
'''simple docstring'''






import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
 from sagemaker import Session, TrainingJobAnalytics
 from sagemaker.huggingface import HuggingFace

@pytest.mark.skipif(
    literal_eval(os.getenv('TEST_SAGEMAKER'						,	'False' ) ) is not True						,	reason='Skipping test because should only be run when releasing minor transformers version'						,	)
@pytest.mark.usefixtures('sm_env' )
@parameterized_class(
    [
        {
            'framework': 'pytorch',
            'script': 'run_glue.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.p3.16xlarge',
            'results': {'train_runtime': 650, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
        },
        {
            'framework': 'pytorch',
            'script': 'run_ddp.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.p3.16xlarge',
            'results': {'train_runtime': 600, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
        },
        {
            'framework': 'tensorflow',
            'script': 'run_tf_dist.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.p3.16xlarge',
            'results': {'train_runtime': 600, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
        },
    ] )
class  UpperCAmelCase__						(       unittest.TestCase ):



    def   lowerCamelCase_  (	self      :       Tuple     ):
          if self.framework == "pytorch":
                subprocess.run(
                    f'cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'.split(),encoding="utf-8",check=__A,)
          assert hasattr(self,"env"     )



    def   lowerCamelCase_  (	self      :       Optional[Any],__A      :       int     ):
          _lowerCamelCase				:   Union[str, Any]								=   f'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
          # distributed data settings
          _lowerCamelCase				:   Tuple								=   {"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None

          # creates estimator
          return HuggingFace(
              entry_point=self.script,source_dir=self.env.test_path,role=self.env.role,image_uri=self.env.image_uri,base_job_name=__A,instance_count=__A,instance_type=self.instance_type,debugger_hook_config=__A,hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path},metric_definitions=self.env.metric_definitions,distribution=__A,py_version="py36",)



    def   lowerCamelCase_  (	self      :       Dict,__A      :       Optional[Any]     ):
          TrainingJobAnalytics(__A     ).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv'     )



    @parameterized.expand([(2,)]     )
    def   lowerCamelCase_  (	self      :       Tuple,__A      :       Dict     ):
          # create estimator
          _lowerCamelCase				:   Any								=   self.create_estimator(__A     )

          # run training
          estimator.fit()

          # result dataframe
          _lowerCamelCase				:   List[str]								=   TrainingJobAnalytics(estimator.latest_training_job.name     ).dataframe()

          # extract kpis
          _lowerCamelCase				:   Optional[int]								=   list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]     )
          _lowerCamelCase				:   Optional[int]								=   list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]     )
          # get train time from SageMaker job, this includes starting, preprocessing, stopping
          _lowerCamelCase				:   Union[str, Any]								=   (
              Session().describe_training_job(estimator.latest_training_job.name     ).get("TrainingTimeInSeconds",9_9_9_9_9_9     )
          )

          # assert kpis
          assert train_runtime <= self.results["train_runtime"]
          assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy     )
          assert all(t <= self.results["eval_loss"] for t in eval_loss     )

          # dump tests result into json file to share in PR
          with open(f'{estimator.latest_training_job.name}.json',"w"     ) as outfile:
                json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss},__A     )

'''simple docstring'''






import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
 import transformers







def 		A_       (	_lowerCAmelCase   :    int							):



      """simple docstring"""




      random.seed(_lowerCAmelCase							)
      np.random.seed(_lowerCAmelCase							)
      torch.manual_seed(_lowerCAmelCase							)
      torch.cuda.manual_seed_all(_lowerCAmelCase							)
      # ^^ safe to call this function even if cuda is not available



class  UpperCAmelCase__						:



    def __init__(	self      :       List[str],__A      :       Iterable[torch.nn.Parameter],__A      :       float = 0.9999,__A      :       float = 0.0,__A      :       int = 0,__A      :       bool = False,__A      :       Union[float, int] = 1.0,__A      :       Union[float, int] = 2 / 3,__A      :       Optional[Any] = None,__A      :       Dict[str, Any] = None,**__A      :       Optional[Any],):

          if isinstance(__A,torch.nn.Module     ):
                _lowerCamelCase				:   Any								=   (
                    "Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. "
                    "Please pass the parameters of the module instead."
                )
                deprecate(
                    "passing a `torch.nn.Module` to `ExponentialMovingAverage`","1.0.0",__A,standard_warn=__A,)
                _lowerCamelCase				:   Dict								=   parameters.parameters()

                # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
                _lowerCamelCase				:   Optional[Any]								=   True

          if kwargs.get("max_value",__A     ) is not None:
                _lowerCamelCase				:   Optional[int]								=   "The `max_value` argument is deprecated. Please use `decay` instead."
                deprecate("max_value","1.0.0",__A,standard_warn=__A     )
                _lowerCamelCase				:   Optional[Any]								=   kwargs["max_value"]

          if kwargs.get("min_value",__A     ) is not None:
                _lowerCamelCase				:   Tuple								=   "The `min_value` argument is deprecated. Please use `min_decay` instead."
                deprecate("min_value","1.0.0",__A,standard_warn=__A     )
                _lowerCamelCase				:   Any								=   kwargs["min_value"]

          _lowerCamelCase				:   Optional[int]								=   list(__A     )
          _lowerCamelCase				:   List[Any]								=   [p.clone().detach() for p in parameters]

          if kwargs.get("device",__A     ) is not None:
                _lowerCamelCase				:   Optional[int]								=   "The `device` argument is deprecated. Please use `to` instead."
                deprecate("device","1.0.0",__A,standard_warn=__A     )
                self.to(device=kwargs["device"]     )

          _lowerCamelCase				:   Tuple								=   None

          _lowerCamelCase				:   Dict								=   decay
          _lowerCamelCase				:   List[Any]								=   min_decay
          _lowerCamelCase				:   Optional[Any]								=   update_after_step
          _lowerCamelCase				:   Any								=   use_ema_warmup
          _lowerCamelCase				:   Union[str, Any]								=   inv_gamma
          _lowerCamelCase				:   str								=   power
          _lowerCamelCase				:   Union[str, Any]								=   0
          _lowerCamelCase				:   Union[str, Any]								=   None  # set in `step()`

          _lowerCamelCase				:   List[str]								=   model_cls
          _lowerCamelCase				:   Dict								=   model_config



    @classmethod
    def   lowerCamelCase_  (	cls      :       Union[str, Any],__A      :       List[str],__A      :       Optional[int]     ):
          _lowerCamelCase						,		_lowerCamelCase				:   Optional[int]								=   model_cls.load_config(__A,return_unused_kwargs=__A     )
          _lowerCamelCase				:   Optional[Any]								=   model_cls.from_pretrained(__A     )

          _lowerCamelCase				:   Union[str, Any]								=   cls(model.parameters(),model_cls=__A,model_config=model.config     )

          ema_model.load_state_dict(__A     )
          return ema_model



    def   lowerCamelCase_  (	self      :       str,__A      :       int     ):
          if self.model_cls is None:
                raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__."     )

          if self.model_config is None:
                raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__."     )

          _lowerCamelCase				:   Tuple								=   self.model_cls.from_config(self.model_config     )
          _lowerCamelCase				:   List[str]								=   self.state_dict()
          state_dict.pop("shadow_params",__A     )

          model.register_to_config(**__A     )
          self.copy_to(model.parameters()     )
          model.save_pretrained(__A     )



    def   lowerCamelCase_  (	self      :       Optional[int],__A      :       int     ):
          _lowerCamelCase				:   str								=   max(0,optimization_step - self.update_after_step - 1     )

          if step <= 0:
                return 0.0

          if self.use_ema_warmup:
                _lowerCamelCase				:   Tuple								=   1 - (1 + step / self.inv_gamma) ** -self.power
          else:
                _lowerCamelCase				:   List[str]								=   (1 + step) / (1_0 + step)

          _lowerCamelCase				:   Union[str, Any]								=   min(__A,self.decay     )
          # make sure decay is not smaller than min_decay
          _lowerCamelCase				:   Union[str, Any]								=   max(__A,self.min_decay     )
          return cur_decay_value



    @torch.no_grad()
    def   lowerCamelCase_  (	self      :       Any,__A      :       Iterable[torch.nn.Parameter]     ):
          if isinstance(__A,torch.nn.Module     ):
                _lowerCamelCase				:   Dict								=   (
                    "Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. "
                    "Please pass the parameters of the module instead."
                )
                deprecate(
                    "passing a `torch.nn.Module` to `ExponentialMovingAverage.step`","1.0.0",__A,standard_warn=__A,)
                _lowerCamelCase				:   Any								=   parameters.parameters()

          _lowerCamelCase				:   str								=   list(__A     )

          self.optimization_step += 1

          # Compute the decay factor for the exponential moving average.
          _lowerCamelCase				:   Dict								=   self.get_decay(self.optimization_step     )
          _lowerCamelCase				:   Optional[Any]								=   decay
          _lowerCamelCase				:   List[Any]								=   1 - decay

          _lowerCamelCase				:   List[Any]								=   contextlib.nullcontext
          if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                import deepspeed

          for s_param, param in zip(self.shadow_params,__A     ):
                if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                      _lowerCamelCase				:   List[Any]								=   deepspeed.zero.GatheredParameters(__A,modifier_rank=__A     )

                with context_manager():
                      if param.requires_grad:
                            s_param.sub_(one_minus_decay * (s_param - param)     )
                      else:
                            s_param.copy_(__A     )



    def   lowerCamelCase_  (	self      :       Dict,__A      :       Iterable[torch.nn.Parameter]     ):
          _lowerCamelCase				:   Tuple								=   list(__A     )
          for s_param, param in zip(self.shadow_params,__A     ):
                param.data.copy_(s_param.to(param.device     ).data     )



    def   lowerCamelCase_  (	self      :       List[str],__A      :       Dict=None,__A      :       Any=None     ):
          _lowerCamelCase				:   int								=   [
              p.to(device=__A,dtype=__A     ) if p.is_floating_point() else p.to(device=__A     )
              for p in self.shadow_params
          ]



    def   lowerCamelCase_  (	self      :       List[Any]     ):
          return {
              "decay": self.decay,
              "min_decay": self.min_decay,
              "optimization_step": self.optimization_step,
              "update_after_step": self.update_after_step,
              "use_ema_warmup": self.use_ema_warmup,
              "inv_gamma": self.inv_gamma,
              "power": self.power,
              "shadow_params": self.shadow_params,
          }



    def   lowerCamelCase_  (	self      :       Tuple,__A      :       Iterable[torch.nn.Parameter]     ):
          _lowerCamelCase				:   Tuple								=   [param.detach().cpu().clone() for param in parameters]



    def   lowerCamelCase_  (	self      :       int,__A      :       Iterable[torch.nn.Parameter]     ):
          if self.temp_stored_params is None:
                raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`"     )
          for c_param, param in zip(self.temp_stored_params,__A     ):
                param.data.copy_(c_param.data     )

          # Better memory-wise.
          _lowerCamelCase				:   List[str]								=   None



    def   lowerCamelCase_  (	self      :       Dict,__A      :       dict     ):
          _lowerCamelCase				:   List[str]								=   copy.deepcopy(__A     )

          _lowerCamelCase				:   Optional[Any]								=   state_dict.get("decay",self.decay     )
          if self.decay < 0.0 or self.decay > 1.0:
                raise ValueError("Decay must be between 0 and 1"     )

          _lowerCamelCase				:   Dict								=   state_dict.get("min_decay",self.min_decay     )
          if not isinstance(self.min_decay,__A     ):
                raise ValueError("Invalid min_decay"     )

          _lowerCamelCase				:   Tuple								=   state_dict.get("optimization_step",self.optimization_step     )
          if not isinstance(self.optimization_step,__A     ):
                raise ValueError("Invalid optimization_step"     )

          _lowerCamelCase				:   Any								=   state_dict.get("update_after_step",self.update_after_step     )
          if not isinstance(self.update_after_step,__A     ):
                raise ValueError("Invalid update_after_step"     )

          _lowerCamelCase				:   List[Any]								=   state_dict.get("use_ema_warmup",self.use_ema_warmup     )
          if not isinstance(self.use_ema_warmup,__A     ):
                raise ValueError("Invalid use_ema_warmup"     )

          _lowerCamelCase				:   Tuple								=   state_dict.get("inv_gamma",self.inv_gamma     )
          if not isinstance(self.inv_gamma,(float, int)     ):
                raise ValueError("Invalid inv_gamma"     )

          _lowerCamelCase				:   Union[str, Any]								=   state_dict.get("power",self.power     )
          if not isinstance(self.power,(float, int)     ):
                raise ValueError("Invalid power"     )

          _lowerCamelCase				:   Optional[Any]								=   state_dict.get("shadow_params",__A     )
          if shadow_params is not None:
                _lowerCamelCase				:   str								=   shadow_params
                if not isinstance(self.shadow_params,__A     ):
                      raise ValueError("shadow_params must be a list"     )
                if not all(isinstance(__A,torch.Tensor     ) for p in self.shadow_params     ):
                      raise ValueError("shadow_params must all be Tensors"     )