"""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 							_UpperCAmelCase		(       lowerCamelCase__     )     ->   Optional[Any]:





		"""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       a_      :




					def __init__(	self						:     List[Any]  ,						snake_case__						:     Any  ,						snake_case__						:     Dict = 0.9999  ,						snake_case__						:     Tuple = 0.0  ,						snake_case__						:     Dict = 0  ,						snake_case__						:     Any = False  ,						snake_case__						:     Dict = 1.0  ,						snake_case__						:     Optional[int] = 2 / 3  ,						snake_case__						:     Dict = None  ,						snake_case__						:     Tuple = None  ,						**snake_case__						:     List[Any]  ,						):

							if isinstance(snake_case__  ,						torch.nn.Module     ):
									lowerCAmelCase__        =   (
									    """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"""  ,						snake_case__  ,						standard_warn=snake_case__  ,						)
									lowerCAmelCase__        =   parameters.parameters()

									# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
									lowerCAmelCase__        =   True

							if kwargs.get("""max_value"""  ,						snake_case__     ) is not None:
									lowerCAmelCase__        =   """The `max_value` argument is deprecated. Please use `decay` instead."""
									deprecate("""max_value"""  ,						"""1.0.0"""  ,						snake_case__  ,						standard_warn=snake_case__     )
									lowerCAmelCase__        =   kwargs["""max_value"""]

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

							lowerCAmelCase__        =   list(snake_case__     )
							lowerCAmelCase__        =   [p.clone().detach() for p in parameters]

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

							lowerCAmelCase__        =   None

							lowerCAmelCase__        =   decay
							lowerCAmelCase__        =   min_decay
							lowerCAmelCase__        =   update_after_step
							lowerCAmelCase__        =   use_ema_warmup
							lowerCAmelCase__        =   inv_gamma
							lowerCAmelCase__        =   power
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   None  # set in `step()`

							lowerCAmelCase__        =   model_cls
							lowerCAmelCase__        =   model_config




					@classmethod
					def 						_SCREAMING_SNAKE_CASE						(	cls						:     Any  ,						snake_case__						:     int  ,						snake_case__						:     Union[str, Any]     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   model_cls.load_config(snake_case__  ,						return_unused_kwargs=snake_case__     )
							lowerCAmelCase__        =   model_cls.from_pretrained(snake_case__     )

							lowerCAmelCase__        =   cls(model.parameters()  ,						model_cls=snake_case__  ,						model_config=model.config     )

							ema_model.load_state_dict(snake_case__     )
							return ema_model




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     List[str]     ):
							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__        =   self.model_cls.from_config(self.model_config     )
							lowerCAmelCase__        =   self.state_dict()
							state_dict.pop("""shadow_params"""  ,						snake_case__     )

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     Optional[int]     ):
							lowerCAmelCase__        =   max(0  ,						optimization_step - self.update_after_step - 1     )

							if step <= 0:
									return 0.0

							if self.use_ema_warmup:
									lowerCAmelCase__        =   1 - (1 + step / self.inv_gamma) ** -self.power
							else:
									lowerCAmelCase__        =   (1 + step) / (10 + step)

							lowerCAmelCase__        =   min(snake_case__  ,						self.decay     )
							# make sure decay is not smaller than min_decay
							lowerCAmelCase__        =   max(snake_case__  ,						self.min_decay     )
							return cur_decay_value




					@torch.no_grad()
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     Union[str, Any]     ):
							if isinstance(snake_case__  ,						torch.nn.Module     ):
									lowerCAmelCase__        =   (
									    """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"""  ,						snake_case__  ,						standard_warn=snake_case__  ,						)
									lowerCAmelCase__        =   parameters.parameters()

							lowerCAmelCase__        =   list(snake_case__     )

							self.optimization_step += 1

							# Compute the decay factor for the exponential moving average.
							lowerCAmelCase__        =   self.get_decay(self.optimization_step     )
							lowerCAmelCase__        =   decay
							lowerCAmelCase__        =   1 - decay

							lowerCAmelCase__        =   contextlib.nullcontext
							if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
									import deepspeed

							for s_param, param in zip(self.shadow_params  ,						snake_case__     ):
									if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
											lowerCAmelCase__        =   deepspeed.zero.GatheredParameters(snake_case__  ,						modifier_rank=snake_case__     )

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any  ,						snake_case__						:     Optional[int]     ):
							lowerCAmelCase__        =   list(snake_case__     )
							for s_param, param in zip(self.shadow_params  ,						snake_case__     ):
									param.data.copy_(s_param.to(param.device     ).data     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     Optional[Any]=None  ,						snake_case__						:     List[str]=None     ):
							lowerCAmelCase__        =   [
							    p.to(device=snake_case__  ,						dtype=snake_case__     ) if p.is_floating_point() else p.to(device=snake_case__     )
							    for p in self.shadow_params
							]




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, 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 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     Optional[int]     ):
							lowerCAmelCase__        =   [param.detach().cpu().clone() for param in parameters]




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     List[Any]     ):
							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  ,						snake_case__     ):
									param.data.copy_(c_param.data     )

							# Better memory-wise.
							lowerCAmelCase__        =   None




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     int     ):
							lowerCAmelCase__        =   copy.deepcopy(snake_case__     )

							lowerCAmelCase__        =   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__        =   state_dict.get("""min_decay"""  ,						self.min_decay     )
							if not isinstance(self.min_decay  ,						snake_case__     ):
									raise ValueError("""Invalid min_decay"""     )

							lowerCAmelCase__        =   state_dict.get("""optimization_step"""  ,						self.optimization_step     )
							if not isinstance(self.optimization_step  ,						snake_case__     ):
									raise ValueError("""Invalid optimization_step"""     )

							lowerCAmelCase__        =   state_dict.get("""update_after_step"""  ,						self.update_after_step     )
							if not isinstance(self.update_after_step  ,						snake_case__     ):
									raise ValueError("""Invalid update_after_step"""     )

							lowerCAmelCase__        =   state_dict.get("""use_ema_warmup"""  ,						self.use_ema_warmup     )
							if not isinstance(self.use_ema_warmup  ,						snake_case__     ):
									raise ValueError("""Invalid use_ema_warmup"""     )

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

							lowerCAmelCase__        =   state_dict.get("""power"""  ,						self.power     )
							if not isinstance(self.power  ,						(float, int)     ):
									raise ValueError("""Invalid power"""     )

							lowerCAmelCase__        =   state_dict.get("""shadow_params"""  ,						snake_case__     )
							if shadow_params is not None:
									lowerCAmelCase__        =   shadow_params
									if not isinstance(self.shadow_params  ,						snake_case__     ):
											raise ValueError("""shadow_params must be a list"""     )
									if not all(isinstance(snake_case__  ,						torch.Tensor     ) for p in self.shadow_params     ):
											raise ValueError("""shadow_params must all be Tensors"""     )





"""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_model_parallelism.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]			)
class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							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=snake_case__  ,						)
							assert hasattr(self  ,						"""env"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     Optional[Any]     ):
							# configuration for running training on smdistributed Model Parallel
							lowerCAmelCase__        =   {
							    """enabled""": True,
							    """processes_per_host""": 8,
							}
							lowerCAmelCase__        =   {
							    """enabled""": True,
							    """parameters""": {
							        """microbatches""": 4,
							        """placement_strategy""": """spread""",
							        """pipeline""": """interleaved""",
							        """optimize""": """speed""",
							        """partitions""": 4,
							        """ddp""": True,
							    },
							}

							lowerCAmelCase__        =   {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

							lowerCAmelCase__        =   """trainer""" if self.script == """run_glue.py""" else """smtrainer"""
							# 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=F"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}"""  ,						instance_count=snake_case__  ,						instance_type=self.instance_type  ,						debugger_hook_config=snake_case__  ,						hyperparameters={
							        **self.env.hyperparameters,
							        """model_name_or_path""": self.model_name_or_path,
							        """max_steps""": 500,
							    }  ,						metric_definitions=self.env.metric_definitions  ,						distribution=snake_case__  ,						py_version="""py36"""  ,						)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     str     ):
							TrainingJobAnalytics(snake_case__     ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""     )




					@parameterized.expand([(1,)]     )
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     List[str]     ):
							# create estimator
							lowerCAmelCase__        =   self.create_estimator(snake_case__     )

							# run training
							estimator.fit()

							# result dataframe
							lowerCAmelCase__        =   TrainingJobAnalytics(estimator.latest_training_job.name     ).dataframe()

							# extract kpis
							lowerCAmelCase__        =   list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]     )
							lowerCAmelCase__        =   list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]     )
							# get train time from SageMaker job, this includes starting, preprocessing, stopping
							lowerCAmelCase__        =   (
							    Session().describe_training_job(estimator.latest_training_job.name     ).get("""TrainingTimeInSeconds"""  ,						999999     )
							)

							# 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}  ,						snake_case__     )




