






"""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   UpperCamelCase_ (       a_  ):
  _A       :					Dict				=	'philschmid/bart-large-cnn-samsum'
  _A       :					Optional[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.'
  )
  _A       :					str				=	'summarizer'
  _A       :					List[Any]				=	AutoTokenizer
  _A       :					Any				=	AutoModelForSeqaSeqLM

  _A       :					str				=	['text']
  _A       :					List[str]				=	['text']



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			str:



         """simple docstring"""





         return self.pre_processor(snake_case__ ,		return_tensors="""pt""" ,		truncation=snake_case__				)



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			Tuple:



         """simple docstring"""





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



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			List[Any]:



         """simple docstring"""





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








"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_	:    Tuple            =      logging.get_logger(__name__)

lowerCAmelCase_	:    List[str]            =      {
    '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json''',
    '''google/bigbird-roberta-large''': '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json''',
    '''google/bigbird-base-trivia-itc''': '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json''',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}





class   UpperCamelCase_ (       a_  ):
  _A       :					Any				=	'big_bird'



  def __init__(      self ,		snake_case__=5_03_58 ,		snake_case__=7_68 ,		snake_case__=12 ,		snake_case__=12 ,		snake_case__=30_72 ,		snake_case__="gelu_new" ,		snake_case__=0.1 ,		snake_case__=0.1 ,		snake_case__=40_96 ,		snake_case__=2 ,		snake_case__=0.02 ,		snake_case__=1e-12 ,		snake_case__=True ,		snake_case__=0 ,		snake_case__=1 ,		snake_case__=2 ,		snake_case__=66 ,		snake_case__="block_sparse" ,		snake_case__=True ,		snake_case__=False ,		snake_case__=64 ,		snake_case__=3 ,		snake_case__=None ,		**snake_case__ ,		)			->			List[str]:



         """simple docstring"""





         super().__init__(
             pad_token_id=snake_case__ ,		bos_token_id=snake_case__ ,		eos_token_id=snake_case__ ,		sep_token_id=snake_case__ ,		**snake_case__ ,		)

         UpperCAmelCase	    =			vocab_size
         UpperCAmelCase	    =			max_position_embeddings
         UpperCAmelCase	    =			hidden_size
         UpperCAmelCase	    =			num_hidden_layers
         UpperCAmelCase	    =			num_attention_heads
         UpperCAmelCase	    =			intermediate_size
         UpperCAmelCase	    =			hidden_act
         UpperCAmelCase	    =			hidden_dropout_prob
         UpperCAmelCase	    =			attention_probs_dropout_prob
         UpperCAmelCase	    =			initializer_range
         UpperCAmelCase	    =			type_vocab_size
         UpperCAmelCase	    =			layer_norm_eps
         UpperCAmelCase	    =			use_cache

         UpperCAmelCase	    =			rescale_embeddings
         UpperCAmelCase	    =			attention_type
         UpperCAmelCase	    =			use_bias
         UpperCAmelCase	    =			block_size
         UpperCAmelCase	    =			num_random_blocks
         UpperCAmelCase	    =			classifier_dropout





class   UpperCamelCase_ (       a_  ):



  @property
  def 		UpperCamelCase_				(      self				)			->			Mapping[str, Mapping[int, str]]:



         """simple docstring"""





         if self.task == "multiple-choice":
                UpperCAmelCase	    =			{0: """batch""", 1: """choice""", 2: """sequence"""}
         else:
                UpperCAmelCase	    =			{0: """batch""", 1: """sequence"""}
         return OrderedDict(
             [
                 ("""input_ids""", dynamic_axis),
                 ("""attention_mask""", dynamic_axis),
             ]				)
