
'''simple docstring'''



import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_="attention"						)     ->     Tuple:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				params[F"{prefix}/layers_{i}/{layer_name}/key/kernel"]
   _SCREAMING_SNAKE_CASE      =				params[F"{prefix}/layers_{i}/{layer_name}/out/kernel"]
   _SCREAMING_SNAKE_CASE      =				params[F"{prefix}/layers_{i}/{layer_name}/query/kernel"]
   _SCREAMING_SNAKE_CASE      =				params[F"{prefix}/layers_{i}/{layer_name}/value/kernel"]
   return k, o, q, v
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=False						)     ->     Dict:




   """simple docstring"""
   if split_mlp_wi:
      _SCREAMING_SNAKE_CASE      =				params[F"{prefix}/layers_{i}/mlp/wi_0/kernel"]
      _SCREAMING_SNAKE_CASE      =				params[F"{prefix}/layers_{i}/mlp/wi_1/kernel"]
      _SCREAMING_SNAKE_CASE      =				(wi_a, wi_a)
   else:
      _SCREAMING_SNAKE_CASE      =				params[F"{prefix}/layers_{i}/mlp/wi/kernel"]

   _SCREAMING_SNAKE_CASE      =				params[F"{prefix}/layers_{i}/mlp/wo/kernel"]
   return wi, wo
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   return params[F"{prefix}/layers_{i}/{layer_name}/scale"]
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      *, SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				traverse_util.flatten_dict(variables["""target"""]						)
   _SCREAMING_SNAKE_CASE      =				{"""/""".join(SCREAMING_SNAKE_CASE_						): v for k, v in old.items()}

   # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
   _SCREAMING_SNAKE_CASE      =				"""encoder/layers_0/mlp/wi_0/kernel""" in old
   print("""Split MLP:"""    ,      SCREAMING_SNAKE_CASE_						)

   _SCREAMING_SNAKE_CASE      =				collections.OrderedDict()

   # Shared embeddings.
   _SCREAMING_SNAKE_CASE      =				old["""token_embedder/embedding"""]

   # Encoder.
   for i in range(SCREAMING_SNAKE_CASE_						):
      # Block i, layer 0 (Self Attention).
      _SCREAMING_SNAKE_CASE      =				tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      """encoder"""    ,      """pre_attention_layer_norm"""						)
      _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				tax_attention_lookup(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      """encoder"""    ,      """attention"""						)
      _SCREAMING_SNAKE_CASE      =				layer_norm
      _SCREAMING_SNAKE_CASE      =				k.T
      _SCREAMING_SNAKE_CASE      =				o.T
      _SCREAMING_SNAKE_CASE      =				q.T
      _SCREAMING_SNAKE_CASE      =				v.T

      # Block i, layer 1 (MLP).
      _SCREAMING_SNAKE_CASE      =				tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      """encoder"""    ,      """pre_mlp_layer_norm"""						)
      _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				tax_mlp_lookup(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      """encoder"""    ,      SCREAMING_SNAKE_CASE_						)
      _SCREAMING_SNAKE_CASE      =				layer_norm
      if split_mlp_wi:
         _SCREAMING_SNAKE_CASE      =				wi[0].T
         _SCREAMING_SNAKE_CASE      =				wi[1].T
      else:
         _SCREAMING_SNAKE_CASE      =				wi.T
      _SCREAMING_SNAKE_CASE      =				wo.T

   _SCREAMING_SNAKE_CASE      =				old[
       """encoder/relpos_bias/rel_embedding"""
   ].T
   _SCREAMING_SNAKE_CASE      =				old["""encoder/encoder_norm/scale"""]

   if not is_encoder_only:
      # Decoder.
      for i in range(SCREAMING_SNAKE_CASE_						):
         # Block i, layer 0 (Self Attention).
         _SCREAMING_SNAKE_CASE      =				tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      """decoder"""    ,      """pre_self_attention_layer_norm"""						)
         _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				tax_attention_lookup(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      """decoder"""    ,      """self_attention"""						)
         _SCREAMING_SNAKE_CASE      =				layer_norm
         _SCREAMING_SNAKE_CASE      =				k.T
         _SCREAMING_SNAKE_CASE      =				o.T
         _SCREAMING_SNAKE_CASE      =				q.T
         _SCREAMING_SNAKE_CASE      =				v.T

         # Block i, layer 1 (Cross Attention).
         _SCREAMING_SNAKE_CASE      =				tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      """decoder"""    ,      """pre_cross_attention_layer_norm"""						)
         _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				tax_attention_lookup(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      """decoder"""    ,      """encoder_decoder_attention"""						)
         _SCREAMING_SNAKE_CASE      =				layer_norm
         _SCREAMING_SNAKE_CASE      =				k.T
         _SCREAMING_SNAKE_CASE      =				o.T
         _SCREAMING_SNAKE_CASE      =				q.T
         _SCREAMING_SNAKE_CASE      =				v.T

         # Block i, layer 2 (MLP).
         _SCREAMING_SNAKE_CASE      =				tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      """decoder"""    ,      """pre_mlp_layer_norm"""						)
         _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				tax_mlp_lookup(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      """decoder"""    ,      SCREAMING_SNAKE_CASE_						)
         _SCREAMING_SNAKE_CASE      =				layer_norm
         if split_mlp_wi:
            _SCREAMING_SNAKE_CASE      =				wi[0].T
            _SCREAMING_SNAKE_CASE      =				wi[1].T
         else:
            _SCREAMING_SNAKE_CASE      =				wi.T
         _SCREAMING_SNAKE_CASE      =				wo.T

      _SCREAMING_SNAKE_CASE      =				old["""decoder/decoder_norm/scale"""]
      _SCREAMING_SNAKE_CASE      =				old[
          """decoder/relpos_bias/rel_embedding"""
      ].T

      # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
      if "decoder/logits_dense/kernel" in old:
         _SCREAMING_SNAKE_CASE      =				old["""decoder/logits_dense/kernel"""].T

   return new
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Dict:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				collections.OrderedDict([(k, torch.from_numpy(v.copy()						)) for (k, v) in converted_params.items()]						)

   # Add what is missing.
   if "encoder.embed_tokens.weight" not in state_dict:
      _SCREAMING_SNAKE_CASE      =				state_dict["""shared.weight"""]

   if not is_encoder_only:
      if "decoder.embed_tokens.weight" not in state_dict:
         _SCREAMING_SNAKE_CASE      =				state_dict["""shared.weight"""]

      if "lm_head.weight" not in state_dict:  # For old 1.0 models.
         print("""Using shared word embeddings as lm_head."""						)
         _SCREAMING_SNAKE_CASE      =				state_dict["""shared.weight"""]

   return state_dict
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Dict:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				convert_tax_to_pytorch(SCREAMING_SNAKE_CASE_    ,      num_layers=config.num_layers    ,      is_encoder_only=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				make_state_dict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   model.load_state_dict(SCREAMING_SNAKE_CASE_    ,      strict=SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = False						)     ->     Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				TaConfig.from_json_file(SCREAMING_SNAKE_CASE_						)
   print(F"Building PyTorch model from configuration: {config}"						)
   # Non-v1.1 checkpoints could also use T5Model, but this works for all.
   # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
   if is_encoder_only:
      _SCREAMING_SNAKE_CASE      =				TaEncoderModel(SCREAMING_SNAKE_CASE_						)
   else:
      _SCREAMING_SNAKE_CASE      =				TaForConditionalGeneration(SCREAMING_SNAKE_CASE_						)

   # Load weights from tf checkpoint
   load_tax_weights_in_ta(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)

   # Save pytorch-model
   print(F"Save PyTorch model to {pytorch_dump_path}"						)
   model.save_pretrained(SCREAMING_SNAKE_CASE_						)

   # Verify that we can load the checkpoint.
   model.from_pretrained(SCREAMING_SNAKE_CASE_						)
   print("""Done"""						)


if __name__ == "__main__":
     UpperCamelCase__      :		Optional[Any]       							=  argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
     # Required parameters
     parser.add_argument(
         "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
     )
     parser.add_argument(
         "--config_file",
         default=None,
         type=str,
         required=True,
         help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
     )
     parser.add_argument(
         "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
     )
     parser.add_argument(
         "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
     )
     UpperCamelCase__      :		int       							=  parser.parse_args()
     convert_tax_checkpoint_to_pytorch(
         args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
     )








'''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(42)
UpperCamelCase__      :		Union[str, Any]       							=  "sshleifer/student_marian_en_ro_6_1"
UpperCamelCase__      :		str       							=  "sshleifer/tiny-mbart"

@require_torch
class    _a				(_lowerCamelCase):





       """simple docstring"""




       def        UpperCamelCase				(							self       ,							A__=False       ,							A__=None       ,							A__=True       ,							A__=True       ,							A__=True       ,							A__=True       ,							)    ->			Optional[Any]:
          _SCREAMING_SNAKE_CASE      =				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__       ,							)
          _SCREAMING_SNAKE_CASE      =				TrainerState.load_from_json(os.path.join(A__       ,							"""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"""]       ,							A__      )
             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]:
          self.run_seqaseq_quick()






       @require_torch_multi_gpu
       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          self.run_seqaseq_quick(distributed=A__      )






       @require_torch_multi_gpu
       def        UpperCamelCase				(							self      )    ->			Union[str, 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        UpperCamelCase				(							self      )    ->			Any:
          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        UpperCamelCase				(							self      )    ->			Tuple:
          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        UpperCamelCase				(							self      )    ->			str:
          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        UpperCamelCase				(							self      )    ->			List[str]:
          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        UpperCamelCase				(							self      )    ->			Optional[Any]:
          # 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        UpperCamelCase				(							self       ,							A__      )    ->			List[Any]:
          # 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(**A__       ,							extra_args_str=data["""extra_args_str"""]      )
          _SCREAMING_SNAKE_CASE      =				len(re.findall(A__       ,							cl.err      )      )
          self.assertEqual(A__       ,							data["""n_matches"""]      )






       @slow
       def        UpperCamelCase				(							self      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				self.run_trainer(
              eval_steps=2       ,							max_len=1_28       ,							model_name=A__       ,							learning_rate=3E-4       ,							num_train_epochs=10       ,							distributed=A__       ,							)

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

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






       @slow
       @require_bitsandbytes
       def        UpperCamelCase				(							self      )    ->			Dict:
          from transformers.training_args import OptimizerNames

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

             _SCREAMING_SNAKE_CASE      =				self.run_trainer(
                 max_len=1_28       ,							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
             _SCREAMING_SNAKE_CASE      =				TrainerState.load_from_json(Path(A__       ,							"""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      =				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(
              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        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__       ,							A__ = 3E-3       ,							A__ = "adafactor"       ,							A__ = False       ,							A__ = None       ,							A__ = 0       ,							A__ = True       ,							A__ = True       ,							A__ = True       ,							A__ = True       ,							A__ = None       ,							)    ->			Dict:
          _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(A__      )}\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(A__      )}\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(A__      )}\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(A__       ,							env=self.get_env()      )
          else:
             _SCREAMING_SNAKE_CASE      =				["""run_translation.py"""] + args
             with patch.object(A__       ,							"""argv"""       ,							A__      ):
                main()

          return output_dir






