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        lowercase_   (					unittest.TestCase		):



							def 	UpperCamelCase	(						self    ):
													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=lowercase_				,    )
													assert hasattr(self				,    "env"    )



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													_snake_case						:					Dict    =   f"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
													# distributed data settings
													_snake_case						:					Optional[int]    =   {"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=lowercase_				,    instance_count=lowercase_				,    instance_type=self.instance_type				,    debugger_hook_config=lowercase_				,    hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}				,    metric_definitions=self.env.metric_definitions				,    distribution=lowercase_				,    py_version="py36"				,    )



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													TrainingJobAnalytics(lowercase_    ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""    )



							@parameterized.expand([(2,)]    )
							def 	UpperCamelCase	(						self				,    lowercase_    ):
													# create estimator
													_snake_case						:					str    =   self.create_estimator(lowercase_    )

													# run training
													estimator.fit()

													# result dataframe
													_snake_case						:					Any    =   TrainingJobAnalytics(estimator.latest_training_job.name    ).dataframe()

													# extract kpis
													_snake_case						:					Optional[int]    =   list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]    )
													_snake_case						:					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
													_snake_case						:					List[Any]    =   (
													    Session().describe_training_job(estimator.latest_training_job.name    ).get("TrainingTimeInSeconds"				,    999_999    )
													)

													# 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}				,    lowercase_    )






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE						:   Any         =			{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			[
				    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MraForMaskedLM',
				    'MraForMultipleChoice',
				    'MraForQuestionAnswering',
				    'MraForSequenceClassification',
				    'MraForTokenClassification',
				    'MraLayer',
				    'MraModel',
				    'MraPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mra import (
								    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MraForMaskedLM,
								    MraForMultipleChoice,
								    MraForQuestionAnswering,
								    MraForSequenceClassification,
								    MraForTokenClassification,
								    MraLayer,
								    MraModel,
								    MraPreTrainedModel,
								)
else:
				import sys

				__SCREAMING_SNAKE_CASE						:   str         =			_LazyModule(__name__, globals()['__file__'], _import_structure)