import os
import sys
import unittest


__UpperCamelCase	: Dict    		=  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
__UpperCamelCase	: str    		=  os.path.join(git_repo_path, 'src', 'transformers')

__UpperCamelCase	: Optional[int]    		=  '\n{0} = None\n'

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


__UpperCamelCase	: Dict    		=  '\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'





class       lowercase__		(     unittest.TestCase):


						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							find_backend('''    _import_structure["models.albert"].append("AlbertTokenizerFast")'''		)
												self.assertIsNone(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Dict           =							find_backend('''    if not is_tokenizers_available():'''		)
												self.assertEqual(UpperCamelCase__					,	'''tokenizers'''		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							find_backend('''    if not is_tensorflow_text_available():'''		)
												self.assertEqual(UpperCamelCase__					,	'''tensorflow_text'''		)

												SCREAMING_SNAKE_CASE							:					Dict           =							find_backend('''    if not (is_sentencepiece_available() and is_tokenizers_available()):'''		)
												self.assertEqual(UpperCamelCase__					,	'''sentencepiece_and_tokenizers'''		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							find_backend(
												    '''    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'''		)
												self.assertEqual(UpperCamelCase__					,	'''sentencepiece_and_tensorflow_text'''		)

												SCREAMING_SNAKE_CASE							:					int           =							find_backend(
												    '''    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'''		)
												self.assertEqual(UpperCamelCase__					,	'''sentencepiece_and_tokenizers_and_vision'''		)


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							read_init()
												# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
												self.assertIn('''torch'''					,	UpperCamelCase__		)
												self.assertIn('''tensorflow_text'''					,	UpperCamelCase__		)
												self.assertIn('''sentencepiece_and_tokenizers'''					,	UpperCamelCase__		)

												# 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  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							create_dummy_object('''CONSTANT'''					,	'''\'torch\''''		)
												self.assertEqual(UpperCamelCase__					,	'''\nCONSTANT = None\n'''		)

												SCREAMING_SNAKE_CASE							:					Tuple           =							create_dummy_object('''function'''					,	'''\'torch\''''		)
												self.assertEqual(
												    UpperCamelCase__					,	'''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'''		)

												SCREAMING_SNAKE_CASE							:					str           =							'''
class FakeClass(metaclass=DummyObject):
    _backends = \'torch\'

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')
'''
												SCREAMING_SNAKE_CASE							:					str           =							create_dummy_object('''FakeClass'''					,	'''\'torch\''''		)
												self.assertEqual(UpperCamelCase__					,	UpperCamelCase__		)



						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							'''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
'''
												SCREAMING_SNAKE_CASE							:					str           =							create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']}		)
												self.assertEqual(dummy_files['''torch''']					,	UpperCamelCase__		)






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  __A ( self			:       Any		):





												'''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=UpperCamelCase__					,	)
												assert hasattr(self					,	'''env'''		)


						def  __A ( self			:       str					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





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


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       List[str]		):





												'''simple docstring'''





												TrainingJobAnalytics(UpperCamelCase__		).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""		)



						@parameterized.expand([(2,)]		)
						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							self.create_estimator(UpperCamelCase__		)

												# run training
												estimator.fit()

												# result dataframe
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

												# extract kpis
												SCREAMING_SNAKE_CASE							:					List[str]           =							list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']		)
												SCREAMING_SNAKE_CASE							:					Dict           =							list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']		)
												# get train time from SageMaker job, this includes starting, preprocessing, stopping
												SCREAMING_SNAKE_CASE							:					List[Any]           =							(
												    Session().describe_training_job(estimator.latest_training_job.name		).get('''TrainingTimeInSeconds'''					,	99_9999		)
												)

												# 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}					,	UpperCamelCase__		)





