





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_					:   Dict 							=						os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F"""{bindir}/../../examples/pytorch/translation"""):
      from run_translation import main  # noqa


set_seed(42)
UpperCAmelCase_					:   Dict 							=						"""sshleifer/student_marian_en_ro_6_1"""
UpperCAmelCase_					:   int 							=						"""sshleifer/tiny-mbart"""





@require_torch
class      lowercase__    (							__A  ):







  def 	UpperCAmelCase__		(	self    ,						_lowercase=False    ,						_lowercase=None    ,						_lowercase=True    ,						_lowercase=True    ,						_lowercase=True    ,						_lowercase=True    ,						):
       lowerCAmelCase_					:       int    =		self.run_trainer(
           eval_steps=1    ,						max_len=12    ,						model_name=_lowercase    ,						num_train_epochs=1    ,						distributed=_lowercase    ,						extra_args_str=_lowercase    ,						predict_with_generate=_lowercase    ,						do_train=_lowercase    ,						do_eval=_lowercase    ,						do_predict=_lowercase    ,						)
       lowerCAmelCase_					:       str    =		TrainerState.load_from_json(os.path.join(_lowercase    ,						"""trainer_state.json"""						)						).log_history

       if not do_eval:
            return

       lowerCAmelCase_					:       Optional[Any]    =		[log for log in logs if """eval_loss""" in log.keys()]

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

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







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







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







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







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







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







  @require_apex
  @require_torch_gpu
  def 	UpperCAmelCase__		(	self						):
       # 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=_lowercase    ,						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=_lowercase    ,						extra_args_str="""--fp16 --fp16_backend=apex"""						)







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

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







  @slow
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Tuple    =		self.run_trainer(
           eval_steps=2    ,						max_len=128    ,						model_name=_lowercase    ,						learning_rate=3e-4    ,						num_train_epochs=10    ,						distributed=_lowercase    ,						)

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

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

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







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

       def train_and_return_metrics(_lowercase						) -> Tuple[int, float]:
            lowerCAmelCase_					:       Any    =		"""--skip_memory_metrics 0"""

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

            # Check metrics
            lowerCAmelCase_					:       Tuple    =		TrainerState.load_from_json(Path(_lowercase    ,						"""trainer_state.json"""						)						).log_history
            lowerCAmelCase_					:       Dict    =		int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20						)
            lowerCAmelCase_					:       Dict    =		int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20						)

            lowerCAmelCase_					:       Optional[Any]    =		logs[0]["""train_loss"""]
            return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

       lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_					:       Any    =		train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value						)
       lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_					:       Any    =		train_and_return_metrics(OptimizerNames.ADAMW_BNB.value						)

       lowerCAmelCase_					:       str    =		gpu_alloc_mem_orig - gpu_alloc_mem_bnb

       lowerCAmelCase_					:       int    =		gpu_peak_mem_orig + gpu_alloc_mem_orig
       lowerCAmelCase_					:       Union[str, Any]    =		gpu_peak_mem_bnb + gpu_alloc_mem_bnb
       lowerCAmelCase_					:       Dict    =		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
       lowerCAmelCase_					:       Dict    =		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(
           _lowercase    ,						_lowercase    ,						"""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(
           _lowercase    ,						_lowercase    ,						"""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(
           _lowercase    ,						_lowercase    ,						F'loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase = 3e-3    ,						_lowercase = "adafactor"    ,						_lowercase = False    ,						_lowercase = None    ,						_lowercase = 0    ,						_lowercase = True    ,						_lowercase = True    ,						_lowercase = True    ,						_lowercase = True    ,						_lowercase = None    ,						):
       lowerCAmelCase_					:       Tuple    =		self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
       lowerCAmelCase_					:       Dict    =		self.get_auto_remove_tmp_dir()
       lowerCAmelCase_					:       int    =		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(_lowercase						)}\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(_lowercase						)}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        '.split()

       lowerCAmelCase_					:       List[Any]    =		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(_lowercase						)}\n        '.split()

       lowerCAmelCase_					:       Union[str, Any]    =		"""
            --do_predict
        """.split()

       lowerCAmelCase_					:       List[str]    =		[]
       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:
                 lowerCAmelCase_					:       Optional[int]    =		get_gpu_count()
            lowerCAmelCase_					:       Tuple    =		get_torch_dist_unique_port()
            lowerCAmelCase_					:       int    =		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()
            lowerCAmelCase_					:       Optional[Any]    =		[sys.executable] + distributed_args + args
            # keep for quick debug
            # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
            execute_subprocess_async(_lowercase    ,						env=self.get_env()						)
       else:
            lowerCAmelCase_					:       int    =		["""run_translation.py"""] + args
            with patch.object(_lowercase    ,						"""argv"""    ,						_lowercase						):
                 main()

       return output_dir







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase_					:   Dict 							=						{
    """configuration_poolformer""": [
        """POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """PoolFormerConfig""",
        """PoolFormerOnnxConfig""",
    ]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   Union[str, Any] 							=						["""PoolFormerFeatureExtractor"""]
      UpperCAmelCase_					:   str 							=						["""PoolFormerImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   List[str] 							=						[
          """POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """PoolFormerForImageClassification""",
          """PoolFormerModel""",
          """PoolFormerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_poolformer import (
          POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          PoolFormerConfig,
          PoolFormerOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_poolformer import PoolFormerFeatureExtractor
            from .image_processing_poolformer import PoolFormerImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_poolformer import (
                POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                PoolFormerForImageClassification,
                PoolFormerModel,
                PoolFormerPreTrainedModel,
            )


else:
      import sys

      UpperCAmelCase_					:   Optional[Any] 							=						_LazyModule(__name__, globals()["""__file__"""], _import_structure)
