


'''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': 16_00, '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': 16_00, 'eval_accuracy': 0.3, 'eval_loss': 1.2},
        },
    ])
class    __snake_case       (   unittest.TestCase):







    """simple docstring"""



    def __lowercase   (   self    :  Optional[int]		)   ->  Optional[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=_A    ,						)
      assert hasattr(self    ,						"""env"""		)

    def __lowercase   (   self    :  Any    ,						lowerCamelCase    :  List[Any]		)   ->  List[Any]:
      lowerCAmelCase_			:      Union[str, Any]			=   {
          '''enabled''': True,
          '''processes_per_host''': 8,
      }
      lowerCAmelCase_			:      str			=   {
          '''enabled''': True,
          '''parameters''': {
              '''microbatches''': 4,
              '''placement_strategy''': '''spread''',
              '''pipeline''': '''interleaved''',
              '''optimize''': '''speed''',
              '''partitions''': 4,
              '''ddp''': True,
          },
      }

      lowerCAmelCase_			:      List[str]			=   {'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

      lowerCAmelCase_			:      Optional[int]			=   '''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=_A    ,						instance_type=self.instance_type    ,						debugger_hook_config=_A    ,						hyperparameters={
              **self.env.hyperparameters,
              """model_name_or_path""": self.model_name_or_path,
              """max_steps""": 5_00,
          }    ,						metric_definitions=self.env.metric_definitions    ,						distribution=_A    ,						py_version="""py36"""    ,						)

    def __lowercase   (   self    :  Optional[Any]    ,						lowerCamelCase    :  List[str]		)   ->  Optional[int]:
      TrainingJobAnalytics(_A		).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv'		)





    @parameterized.expand([(1,)]		)
    def __lowercase   (   self    :  Optional[int]    ,						lowerCamelCase    :  List[str]		)   ->  List[str]:
      lowerCAmelCase_			:      List[str]			=   self.create_estimator(_A		)

      # run training
      estimator.fit()

      # result dataframe
      lowerCAmelCase_			:      str			=   TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

      # extract kpis
      lowerCAmelCase_			:      Dict			=   list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]		)
      lowerCAmelCase_			:      Union[str, Any]			=   list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]		)
      # get train time from SageMaker job, this includes starting, preprocessing, stopping
      lowerCAmelCase_			:      Dict			=   (
          Session().describe_training_job(estimator.latest_training_job.name		).get("""TrainingTimeInSeconds"""    ,						99_99_99		)
      )

      # 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 re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


UpperCamelCase__    					=      logging.get_logger(__name__)





def 					a__       (					lowerCAmelCase__						)    ->	Optional[Any]:
    UpperCAmelCase__			:		str													=				R'''\w+[.]\d+'''
    UpperCAmelCase__			:		List[Any]													=				re.findall(lowerCAmelCase__  ,					lowerCAmelCase__						)
    for pat in pats:
        UpperCAmelCase__			:		Union[str, Any]													=				key.replace(lowerCAmelCase__  ,					'''_'''.join(pat.split('''.'''						)						)						)
    return key





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Union[str, Any]:
    UpperCAmelCase__			:		Optional[Any]													=				pt_tuple_key[:-1] + ('''scale''',)
    if (
        any('''norm''' in str_ for str_ in pt_tuple_key						)
        and (pt_tuple_key[-1] == "bias")
        and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
        and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
    ):
        UpperCAmelCase__			:		Union[str, Any]													=				pt_tuple_key[:-1] + ('''scale''',)
        return renamed_pt_tuple_key, pt_tensor
    elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
        UpperCAmelCase__			:		Optional[int]													=				pt_tuple_key[:-1] + ('''scale''',)
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
        UpperCAmelCase__			:		str													=				pt_tuple_key[:-1] + ('''embedding''',)
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    UpperCAmelCase__			:		Optional[Any]													=				pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
        UpperCAmelCase__			:		List[Any]													=				pt_tensor.transpose(2  ,					3  ,					1  ,					0						)
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    UpperCAmelCase__			:		int													=				pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight":
        UpperCAmelCase__			:		Optional[Any]													=				pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    UpperCAmelCase__			:		Union[str, Any]													=				pt_tuple_key[:-1] + ('''weight''',)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    UpperCAmelCase__			:		Optional[Any]													=				pt_tuple_key[:-1] + ('''bias''',)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor







def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__=42						)    ->	Tuple:
    # Step 1: Convert pytorch tensor to numpy
    UpperCAmelCase__			:		int													=				{k: v.numpy() for k, v in pt_state_dict.items()}

    # Step 2: Since the model is stateless, get random Flax params
    UpperCAmelCase__			:		Tuple													=				flax_model.init_weights(PRNGKey(lowerCAmelCase__						)						)

    UpperCAmelCase__			:		Optional[Any]													=				flatten_dict(lowerCAmelCase__						)
    UpperCAmelCase__			:		List[str]													=				{}

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        UpperCAmelCase__			:		Optional[int]													=				rename_key(lowerCAmelCase__						)
        UpperCAmelCase__			:		str													=				tuple(renamed_pt_key.split('''.'''						)						)

        # Correctly rename weight parameters
        UpperCAmelCase__						,						UpperCAmelCase__			:		Optional[int]													=				rename_key_and_reshape_tensor(lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)

        if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                raise ValueError(
                    F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                    F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""						)

        # also add unexpected weight so that warning is thrown
        UpperCAmelCase__			:		List[str]													=				jnp.asarray(lowerCAmelCase__						)

    return unflatten_dict(lowerCAmelCase__						)
