

import pytest


lowerCAmelCase_       	=	'''__dummy_dataset1__'''

lowerCAmelCase_       	=	'''
import json
import os

import datasets


REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}


class __DummyDataset1__(datasets.GeneratorBasedBuilder):

    def _info(self):
        features = datasets.Features(
            {
                "tokens": datasets.Sequence(datasets.Value("string")),
                "ner_tags": datasets.Sequence(
                    datasets.features.ClassLabel(
                        names=[
                            "O",
                            "B-PER",
                            "I-PER",
                            "B-ORG",
                            "I-ORG",
                            "B-LOC",
                            "I-LOC",
                        ]
                    )
                ),
                "langs": datasets.Sequence(datasets.Value("string")),
                "spans": datasets.Sequence(datasets.Value("string")),
            }
        )
        return datasets.DatasetInfo(features=features)

    def _split_generators(self, dl_manager):
        dl_path = dl_manager.download(URLS)
        return [
            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),
            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),
        ]

    def _generate_examples(self, filepath):
        with open(filepath, "r", encoding="utf-8") as f:
            for i, line in enumerate(f):
                yield i, json.loads(line)
'''


@pytest.fixture
def   __SCREAMING_SNAKE_CASE					():
						return DATASET_LOADING_SCRIPT_NAME


@pytest.fixture
def   __SCREAMING_SNAKE_CASE					():
						return DATASET_LOADING_SCRIPT_CODE


@pytest.fixture
def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	dataset_loading_script_name
						snake_case_   =	tmp_path / '''datasets''' / script_name
						script_dir.mkdir(parents=SCREAMING_SNAKE_CASE__       )
						snake_case_   =	script_dir / F'''{script_name}.py'''
						with open(SCREAMING_SNAKE_CASE__  ,		'''w'''       ) as f:
												f.write(SCREAMING_SNAKE_CASE__       )
						return str(SCREAMING_SNAKE_CASE__       )


# 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





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							)