





"""simple docstring"""


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


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


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



@require_torch
class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  def  __SCREAMING_SNAKE_CASE					(		self :							Any       , __a :							Tuple=False       , __a :							Union[str, Any]=None       , __a :							List[Any]=True       , __a :							List[Any]=True       , __a :							Optional[Any]=True       , __a :							str=True       , )		->					List[Any]:
        _UpperCamelCase   :						int    =							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       , )
        _UpperCamelCase   :						Any    =							TrainerState.load_from_json(os.path.join(__a       , "trainer_state.json"						)						).log_history

        if not do_eval:
              return

        _UpperCamelCase   :						Union[str, Any]    =							[log for log in logs if "eval_loss" in log.keys()]

        _UpperCamelCase   :						List[str]    =							eval_metrics[0]
        if predict_with_generate:
              assert "eval_bleu" in first_step_stats

              _UpperCamelCase   :						Tuple    =							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  __SCREAMING_SNAKE_CASE					(		self :							Any						)		->					Dict:
        self.run_seqaseq_quick()

  @require_torch_multi_gpu
  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple						)		->					Optional[int]:
        self.run_seqaseq_quick(distributed=__a						)

  @require_torch_multi_gpu
  def  __SCREAMING_SNAKE_CASE					(		self :							int						)		->					Optional[Any]:
        self.run_seqaseq_quick(distributed=__a						)

  @unittest.skip("Requires an update of the env running those tests"						)
  @require_torch_multi_gpu
  @require_fairscale
  def  __SCREAMING_SNAKE_CASE					(		self :							int						)		->					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  __SCREAMING_SNAKE_CASE					(		self :							Dict						)		->					str:
        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  __SCREAMING_SNAKE_CASE					(		self :							Tuple						)		->					Any:
        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  __SCREAMING_SNAKE_CASE					(		self :							int						)		->					List[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  __SCREAMING_SNAKE_CASE					(		self :							Any						)		->					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  __SCREAMING_SNAKE_CASE					(		self :							str       , __a :							Union[str, Any]						)		->					Union[str, Any]:
        # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
        _UpperCamelCase   :						int    =							{
            # 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   :						int    =							experiments[experiment_id]
        _UpperCamelCase   :						List[Any]    =							{"distributed": True, "predict_with_generate": False, "do_eval": False, "do_predict": False}
        _UpperCamelCase   :						Any    =							"Running training"
        with CaptureStderr() as cl:
              self.run_seqaseq_quick(**__a       , extra_args_str=data["extra_args_str"]						)
        _UpperCamelCase   :						List[str]    =							len(re.findall(__a       , cl.err						)						)
        self.assertEqual(__a       , data["n_matches"]						)

  @slow
  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]						)		->					Dict:
        _UpperCamelCase   :						Any    =							self.run_trainer(
            eval_steps=2       , max_len=128       , model_name=__a       , learning_rate=3e-4       , num_train_epochs=10       , distributed=__a       , )

        # Check metrics
        _UpperCamelCase   :						Dict    =							TrainerState.load_from_json(os.path.join(__a       , "trainer_state.json"						)						).log_history
        _UpperCamelCase   :						Union[str, Any]    =							[log for log in logs if "eval_loss" in log.keys()]
        _UpperCamelCase   :						int    =							eval_metrics[0]
        _UpperCamelCase   :						str    =							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
        _UpperCamelCase   :						Optional[int]    =							os.listdir(__a						)
        _UpperCamelCase   :						Dict    =							{os.path.basename(__a						) for p in contents}
        assert "generated_predictions.txt" in contents
        assert "predict_results.json" in contents

  @slow
  @require_bitsandbytes
  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Any:
        from transformers.training_args import OptimizerNames

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

              _UpperCamelCase   :						Optional[int]    =							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
              _UpperCamelCase   :						Any    =							TrainerState.load_from_json(Path(__a       , "trainer_state.json"						)						).log_history
              _UpperCamelCase   :						str    =							int(logs[0]["train_mem_gpu_peaked_delta"] / 2**20						)
              _UpperCamelCase   :						List[Any]    =							int(logs[0]["train_mem_gpu_alloc_delta"] / 2**20						)

              _UpperCamelCase   :						int    =							logs[0]["train_loss"]
              return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

        _UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase   :						Union[str, Any]    =							train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value						)
        _UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase   :						Optional[Any]    =							train_and_return_metrics(OptimizerNames.ADAMW_BNB.value						)

        _UpperCamelCase   :						Optional[Any]    =							gpu_alloc_mem_orig - gpu_alloc_mem_bnb

        _UpperCamelCase   :						List[Any]    =							gpu_peak_mem_orig + gpu_alloc_mem_orig
        _UpperCamelCase   :						List[Any]    =							gpu_peak_mem_bnb + gpu_alloc_mem_bnb
        _UpperCamelCase   :						Optional[int]    =							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   :						List[str]    =							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  __SCREAMING_SNAKE_CASE					(		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       , )		->					List[str]:
        _UpperCamelCase   :						List[str]    =							self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
        _UpperCamelCase   :						Dict    =							self.get_auto_remove_tmp_dir()
        _UpperCamelCase   :						Union[str, Any]    =							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()

        _UpperCamelCase   :						Dict    =							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()

        _UpperCamelCase   :						Any    =							"\n            --do_predict\n        ".split()

        _UpperCamelCase   :						Union[str, 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:
                    _UpperCamelCase   :						Union[str, Any]    =							get_gpu_count()
              _UpperCamelCase   :						List[str]    =							get_torch_dist_unique_port()
              _UpperCamelCase   :						Union[str, Any]    =							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()
              _UpperCamelCase   :						Any    =							[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:
              _UpperCamelCase   :						Any    =							["run_translation.py"] + args
              with patch.object(__a       , "argv"       , __a						):
                    main()

        return output_dir







"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def        lowercase__     (       lowercase_      )       ->		Tuple:




      """simple docstring"""

      _UpperCamelCase   :						Optional[int]    =							[
          "encoder.version",
          "decoder.version",
          "model.encoder.version",
          "model.decoder.version",
          "_float_tensor",
          "decoder.output_projection.weight",
      ]
      for k in ignore_keys:
            state_dict.pop(lowercase_		,lowercase_      )




def        lowercase__     (       lowercase_      )       ->		List[Any]:




      """simple docstring"""

      _UpperCamelCase,	_UpperCamelCase   :						Union[str, Any]    =							emb.weight.shape
      _UpperCamelCase   :						str    =							nn.Linear(lowercase_		,lowercase_		,bias=lowercase_      )
      _UpperCamelCase   :						List[Any]    =							emb.weight.data
      return lin_layer




def        lowercase__     (       lowercase_		,lowercase_="facebook/mbart-large-en-ro"		,lowercase_=False		,lowercase_=False      )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						int    =							torch.load(lowercase_		,map_location="cpu"      )["model"]
      remove_ignore_keys_(lowercase_      )
      _UpperCamelCase   :						str    =							state_dict["encoder.embed_tokens.weight"].shape[0]

      _UpperCamelCase   :						int    =							MBartConfig.from_pretrained(lowercase_		,vocab_size=lowercase_      )
      if mbart_aa and finetuned:
            _UpperCamelCase   :						Union[str, Any]    =							"relu"

      _UpperCamelCase   :						Optional[int]    =							state_dict["decoder.embed_tokens.weight"]
      _UpperCamelCase   :						List[str]    =							MBartForConditionalGeneration(lowercase_      )
      model.model.load_state_dict(lowercase_      )

      if finetuned:
            _UpperCamelCase   :						Optional[Any]    =							make_linear_from_emb(model.model.shared      )

      return model


if __name__ == "__main__":
  lowerCamelCase__						= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "fairseq_path", type=str, help="bart.large, bart.large.cnn or a path to a model.pt on local filesystem."
  )
  parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument(
      "--hf_config",
      default="facebook/mbart-large-cc25",
      type=str,
      help="Which huggingface architecture to use: mbart-large",
  )
  parser.add_argument("--mbart_50", action="store_true", help="whether the model is mMART-50 checkpoint")
  parser.add_argument("--finetuned", action="store_true", help="whether the model is a fine-tuned checkpoint")
  lowerCamelCase__						= parser.parse_args()
  lowerCamelCase__						= convert_fairseq_mbart_checkpoint_from_disk(
      args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
  )
  model.save_pretrained(args.pytorch_dump_folder_path)
