


"""simple docstring"""






import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
    '''files''',   [
        ['''full:README.md''', '''dataset_infos.json'''],
        ['''empty:README.md''', '''dataset_infos.json'''],
        ['''dataset_infos.json'''],
        ['''full:README.md'''],
    ],   )
def 					A_      (      _lowerCAmelCase   :    List[str],   _lowerCAmelCase   :    List[str]				):






     """simple docstring"""

     _a					= tmp_path_factory.mktemp('''dset_infos_dir'''				)
     if "full:README.md" in files:
          with open(dataset_infos_dir / '''README.md''',   '''w'''				) as f:
               f.write('''---\ndataset_info:\n  dataset_size: 42\n---'''				)
     if "empty:README.md" in files:
          with open(dataset_infos_dir / '''README.md''',   '''w'''				) as f:
               f.write(''''''				)
          # we want to support dataset_infos.json for backward compatibility
     if "dataset_infos.json" in files:
          with open(dataset_infos_dir / '''dataset_infos.json''',   '''w'''				) as f:
               f.write('''{\"default\": {\"dataset_size\": 42}}'''				)
     _a					= DatasetInfosDict.from_directory(_lowerCAmelCase				)
     assert dataset_infos
     assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
    '''dataset_info''',   [
        DatasetInfo(),
        DatasetInfo(
            description='''foo''',   features=Features({'''a''': Value('''int32'''				)}				),   builder_name='''builder''',   config_name='''config''',   version='''1.0.0''',   splits=[{'''name''': '''train'''}],   download_size=42,   ),
    ],   )
def 					A_      (      _lowerCAmelCase   :    Any,   _lowerCAmelCase   :    DatasetInfo				):






     """simple docstring"""

     _a					= str(_lowerCAmelCase				)
     dataset_info.write_to_directory(_lowerCAmelCase				)
     _a					= DatasetInfo.from_directory(_lowerCAmelCase				)
     assert dataset_info == reloaded
     assert os.path.exists(os.path.join(_lowerCAmelCase,   '''dataset_info.json'''				)				)
def 					A_      (      ):






     """simple docstring"""

     _a					= DatasetInfo(
         description='''foo''',   citation='''bar''',   homepage='''https://foo.bar''',   license='''CC0''',   features=Features({'''a''': Value('''int32'''				)}				),   post_processed={},   supervised_keys=(),   task_templates=[],   builder_name='''builder''',   config_name='''config''',   version='''1.0.0''',   splits=[{'''name''': '''train''', '''num_examples''': 42}],   download_checksums={},   download_size=13_37,   post_processing_size=4_42,   dataset_size=12_34,   size_in_bytes=13_37 + 4_42 + 12_34,   )
     _a					= dataset_info._to_yaml_dict()
     assert sorted(_lowerCAmelCase				) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML				)
     for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
          assert key in dataset_info_yaml_dict
          assert isinstance(dataset_info_yaml_dict[key],   (list, dict, int, str)				)
     _a					= yaml.safe_dump(_lowerCAmelCase				)
     _a					= yaml.safe_load(_lowerCAmelCase				)
     assert dataset_info_yaml_dict == reloaded
def 					A_      (      ):






     """simple docstring"""

     _a					= DatasetInfo()
     _a					= dataset_info._to_yaml_dict()
     assert dataset_info_yaml_dict == {}

@pytest.mark.parametrize(
    '''dataset_infos_dict''',   [
        DatasetInfosDict(),
        DatasetInfosDict({'''default''': DatasetInfo()}				),
        DatasetInfosDict({'''my_config_name''': DatasetInfo()}				),
        DatasetInfosDict(
            {
                '''default''': DatasetInfo(
                    description='''foo''',   features=Features({'''a''': Value('''int32'''				)}				),   builder_name='''builder''',   config_name='''config''',   version='''1.0.0''',   splits=[{'''name''': '''train'''}],   download_size=42,   )
            }				),
        DatasetInfosDict(
            {
                '''v1''': DatasetInfo(dataset_size=42				),
                '''v2''': DatasetInfo(dataset_size=13_37				),
            }				),
    ],   )
def 					A_      (      _lowerCAmelCase   :    Tuple,   _lowerCAmelCase   :    DatasetInfosDict				):






     """simple docstring"""

     _a					= str(_lowerCAmelCase				)
     dataset_infos_dict.write_to_directory(_lowerCAmelCase				)
     _a					= DatasetInfosDict.from_directory(_lowerCAmelCase				)

     # the config_name of the dataset_infos_dict take over the attribute
     for config_name, dataset_info in dataset_infos_dict.items():
          _a					= config_name
          # the yaml representation doesn't include fields like description or citation
          # so we just test that we can recover what we can from the yaml
          _a					= DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()				)
     assert dataset_infos_dict == reloaded

     if dataset_infos_dict:
          assert os.path.exists(os.path.join(_lowerCAmelCase,   '''README.md'''				)				)






# 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 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Optional[Any]						=			"""philschmid/bart-large-cnn-samsum"""
      _lowercase				:					List[Any]						=			(
          """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."""
      )
      _lowercase				:					Any						=			"""summarizer"""
      _lowercase				:					Any						=			AutoTokenizer
      _lowercase				:					str						=			AutoModelForSeqaSeqLM

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







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Tuple:



        '''simple docstring'''




        return self.pre_processor(lowerCAmelCase__		,      return_tensors="pt"		,      truncation=lowerCAmelCase__							)







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Union[str, Any]:



        '''simple docstring'''




        return self.model.generate(**lowerCAmelCase__							)[0]







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Any:



        '''simple docstring'''




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






