"""simple docstring"""







import io
import json
import subprocess


_a	:	Tuple=					[
    ["en", "ru"],
    ["ru", "en"],
    ["en", "de"],
    ["de", "en"],
]

_a	:	List[str]=					8
def     __UpperCAmelCase							(       UpperCAmelCase_       :							str     ,				UpperCAmelCase_       :							List[str]    )				->     str:

	'''simple docstring'''





	__snake_case     :				str 						=					{}
	for src, tgt in pairs:
		__snake_case     :				str 						=					F"{src}-{tgt}"
		__snake_case     :				List[str] 						=					F"sacrebleu -t wmt19 -l {pair} --echo src".split()
		__snake_case     :				Dict 						=					subprocess.run(UpperCAmelCase_     ,				stdout=subprocess.PIPE    ).stdout.decode('utf-8'    ).splitlines()
		__snake_case     :				Optional[Any] 						=					F"sacrebleu -t wmt19 -l {pair} --echo ref".split()
		__snake_case     :				Any 						=					subprocess.run(UpperCAmelCase_     ,				stdout=subprocess.PIPE    ).stdout.decode('utf-8'    ).splitlines()
		__snake_case     :				int 						=					{'src': src_lines[:n_objs], 'tgt': tgt_lines[:n_objs]}
	return text


_a	:	Optional[Any]=					get_all_data(pairs, n_objs)
_a	:	List[str]=					"./fsmt_val_data.json"
with io.open(filename, "w", encoding="utf-8") as f:
	_a	:	str=					json.dump(text, f, indent=2, ensure_ascii=False)






"""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)





