"""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       			=      """Here is a text containing a lot of information: '''{text}'''.

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






class 					a					(				A_							):
  A_ :				Optional[Any] 			=							'''google/flan-t5-base'''
  A_ :				Optional[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.'''
  )
  A_ :				Dict 			=							'''text_qa'''
  A_ :				Dict 			=							AutoTokenizer
  A_ :				Optional[Any] 			=							AutoModelForSeqaSeqLM

  A_ :				str 			=							['''text''', '''text''']
  A_ :				Union[str, Any] 			=							['''text''']



  def        lowerCAmelCase_				(		self   :    Any						,  lowerCamelCase_   :    str						,  lowerCamelCase_   :    str     )      -> Union[str, Any]:
      __a			      =						QA_PROMPT.format(text=lowerCamelCase_						,  question=lowerCamelCase_     )
      return self.pre_processor(lowerCamelCase_						,  return_tensors="""pt"""     )



  def        lowerCAmelCase_				(		self   :    Optional[Any]						,  lowerCamelCase_   :    Optional[int]     )      -> Optional[int]:
      __a			      =						self.model.generate(**lowerCamelCase_     )

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

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



  def        lowerCAmelCase_				(		self   :    List[Any]						,  lowerCamelCase_   :    Optional[Any]     )      -> Optional[int]:
      return self.pre_processor.decode(lowerCamelCase_						,  skip_special_tokens=lowerCamelCase_						,  clean_up_tokenization_spaces=lowerCamelCase_     )

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A       			=      {
    """configuration_realm""": ["""REALM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RealmConfig"""],
    """tokenization_realm""": ["""RealmTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       			=      ["""RealmTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       			=      [
     """REALM_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """RealmEmbedder""",
     """RealmForOpenQA""",
     """RealmKnowledgeAugEncoder""",
     """RealmPreTrainedModel""",
     """RealmReader""",
     """RealmScorer""",
     """load_tf_weights_in_realm""",
 ]
 __A       			=      ["""RealmRetriever"""]


if TYPE_CHECKING:
 from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
 from .tokenization_realm import RealmTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_realm import RealmTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_realm import (
      REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
      RealmEmbedder,
      RealmForOpenQA,
      RealmKnowledgeAugEncoder,
      RealmPreTrainedModel,
      RealmReader,
      RealmScorer,
      load_tf_weights_in_realm,
  )
  from .retrieval_realm import RealmRetriever


else:
 import sys

 __A       			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
