



"""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    SCREAMING_SNAKE_CASE__ (							unittest.TestCase   ):

    def 					_UpperCAmelCase	(				self			:				Dict):







      """simple docstring"""







      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=lowerCAmelCase_ ,  )
      assert hasattr(self ,  """env""")

    def 					_UpperCAmelCase	(				self			:				Optional[Any] ,  lowerCAmelCase_			:				int):







      """simple docstring"""







      lowercase_								= F'''{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'''
      # distributed data settings
      lowercase_								= {"""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=lowerCAmelCase_ ,  instance_count=lowerCAmelCase_ ,  instance_type=self.instance_type ,  debugger_hook_config=lowerCAmelCase_ ,  hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path} ,  metric_definitions=self.env.metric_definitions ,  distribution=lowerCAmelCase_ ,  py_version="""py36""" ,  )

    def 					_UpperCAmelCase	(				self			:				List[str] ,  lowerCAmelCase_			:				int):







      """simple docstring"""







      TrainingJobAnalytics(lowerCAmelCase_).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv''')




    @parameterized.expand([(2,)])
    def 					_UpperCAmelCase	(				self			:				Optional[Any] ,  lowerCAmelCase_			:				Optional[Any]):







      """simple docstring"""







      lowercase_								= self.create_estimator(lowerCAmelCase_)

      # run training
      estimator.fit()

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

      # extract kpis
      lowercase_								= list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""])
      lowercase_								= list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""])
      # get train time from SageMaker job, this includes starting, preprocessing, stopping
      lowercase_								= (
          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} ,  lowerCAmelCase_)





"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin


@flax.struct.dataclass
class    SCREAMING_SNAKE_CASE__ :
    # setable values
    lowercase__      =   None
    lowercase__      =   None
    lowercase__      =   None  # sigma(t_i)

    @classmethod
    def 					_UpperCAmelCase	(				cls			:				Any):







      """simple docstring"""







      return cls()







@dataclass
class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   42
    lowercase__      =   42
    lowercase__      =   42







class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase      ,  __UpperCAmelCase   ):

    @property
    def 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







      return True

    @register_to_config
    def __init__(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				float = 0.02 ,  lowerCAmelCase_			:				float = 1_0_0 ,  lowerCAmelCase_			:				float = 1.007 ,  lowerCAmelCase_			:				float = 8_0 ,  lowerCAmelCase_			:				float = 0.05 ,  lowerCAmelCase_			:				float = 5_0 ,  ):







      """simple docstring"""







      pass

    def 					_UpperCAmelCase	(				self			:				Optional[int]):







      """simple docstring"""







      return KarrasVeSchedulerState.create()

    def 					_UpperCAmelCase	(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				Tuple = ()):







      """simple docstring"""







      lowercase_								= jnp.arange(0 ,  lowerCAmelCase_)[::-1].copy()
      lowercase_								= [
          (
              self.config.sigma_max**2
              * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
          )
          for i in timesteps
      ]

      return state.replace(
          num_inference_steps=lowerCAmelCase_ ,  schedule=jnp.array(lowerCAmelCase_ ,  dtype=jnp.floataa) ,  timesteps=lowerCAmelCase_ ,  )

    def 					_UpperCAmelCase	(				self			:				Optional[int] ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				random.KeyArray ,  ):







      """simple docstring"""







      if self.config.s_min <= sigma <= self.config.s_max:
        lowercase_								= min(self.config.s_churn / state.num_inference_steps ,  2**0.5 - 1)
      else:
        lowercase_								= 0

      # sample eps ~ N(0, S_noise^2 * I)
      lowercase_								= random.split(lowerCAmelCase_ ,  num=1)
      lowercase_								= self.config.s_noise * random.normal(key=lowerCAmelCase_ ,  shape=sample.shape)
      lowercase_								= sigma + gamma * sigma
      lowercase_								= sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

      return sample_hat, sigma_hat

    def 					_UpperCAmelCase	(				self			:				Tuple ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				bool = True ,  ):







      """simple docstring"""







      lowercase_								= sample_hat + sigma_hat * model_output
      lowercase_								= (sample_hat - pred_original_sample) / sigma_hat
      lowercase_								= sample_hat + (sigma_prev - sigma_hat) * derivative

      if not return_dict:
        return (sample_prev, derivative, state)

      return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_ ,  derivative=lowerCAmelCase_ ,  state=lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Dict ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				bool = True ,  ):







      """simple docstring"""







      lowercase_								= sample_prev + sigma_prev * model_output
      lowercase_								= (sample_prev - pred_original_sample) / sigma_prev
      lowercase_								= sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

      if not return_dict:
        return (sample_prev, derivative, state)

      return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_ ,  derivative=lowerCAmelCase_ ,  state=lowerCAmelCase_)




    def 					_UpperCAmelCase	(				self			:				List[Any] ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				List[Any]):







      """simple docstring"""







      raise NotImplementedError()

