




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)
snake_case_ : str    		=       logging.getLogger()


def    A						(__A   :    Path   ,				__A   :    list       )   ->      Tuple:







     """simple docstring"""


     UpperCAmelCase_          =							'''\n'''.join(__A       )
     Path(__A       ).open('''w'''       ).writelines(__A       )


snake_case_ : Union[str, Any]    		=       "patrickvonplaten/t5-tiny-random"
snake_case_ : int    		=       "sshleifer/bart-tiny-random"
snake_case_ : List[str]    		=       "sshleifer/tiny-mbart"

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







class __snake_case			(			a		):







   def lowerCamelCase						(      self		:						List[str]					,						_snake_case		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							Path(self.get_auto_remove_tmp_dir()) / '''utest_input.source'''
        UpperCAmelCase_          =							input_file_name.parent / '''utest_output.txt'''
        assert not output_file_name.exists()
        UpperCAmelCase_          =							[''' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.''']
        _dump_articles(_snake_case					,						_snake_case)

        UpperCAmelCase_          =							str(Path(self.get_auto_remove_tmp_dir()) / '''scores.json''')
        UpperCAmelCase_          =							'''translation_en_to_de''' if model == T5_TINY else '''summarization'''
        UpperCAmelCase_          =							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(_snake_case					,						'''argv'''					,						_snake_case):
             run_generate()
             assert Path(_snake_case).exists()
             # os.remove(Path(output_file_name))







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        self.run_eval_tester(_snake_case)







   @parameterized.expand([BART_TINY, MBART_TINY])
   @slow
   def lowerCamelCase						(      self		:						List[Any]					,						_snake_case		:						Tuple):

        """simple docstring"""


        self.run_eval_tester(_snake_case)







   @parameterized.expand([T5_TINY, MBART_TINY])
   @slow
   def lowerCamelCase						(      self		:						str					,						_snake_case		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							Path(self.get_auto_remove_tmp_dir()) / '''utest_input.source'''
        UpperCAmelCase_          =							input_file_name.parent / '''utest_output.txt'''
        assert not output_file_name.exists()

        UpperCAmelCase_          =							{
            '''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!''',
            ],
        }

        UpperCAmelCase_          =							Path(self.get_auto_remove_tmp_dir())
        UpperCAmelCase_          =							str(tmp_dir / '''scores.json''')
        UpperCAmelCase_          =							str(tmp_dir / '''val.target''')
        _dump_articles(_snake_case					,						text['''en'''])
        _dump_articles(_snake_case					,						text['''de'''])
        UpperCAmelCase_          =							'''translation_en_to_de''' if model == T5_TINY else '''summarization'''
        UpperCAmelCase_          =							F"""
            run_eval_search.py
            {model}
            {str(_snake_case)}
            {str(_snake_case)}
            --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(_snake_case					,						'''argv'''					,						_snake_case):
             with CaptureStdout() as cs:
                  run_search()
             UpperCAmelCase_          =							[''' num_beams | length_penalty''', model, '''Best score args''']
             UpperCAmelCase_          =							['''Info''']
             if "translation" in task:
                  expected_strings.append('''bleu''')
             else:
                  expected_strings.extend(_snake_case)
             for w in expected_strings:
                  assert w in cs.out
             for w in un_expected_strings:
                  assert w not in cs.out
             assert Path(_snake_case).exists()
             os.remove(Path(_snake_case))










# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


lowerCAmelCase__      		= logging.get_logger(__name__)

lowerCAmelCase__      		= {}
lowerCAmelCase__      		= {}
lowerCAmelCase__      		= {}
def 							_UpperCAmelCase				(UpperCamelCase__     :  type				,							UpperCamelCase__     :  Optional[str]				,							UpperCamelCase__     :  Optional[List[str]] = None				,							):
       _A      :       Union[str, Any]				    = aliases if aliases is not None else []
       if format_type in _FORMAT_TYPES:
              logger.warning(
                  f"Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"						)
       _A      :       Dict				    = formatter_cls
       for alias in set(aliases + [format_type]						):
              if alias in _FORMAT_TYPES_ALIASES:
                     logger.warning(
                         f"Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"						)
              _A      :       Dict				    = format_type
def 							_UpperCAmelCase				(UpperCamelCase__     :  Exception				,							UpperCamelCase__     :  Optional[str]				,							UpperCamelCase__     :  Optional[List[str]] = None						):
       _A      :       Union[str, Any]				    = aliases if aliases is not None else []
       for alias in set(aliases + [format_type]						):
              _A      :       Union[str, Any]				    = unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['python'])
_register_formatter(ArrowFormatter, 'arrow', aliases=['pa', 'pyarrow'])
_register_formatter(NumpyFormatter, 'numpy', aliases=['np'])
_register_formatter(PandasFormatter, 'pandas', aliases=['pd'])
_register_formatter(CustomFormatter, 'custom')

if config.TORCH_AVAILABLE:
    from .torch_formatter import TorchFormatter

    _register_formatter(TorchFormatter, 'torch', aliases=['pt', 'pytorch'])
else:
    lowerCAmelCase__      		= ValueError('PyTorch needs to be installed to be able to return PyTorch tensors.')
    _register_unavailable_formatter(_torch_error, 'torch', aliases=['pt', 'pytorch'])

if config.TF_AVAILABLE:
    from .tf_formatter import TFFormatter

    _register_formatter(TFFormatter, 'tensorflow', aliases=['tf'])
else:
    lowerCAmelCase__      		= ValueError('Tensorflow needs to be installed to be able to return Tensorflow tensors.')
    _register_unavailable_formatter(_tf_error, 'tensorflow', aliases=['tf'])

if config.JAX_AVAILABLE:
    from .jax_formatter import JaxFormatter

    _register_formatter(JaxFormatter, 'jax', aliases=[])
else:
    lowerCAmelCase__      		= ValueError('JAX needs to be installed to be able to return JAX arrays.')
    _register_unavailable_formatter(_jax_error, 'jax', aliases=[])
def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[str]						):
       if format_type in _FORMAT_TYPES_ALIASES:
              return _FORMAT_TYPES_ALIASES[format_type]
       else:
              return format_type






def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[str]				,							**UpperCamelCase__     :  List[Any]						):
       _A      :       List[str]				    = get_format_type_from_alias(UpperCamelCase__						)
       if format_type in _FORMAT_TYPES:
              return _FORMAT_TYPES[format_type](**UpperCamelCase__						)
       if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
              raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
       else:
              raise ValueError(
                  f"Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None						)}, but got '{format_type}'"						)
