


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


set_seed(42)
A      = "sshleifer/student_marian_en_ro_6_1"
A      = "sshleifer/tiny-mbart"


@require_torch
class     lowercase__  (					__UpperCAmelCase		):






  def 			_UpperCAmelCase  (					self      :					List[Any]		,						_lowercase      :					List[Any]=False		,						_lowercase      :					List[Any]=None		,						_lowercase      :					int=True		,						_lowercase      :					Optional[int]=True		,						_lowercase      :					Any=True		,						_lowercase      :					Any=True		,						):




         """simple docstring"""







         UpperCAmelCase__										=     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		,						)
         UpperCAmelCase__										=     TrainerState.load_from_json(os.path.join(_lowerCamelCase		,						"trainer_state.json" ) ).log_history

         if not do_eval:
                return

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

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

                UpperCAmelCase__										=     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 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         self.run_seqaseq_quick()






  @require_torch_multi_gpu
  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         self.run_seqaseq_quick(distributed=_lowerCamelCase )






  @require_torch_multi_gpu
  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         self.run_seqaseq_quick(distributed=_lowerCamelCase )






  @unittest.skip("Requires an update of the env running those tests" )
  @require_torch_multi_gpu
  @require_fairscale
  def 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         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 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         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 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         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 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         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 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         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 			_UpperCAmelCase  (					self      :					Union[str, Any]		,						_lowercase      :					Dict ):




         """simple docstring"""







         UpperCAmelCase__										=     {
             # 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},
         }

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






  @slow
  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         UpperCAmelCase__										=     self.run_trainer(
             eval_steps=2		,						max_len=1_28		,						model_name=_lowerCamelCase		,						learning_rate=3E-4		,						num_train_epochs=10		,						distributed=_lowerCamelCase		,						)

         # Check metrics
         UpperCAmelCase__										=     TrainerState.load_from_json(os.path.join(_lowerCamelCase		,						"trainer_state.json" ) ).log_history
         UpperCAmelCase__										=     [log for log in logs if "eval_loss" in log.keys()]
         UpperCAmelCase__										=     eval_metrics[0]
         UpperCAmelCase__										=     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
         UpperCAmelCase__										=     os.listdir(_lowerCamelCase )
         UpperCAmelCase__										=     {os.path.basename(_lowerCamelCase ) for p in contents}
         assert "generated_predictions.txt" in contents
         assert "predict_results.json" in contents






  @slow
  @require_bitsandbytes
  def 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         from transformers.training_args import OptimizerNames

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

                UpperCAmelCase__										=     self.run_trainer(
                    max_len=1_28		,						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
                UpperCAmelCase__										=     TrainerState.load_from_json(Path(_lowerCamelCase		,						"trainer_state.json" ) ).log_history
                UpperCAmelCase__										=     int(logs[0]["train_mem_gpu_peaked_delta"] / 2**20 )
                UpperCAmelCase__										=     int(logs[0]["train_mem_gpu_alloc_delta"] / 2**20 )

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

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

         UpperCAmelCase__										=     gpu_alloc_mem_orig - gpu_alloc_mem_bnb

         UpperCAmelCase__										=     gpu_peak_mem_orig + gpu_alloc_mem_orig
         UpperCAmelCase__										=     gpu_peak_mem_bnb + gpu_alloc_mem_bnb
         UpperCAmelCase__										=     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
         UpperCAmelCase__										=     1_20

         # 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 			_UpperCAmelCase  (					self      :					Union[str, Any]		,						_lowercase      :					int		,						_lowercase      :					str		,						_lowercase      :					int		,						_lowercase      :					float = 3E-3		,						_lowercase      :					str = "adafactor"		,						_lowercase      :					bool = False		,						_lowercase      :					str = None		,						_lowercase      :					int = 0		,						_lowercase      :					bool = True		,						_lowercase      :					bool = True		,						_lowercase      :					bool = True		,						_lowercase      :					bool = True		,						_lowercase      :					int = None		,						):




         """simple docstring"""







         UpperCAmelCase__										=     self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
         UpperCAmelCase__										=     self.get_auto_remove_tmp_dir()
         UpperCAmelCase__										=     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()

         UpperCAmelCase__										=     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()

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

         UpperCAmelCase__										=     []
         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:
                       UpperCAmelCase__										=     get_gpu_count()
                UpperCAmelCase__										=     get_torch_dist_unique_port()
                UpperCAmelCase__										=     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()
                UpperCAmelCase__										=     [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:
                UpperCAmelCase__										=     ["run_translation.py"] + args
                with patch.object(_lowerCamelCase		,						"argv"		,						_lowerCamelCase ):
                       main()

         return output_dir









import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


A      = logging.get_logger(__name__)


class     lowercase__  (					__SCREAMING_SNAKE_CASE		):






  def __init__(					self      :					Union[str, Any]		,						*_lowercase      :					Any		,						**_lowercase      :					Optional[int] ):




         """simple docstring"""







         warnings.warn(
             "The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
             " use DPTImageProcessor instead."		,						_lowercase		,						)
         super().__init__(*_lowercase		,						**_lowercase )





