
# 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__					:       List[Any]		=       '''Here is a text containing a lot of information: \'\'\'{text}\'\'\'.\n\nCan you answer this question about the text: \'{question}\''''





class 			__snake_case  (      __A     ):
					__lowerCamelCase	 =       """google/flan-t5-base"""
					__lowerCamelCase	 =       (
					    """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	 =       """text_qa"""
					__lowerCamelCase	 =       AutoTokenizer
					__lowerCamelCase	 =       AutoModelForSeqaSeqLM

					__lowerCamelCase	 =       ["""text""", """text"""]
					__lowerCamelCase	 =       ["""text"""]




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase		)					->  Union[str, Any]:



									'''simple docstring'''







									snake_case__ :     Dict 							=				QA_PROMPT.format(text=__UpperCamelCase   ,	question=__UpperCamelCase		)
									return self.pre_processor(__UpperCamelCase   ,	return_tensors='pt'		)




					def       __a      (       self   ,	__UpperCamelCase		)					->  str:



									'''simple docstring'''







									snake_case__ :     List[str] 							=				self.model.generate(**__UpperCamelCase		)

									snake_case__ :     Dict 							=				inputs["input_ids"].shape
									snake_case__ :     Optional[Any] 							=				output_ids.shape[0]

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




					def       __a      (       self   ,	__UpperCamelCase		)					->  Optional[int]:



									'''simple docstring'''







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



from typing import TYPE_CHECKING

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


lowerCAmelCase__					:       Optional[int]		=       {}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					:       Tuple		=       ['''MBart50Tokenizer''']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					:       Tuple		=       ['''MBart50TokenizerFast''']


if TYPE_CHECKING:
				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mbartaa import MBartaaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
				import sys

				lowerCAmelCase__					:       Optional[int]		=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


