"""simple docstring"""
from typing import TYPE_CHECKING

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


lowercase__   			=      {
    'configuration_align': [
        'ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AlignConfig',
        'AlignTextConfig',
        'AlignVisionConfig',
    ],
    'processing_align': ['AlignProcessor'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      [
     'ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST',
     'AlignModel',
     'AlignPreTrainedModel',
     'AlignTextModel',
     'AlignVisionModel',
 ]

if TYPE_CHECKING:
 from .configuration_align import (
     ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
     AlignConfig,
     AlignTextConfig,
     AlignVisionConfig,
 )
 from .processing_align import AlignProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_align import (
      ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
      AlignModel,
      AlignPreTrainedModel,
      AlignTextModel,
      AlignVisionModel,
  )

else:
 import sys

 lowercase__   			=      _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


_a	:	Union[str, Any]=					"Here is a text containing a lot of information: '''{text}'''.\n\nCan you answer this question about the text: '{question}'"

class 						UpperCamelCase     (       lowercase		):
				UpperCAmelCase							: str       =						"""google/flan-t5-base"""
				UpperCAmelCase							: int       =						(
				    """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."""
				)
				UpperCAmelCase							: Dict       =						"""text_qa"""
				UpperCAmelCase							: Dict       =						AutoTokenizer
				UpperCAmelCase							: Optional[Any]       =						AutoModelForSeqaSeqLM

				UpperCAmelCase							: List[str]       =						["""text""", """text"""]
				UpperCAmelCase							: Any       =						["""text"""]



				def    _lowercase	(self							: Tuple      ,    _A							: str      ,    _A							: str)       ->      Tuple:
					__snake_case     :				List[Any] 						=					QA_PROMPT.format(text=_A      ,    question=_A)
					return self.pre_processor(_A      ,    return_tensors='pt')



				def    _lowercase	(self							: Tuple      ,    _A							: Optional[Any])       ->      List[Any]:
					__snake_case     :				List[Any] 						=					self.model.generate(**_A)

					__snake_case						,							__snake_case     :				Any 						=					inputs['input_ids'].shape
					__snake_case     :				Any 						=					output_ids.shape[0]

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






				def    _lowercase	(self							: Optional[int]      ,    _A							: int)       ->      str:
					return self.pre_processor.decode(_A      ,    skip_special_tokens=_A      ,    clean_up_tokenization_spaces=_A)





