'''simple docstring'''





import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class  __magic_name__  (		__a    ):





   """simple docstring"""




   def __init__(	self					:  Any		,  _lowercase					:  str		,  _lowercase					:  List[Any] = None		,  _lowercase					:  List[Any] = None		,  _lowercase					:  Tuple = None		,  _lowercase					:  str = False		,  _lowercase					:  Dict = False		,  _lowercase					:  Tuple = None		,  _lowercase					:  Optional[Any] = None		,  **_lowercase					:  List[str]		,  ):



    """simple docstring"""



    super().__init__(
        _SCREAMING_SNAKE_CASE		,  split=_SCREAMING_SNAKE_CASE		,  features=_SCREAMING_SNAKE_CASE		,  cache_dir=_SCREAMING_SNAKE_CASE		,  keep_in_memory=_SCREAMING_SNAKE_CASE		,  streaming=_SCREAMING_SNAKE_CASE		,  num_proc=_SCREAMING_SNAKE_CASE		,  **_SCREAMING_SNAKE_CASE		,  )
    _UpperCamelCase:     Any							= field
    _UpperCamelCase:     Optional[int]							= path_or_paths if isinstance(_SCREAMING_SNAKE_CASE		,  _SCREAMING_SNAKE_CASE							) else {self.split: path_or_paths}
    _UpperCamelCase:     Tuple							= Json(
        cache_dir=_SCREAMING_SNAKE_CASE		,  data_files=_SCREAMING_SNAKE_CASE		,  features=_SCREAMING_SNAKE_CASE		,  field=_SCREAMING_SNAKE_CASE		,  **_SCREAMING_SNAKE_CASE		,  )



   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



    if self.streaming:
     _UpperCamelCase:     Any							= self.builder.as_streaming_dataset(split=self.split							)
    # Build regular (map-style) dataset
    else:
     _UpperCamelCase:     Optional[Any]							= None
     _UpperCamelCase:     Optional[int]							= None
     _UpperCamelCase:     Union[str, Any]							= None
     _UpperCamelCase:     Union[str, Any]							= None

     self.builder.download_and_prepare(
         download_config=_SCREAMING_SNAKE_CASE		,  download_mode=_SCREAMING_SNAKE_CASE		,  verification_mode=_SCREAMING_SNAKE_CASE		,  base_path=_SCREAMING_SNAKE_CASE		,  num_proc=self.num_proc		,  )
     _UpperCamelCase:     Optional[int]							= self.builder.as_dataset(
         split=self.split		,  verification_mode=_SCREAMING_SNAKE_CASE		,  in_memory=self.keep_in_memory							)
    return dataset




class  __magic_name__  :





   """simple docstring"""




   def __init__(	self					:  Optional[Any]		,  _lowercase					:  str		,  _lowercase					:  str		,  _lowercase					:  str = None		,  _lowercase					:  Optional[int] = None		,  **_lowercase					:  Any		,  ):



    """simple docstring"""



    if num_proc is not None and num_proc <= 0:
     raise ValueError(f"""num_proc {num_proc} must be an integer > 0."""							)

    _UpperCamelCase:     str							= dataset
    _UpperCamelCase:     Tuple							= path_or_buf
    _UpperCamelCase:     Tuple							= batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
    _UpperCamelCase:     str							= num_proc
    _UpperCamelCase:     str							= '''utf-8'''
    _UpperCamelCase:     List[str]							= to_json_kwargs



   def   lowerCAmelCase						(	self					:  int							):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= self.to_json_kwargs.pop('''path_or_buf'''		,  _SCREAMING_SNAKE_CASE							)
    _UpperCamelCase:     Union[str, Any]							= self.to_json_kwargs.pop('''orient'''		,  '''records'''							)
    _UpperCamelCase:     Any							= self.to_json_kwargs.pop('''lines'''		,  True if orient == '''records''' else False							)
    _UpperCamelCase:     str							= self.to_json_kwargs.pop('''index'''		,  False if orient in ['''split''', '''table'''] else True							)
    _UpperCamelCase:     Dict							= self.to_json_kwargs.pop('''compression'''		,  _SCREAMING_SNAKE_CASE							)

    if compression not in [None, "infer", "gzip", "bz2", "xz"]:
     raise NotImplementedError(f"""`datasets` currently does not support {compression} compression"""							)

    if isinstance(self.path_or_buf		,  (str, bytes, os.PathLike)							):
     with fsspec.open(self.path_or_buf		,  '''wb'''		,  compression=_SCREAMING_SNAKE_CASE							) as buffer:
      _UpperCamelCase:     Optional[int]							= self._write(file_obj=_SCREAMING_SNAKE_CASE		,  orient=_SCREAMING_SNAKE_CASE		,  lines=_SCREAMING_SNAKE_CASE		,  index=_SCREAMING_SNAKE_CASE		,  **self.to_json_kwargs							)
    else:
     if compression:
      raise NotImplementedError(
          f"""The compression parameter is not supported when writing to a buffer, but compression={compression}"""
          ''' was passed. Please provide a local path instead.'''							)
     _UpperCamelCase:     int							= self._write(
         file_obj=self.path_or_buf		,  orient=_SCREAMING_SNAKE_CASE		,  lines=_SCREAMING_SNAKE_CASE		,  index=_SCREAMING_SNAKE_CASE		,  **self.to_json_kwargs							)
    return written



   def   lowerCAmelCase						(	self					:  Optional[Any]		,  _lowercase					:  Optional[int]							):



    """simple docstring"""



    _UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase:     Optional[Any]							= args

    _UpperCamelCase:     Union[str, Any]							= query_table(
        table=self.dataset.data		,  key=slice(_SCREAMING_SNAKE_CASE		,  offset + self.batch_size							)		,  indices=self.dataset._indices		,  )
    _UpperCamelCase:     Tuple							= batch.to_pandas().to_json(
        path_or_buf=_SCREAMING_SNAKE_CASE		,  orient=_SCREAMING_SNAKE_CASE		,  lines=_SCREAMING_SNAKE_CASE		,  index=_SCREAMING_SNAKE_CASE		,  **_SCREAMING_SNAKE_CASE							)
    if not json_str.endswith('''\n'''							):
     json_str += "\n"
    return json_str.encode(self.encoding							)



   def   lowerCAmelCase						(	self					:  Tuple		,  _lowercase					:  List[str]		,  _lowercase					:  int		,  _lowercase					:  Any		,  _lowercase					:  Union[str, Any]		,  **_lowercase					:  Tuple		,  ):



    """simple docstring"""



    _UpperCamelCase:     List[Any]							= 0

    if self.num_proc is None or self.num_proc == 1:
     for offset in logging.tqdm(
         range(0		,  len(self.dataset							)		,  self.batch_size							)		,  unit='''ba'''		,  disable=not logging.is_progress_bar_enabled()		,  desc='''Creating json from Arrow format'''		,  ):
      _UpperCamelCase:     Optional[int]							= self._batch_json((offset, orient, lines, index, to_json_kwargs)							)
      written += file_obj.write(_SCREAMING_SNAKE_CASE							)
    else:
     _UpperCamelCase	,							_UpperCamelCase:     Dict							= len(self.dataset							), self.batch_size
     with multiprocessing.Pool(self.num_proc							) as pool:
      for json_str in logging.tqdm(
          pool.imap(
              self._batch_json		,  [(offset, orient, lines, index, to_json_kwargs) for offset in range(0		,  _SCREAMING_SNAKE_CASE		,  _SCREAMING_SNAKE_CASE							)]		,  )		,  total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size		,  unit='''ba'''		,  disable=not logging.is_progress_bar_enabled()		,  desc='''Creating json from Arrow format'''		,  ):
       written += file_obj.write(_SCREAMING_SNAKE_CASE							)

    return written
# 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  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    Optional[Any]  			=   '''philschmid/bart-large-cnn-samsum'''
   lowerCAmelCase      :    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.'''
   )
   lowerCAmelCase      :    Any  			=   '''summarizer'''
   lowerCAmelCase      :    Tuple  			=   AutoTokenizer
   lowerCAmelCase      :    Optional[Any]  			=   AutoModelForSeqaSeqLM

   lowerCAmelCase      :    Union[str, Any]  			=   ['''text''']
   lowerCAmelCase      :    Dict  			=   ['''text''']



   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  Union[str, Any]							):



    """simple docstring"""



    return self.pre_processor(_lowercase		,  return_tensors='''pt'''		,  truncation=_lowercase							)



   def   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  Optional[Any]							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  int		,  _lowercase					:  List[str]							):



    """simple docstring"""



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