


'''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)
__lowercase	:						Dict       =	logging.getLogger()






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Path						,      _SCREAMING_SNAKE_CASE      :   list   ):
					__a			:       Union[str, Any]					    =  '\n'.join(_SCREAMING_SNAKE_CASE   )
					Path(_SCREAMING_SNAKE_CASE   ).open('w'   ).writelines(_SCREAMING_SNAKE_CASE   )


__lowercase	:						Any       =	'patrickvonplaten/t5-tiny-random'
__lowercase	:						List[Any]       =	'sshleifer/bart-tiny-random'
__lowercase	:						Dict       =	'sshleifer/tiny-mbart'

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





class 							__UpperCamelCase				(		lowerCAmelCase_					):




	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





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

						__a			:       int					    =  str(Path(self.get_auto_remove_tmp_dir()							) / 'scores.json'							)
						__a			:       Tuple					    =  'translation_en_to_de' if model == T5_TINY else 'summarization'
						__a			:       Optional[Any]					    =  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       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						self.run_eval_tester(__a							)




	@parameterized.expand([BART_TINY, MBART_TINY]							)
	@slow
	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						self.run_eval_tester(__a							)





	@parameterized.expand([T5_TINY, MBART_TINY]							)
	@slow
	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						__a			:       Tuple					    =  Path(self.get_auto_remove_tmp_dir()							) / 'utest_input.source'
						__a			:       Union[str, Any]					    =  input_file_name.parent / 'utest_output.txt'
						assert not output_file_name.exists()

						__a			:       str					    =  {
						    '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!',
						    ],
						}

						__a			:       Optional[int]					    =  Path(self.get_auto_remove_tmp_dir()							)
						__a			:       List[Any]					    =  str(tmp_dir / 'scores.json'							)
						__a			:       Optional[Any]					    =  str(tmp_dir / 'val.target'							)
						_dump_articles(__a						,				text['en']							)
						_dump_articles(__a						,				text['de']							)
						__a			:       List[Any]					    =  'translation_en_to_de' if model == T5_TINY else 'summarization'
						__a			:       Optional[int]					    =  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()
											__a			:       Optional[Any]					    =  [' num_beams | length_penalty', model, 'Best score args']
											__a			:       Dict					    =  ['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 os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil






@pytest.fixture
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict   ):
					__a			:       Optional[Any]					    =  tmp_path / 'file.csv'
					__a			:       Union[str, Any]					    =  textwrap.dedent(
					    '\\n        header1,header2\n        1,2\n        10,20\n        '   )
					with open(_SCREAMING_SNAKE_CASE						,      'w'   ) as f:
										f.write(_SCREAMING_SNAKE_CASE   )
					return str(_SCREAMING_SNAKE_CASE   )






@pytest.fixture
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					__a			:       str					    =  tmp_path / 'malformed_file.csv'
					__a			:       int					    =  textwrap.dedent(
					    '\\n        header1,header2\n        1,2\n        10,20,\n        '   )
					with open(_SCREAMING_SNAKE_CASE						,      'w'   ) as f:
										f.write(_SCREAMING_SNAKE_CASE   )
					return str(_SCREAMING_SNAKE_CASE   )






@pytest.fixture
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[Any]						,      _SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       Optional[Any]					    =  tmp_path / 'csv_with_image.csv'
					__a			:       Dict					    =  textwrap.dedent(
					    F"""\
        image
        {image_file}
        """   )
					with open(_SCREAMING_SNAKE_CASE						,      'w'   ) as f:
										f.write(_SCREAMING_SNAKE_CASE   )
					return str(_SCREAMING_SNAKE_CASE   )






@pytest.fixture
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       Union[str, Any]					    =  tmp_path / 'csv_with_label.csv'
					__a			:       Any					    =  textwrap.dedent(
					    '\\n        label\n        good\n        bad\n        good\n        '   )
					with open(_SCREAMING_SNAKE_CASE						,      'w'   ) as f:
										f.write(_SCREAMING_SNAKE_CASE   )
					return str(_SCREAMING_SNAKE_CASE   )






@pytest.fixture
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[Any]   ):
					__a			:       Dict					    =  tmp_path / 'csv_with_int_list.csv'
					__a			:       Tuple					    =  textwrap.dedent(
					    '\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        '   )
					with open(_SCREAMING_SNAKE_CASE						,      'w'   ) as f:
										f.write(_SCREAMING_SNAKE_CASE   )
					return str(_SCREAMING_SNAKE_CASE   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       int					    =  Csv()
					__a			:       str					    =  csv._generate_tables([[csv_file, malformed_csv_file]]   )
					with pytest.raises(_SCREAMING_SNAKE_CASE						,      match='Error tokenizing data'   ):
										for _ in generator:
															pass
					assert any(
					    record.levelname == 'ERROR'
					    and 'Failed to read file' in record.message
					    and os.path.basename(_SCREAMING_SNAKE_CASE   ) in record.message
					    for record in caplog.records   )






@require_pil
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]   ):
					with open(_SCREAMING_SNAKE_CASE						,      encoding='utf-8'   ) as f:
										__a			:       Tuple					    =  f.read().splitlines()[1]
					__a			:       Tuple					    =  Csv(encoding='utf-8'						,      features=Features({'image': Image()}   )   )
					__a			:       Any					    =  csv._generate_tables([[csv_file_with_image]]   )
					__a			:       int					    =  pa.concat_tables([table for _, table in generator]   )
					assert pa_table.schema.field('image'   ).type == Image()()
					__a			:       Any					    =  pa_table.to_pydict()['image']
					assert generated_content == [{"path": image_file, "bytes": None}]






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					with open(_SCREAMING_SNAKE_CASE						,      encoding='utf-8'   ) as f:
										__a			:       Tuple					    =  f.read().splitlines()[1:]
					__a			:       Optional[int]					    =  Csv(encoding='utf-8'						,      features=Features({'label': ClassLabel(names=['good', 'bad']   )}   )   )
					__a			:       List[str]					    =  csv._generate_tables([[csv_file_with_label]]   )
					__a			:       Dict					    =  pa.concat_tables([table for _, table in generator]   )
					assert pa_table.schema.field('label'   ).type == ClassLabel(names=['good', 'bad']   )()
					__a			:       int					    =  pa_table.to_pydict()['label']
					assert generated_content == [ClassLabel(names=['good', 'bad']   ).straint(_SCREAMING_SNAKE_CASE   ) for label in labels]






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[int]   ):
					__a			:       str					    =  Csv(encoding='utf-8'						,      sep=','						,      converters={'int_list': lambda _SCREAMING_SNAKE_CASE   : [int(_SCREAMING_SNAKE_CASE   ) for i in x.split()]}   )
					__a			:       Any					    =  csv._generate_tables([[csv_file_with_int_list]]   )
					__a			:       Any					    =  pa.concat_tables([table for _, table in generator]   )
					assert pa.types.is_list(pa_table.schema.field('int_list'   ).type   )
					__a			:       Tuple					    =  pa_table.to_pydict()['int_list']
					assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]



