

"""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


SCREAMING_SNAKE_CASE_  :      Optional[int]							=      '''Here is a text containing a lot of information: \'\'\'{text}\'\'\'.

Can you answer this question about the text: \'{question}\''''


class 		_A							(      __a       ):
      __a             =						'google/flan-t5-base'
      __a             =						(
          'This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the '
          'text where to find the answer, and `question`, which is the question, and returns the answer to the question.'
      )
      __a             =						'text_qa'
      __a             =						AutoTokenizer
      __a             =						AutoModelForSeqaSeqLM

      __a             =						['text', 'text']
      __a             =						['text']





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		) ->       Any:
         lowerCamelCase__						       =      QA_PROMPT.format(text=SCREAMING_SNAKE_CASE__     ,    question=SCREAMING_SNAKE_CASE__		)
         return self.pre_processor(SCREAMING_SNAKE_CASE__     ,    return_tensors="pt"		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       Any:
         lowerCamelCase__						       =      self.model.generate(**SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__       ,							lowerCamelCase__						       =      inputs["input_ids"].shape
         lowerCamelCase__						       =      output_ids.shape[0]

         return output_ids.reshape(SCREAMING_SNAKE_CASE__     ,    out_b // in_b     ,    *output_ids.shape[1:]		)[0][0]





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       str:
         return self.pre_processor.decode(SCREAMING_SNAKE_CASE__     ,    skip_special_tokens=SCREAMING_SNAKE_CASE__     ,    clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__		)







"""simple docstring"""




# Copyright 2023 The HuggingFace 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 typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_  :      Tuple							=      {
    '''configuration_convnextv2''': [
        '''CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ConvNextV2Config''',
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_  :      int							=      [
         '''CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''ConvNextV2ForImageClassification''',
         '''ConvNextV2Model''',
         '''ConvNextV2PreTrainedModel''',
         '''ConvNextV2Backbone''',
     ]


if TYPE_CHECKING:
     from .configuration_convnextva import (
         CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         ConvNextVaConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convnextva import (
              CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvNextVaBackbone,
              ConvNextVaForImageClassification,
              ConvNextVaModel,
              ConvNextVaPreTrainedModel,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE_  :      Dict							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure)




