






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)
a_							=    logging.getLogger()


def        _a  (     UpperCamelCase_					:				Path    ,   UpperCamelCase_					:				list   ) ->   Dict:
						"""simple docstring"""







						lowerCAmelCase__       =						"\n".join(UpperCamelCase_   )
						Path(UpperCamelCase_   ).open("w"   ).writelines(UpperCamelCase_   )


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

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





class      lowercase__     (			_UpperCAmelCase	):

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					Tuple:







							'''simple docstring'''





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

							lowerCAmelCase__       =						str(Path(self.get_auto_remove_tmp_dir()       ) / "scores.json"       )
							lowerCAmelCase__       =						"translation_en_to_de" if model == T5_TINY else "summarization"
							lowerCAmelCase__       =						F"\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            ".split()

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

	def 		UpperCAmelCase  (							self       )->					List[str]:







							'''simple docstring'''





							self.run_eval_tester(__UpperCAmelCase       )

	@parameterized.expand([BART_TINY, MBART_TINY]       )
	@slow
	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					Any:







							'''simple docstring'''





							self.run_eval_tester(__UpperCAmelCase       )


	@parameterized.expand([T5_TINY, MBART_TINY]       )
	@slow
	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					Dict:







							'''simple docstring'''





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

							lowerCAmelCase__       =						{
							    "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!",
							    ],
							}

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

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














import json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

a_							=    logging.getLogger()


def        _a  (     UpperCamelCase_					:				Union[str, Any]   ) ->   Dict:
						"""simple docstring"""







						lowerCAmelCase__       =						{}
						lowerCAmelCase__       =						os.path.join(UpperCamelCase_    ,   "all_results.json"   )
						if os.path.exists(UpperCamelCase_   ):
												with open(UpperCamelCase_    ,   "r"   ) as f:
																		lowerCAmelCase__       =						json.load(UpperCamelCase_   )
						else:
												raise ValueError(F"can't find {path}"   )
						return results


a_							=    logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





@require_torch_tpu
class      lowercase__     (			_UpperCAmelCase	):

	def 		UpperCAmelCase  (							self       )->					Optional[Any]:







							'''simple docstring'''





							import xla_spawn

							lowerCAmelCase__       =						self.get_auto_remove_tmp_dir()
							lowerCAmelCase__       =						F"\n            ./examples/pytorch/text-classification/run_glue.py\n            --num_cores=8\n            ./examples/pytorch/text-classification/run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --debug tpu_metrics_debug\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

							with patch.object(__UpperCAmelCase		,    "argv"		,    __UpperCAmelCase       ):
													lowerCAmelCase__       =						time()
													xla_spawn.main()
													lowerCAmelCase__       =						time()

													lowerCAmelCase__       =						get_results(__UpperCAmelCase       )
													self.assertGreaterEqual(result["eval_accuracy"]		,    0.75       )

													# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
													self.assertLess(end - start		,    500       )


	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							import xla_spawn

							lowerCAmelCase__       =						"\n            ./tests/test_trainer_tpu.py\n            --num_cores=8\n            ./tests/test_trainer_tpu.py\n            ".split()
							with patch.object(__UpperCAmelCase		,    "argv"		,    __UpperCAmelCase       ):
													xla_spawn.main()






