


'''simple docstring'''

from typing import TYPE_CHECKING

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


lowerCamelCase							:       Optional[Any]  						=     {
    "configuration_segformer": ["SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SegformerConfig", "SegformerOnnxConfig"]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:       Dict  						=     ["SegformerFeatureExtractor"]
 lowerCamelCase							:       Optional[int]  						=     ["SegformerImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:       str  						=     [
     "SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "SegformerDecodeHead",
     "SegformerForImageClassification",
     "SegformerForSemanticSegmentation",
     "SegformerLayer",
     "SegformerModel",
     "SegformerPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:       Dict  						=     [
     "TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFSegformerDecodeHead",
     "TFSegformerForImageClassification",
     "TFSegformerForSemanticSegmentation",
     "TFSegformerModel",
     "TFSegformerPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_segformer import SegformerFeatureExtractor
  from .image_processing_segformer import SegformerImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_segformer import (
      SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      SegformerDecodeHead,
      SegformerForImageClassification,
      SegformerForSemanticSegmentation,
      SegformerLayer,
      SegformerModel,
      SegformerPreTrainedModel,
  )
 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_segformer import (
      TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFSegformerDecodeHead,
      TFSegformerForImageClassification,
      TFSegformerForSemanticSegmentation,
      TFSegformerModel,
      TFSegformerPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase							:       List[str]  						=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








'''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


lowerCamelCase							:       Any  						=     'Here is a text containing a lot of information: \'\'\'{text}\'\'\'.\n\nCan you answer this question about the text: \'{question}\''
class   __lowerCAmelCase						(lowercase_					):





      '''simple docstring'''






      lowerCAmelCase__ :					Dict   =							"""google/flan-t5-base"""
      lowerCAmelCase__ :					List[Any]   =							(
          """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."""
      )
      lowerCAmelCase__ :					Any   =							"""text_qa"""
      lowerCAmelCase__ :					Union[str, Any]   =							AutoTokenizer
      lowerCAmelCase__ :					Any   =							AutoModelForSeqaSeqLM

      lowerCAmelCase__ :					Any   =							["""text""", """text"""]
      lowerCAmelCase__ :					Optional[int]   =							["""text"""]



      def 				UpperCamelCase__		(self   :   Union[str, Any]       ,						UpperCamelCase   :   str       ,						UpperCamelCase   :   str			):

             '''simple docstring'''







             lowercase__				      =			QA_PROMPT.format(text=UpperCamelCase       ,						question=UpperCamelCase			)
             return self.pre_processor(UpperCamelCase       ,						return_tensors='''pt'''			)



      def 				UpperCamelCase__		(self   :   Dict       ,						UpperCamelCase   :   List[Any]			):

             '''simple docstring'''







             lowercase__				      =			self.model.generate(**UpperCamelCase			)

             lowercase__      ,lowercase__				      =			inputs['''input_ids'''].shape
             lowercase__				      =			output_ids.shape[0]

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






      def 				UpperCamelCase__		(self   :   Tuple       ,						UpperCamelCase   :   Optional[Any]			):

             '''simple docstring'''







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




