





import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


__UpperCAmelCase       :   List[Any]				       =							os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f'{bindir}/../../examples/pytorch/translation'):
      from run_translation import main  # noqa


set_seed(42)
__UpperCAmelCase       :   Any				       =							"sshleifer/student_marian_en_ro_6_1"
__UpperCAmelCase       :   Optional[int]				       =							"sshleifer/tiny-mbart"







@require_torch
class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    List[str]=False							,				A :    Dict=None							,				A :    List[str]=True							,				A :    Optional[Any]=True							,				A :    Dict=True							,				A :    Optional[int]=True							,				):
         __snake_case:						Any   =						self.run_trainer(
             eval_steps=1							,				max_len=12							,				model_name=A							,				num_train_epochs=1							,				distributed=A							,				extra_args_str=A							,				predict_with_generate=A							,				do_train=A							,				do_eval=A							,				do_predict=A							,				)
         __snake_case:						Tuple   =						TrainerState.load_from_json(os.path.join(A							,				"""trainer_state.json"""		)		).log_history

         if not do_eval:
           return

         __snake_case:						List[str]   =						[log for log in logs if """eval_loss""" in log.keys()]

         __snake_case:						Dict   =						eval_metrics[0]
         if predict_with_generate:
           assert "eval_bleu" in first_step_stats

           __snake_case:						Optional[int]   =						eval_metrics[-1]
           assert isinstance(last_step_stats["""eval_bleu"""]							,				A		)
           assert not math.isnan(float(last_step_stats["""eval_loss"""]		)		), "eval_loss must not be `nan`"




       @require_torch_non_multi_gpu
       def 							UpperCAmelCase__ (  self :    Any		):
         self.run_seqaseq_quick()




       @require_torch_multi_gpu
       def 							UpperCAmelCase__ (  self :    List[str]		):
         self.run_seqaseq_quick(distributed=A		)




       @require_torch_multi_gpu
       def 							UpperCAmelCase__ (  self :    Dict		):
         self.run_seqaseq_quick(distributed=A		)




       @unittest.skip("""Requires an update of the env running those tests"""		)
       @require_torch_multi_gpu
       @require_fairscale
       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         self.run_seqaseq_quick(distributed=A							,				extra_args_str="""--sharded_ddp simple"""		)




       @unittest.skip("""Requires an update of the env running those tests"""		)
       @require_torch_multi_gpu
       @require_fairscale
       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         self.run_seqaseq_quick(distributed=A							,				extra_args_str="""--sharded_ddp simple --fp16"""		)




       @unittest.skip("""Requires an update of the env running those tests"""		)
       @require_torch_multi_gpu
       @require_fairscale
       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         self.run_seqaseq_quick(distributed=A							,				extra_args_str="""--sharded_ddp zero_dp_2"""							,				predict_with_generate=A		)




       @unittest.skip("""Requires an update of the env running those tests"""		)
       @require_torch_multi_gpu
       @require_fairscale
       def 							UpperCAmelCase__ (  self :    Union[str, Any]		):
         self.run_seqaseq_quick(
             distributed=A							,				extra_args_str="""--sharded_ddp zero_dp_2 --fp16"""							,				predict_with_generate=A		)




       @require_apex
       @require_torch_gpu
       def 							UpperCAmelCase__ (  self :    int		):
         # XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
         # program and it breaks other tests that run from the same pytest worker, therefore until this is
         # sorted out it must be run only in an external program, that is distributed=True in this
         # test and only under one or more gpus - if we want cpu will need to make a special test
         #
         # specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
         # 2nd main() call it botches the future eval.
         #
         self.run_seqaseq_quick(distributed=A							,				extra_args_str="""--fp16 --fp16_backend=apex"""		)
         # test 2nd time - was getting eval_loss': nan'
         # to reproduce the problem set distributed=False
         self.run_seqaseq_quick(distributed=A							,				extra_args_str="""--fp16 --fp16_backend=apex"""		)




       @parameterized.expand(["""base""", """low""", """high""", """mixed"""]		)
       @require_torch_multi_gpu
       def 							UpperCAmelCase__ (  self :    Union[str, Any]							,				A :    List[Any]		):
         # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
         __snake_case:						Dict   =						{
             # test with the default log_level - should be info and thus log info once
             """base""": {"""extra_args_str""": """""", """n_matches""": 1},
             # test with low log_level and log_level_replica - should be noisy on all processes
             # now the info string should appear twice on 2 processes
             """low""": {"""extra_args_str""": """--log_level debug --log_level_replica debug""", """n_matches""": 2},
             # test with high log_level and low log_level_replica
             # now the info string should appear once only on the replica
             """high""": {"""extra_args_str""": """--log_level error --log_level_replica debug""", """n_matches""": 1},
             # test with high log_level and log_level_replica - should be quiet on all processes
             """mixed""": {"""extra_args_str""": """--log_level error --log_level_replica error""", """n_matches""": 0},
         }

         __snake_case:						Optional[Any]   =						experiments[experiment_id]
         __snake_case:						str   =						{"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
         __snake_case:						Dict   =						"""Running training"""
         with CaptureStderr() as cl:
           self.run_seqaseq_quick(**A							,				extra_args_str=data["""extra_args_str"""]		)
         __snake_case:						Any   =						len(re.findall(A							,				cl.err		)		)
         self.assertEqual(A							,				data["""n_matches"""]		)




       @slow
       def 							UpperCAmelCase__ (  self :    List[str]		):
         __snake_case:						str   =						self.run_trainer(
             eval_steps=2							,				max_len=128							,				model_name=A							,				learning_rate=3E-4							,				num_train_epochs=10							,				distributed=A							,				)

         # Check metrics
         __snake_case:						List[Any]   =						TrainerState.load_from_json(os.path.join(A							,				"""trainer_state.json"""		)		).log_history
         __snake_case:						Optional[int]   =						[log for log in logs if """eval_loss""" in log.keys()]
         __snake_case:						Any   =						eval_metrics[0]
         __snake_case:						Dict   =						eval_metrics[-1]

         assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
         assert isinstance(last_step_stats["""eval_bleu"""]							,				A		)

         # test if do_predict saves generations and metrics
         __snake_case:						Tuple   =						os.listdir(A		)
         __snake_case:						Optional[int]   =						{os.path.basename(A		) for p in contents}
         assert "generated_predictions.txt" in contents
         assert "predict_results.json" in contents




       @slow
       @require_bitsandbytes
       def 							UpperCAmelCase__ (  self :    Tuple		):
         from transformers.training_args import OptimizerNames

         def train_and_return_metrics(A :    str		) -> Tuple[int, float]:
           __snake_case:						List[str]   =						"""--skip_memory_metrics 0"""

           __snake_case:						List[str]   =						self.run_trainer(
               max_len=128							,				model_name=A							,				learning_rate=3E-4							,				num_train_epochs=1							,				optim=A							,				distributed=A							,				extra_args_str=A							,				do_eval=A							,				do_predict=A							,				n_gpus_to_use=1							,				)

           # Check metrics
           __snake_case:						int   =						TrainerState.load_from_json(Path(A							,				"""trainer_state.json"""		)		).log_history
           __snake_case:						List[str]   =						int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20		)
           __snake_case:						Optional[int]   =						int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20		)

           __snake_case:						List[str]   =						logs[0]["""train_loss"""]
           return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

         __snake_case  ,   __snake_case  ,   __snake_case:						Optional[Any]   =						train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value		)
         __snake_case  ,   __snake_case  ,   __snake_case:						Tuple   =						train_and_return_metrics(OptimizerNames.ADAMW_BNB.value		)

         __snake_case:						Optional[int]   =						gpu_alloc_mem_orig - gpu_alloc_mem_bnb

         __snake_case:						Union[str, Any]   =						gpu_peak_mem_orig + gpu_alloc_mem_orig
         __snake_case:						int   =						gpu_peak_mem_bnb + gpu_alloc_mem_bnb
         __snake_case:						str   =						gpu_total_mem_orig - gpu_total_mem_bnb

         # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
         # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
         # in 2 bytes and the diff in optim memory usage is derived as so:
         #
         # - normal 25*8=~200MB (8 bytes per param)
         # - bnb    25*2= ~50MB (2 bytes per param)
         #
         # Thus we should expect ~150MB total memory saved.
         #
         # Peak memory should be the same - the total should be different by about that same margin
         #
         # After leaving a small margin to accommodate for differences between gpus let's check
         # that we have at least 120MB in savings
         __snake_case:						Union[str, Any]   =						120

         # uncomment the following if this test starts failing - requires py38 for a new print feature
         # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
         # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
         # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
         # print(f"{gpu_alloc_mem_diff=}MB")
         # print(f"{gpu_peak_mem_diff=}MB")
         # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
         # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

         self.assertGreater(
             A							,				A							,				"""should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"""
             f''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
             f''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'''							,				)

         self.assertGreater(
             A							,				A							,				"""should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"""
             f''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
             f''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'''							,				)

         self.assertEqual(
             A							,				A							,				f'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''		)






       def 							UpperCAmelCase__ (  self :    str							,				A :    int							,				A :    str							,				A :    int							,				A :    float = 3E-3							,				A :    str = "adafactor"							,				A :    bool = False							,				A :    str = None							,				A :    int = 0							,				A :    bool = True							,				A :    bool = True							,				A :    bool = True							,				A :    bool = True							,				A :    int = None							,				):
         __snake_case:						List[Any]   =						self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
         __snake_case:						List[str]   =						self.get_auto_remove_tmp_dir()
         __snake_case:						int   =						f'''
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(A		)}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(A		)}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

         __snake_case:						Union[str, Any]   =						f'''
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(A		)}
        '''.split()

         __snake_case:						Optional[Any]   =						"""
            --do_predict
        """.split()

         __snake_case:						Optional[Any]   =						[]
         if do_train:
           args += args_train

         if do_eval:
           args += args_eval

         if do_predict:
           args += args_predict

         if predict_with_generate:
           args += "--predict_with_generate".split()

         if do_train:
           if optim == "adafactor":
             args += "--adafactor".split()
           else:
             args += f'''--optim {optim}'''.split()

         if extra_args_str is not None:
           args += extra_args_str.split()

         if distributed:
           if n_gpus_to_use is None:
             __snake_case:						Optional[Any]   =						get_gpu_count()
           __snake_case:						Optional[Any]   =						get_torch_dist_unique_port()
           __snake_case:						Optional[int]   =						f'''
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            '''.split()
           __snake_case:						Tuple   =						[sys.executable] + distributed_args + args
           # keep for quick debug
           # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
           execute_subprocess_async(A							,				env=self.get_env()		)
         else:
           __snake_case:						Any   =						["""run_translation.py"""] + args
           with patch.object(A							,				"""argv"""							,				A		):
             main()

         return output_dir













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,
)






