"""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 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""

				def 						snake_case	(			self      ):






										"""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 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     {
										    'enabled': True,
										    'processes_per_host': 8,
										}
										snake_case        =     {
										    'enabled': True,
										    'parameters': {
										        'microbatches': 4,
										        'placement_strategy': 'spread',
										        'pipeline': 'interleaved',
										        'optimize': 'speed',
										        'partitions': 4,
										        'ddp': True,
										    },
										}

										snake_case        =     {'smdistributed': {'modelparallel': smp_options}, 'mpi': mpi_options}

										snake_case        =     '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': 5_00,
										    }						,      metric_definitions=self.env.metric_definitions						,      distribution=lowerCAmelCase						,      py_version='py36'						,      )

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										TrainingJobAnalytics(lowerCAmelCase      ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""      )

				@parameterized.expand([(1,)]      )
				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     self.create_estimator(lowerCAmelCase      )

										# run training
										estimator.fit()

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

										# extract kpis
										snake_case        =     list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']      )
										snake_case        =     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        =     (
										    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}						,      lowerCAmelCase      )



"""simple docstring"""
import os
import sys
import unittest


SCREAMING_SNAKE_CASE__   =					os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
SCREAMING_SNAKE_CASE__   =					os.path.join(git_repo_path, "src", "transformers")

SCREAMING_SNAKE_CASE__   =					"\n{0} = None\n"

SCREAMING_SNAKE_CASE__   =					"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


SCREAMING_SNAKE_CASE__   =					"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"



class 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")'      )
										self.assertIsNone(lowerCAmelCase      )

										snake_case        =     find_backend('    if not is_tokenizers_available():'      )
										self.assertEqual(lowerCAmelCase						,      'tokenizers'      )

										snake_case        =     find_backend('    if not is_tensorflow_text_available():'      )
										self.assertEqual(lowerCAmelCase						,      'tensorflow_text'      )

										snake_case        =     find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tokenizers'      )

										snake_case        =     find_backend(
										    '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tensorflow_text'      )

										snake_case        =     find_backend(
										    '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tokenizers_and_vision'      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     read_init()
										# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
										self.assertIn('torch'						,      lowerCAmelCase      )
										self.assertIn('tensorflow_text'						,      lowerCAmelCase      )
										self.assertIn('sentencepiece_and_tokenizers'						,      lowerCAmelCase      )

										# Likewise, we can't assert on the exact content of a key
										self.assertIn('BertModel'						,      objects['torch']      )
										self.assertIn('TFBertModel'						,      objects['tf']      )
										self.assertIn('FlaxBertModel'						,      objects['flax']      )
										self.assertIn('BertModel'						,      objects['torch']      )
										self.assertIn('TFBertTokenizer'						,      objects['tensorflow_text']      )
										self.assertIn('convert_slow_tokenizer'						,      objects['sentencepiece_and_tokenizers']      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     create_dummy_object('CONSTANT'						,      '\'torch\''      )
										self.assertEqual(lowerCAmelCase						,      '\nCONSTANT = None\n'      )

										snake_case        =     create_dummy_object('function'						,      '\'torch\''      )
										self.assertEqual(
										    lowerCAmelCase						,      '\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'      )

										snake_case        =     '\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
										snake_case        =     create_dummy_object('FakeClass'						,      '\'torch\''      )
										self.assertEqual(lowerCAmelCase						,      lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
										snake_case        =     create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}      )
										self.assertEqual(dummy_files['torch']						,      lowerCAmelCase      )


