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#
# 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
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from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Optional[Any]									=				"philschmid/bart-large-cnn-samsum"
			SCREAMING_SNAKE_CASE     :   Tuple									=				(
			    "This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, "
			    "and returns a summary of the text."
			)
			SCREAMING_SNAKE_CASE     :   str									=				"summarizer"
			SCREAMING_SNAKE_CASE     :   str									=				AutoTokenizer
			SCREAMING_SNAKE_CASE     :   str									=				AutoModelForSeqaSeqLM

			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["text"]
			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["text"]







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				int							)      ->Optional[int]:
									return self.pre_processor(_UpperCamelCase							,   return_tensors='''pt'''							,   truncation=_UpperCamelCase							)







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				Optional[int]							)      ->Tuple:
									return self.model.generate(**_UpperCamelCase							)[0]







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


from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader





class 	snake_case_   (		__A					):







			'''simple docstring'''

			def __init__(     self :				int							,   _UpperCamelCase :				pyspark.sql.DataFrame							,   _UpperCamelCase :				Optional[NamedSplit] = None							,   _UpperCamelCase :				Optional[Features] = None							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				str = None							,   _UpperCamelCase :				bool = False							,   _UpperCamelCase :				str = None							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				str = "arrow"							,   **_UpperCamelCase :				Tuple							,   )      ->str:
									super().__init__(
									    split=_UpperCamelCase							,   features=_UpperCamelCase							,   cache_dir=_UpperCamelCase							,   keep_in_memory=_UpperCamelCase							,   streaming=_UpperCamelCase							,   **_UpperCamelCase							,   )
									snake_case_   =	load_from_cache_file
									snake_case_   =	file_format
									snake_case_   =	Spark(
									    df=_UpperCamelCase							,   features=_UpperCamelCase							,   cache_dir=_UpperCamelCase							,   working_dir=_UpperCamelCase							,   **_UpperCamelCase							,   )







			def 				snake_case__(     self :				int							)      ->Tuple:
									if self.streaming:
															return self.builder.as_streaming_dataset(split=self.split							)
									snake_case_   =	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
									self.builder.download_and_prepare(
									    download_mode=_UpperCamelCase							,   file_format=self._file_format							,   )
									return self.builder.as_dataset(split=self.split							)