


"""simple docstring"""





import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows


def      _snake_case						(						_snake_case				:			Dict			):  # picklable for multiprocessing
      return i + 1


@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def      _snake_case						(						):
      with parallel_backend('''spark'''			):
            assert ParallelBackendConfig.backend_name == "spark"

      lowerCAmelCase					:	str       =   [1, 2, 3]
      with pytest.raises(_snake_case			):
            with parallel_backend('''unsupported backend'''			):
                  map_nested(_snake_case ,   _snake_case ,   num_proc=2			)

      with pytest.raises(_snake_case			):
            with parallel_backend('''unsupported backend'''			):
                  map_nested(_snake_case ,   _snake_case ,   num_proc=-1			)






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize('''num_proc''' ,   [2, -1]			)
def      _snake_case						(						_snake_case				:			str			):
      lowerCAmelCase					:	Any       =   [1, 2]
      lowerCAmelCase					:	List[str]       =   {'''a''': 1, '''b''': 2}
      lowerCAmelCase					:	int       =   {'''a''': [1, 2], '''b''': [3, 4]}
      lowerCAmelCase					:	str       =   {'''a''': {'''1''': 1}, '''b''': 2}
      lowerCAmelCase					:	List[str]       =   {'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
      lowerCAmelCase					:	Optional[Any]       =   [2, 3]
      lowerCAmelCase					:	Optional[Any]       =   {'''a''': 2, '''b''': 3}
      lowerCAmelCase					:	Optional[int]       =   {'''a''': [2, 3], '''b''': [4, 5]}
      lowerCAmelCase					:	str       =   {'''a''': {'''1''': 2}, '''b''': 3}
      lowerCAmelCase					:	Any       =   {'''a''': 2, '''b''': 3, '''c''': 4, '''d''': 5}

      with parallel_backend('''spark'''			):
            assert map_nested(_snake_case ,   _snake_case ,   num_proc=_snake_case			) == expected_map_nested_sa
            assert map_nested(_snake_case ,   _snake_case ,   num_proc=_snake_case			) == expected_map_nested_sa
            assert map_nested(_snake_case ,   _snake_case ,   num_proc=_snake_case			) == expected_map_nested_sa
            assert map_nested(_snake_case ,   _snake_case ,   num_proc=_snake_case			) == expected_map_nested_sa
            assert map_nested(_snake_case ,   _snake_case ,   num_proc=_snake_case			) == expected_map_nested_sa







"""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



class 		snake_case_(       a__			):
     __UpperCamelCase  =					'''philschmid/bart-large-cnn-samsum'''
     __UpperCamelCase  =					(
         '''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.'''
     )
     __UpperCamelCase  =					'''summarizer'''
     __UpperCamelCase  =					AutoTokenizer
     __UpperCamelCase  =					AutoModelForSeqaSeqLM

     __UpperCamelCase  =					['''text''']
     __UpperCamelCase  =					['''text''']






     def      lowerCamelCase__     (	self  :					Dict					,				UpperCamelCase_  :					int				):
           return self.pre_processor(UpperCamelCase_					,				return_tensors='''pt'''					,				truncation=UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					str				):
           return self.model.generate(**UpperCamelCase_				)[0]






     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					Tuple				):
           return self.pre_processor.decode(UpperCamelCase_					,				skip_special_tokens=UpperCamelCase_					,				clean_up_tokenization_spaces=UpperCamelCase_				)


