


import logging
import os
from typing import Dict, List, Optional, Union

import torch
import torch.nn as nn

from accelerate.utils.imports import (
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_bnb_available,
)

from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
    find_tied_parameters,
    get_balanced_memory,
    infer_auto_device_map,
    load_checkpoint_in_model,
    offload_weight,
    set_module_tensor_to_device,
)


if is_bnb_available():
 import bitsandbytes as bnb

from copy import deepcopy


__a     =			logging.getLogger(__name__)
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = False  ,						)						->Optional[int]:
   UpperCAmelCase							=     bnb_quantization_config.load_in_abit
   UpperCAmelCase							=     bnb_quantization_config.load_in_abit

   if load_in_abit and not is_abit_bnb_available():
      raise ImportError(
          """You have a version of `bitsandbytes` that is not compatible with 8bit quantization,"""
          """ make sure you have the latest version of `bitsandbytes` installed."""   )
   if load_in_abit and not is_abit_bnb_available():
      raise ValueError(
          """You have a version of `bitsandbytes` that is not compatible with 4bit quantization,"""
          """make sure you have the latest version of `bitsandbytes` installed."""   )

   UpperCAmelCase							=     []
   # custom device map
   if isinstance(lowerCAmelCase_  ,						lowerCAmelCase_   ) and len(device_map.keys()   ) > 1:
      UpperCAmelCase							=     [key for key, value in device_map.items() if value in ["""disk""", """cpu"""]]

   # We keep some modules such as the lm_head in their original dtype for numerical stability reasons
   if bnb_quantization_config.skip_modules is None:
      UpperCAmelCase							=     get_keys_to_not_convert(lowerCAmelCase_   )

   # add cpu modules to skip modules only for 4-bit modules
   if load_in_abit:
      bnb_quantization_config.skip_modules.extend(lowerCAmelCase_   )
   UpperCAmelCase							=     bnb_quantization_config.skip_modules

   # We add the modules we want to keep in full precision
   if bnb_quantization_config.keep_in_fpaa_modules is None:
      UpperCAmelCase							=     []
   UpperCAmelCase							=     bnb_quantization_config.keep_in_fpaa_modules
   modules_to_not_convert.extend(lowerCAmelCase_   )

   # compatibility with peft
   UpperCAmelCase							=     load_in_abit
   UpperCAmelCase							=     load_in_abit

   UpperCAmelCase							=     get_parameter_device(lowerCAmelCase_   )
   if model_device.type != "meta":
      # quantization of an already loaded model
      logger.warning(
          """It is not recommended to quantize a loaded model. """
          """The model should be instantiated under the `init_empty_weights` context manager."""   )
      UpperCAmelCase							=     replace_with_bnb_layers(lowerCAmelCase_  ,						lowerCAmelCase_  ,						modules_to_not_convert=lowerCAmelCase_   )
      # convert param to the right dtype
      UpperCAmelCase							=     bnb_quantization_config.torch_dtype
      for name, param in model.state_dict().items():
         if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules   ):
            param.to(torch.floataa   )
            if param.dtype != torch.floataa:
               UpperCAmelCase							=     name.replace(""".weight"""  ,						""""""   ).replace(""".bias"""  ,						""""""   )
               UpperCAmelCase							=     getattr(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )
               if param is not None:
                  param.to(torch.floataa   )
         elif torch.is_floating_point(lowerCAmelCase_   ):
            param.to(lowerCAmelCase_   )
      if model_device.type == "cuda":
         # move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
         model.cuda(torch.cuda.current_device()   )
         torch.cuda.empty_cache()
      elif torch.cuda.is_available():
         model.to(torch.cuda.current_device()   )
      else:
         raise RuntimeError("""No GPU found. A GPU is needed for quantization."""   )
      logger.info(
          F"""The model device type is {model_device.type}. However, cuda is needed for quantization."""
          """We move the model to cuda."""   )
      return model

   elif weights_location is None:
      raise RuntimeError(
          F"""`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} """   )

   else:
      with init_empty_weights():
         UpperCAmelCase							=     replace_with_bnb_layers(
             lowerCAmelCase_  ,						lowerCAmelCase_  ,						modules_to_not_convert=lowerCAmelCase_   )

      UpperCAmelCase							=     get_quantized_model_device_map(
          lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						max_memory=lowerCAmelCase_  ,						no_split_module_classes=lowerCAmelCase_  ,						)
      if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
         UpperCAmelCase							=     True

      UpperCAmelCase							=     any(x in list(device_map.values()   ) for x in ["""cpu""", """disk"""]   )

      load_checkpoint_in_model(
          lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						dtype=bnb_quantization_config.torch_dtype  ,						offload_folder=lowerCAmelCase_  ,						offload_state_dict=lowerCAmelCase_  ,						keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules  ,						offload_abit_bnb=load_in_abit and offload  ,						)
      return dispatch_model(lowerCAmelCase_  ,						device_map=lowerCAmelCase_  ,						offload_dir=lowerCAmelCase_   )
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None   )						->Tuple:
   if device_map is None:
      if torch.cuda.is_available():
         UpperCAmelCase							=     {"""""": torch.cuda.current_device()}
      else:
         raise RuntimeError("""No GPU found. A GPU is needed for quantization."""   )
      logger.info("""The device_map was not initialized.""" """Setting device_map to `{'':torch.cuda.current_device()}`."""   )

   if isinstance(lowerCAmelCase_  ,						lowerCAmelCase_   ):
      if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
         raise ValueError(
             """If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or """
             """'sequential'."""   )

      UpperCAmelCase							=     {}
      special_dtypes.update(
          {
              name: bnb_quantization_config.torch_dtype
              for name, _ in model.named_parameters()
              if any(m in name for m in bnb_quantization_config.skip_modules   )
          }   )
      special_dtypes.update(
          {
              name: torch.floataa
              for name, _ in model.named_parameters()
              if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules   )
          }   )

      UpperCAmelCase							=     {}
      UpperCAmelCase							=     special_dtypes
      UpperCAmelCase							=     no_split_module_classes
      UpperCAmelCase							=     bnb_quantization_config.target_dtype

      # get max_memory for each device.
      if device_map != "sequential":
         UpperCAmelCase							=     get_balanced_memory(
             lowerCAmelCase_  ,						low_zero=(device_map == """balanced_low_0""")  ,						max_memory=lowerCAmelCase_  ,						**lowerCAmelCase_  ,						)

      UpperCAmelCase							=     max_memory
      UpperCAmelCase							=     infer_auto_device_map(lowerCAmelCase_  ,						**lowerCAmelCase_   )

   if isinstance(lowerCAmelCase_  ,						lowerCAmelCase_   ):
      # check if don't have any quantized module on the cpu
      UpperCAmelCase							=     bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules

      UpperCAmelCase							=     {
          key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
      }
      for device in ["cpu", "disk"]:
         if device in device_map_without_some_modules.values():
            if bnb_quantization_config.load_in_abit:
               raise ValueError(
                   """
                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit
                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping
                        these modules in `torch_dtype`, you need to pass a custom `device_map` to
                        `load_and_quantize_model`. Check
                        https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk
                        for more details.
                        """   )
            else:
               logger.info(
                   """Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit"""   )
      del device_map_without_some_modules
   return device_map
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None   )						->Optional[int]:

   if modules_to_not_convert is None:
      UpperCAmelCase							=     []

   UpperCAmelCase				,  UpperCAmelCase							=     _replace_with_bnb_layers(
       lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )
   if not has_been_replaced:
      logger.warning(
          """You are loading your model in 8bit or 4bit but no linear modules were found in your model."""
          """ this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers."""
          """ Please double check your model architecture, or submit an issue on github if you think this is"""
          """ a bug."""   )
   return model
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						)						->Optional[int]:
   UpperCAmelCase							=     False
   for name, module in model.named_children():
      if current_key_name is None:
         UpperCAmelCase							=     []
      current_key_name.append(lowerCAmelCase_   )
      if isinstance(lowerCAmelCase_  ,						nn.Linear   ) and name not in modules_to_not_convert:
         # Check if the current key is not in the `modules_to_not_convert`
         UpperCAmelCase							=     """.""".join(lowerCAmelCase_   )
         UpperCAmelCase							=     True
         for key in modules_to_not_convert:
            if (
                (key in current_key_name_str) and (key + "." in current_key_name_str)
            ) or key == current_key_name_str:
               UpperCAmelCase							=     False
               break
         if proceed:
            # Load bnb module with empty weight and replace ``nn.Linear` module
            if bnb_quantization_config.load_in_abit:
               UpperCAmelCase							=     bnb.nn.LinearabitLt(
                   module.in_features  ,						module.out_features  ,						module.bias is not None  ,						has_fpaa_weights=lowerCAmelCase_  ,						threshold=bnb_quantization_config.llm_inta_threshold  ,						)
            elif bnb_quantization_config.load_in_abit:
               UpperCAmelCase							=     bnb.nn.Linearabit(
                   module.in_features  ,						module.out_features  ,						module.bias is not None  ,						bnb_quantization_config.bnb_abit_compute_dtype  ,						compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant  ,						quant_type=bnb_quantization_config.bnb_abit_quant_type  ,						)
            else:
               raise ValueError("""load_in_8bit and load_in_4bit can't be both False"""   )
            UpperCAmelCase							=     module.weight.data
            if module.bias is not None:
               UpperCAmelCase							=     module.bias.data
            bnb_module.requires_grad_(lowerCAmelCase_   )
            setattr(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )
            UpperCAmelCase							=     True
      if len(list(module.children()   )   ) > 0:
         UpperCAmelCase				,  UpperCAmelCase							=     _replace_with_bnb_layers(
             lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )
         UpperCAmelCase							=     has_been_replaced | _has_been_replaced
      # Remove the last key for recursion
      current_key_name.pop(-1   )
   return model, has_been_replaced
def   _UpperCamelCase				(						lowerCAmelCase_   )						->Optional[Any]:
   # Create a copy of the model
   with init_empty_weights():
      UpperCAmelCase							=     deepcopy(lowerCAmelCase_   )  # this has 0 cost since it is done inside `init_empty_weights` context manager`

   UpperCAmelCase							=     find_tied_parameters(lowerCAmelCase_   )
   # For compatibility with Accelerate < 0.18
   if isinstance(lowerCAmelCase_  ,						lowerCAmelCase_   ):
      UpperCAmelCase							=     sum(list(tied_params.values()   )  ,						[]   ) + list(tied_params.keys()   )
   else:
      UpperCAmelCase							=     sum(lowerCAmelCase_  ,						[]   )
   UpperCAmelCase							=     len(lowerCAmelCase_   ) > 0

   # Check if it is a base model
   UpperCAmelCase							=     False
   if hasattr(lowerCAmelCase_  ,						"""base_model_prefix"""   ):
      UpperCAmelCase							=     not hasattr(lowerCAmelCase_  ,						model.base_model_prefix   )

   # Ignore this for base models (BertModel, GPT2Model, etc.)
   if (not has_tied_params) and is_base_model:
      return []

   # otherwise they have an attached head
   UpperCAmelCase							=     list(model.named_children()   )
   UpperCAmelCase							=     [list_modules[-1][0]]

   # add last module together with tied weights
   UpperCAmelCase							=     set(lowerCAmelCase_   ) - set(lowerCAmelCase_   )
   UpperCAmelCase							=     list(set(lowerCAmelCase_   )   ) + list(lowerCAmelCase_   )

   # remove ".weight" from the keys
   UpperCAmelCase							=     [""".weight""", """.bias"""]
   UpperCAmelCase							=     []
   for name in list_untouched:
      for name_to_remove in names_to_remove:
         if name_to_remove in name:
            UpperCAmelCase							=     name.replace(lowerCAmelCase_  ,						""""""   )
      filtered_module_names.append(lowerCAmelCase_   )

   return filtered_module_names
def   _UpperCamelCase				(						lowerCAmelCase_   )						->List[str]:
   for m in model.modules():
      if isinstance(lowerCAmelCase_  ,						bnb.nn.Linearabit   ):
         return True
   return False
def   _UpperCamelCase				(						lowerCAmelCase_   )						->str:
   return next(parameter.parameters()   ).device







def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Any:
   # if it is not quantized, we quantize and offload the quantized weights and the SCB stats
   if fpaa_statistics is None:
      set_module_tensor_to_device(lowerCAmelCase_  ,						lowerCAmelCase_  ,						0  ,						dtype=lowerCAmelCase_  ,						value=lowerCAmelCase_   )
      UpperCAmelCase							=     param_name
      UpperCAmelCase							=     model
      if "." in tensor_name:
         UpperCAmelCase							=     tensor_name.split("""."""   )
         for split in splits[:-1]:
            UpperCAmelCase							=     getattr(lowerCAmelCase_  ,						lowerCAmelCase_   )
            if new_module is None:
               raise ValueError(F"""{module} has no attribute {split}."""   )
            UpperCAmelCase							=     new_module
         UpperCAmelCase							=     splits[-1]
      # offload weights
      UpperCAmelCase							=     False
      offload_weight(module._parameters[tensor_name]  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						index=lowerCAmelCase_   )
      if hasattr(module._parameters[tensor_name]  ,						"""SCB"""   ):
         offload_weight(
             module._parameters[tensor_name].SCB  ,						param_name.replace("""weight"""  ,						"""SCB"""   )  ,						lowerCAmelCase_  ,						index=lowerCAmelCase_  ,						)
   else:
      offload_weight(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						index=lowerCAmelCase_   )
      offload_weight(lowerCAmelCase_  ,						param_name.replace("""weight"""  ,						"""SCB"""   )  ,						lowerCAmelCase_  ,						index=lowerCAmelCase_   )

   set_module_tensor_to_device(lowerCAmelCase_  ,						lowerCAmelCase_  ,						"""meta"""  ,						dtype=lowerCAmelCase_  ,						value=torch.empty(*param.size()   )   )







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				(  __snake_case  ):





      def        _lowercase				(		self						:					Dict					,       __lowerCamelCase						:					List[Any]=False					,       __lowerCamelCase						:					str=None					,       __lowerCamelCase						:					Any=True					,       __lowerCamelCase						:					Union[str, Any]=True					,       __lowerCamelCase						:					str=True					,       __lowerCamelCase						:					List[str]=True					,       )    ->   List[Any]:






         """simple docstring"""







         UpperCAmelCase							=     self.run_trainer(
             eval_steps=1					,       max_len=1_2					,       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        _lowercase				(		self						:					Dict       )    ->   str:






         """simple docstring"""







         self.run_seqaseq_quick()





      @require_torch_multi_gpu
      def        _lowercase				(		self						:					Tuple       )    ->   Any:






         """simple docstring"""







         self.run_seqaseq_quick(distributed=__lowerCamelCase       )





      @require_torch_multi_gpu
      def        _lowercase				(		self						:					Optional[int]       )    ->   Optional[int]:






         """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        _lowercase				(		self						:					Union[str, Any]       )    ->   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        _lowercase				(		self						:					List[Any]       )    ->   Union[str, 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        _lowercase				(		self						:					Dict       )    ->   Tuple:






         """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        _lowercase				(		self						:					Optional[int]       )    ->   Dict:






         """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        _lowercase				(		self						:					str       )    ->   Optional[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        _lowercase				(		self						:					Union[str, Any]					,       __lowerCamelCase						:					Optional[Any]       )    ->   Optional[Any]:






         """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        _lowercase				(		self						:					Union[str, Any]       )    ->   Any:






         """simple docstring"""







         UpperCAmelCase							=     self.run_trainer(
             eval_steps=2					,       max_len=1_2_8					,       model_name=__lowerCamelCase					,       learning_rate=3e-4					,       num_train_epochs=1_0					,       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        _lowercase				(		self						:					str       )    ->   int:






         """simple docstring"""







         from transformers.training_args import OptimizerNames

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

            UpperCAmelCase							=     self.run_trainer(
                max_len=1_2_8					,       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**2_0       )
            UpperCAmelCase							=     int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**2_0       )

            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_2_0

         # 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        _lowercase				(		self						:					Any					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					str					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					float = 3e-3					,       __lowerCamelCase						:					str = "adafactor"					,       __lowerCamelCase						:					bool = False					,       __lowerCamelCase						:					str = None					,       __lowerCamelCase						:					int = 0					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					int = None					,       )    ->   Dict:






         """simple docstring"""







         UpperCAmelCase							=     self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
         UpperCAmelCase							=     self.get_auto_remove_tmp_dir()
         UpperCAmelCase							=     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(__lowerCamelCase       )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(__lowerCamelCase       )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        """.split()

         UpperCAmelCase							=     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(__lowerCamelCase       )}
        """.split()

         UpperCAmelCase							=     """
            --do_predict
        """.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"""
                -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							=     [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



