

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


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


set_seed(4_2)
__A			:					Tuple						=			'sshleifer/student_marian_en_ro_6_1'
__A			:					List[Any]						=			'sshleifer/tiny-mbart'





@require_torch
class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




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

       if not do_eval:
           return

       SCREAMING_SNAKE_CASE										=   [log for log in logs if "eval_loss" in log.keys()]

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

           SCREAMING_SNAKE_CASE										=   eval_metrics[-1]
           assert isinstance(last_step_stats["eval_bleu"]     , __lowerCamelCase			)
           assert not math.isnan(float(last_step_stats["eval_loss"]			)			), "eval_loss must not be `nan`"





   @require_torch_non_multi_gpu
   def      _snake_case		(       self					:			List[str]			):
       self.run_seqaseq_quick()





   @require_torch_multi_gpu
   def      _snake_case		(       self					:			str			):
       self.run_seqaseq_quick(distributed=__lowerCamelCase			)





   @require_torch_multi_gpu
   def      _snake_case		(       self					:			List[Any]			):
       self.run_seqaseq_quick(distributed=__lowerCamelCase			)





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





   @unittest.skip("Requires an update of the env running those tests"			)
   @require_torch_multi_gpu
   @require_fairscale
   def      _snake_case		(       self					:			Dict			):
       self.run_seqaseq_quick(distributed=__lowerCamelCase     , 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      _snake_case		(       self					:			str			):
       self.run_seqaseq_quick(distributed=__lowerCamelCase     , extra_args_str="--sharded_ddp zero_dp_2"     , predict_with_generate=__lowerCamelCase			)





   @unittest.skip("Requires an update of the env running those tests"			)
   @require_torch_multi_gpu
   @require_fairscale
   def      _snake_case		(       self					:			Optional[int]			):
       self.run_seqaseq_quick(
           distributed=__lowerCamelCase     , extra_args_str="--sharded_ddp zero_dp_2 --fp16"     , predict_with_generate=__lowerCamelCase			)





   @require_apex
   @require_torch_gpu
   def      _snake_case		(       self					:			Tuple			):
       # 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=__lowerCamelCase     , 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=__lowerCamelCase     , extra_args_str="--fp16 --fp16_backend=apex"			)





   @parameterized.expand(["base", "low", "high", "mixed"]			)
   @require_torch_multi_gpu
   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			str			):
       # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
       SCREAMING_SNAKE_CASE										=   {
           # 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},
       }

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





   @slow
   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   self.run_trainer(
           eval_steps=2     , max_len=128     , model_name=__lowerCamelCase     , learning_rate=3e-4     , num_train_epochs=10     , distributed=__lowerCamelCase     , )

       # Check metrics
       SCREAMING_SNAKE_CASE										=   TrainerState.load_from_json(os.path.join(__lowerCamelCase     , "trainer_state.json"			)			).log_history
       SCREAMING_SNAKE_CASE										=   [log for log in logs if "eval_loss" in log.keys()]
       SCREAMING_SNAKE_CASE										=   eval_metrics[0]
       SCREAMING_SNAKE_CASE										=   eval_metrics[-1]

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

       # test if do_predict saves generations and metrics
       SCREAMING_SNAKE_CASE										=   os.listdir(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   {os.path.basename(__lowerCamelCase			) for p in contents}
       assert "generated_predictions.txt" in contents
       assert "predict_results.json" in contents





   @slow
   @require_bitsandbytes
   def      _snake_case		(       self					:			List[str]			):
       from transformers.training_args import OptimizerNames

       def train_and_return_metrics(__lowerCamelCase					:			str			) -> Tuple[int, float]:
           SCREAMING_SNAKE_CASE										=   "--skip_memory_metrics 0"

           SCREAMING_SNAKE_CASE										=   self.run_trainer(
               max_len=128     , model_name=__lowerCamelCase     , learning_rate=3e-4     , num_train_epochs=1     , optim=__lowerCamelCase     , distributed=__lowerCamelCase     , extra_args_str=__lowerCamelCase     , do_eval=__lowerCamelCase     , do_predict=__lowerCamelCase     , n_gpus_to_use=1     , )

           # Check metrics
           SCREAMING_SNAKE_CASE										=   TrainerState.load_from_json(Path(__lowerCamelCase     , "trainer_state.json"			)			).log_history
           SCREAMING_SNAKE_CASE										=   int(logs[0]["train_mem_gpu_peaked_delta"] / 2**20			)
           SCREAMING_SNAKE_CASE										=   int(logs[0]["train_mem_gpu_alloc_delta"] / 2**20			)

           SCREAMING_SNAKE_CASE										=   logs[0]["train_loss"]
           return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value			)
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   train_and_return_metrics(OptimizerNames.ADAMW_BNB.value			)

       SCREAMING_SNAKE_CASE										=   gpu_alloc_mem_orig - gpu_alloc_mem_bnb

       SCREAMING_SNAKE_CASE										=   gpu_peak_mem_orig + gpu_alloc_mem_orig
       SCREAMING_SNAKE_CASE										=   gpu_peak_mem_bnb + gpu_alloc_mem_bnb
       SCREAMING_SNAKE_CASE										=   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
       SCREAMING_SNAKE_CASE										=   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(
           __lowerCamelCase     , __lowerCamelCase     , "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(
           __lowerCamelCase     , __lowerCamelCase     , "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(
           __lowerCamelCase     , __lowerCamelCase     , f"loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"			)





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

       SCREAMING_SNAKE_CASE										=   f"\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(__lowerCamelCase			)}\n        ".split()

       SCREAMING_SNAKE_CASE										=   "\n            --do_predict\n        ".split()

       SCREAMING_SNAKE_CASE										=   []
       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:
               SCREAMING_SNAKE_CASE										=   get_gpu_count()
           SCREAMING_SNAKE_CASE										=   get_torch_dist_unique_port()
           SCREAMING_SNAKE_CASE										=   f"\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            ".split()
           SCREAMING_SNAKE_CASE										=   [sys.executable] + distributed_args + args
           # keep for quick debug
           # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
           execute_subprocess_async(__lowerCamelCase     , env=self.get_env()			)
       else:
           SCREAMING_SNAKE_CASE										=   ["run_translation.py"] + args
           with patch.object(__lowerCamelCase     , "argv"     , __lowerCamelCase			):
               main()

       return output_dir






'''simple docstring'''




from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_pt_objects import *  # noqa F403
else:
 from .scheduling_consistency_models import CMStochasticIterativeScheduler
 from .scheduling_ddim import DDIMScheduler
 from .scheduling_ddim_inverse import DDIMInverseScheduler
 from .scheduling_ddim_parallel import DDIMParallelScheduler
 from .scheduling_ddpm import DDPMScheduler
 from .scheduling_ddpm_parallel import DDPMParallelScheduler
 from .scheduling_deis_multistep import DEISMultistepScheduler
 from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
 from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
 from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
 from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
 from .scheduling_euler_discrete import EulerDiscreteScheduler
 from .scheduling_heun_discrete import HeunDiscreteScheduler
 from .scheduling_ipndm import IPNDMScheduler
 from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
 from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
 from .scheduling_karras_ve import KarrasVeScheduler
 from .scheduling_pndm import PNDMScheduler
 from .scheduling_repaint import RePaintScheduler
 from .scheduling_sde_ve import ScoreSdeVeScheduler
 from .scheduling_sde_vp import ScoreSdeVpScheduler
 from .scheduling_unclip import UnCLIPScheduler
 from .scheduling_unipc_multistep import UniPCMultistepScheduler
 from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
 from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_flax_objects import *  # noqa F403
else:
 from .scheduling_ddim_flax import FlaxDDIMScheduler
 from .scheduling_ddpm_flax import FlaxDDPMScheduler
 from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
 from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
 from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
 from .scheduling_pndm_flax import FlaxPNDMScheduler
 from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
 from .scheduling_utils_flax import (
     FlaxKarrasDiffusionSchedulers,
     FlaxSchedulerMixin,
     FlaxSchedulerOutput,
     broadcast_to_shape_from_left,
 )


try:
 if not (is_torch_available() and is_scipy_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
 from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
 if not (is_torch_available() and is_torchsde_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
 from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler

