






'''simple docstring'''
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
UpperCamelCase__ =			logging.getLogger()




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Any           =     "\n".join(_UpperCamelCase							)
   Path(_UpperCamelCase							).open("w"							).writelines(_UpperCamelCase							)


UpperCamelCase__ =			'patrickvonplaten/t5-tiny-random'
UpperCamelCase__ =			'sshleifer/bart-tiny-random'
UpperCamelCase__ =			'sshleifer/tiny-mbart'

UpperCamelCase__ =			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks





class        _UpperCAmelCase						(		snake_case			):




    def 				lowerCAmelCase__				(			self  :       int  ,     a  :       Optional[Any]			):






       '''simple docstring'''






       lowercase_	:    Union[str, Any]           =     Path(self.get_auto_remove_tmp_dir()			) / "utest_input.source"
       lowercase_	:    Optional[int]           =     input_file_name.parent / "utest_output.txt"
       assert not output_file_name.exists()
       lowercase_	:    Tuple           =     [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
       _dump_articles(a  ,     a			)

       lowercase_	:    Tuple           =     str(Path(self.get_auto_remove_tmp_dir()			) / "scores.json"			)
       lowercase_	:    Dict           =     "translation_en_to_de" if model == T5_TINY else "summarization"
       lowercase_	:    List[str]           =     f"""
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            """.split()

       with patch.object(a  ,     "argv"  ,     a			):
          run_generate()
          assert Path(a			).exists()
          # os.remove(Path(output_file_name))




    def 				lowerCAmelCase__				(			self  :       Dict			):






       '''simple docstring'''






       self.run_eval_tester(a			)




    @parameterized.expand([BART_TINY, MBART_TINY]			)
    @slow
    def 				lowerCAmelCase__				(			self  :       str  ,     a  :       str			):






       '''simple docstring'''






       self.run_eval_tester(a			)





    @parameterized.expand([T5_TINY, MBART_TINY]			)
    @slow
    def 				lowerCAmelCase__				(			self  :       str  ,     a  :       Union[str, Any]			):






       '''simple docstring'''






       lowercase_	:    str           =     Path(self.get_auto_remove_tmp_dir()			) / "utest_input.source"
       lowercase_	:    Any           =     input_file_name.parent / "utest_output.txt"
       assert not output_file_name.exists()

       lowercase_	:    Union[str, Any]           =     {
           "en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
           "de": [
               "Maschinelles Lernen ist großartig, oder?",
               "Ich esse gerne Bananen",
               "Morgen ist wieder ein toller Tag!",
           ],
       }

       lowercase_	:    List[str]           =     Path(self.get_auto_remove_tmp_dir()			)
       lowercase_	:    Optional[Any]           =     str(tmp_dir / "scores.json"			)
       lowercase_	:    Optional[Any]           =     str(tmp_dir / "val.target"			)
       _dump_articles(a  ,     text["en"]			)
       _dump_articles(a  ,     text["de"]			)
       lowercase_	:    str           =     "translation_en_to_de" if model == T5_TINY else "summarization"
       lowercase_	:    List[str]           =     f"""
            run_eval_search.py
            {model}
            {str(a			)}
            {str(a			)}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            """.split()
       testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"]			)

       with patch.object(a  ,     "argv"  ,     a			):
          with CaptureStdout() as cs:
             run_search()
          lowercase_	:    Dict           =     [" num_beams | length_penalty", model, "Best score args"]
          lowercase_	:    int           =     ["Info"]
          if "translation" in task:
             expected_strings.append("bleu"			)
          else:
             expected_strings.extend(a			)
          for w in expected_strings:
             assert w in cs.out
          for w in un_expected_strings:
             assert w not in cs.out
          assert Path(a			).exists()
          os.remove(Path(a			)			)












'''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        _UpperCAmelCase						(		snake_case			):




    def __init__(			self  :       Tuple  ,     a  :       NestedDataStructureLike[PathLike]  ,     a  :       Optional[NamedSplit] = None  ,     a  :       Optional[Features] = None  ,     a  :       str = None  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       Optional[str] = None  ,     a  :       Optional[int] = None  ,     **a  :       List[Any]  ,     ):






       '''simple docstring'''






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





    def 				lowerCAmelCase__				(			self  :       Optional[int]			):






       '''simple docstring'''






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

          self.builder.download_and_prepare(
              download_config=a  ,     download_mode=a  ,     verification_mode=a  ,     base_path=a  ,     num_proc=self.num_proc  ,     )
          lowercase_	:    int           =     self.builder.as_dataset(
              split=self.split  ,     verification_mode=a  ,     in_memory=self.keep_in_memory			)
       return dataset


class        _UpperCAmelCase						:




    def __init__(			self  :       str  ,     a  :       Dataset  ,     a  :       Union[PathLike, BinaryIO]  ,     a  :       Optional[int] = None  ,     a  :       Optional[int] = None  ,     **a  :       List[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."""			)

       lowercase_	:    Dict           =     dataset
       lowercase_	:    Optional[int]           =     path_or_buf
       lowercase_	:    List[Any]           =     batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
       lowercase_	:    Optional[Any]           =     num_proc
       lowercase_	:    List[Any]           =     "utf-8"
       lowercase_	:    List[str]           =     to_json_kwargs




    def 				lowerCAmelCase__				(			self  :       Optional[int]			):






       '''simple docstring'''






       lowercase_	:    str           =     self.to_json_kwargs.pop("path_or_buf"  ,     a			)
       lowercase_	:    Any           =     self.to_json_kwargs.pop("orient"  ,     "records"			)
       lowercase_	:    Any           =     self.to_json_kwargs.pop("lines"  ,     True if orient == "records" else False			)
       lowercase_	:    List[str]           =     self.to_json_kwargs.pop("index"  ,     False if orient in ["split", "table"] else True			)
       lowercase_	:    int           =     self.to_json_kwargs.pop("compression"  ,     a			)

       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=a			) as buffer:
             lowercase_	:    Dict           =     self._write(file_obj=a  ,     orient=a  ,     lines=a  ,     index=a  ,     **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."			)
          lowercase_	:    Dict           =     self._write(
              file_obj=self.path_or_buf  ,     orient=a  ,     lines=a  ,     index=a  ,     **self.to_json_kwargs			)
       return written




    def 				lowerCAmelCase__				(			self  :       Optional[int]  ,     a  :       List[str]			):






       '''simple docstring'''






       lowercase_    ,		lowercase_    ,		lowercase_    ,		lowercase_    ,		lowercase_	:    List[Any]           =     args

       lowercase_	:    Optional[int]           =     query_table(
           table=self.dataset.data  ,     key=slice(a  ,     offset + self.batch_size			)  ,     indices=self.dataset._indices  ,     )
       lowercase_	:    Dict           =     batch.to_pandas().to_json(
           path_or_buf=a  ,     orient=a  ,     lines=a  ,     index=a  ,     **a			)
       if not json_str.endswith("\n"			):
          json_str += "\n"
       return json_str.encode(self.encoding			)





    def 				lowerCAmelCase__				(			self  :       int  ,     a  :       BinaryIO  ,     a  :       int  ,     a  :       str  ,     a  :       Union[str, Any]  ,     **a  :       str  ,     ):






       '''simple docstring'''






       lowercase_	:    Union[str, 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"  ,     ):
             lowercase_	:    Dict           =     self._batch_json((offset, orient, lines, index, to_json_kwargs)			)
             written += file_obj.write(a			)
       else:
          lowercase_    ,		lowercase_	:    Any           =     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  ,     a  ,     a			)]  ,     )  ,     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(a			)

       return written




