


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				):




							"""simple docstring"""


							def    __snake_case (							self	:  Optional[int]    )     ->   Any:
														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    __snake_case (							self	:  Tuple   ,     lowerCamelCase	:  str    )     ->   List[str]:
														# configuration for running training on smdistributed Model Parallel
														__snake_case		:      int										=       {
														    "enabled": True,
														    "processes_per_host": 8,
														}
														__snake_case		:      Tuple										=       {
														    "enabled": True,
														    "parameters": {
														        "microbatches": 4,
														        "placement_strategy": "spread",
														        "pipeline": "interleaved",
														        "optimize": "speed",
														        "partitions": 4,
														        "ddp": True,
														    },
														}

														__snake_case		:      Dict										=       {"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

														__snake_case		:      List[Any]										=       "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=lowerCamelCase   ,     instance_type=self.instance_type   ,     debugger_hook_config=lowerCamelCase   ,     hyperparameters={
														        **self.env.hyperparameters,
														        "model_name_or_path": self.model_name_or_path,
														        "max_steps": 500,
														    }   ,     metric_definitions=self.env.metric_definitions   ,     distribution=lowerCamelCase   ,     py_version="py36"   ,     )

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






							@parameterized.expand([(1,)]    )
							def    __snake_case (							self	:  List[str]   ,     lowerCamelCase	:  Optional[int]    )     ->   Any:
														# create estimator
														__snake_case		:      List[str]										=       self.create_estimator(lowerCamelCase    )

														# run training
														estimator.fit()

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

														# extract kpis
														__snake_case		:      Optional[Any]										=       list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]    )
														__snake_case		:      str										=       list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]    )
														# get train time from SageMaker job, this includes starting, preprocessing, stopping
														__snake_case		:      str										=       (
														    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}   ,     lowerCamelCase    )





from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Any										=       []
							__snake_case		:      Optional[Any]										=       []
							__snake_case		:      List[Any]										=       []

							for rt in rc.restypes:
														__snake_case		:      Any										=       rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
														restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]     )
														__snake_case		:      Tuple										=       {name: i for i, name in enumerate(__lowerCamelCase     )}
														restype_atomaa_to_atomaa_list.append(
														    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]     )

														restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]     )

							# Add dummy mapping for restype 'UNK'
							restype_atomaa_to_atomaa_list.append([0] * 1_4     )
							restype_atomaa_to_atomaa_list.append([0] * 3_7     )
							restype_atomaa_mask_list.append([0.0] * 1_4     )

							__snake_case		:      int										=       torch.tensor(
							    __lowerCamelCase							,     dtype=torch.intaa							,     device=protein["aatype"].device							,     )
							__snake_case		:      List[str]										=       torch.tensor(
							    __lowerCamelCase							,     dtype=torch.intaa							,     device=protein["aatype"].device							,     )
							__snake_case		:      Optional[Any]										=       torch.tensor(
							    __lowerCamelCase							,     dtype=torch.floataa							,     device=protein["aatype"].device							,     )
							__snake_case		:      Optional[int]										=       protein["aatype"].to(torch.long     )

							# create the mapping for (residx, atom14) --> atom37, i.e. an array
							# with shape (num_res, 14) containing the atom37 indices for this protein
							__snake_case		:      Optional[Any]										=       restype_atomaa_to_atomaa[protein_aatype]
							__snake_case		:      Tuple										=       restype_atomaa_mask[protein_aatype]

							__snake_case		:      Optional[Any]										=       residx_atomaa_mask
							__snake_case		:      Union[str, Any]										=       residx_atomaa_to_atomaa.long()

							# create the gather indices for mapping back
							__snake_case		:      Dict										=       restype_atomaa_to_atomaa[protein_aatype]
							__snake_case		:      Dict										=       residx_atomaa_to_atomaa.long()

							# create the corresponding mask
							__snake_case		:      List[str]										=       torch.zeros([2_1, 3_7]							,     dtype=torch.floataa							,     device=protein["aatype"].device     )
							for restype, restype_letter in enumerate(rc.restypes     ):
														__snake_case		:      List[str]										=       rc.restype_atoa[restype_letter]
														__snake_case		:      List[Any]										=       rc.residue_atoms[restype_name]
														for atom_name in atom_names:
																					__snake_case		:      Union[str, Any]										=       rc.atom_order[atom_name]
																					__snake_case		:      str										=       1

							__snake_case		:      List[str]										=       restype_atomaa_mask[protein_aatype]
							__snake_case		:      List[str]										=       residx_atomaa_mask

							return protein







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      str										=       tree_map(lambda __lowerCamelCase     : torch.tensor(__lowerCamelCase							,     device=batch["aatype"].device     )							,     __lowerCamelCase							,     np.ndarray     )
							__snake_case		:      str										=       tensor_tree_map(lambda __lowerCamelCase     : np.array(__lowerCamelCase     )							,     make_atomaa_masks(__lowerCamelCase     )     )
							return out

