


"""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)
__snake_case            =   logging.getLogger()







def   __lowerCAmelCase						(						lowercase  :  Path						,							lowercase  :  list			)     ->						List[Any]:


    """simple docstring"""





    snake_case   :				Tuple				 =   "\n".join(__lowerCAmelCase			)
    Path(__lowerCAmelCase			).open("w"			).writelines(__lowerCAmelCase			)


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

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







class _lowerCAmelCase    (				lowerCamelCase_     ):







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		Union[str, Any]:

       '''simple docstring'''




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

       snake_case   :				List[Any]				 =   str(Path(self.get_auto_remove_tmp_dir()						) / "scores.json"						)
       snake_case   :				int				 =   "translation_en_to_de" if model == T5_TINY else "summarization"
       snake_case   :				Any				 =   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(__snake_case						,     "argv"						,     __snake_case						):
           run_generate()
           assert Path(__snake_case						).exists()
           # os.remove(Path(output_file_name))







   def 			lowerCamelCase		(     self						)     ->		Union[str, Any]:

       '''simple docstring'''




       self.run_eval_tester(__snake_case						)







   @parameterized.expand([BART_TINY, MBART_TINY]						)
   @slow
   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		str:

       '''simple docstring'''




       self.run_eval_tester(__snake_case						)







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

       '''simple docstring'''




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

       snake_case   :				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!",
           ],
       }

       snake_case   :				Union[str, Any]				 =   Path(self.get_auto_remove_tmp_dir()						)
       snake_case   :				Dict				 =   str(tmp_dir / "scores.json"						)
       snake_case   :				Any				 =   str(tmp_dir / "val.target"						)
       _dump_articles(__snake_case						,     text["en"]						)
       _dump_articles(__snake_case						,     text["de"]						)
       snake_case   :				Optional[Any]				 =   "translation_en_to_de" if model == T5_TINY else "summarization"
       snake_case   :				Dict				 =   F'\n            run_eval_search.py\n            {model}\n            {str(__snake_case						)}\n            {str(__snake_case						)}\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(__snake_case						,     "argv"						,     __snake_case						):
           with CaptureStdout() as cs:
               run_search()
           snake_case   :				int				 =   [" num_beams | length_penalty", model, "Best score args"]
           snake_case   :				List[Any]				 =   ["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						)						)









"""simple docstring"""



from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)





