
"""simple docstring"""




from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


A__   :					List[str]							=   [
    'Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'
    ' final seconds on board Flight 9525. The Germanwings co-pilot says he had a "previous episode of severe'
    ' depression\" German airline confirms it knew of Andreas Lubitz\'s depression years before he took control.',
    'The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal'
    ' accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC\'s'
    ' founding Rome Statute in January. Israel and the United States opposed the Palestinians\' efforts to join the'
    ' body.',
    'Amnesty International releases its annual report on the death penalty. The report catalogs the use of'
    ' state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the'
    ' world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital'
    ' punishment.',
]

A__   :					int							=   [
    'Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .'
    ' Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz'
    ' had informed his Lufthansa training school of an episode of severe depression, airline says .',
    'Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June .'
    ' Israel and the United States opposed the move, which could open the door to war crimes investigations against'
    ' Israelis .',
    'Amnesty\'s annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to'
    ' death . Organization claims that governments around the world are using the threat of terrorism to advance'
    ' executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death'
    ' sentences up by 28% .',
]




def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			List[str]       =     calculate_rouge(_UpperCamelCase ,				_UpperCamelCase ,				bootstrap_aggregation=_UpperCamelCase ,				rouge_keys=['''rouge2''', '''rougeL''']						)
  assert isinstance(_UpperCamelCase ,				_UpperCamelCase						)
  _lowercase:			Union[str, Any]       =     calculate_rouge(_UpperCamelCase ,				_UpperCamelCase ,				bootstrap_aggregation=_UpperCamelCase ,				rouge_keys=['''rouge2''']						)
  assert (
      pd.DataFrame(no_aggregation['''rouge2''']						).fmeasure.mean()
      == pd.DataFrame(no_aggregation_just_ra['''rouge2''']						).fmeasure.mean()
  )




def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			Dict       =     '''rougeLsum'''
  _lowercase:			Tuple       =     calculate_rouge(_UpperCamelCase ,				_UpperCamelCase ,				newline_sep=_UpperCamelCase ,				rouge_keys=[k]						)[k]
  _lowercase:			Optional[Any]       =     calculate_rouge(_UpperCamelCase ,				_UpperCamelCase ,				newline_sep=_UpperCamelCase ,				rouge_keys=[k]						)[k]
  assert score > score_no_sep




def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			Tuple       =     ['''rouge1''', '''rouge2''', '''rougeL''']
  _lowercase:			Optional[Any]       =     calculate_rouge(_UpperCamelCase ,				_UpperCamelCase ,				newline_sep=_UpperCamelCase ,				rouge_keys=_UpperCamelCase						)
  _lowercase:			Any       =     calculate_rouge(_UpperCamelCase ,				_UpperCamelCase ,				newline_sep=_UpperCamelCase ,				rouge_keys=_UpperCamelCase						)
  assert score_sep == score_no_sep




def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			Optional[Any]       =     [
      '''Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.''',
      '''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .''',
  ]
  _lowercase:			List[Any]       =     [
      '''Margot Frank, died in 1945, a month earlier than previously thought.''',
      '''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of'''
      ''' the final seconds on board Flight 9525.''',
  ]
  assert calculate_rouge(_UpperCamelCase ,				_UpperCamelCase ,				newline_sep=_UpperCamelCase						) == calculate_rouge(_UpperCamelCase ,				_UpperCamelCase ,				newline_sep=_UpperCamelCase						)




def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			int       =     [
      '''" "a person who has such a video needs to immediately give it to the investigators," prosecutor says .<n> "it is a very disturbing scene," editor-in-chief of bild online tells "erin burnett: outfront" '''
  ]
  _lowercase:			List[Any]       =     [
      ''' Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports . Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says .'''
  ]

  _lowercase:			Tuple       =     calculate_rouge(_UpperCamelCase ,				_UpperCamelCase ,				rouge_keys=['''rougeLsum'''] ,				newline_sep=_UpperCamelCase						)['''rougeLsum''']
  _lowercase:			str       =     calculate_rouge(_UpperCamelCase ,				_UpperCamelCase ,				rouge_keys=['''rougeLsum''']						)['''rougeLsum''']
  assert new_score > prev_score







def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			int       =     Path('''examples/seq2seq/test_data/wmt_en_ro'''						)
  _lowercase:			Optional[Any]       =     calculate_rouge_path(data_dir.joinpath('''test.source'''						) ,				data_dir.joinpath('''test.target'''						)						)
  assert isinstance(_UpperCamelCase ,				_UpperCamelCase						)
  _lowercase:			Dict       =     calculate_rouge_path(
      data_dir.joinpath('''test.source'''						) ,				data_dir.joinpath('''test.target'''						) ,				bootstrap_aggregation=_UpperCamelCase						)
  assert isinstance(_UpperCamelCase ,				_UpperCamelCase						)


"""simple docstring"""




import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


A__   :					List[Any]							=   datasets.utils.logging.get_logger(__name__)


@dataclass
class 			__magic_name__       (       datasets.BuilderConfig ):
       UpperCamelCase_   =		None
       UpperCamelCase_   =		"utf-8"
       UpperCamelCase_   =		None
       UpperCamelCase_   =		None
       UpperCamelCase_   =		True  # deprecated
       UpperCamelCase_   =		None  # deprecated
       UpperCamelCase_   =		10 << 20  # 10MB
       UpperCamelCase_   =		None


class 			__magic_name__       (       datasets.ArrowBasedBuilder ):
       UpperCamelCase_   =		JsonConfig
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         if self.config.block_size is not None:
           logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''       )
           _lowercase:			List[str]       =     self.config.block_size
         if self.config.use_threads is not True:
           logger.warning(
               '''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'''       )
         if self.config.newlines_in_values is not None:
           raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported'''       )
         return datasets.DatasetInfo(features=self.config.features       )
       def 							lowercase_							(     self ,				A_       )     ->	Any:





         """simple docstring"""




         if not self.config.data_files:
           raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}'''       )
         _lowercase:			int       =     dl_manager.download_and_extract(self.config.data_files       )
         if isinstance(A_ ,				(str, list, tuple)       ):
           _lowercase:			Tuple       =     data_files
           if isinstance(A_ ,				A_       ):
             _lowercase:			Optional[Any]       =     [files]
           _lowercase:			Dict       =     [dl_manager.iter_files(A_       ) for file in files]
           return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,				gen_kwargs={'''files''': files}       )]
         _lowercase:			str       =     []
         for split_name, files in data_files.items():
           if isinstance(A_ ,				A_       ):
             _lowercase:			Optional[Any]       =     [files]
           _lowercase:			str       =     [dl_manager.iter_files(A_       ) for file in files]
           splits.append(datasets.SplitGenerator(name=A_ ,				gen_kwargs={'''files''': files}       )       )
         return splits
       def 							lowercase_							(     self ,				A_       )     ->	pa.Table:





         """simple docstring"""




         if self.config.features is not None:
           # adding missing columns
           for column_name in set(self.config.features       ) - set(pa_table.column_names       ):
             _lowercase:			Any       =     self.config.features.arrow_schema.field(A_       ).type
             _lowercase:			Optional[Any]       =     pa_table.append_column(A_ ,				pa.array([None] * len(A_       ) ,				type=A_       )       )
           # more expensive cast to support nested structures with keys in a different order
           # allows str <-> int/float or str to Audio for example
           _lowercase:			Optional[int]       =     table_cast(A_ ,				self.config.features.arrow_schema       )
         return pa_table



       def 							lowercase_							(     self ,				A_       )     ->	str:





         """simple docstring"""




         for file_idx, file in enumerate(itertools.chain.from_iterable(A_       )       ):
           # If the file is one json object and if we need to look at the list of items in one specific field
           if self.config.field is not None:
             with open(A_ ,				encoding=self.config.encoding ,				errors=self.config.encoding_errors       ) as f:
               _lowercase:			Optional[int]       =     json.load(A_       )

             # We keep only the field we are interested in
             _lowercase:			str       =     dataset[self.config.field]

             # We accept two format: a list of dicts or a dict of lists
             if isinstance(A_ ,				(list, tuple)       ):
               _lowercase:			Dict       =     set().union(*[row.keys() for row in dataset]       )
               _lowercase:			List[str]       =     {col: [row.get(A_       ) for row in dataset] for col in keys}
             else:
               _lowercase:			Dict       =     dataset
             _lowercase:			Union[str, Any]       =     pa.Table.from_pydict(A_       )
             yield file_idx, self._cast_table(A_       )

           # If the file has one json object per line
           else:
             with open(A_ ,				'''rb'''       ) as f:
               _lowercase:			int       =     0
               # Use block_size equal to the chunk size divided by 32 to leverage multithreading
               # Set a default minimum value of 16kB if the chunk size is really small
               _lowercase:			Optional[int]       =     max(self.config.chunksize // 32 ,				16 << 10       )
               _lowercase:			List[Any]       =     (
                   self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
               )
               while True:
                 _lowercase:			Union[str, Any]       =     f.read(self.config.chunksize       )
                 if not batch:
                   break
                 # Finish current line
                 try:
                   batch += f.readline()
                 except (AttributeError, io.UnsupportedOperation):
                   batch += readline(A_       )
                 # PyArrow only accepts utf-8 encoded bytes
                 if self.config.encoding != "utf-8":
                   _lowercase:			Any       =     batch.decode(self.config.encoding ,				errors=A_       ).encode('''utf-8'''       )
                 try:
                   while True:
                     try:
                       _lowercase:			Optional[int]       =     paj.read_json(
                           io.BytesIO(A_       ) ,				read_options=paj.ReadOptions(block_size=A_       )       )
                       break
                     except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                       if (
                           isinstance(A_ ,				pa.ArrowInvalid       )
                           and "straddling" not in str(A_       )
                           or block_size > len(A_       )
                       ):
                         raise
                       else:
                         # Increase the block size in case it was too small.
                         # The block size will be reset for the next file.
                         logger.debug(
                             f'''Batch of {len(A_       )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.'''       )
                         block_size *= 2
                 except pa.ArrowInvalid as e:
                   try:
                     with open(
                         A_ ,				encoding=self.config.encoding ,				errors=self.config.encoding_errors       ) as f:
                       _lowercase:			Optional[Any]       =     json.load(A_       )
                   except json.JSONDecodeError:
                     logger.error(f'''Failed to read file \'{file}\' with error {type(A_       )}: {e}'''       )
                     raise e
                   # If possible, parse the file as a list of json objects and exit the loop
                   if isinstance(A_ ,				A_       ):  # list is the only sequence type supported in JSON
                     try:
                       _lowercase:			Optional[int]       =     set().union(*[row.keys() for row in dataset]       )
                       _lowercase:			Tuple       =     {col: [row.get(A_       ) for row in dataset] for col in keys}
                       _lowercase:			str       =     pa.Table.from_pydict(A_       )
                     except (pa.ArrowInvalid, AttributeError) as e:
                       logger.error(f'''Failed to read file \'{file}\' with error {type(A_       )}: {e}'''       )
                       raise ValueError(f'''Not able to read records in the JSON file at {file}.'''       ) from None
                     yield file_idx, self._cast_table(A_       )
                     break
                   else:
                     logger.error(f'''Failed to read file \'{file}\' with error {type(A_       )}: {e}'''       )
                     raise ValueError(
                         f'''Not able to read records in the JSON file at {file}. '''
                         f'''You should probably indicate the field of the JSON file containing your records. '''
                         f'''This JSON file contain the following fields: {str(list(dataset.keys()       )       )}. '''
                         f'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. '''       ) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                 yield (file_idx, batch_idx), self._cast_table(A_       )
                 batch_idx += 1
