





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase	:	Any		     =       {
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase	:	List[Any]		     =       ["""ConvBertTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase	:	List[str]		     =       [
         """CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """ConvBertForMaskedLM""",
         """ConvBertForMultipleChoice""",
         """ConvBertForQuestionAnswering""",
         """ConvBertForSequenceClassification""",
         """ConvBertForTokenClassification""",
         """ConvBertLayer""",
         """ConvBertModel""",
         """ConvBertPreTrainedModel""",
         """load_tf_weights_in_convbert""",
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase	:	Union[str, Any]		     =       [
         """TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFConvBertForMaskedLM""",
         """TFConvBertForMultipleChoice""",
         """TFConvBertForQuestionAnswering""",
         """TFConvBertForSequenceClassification""",
         """TFConvBertForTokenClassification""",
         """TFConvBertLayer""",
         """TFConvBertModel""",
         """TFConvBertPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
     from .tokenization_convbert import ConvBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_convbert_fast import ConvBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convbert import (
              CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvBertForMaskedLM,
              ConvBertForMultipleChoice,
              ConvBertForQuestionAnswering,
              ConvBertForSequenceClassification,
              ConvBertForTokenClassification,
              ConvBertLayer,
              ConvBertModel,
              ConvBertPreTrainedModel,
              load_tf_weights_in_convbert,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_convbert import (
              TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFConvBertForMaskedLM,
              TFConvBertForMultipleChoice,
              TFConvBertForQuestionAnswering,
              TFConvBertForSequenceClassification,
              TFConvBertForTokenClassification,
              TFConvBertLayer,
              TFConvBertModel,
              TFConvBertPreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase	:	Union[str, Any]		     =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)













# 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       (    UpperCamelCase__):
      _lowercase				:					Optional[Any]						=			"""philschmid/bart-large-cnn-samsum"""
      _lowercase				:					List[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."""
      )
      _lowercase				:					Any						=			"""summarizer"""
      _lowercase				:					Any						=			AutoTokenizer
      _lowercase				:					str						=			AutoModelForSeqaSeqLM

      _lowercase				:					Optional[int]						=			["""text"""]
      _lowercase				:					Optional[int]						=			["""text"""]







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Tuple:



        '''simple docstring'''




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







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Union[str, Any]:



        '''simple docstring'''




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







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Any:



        '''simple docstring'''




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






