





'''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					: List[str]      =    """Here is a text containing a lot of information: '''{text}'''.

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





class     _UpperCAmelCase							(						lowerCAmelCase_   ):
		a   :   Dict     ="""google/flan-t5-base"""
		a   :   List[str]     =(
		    """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   :   int     ="""text_qa"""
		a   :   str     =AutoTokenizer
		a   :   List[Any]     =AutoModelForSeqaSeqLM

		a   :   str     =["""text""", """text"""]
		a   :   Union[str, Any]     =["""text"""]
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__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			):



					'''simple docstring'''

					__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			):



					'''simple docstring'''

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








'''simple docstring'''







import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp





def    _lowerCAmelCase						(						)					->					List[str]:
			__lowerCAmelCase											=				ArgumentParser(
			    description=(
			        """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
			    )	)

			# Optional arguments for the launch helper
			parser.add_argument("""--num_cores"""					,	type=lowercase					,	default=1					,	help="""Number of TPU cores to use (1 or 8)."""	)

			# positional
			parser.add_argument(
			    """training_script"""					,	type=lowercase					,	help=(
			        """The full path to the single TPU training """
			        """program/script to be launched in parallel, """
			        """followed by all the arguments for the """
			        """training script"""
			    )					,	)

			# rest from the training program
			parser.add_argument("""training_script_args"""					,	nargs=lowercase	)

			return parser.parse_args()





def    _lowerCAmelCase						(						)					->					Any:
			__lowerCAmelCase											=				parse_args()

			# Import training_script as a module.
			__lowerCAmelCase											=				Path(args.training_script	)
			sys.path.append(str(script_fpath.parent.resolve()	)	)
			__lowerCAmelCase											=				script_fpath.stem
			__lowerCAmelCase											=				importlib.import_module(lowercase	)

			# Patch sys.argv
			__lowerCAmelCase											=				[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores	)]

			xmp.spawn(mod._mp_fn					,	args=()					,	nprocs=args.num_cores	)


if __name__ == "__main__":
	main()

