ,  attentions=outputs.attentions    ,  )













"""simple docstring"""






# 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 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  """philschmid/bart-large-cnn-samsum"""
       lowerCAmelCase__						 =  (
           """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__						 =  """summarizer"""
       lowerCAmelCase__						 =  AutoTokenizer
       lowerCAmelCase__						 =  AutoModelForSeqaSeqLM

       lowerCAmelCase__						 =  ["""text"""]
       lowerCAmelCase__						 =  ["""text"""]






       def 							__A (   self    ,  a__     ):
              return self.pre_processor(a__    ,  return_tensors='pt'    ,  truncation=a__     )






       def 							__A (   self    ,  a__     ):
              return self.model.generate(**a__     )[0]






       def 							__A (   self    ,  a__     ):
              return self.pre_processor.decode(a__    ,  skip_special_tokens=a__    ,  clean_up_tokenization_spaces=a__     )






