

'''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


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


set_seed(4_2)
UpperCAmelCase					:			str       =	'sshleifer/student_marian_en_ro_6_1'
UpperCAmelCase					:			int       =	'sshleifer/tiny-mbart'







@require_torch
class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 def  UpperCAmelCase__						(						self   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			List[str]=False   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=None   ,				__SCREAMING_SNAKE_CASE   :			List[str]=True   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=True   ,				__SCREAMING_SNAKE_CASE   :			List[str]=True   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=True   ,				)    ->					List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.run_trainer(
          eval_steps=1   ,				max_len=12   ,				model_name=__SCREAMING_SNAKE_CASE   ,				num_train_epochs=1   ,				distributed=__SCREAMING_SNAKE_CASE   ,				extra_args_str=__SCREAMING_SNAKE_CASE   ,				predict_with_generate=__SCREAMING_SNAKE_CASE   ,				do_train=__SCREAMING_SNAKE_CASE   ,				do_eval=__SCREAMING_SNAKE_CASE   ,				do_predict=__SCREAMING_SNAKE_CASE   ,				)
      __SCREAMING_SNAKE_CASE      = TrainerState.load_from_json(os.path.join(__SCREAMING_SNAKE_CASE   ,				"""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"""]   ,				__SCREAMING_SNAKE_CASE     )
           assert not math.isnan(float(last_step_stats["""eval_loss"""]     )     ), "eval_loss must not be `nan`"
 @require_torch_non_multi_gpu
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					Tuple:


      """simple docstring"""




      self.run_seqaseq_quick()
 @require_torch_multi_gpu
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Union[str, Any]:


      """simple docstring"""




      self.run_seqaseq_quick(distributed=__SCREAMING_SNAKE_CASE     )
 @require_torch_multi_gpu
 def  UpperCAmelCase__						(						self   :			Any     )    ->					Union[str, Any]:


      """simple docstring"""




      self.run_seqaseq_quick(distributed=__SCREAMING_SNAKE_CASE     )
 @unittest.skip("""Requires an update of the env running those tests"""     )
 @require_torch_multi_gpu
 @require_fairscale
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					Optional[Any]:


      """simple docstring"""




      self.run_seqaseq_quick(distributed=__SCREAMING_SNAKE_CASE   ,				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   :			str     )    ->					Union[str, Any]:


      """simple docstring"""




      self.run_seqaseq_quick(distributed=__SCREAMING_SNAKE_CASE   ,				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   :			List[str]     )    ->					Optional[Any]:


      """simple docstring"""




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


      """simple docstring"""




      self.run_seqaseq_quick(
          distributed=__SCREAMING_SNAKE_CASE   ,				extra_args_str="""--sharded_ddp zero_dp_2 --fp16"""   ,				predict_with_generate=__SCREAMING_SNAKE_CASE     )
 @require_apex
 @require_torch_gpu
 def  UpperCAmelCase__						(						self   :			str     )    ->					Dict:


      """simple docstring"""




      self.run_seqaseq_quick(distributed=__SCREAMING_SNAKE_CASE   ,				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=__SCREAMING_SNAKE_CASE   ,				extra_args_str="""--fp16 --fp16_backend=apex"""     )
 @parameterized.expand(["""base""", """low""", """high""", """mixed"""]     )
 @require_torch_multi_gpu
 def  UpperCAmelCase__						(						self   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]     )    ->					int:


      """simple docstring"""




      __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(**__SCREAMING_SNAKE_CASE   ,				extra_args_str=data["""extra_args_str"""]     )
      __SCREAMING_SNAKE_CASE      = len(re.findall(__SCREAMING_SNAKE_CASE   ,				cl.err     )     )
      self.assertEqual(__SCREAMING_SNAKE_CASE   ,				data["""n_matches"""]     )
 @slow
 def  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.run_trainer(
          eval_steps=2   ,				max_len=128   ,				model_name=__SCREAMING_SNAKE_CASE   ,				learning_rate=3E-4   ,				num_train_epochs=10   ,				distributed=__SCREAMING_SNAKE_CASE   ,				)

      # Check metrics
      __SCREAMING_SNAKE_CASE      = TrainerState.load_from_json(os.path.join(__SCREAMING_SNAKE_CASE   ,				"""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"""]   ,				__SCREAMING_SNAKE_CASE     )

      # test if do_predict saves generations and metrics
      __SCREAMING_SNAKE_CASE      = os.listdir(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = {os.path.basename(__SCREAMING_SNAKE_CASE     ) for p in contents}
      assert "generated_predictions.txt" in contents
      assert "predict_results.json" in contents
 @slow
 @require_bitsandbytes
 def  UpperCAmelCase__						(						self   :			int     )    ->					Tuple:


      """simple docstring"""




      from transformers.training_args import OptimizerNames

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

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

           # Check metrics
           __SCREAMING_SNAKE_CASE      = TrainerState.load_from_json(Path(__SCREAMING_SNAKE_CASE   ,				"""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(
          __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				"""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(
          __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				"""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(
          __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				f'loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'     )


 def  UpperCAmelCase__						(						self   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			int   ,				__SCREAMING_SNAKE_CASE   :			str   ,				__SCREAMING_SNAKE_CASE   :			int   ,				__SCREAMING_SNAKE_CASE   :			float = 3E-3   ,				__SCREAMING_SNAKE_CASE   :			str = "adafactor"   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			str = None   ,				__SCREAMING_SNAKE_CASE   :			int = 0   ,				__SCREAMING_SNAKE_CASE   :			bool = True   ,				__SCREAMING_SNAKE_CASE   :			bool = True   ,				__SCREAMING_SNAKE_CASE   :			bool = True   ,				__SCREAMING_SNAKE_CASE   :			bool = True   ,				__SCREAMING_SNAKE_CASE   :			int = None   ,				)    ->					Optional[Any]:


      """simple docstring"""




      __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(__SCREAMING_SNAKE_CASE     )}\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(__SCREAMING_SNAKE_CASE     )}\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(__SCREAMING_SNAKE_CASE     )}\n        '.split()

      __SCREAMING_SNAKE_CASE      = """
            --do_predict
        """.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(__SCREAMING_SNAKE_CASE   ,				env=self.get_env()     )
      else:
           __SCREAMING_SNAKE_CASE      = ["""run_translation.py"""] + args
           with patch.object(__SCREAMING_SNAKE_CASE   ,				"""argv"""   ,				__SCREAMING_SNAKE_CASE     ):
                main()

      return output_dir









'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase					:			Union[str, Any]       =	logging.get_logger(__name__)

UpperCAmelCase					:			Any       =	{
    'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = "mra"
 def __init__(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Dict=50_265   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=768   ,				__SCREAMING_SNAKE_CASE   :			int=12   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=12   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=3_072   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]="gelu"   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=512   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Any=0.02   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=1E-5   ,				__SCREAMING_SNAKE_CASE   :			str="absolute"   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=4   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]="full"   ,				__SCREAMING_SNAKE_CASE   :			str=0   ,				__SCREAMING_SNAKE_CASE   :			Dict=0   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Dict=0   ,				__SCREAMING_SNAKE_CASE   :			Dict=2   ,				**__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				)    ->					Tuple:


      """simple docstring"""




      super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE   ,				bos_token_id=__SCREAMING_SNAKE_CASE   ,				eos_token_id=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = vocab_size
      __SCREAMING_SNAKE_CASE      = max_position_embeddings
      __SCREAMING_SNAKE_CASE      = hidden_size
      __SCREAMING_SNAKE_CASE      = num_hidden_layers
      __SCREAMING_SNAKE_CASE      = num_attention_heads
      __SCREAMING_SNAKE_CASE      = intermediate_size
      __SCREAMING_SNAKE_CASE      = hidden_act
      __SCREAMING_SNAKE_CASE      = hidden_dropout_prob
      __SCREAMING_SNAKE_CASE      = attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE      = initializer_range
      __SCREAMING_SNAKE_CASE      = type_vocab_size
      __SCREAMING_SNAKE_CASE      = layer_norm_eps
      __SCREAMING_SNAKE_CASE      = position_embedding_type
      __SCREAMING_SNAKE_CASE      = block_per_row
      __SCREAMING_SNAKE_CASE      = approx_mode
      __SCREAMING_SNAKE_CASE      = initial_prior_first_n_blocks
      __SCREAMING_SNAKE_CASE      = initial_prior_diagonal_n_blocks






