



"""simple docstring"""






import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil



class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       pa.array(TypedSequence([1, 2, 3]))
				self.assertEqual(arr.type       ,			pa.intaa())

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       Tuple:

				"""simple docstring"""

				with self.assertRaises(_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase      :  int												=       pa.array(TypedSequence([1, 2, 3])       ,			type=pa.intaa())

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       str:

				"""simple docstring"""

				with self.assertRaises(_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase      :  Optional[Any]												=       pa.array(TypedSequence([1, 2, 3]       ,			try_type=Value("bool")       ,			type=Value("int64")))

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any])				->       Dict:

				"""simple docstring"""

				__lowerCAmelCase      :  str												=       pa.array(TypedSequence([1, 2, 3]       ,			type=Value("int32")))
				self.assertEqual(arr.type       ,			pa.intaa())

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any])				->       Tuple:

				"""simple docstring"""

				with self.assertRaises((TypeError, pa.lib.ArrowInvalid)):
							__lowerCAmelCase      :  Optional[Any]												=       pa.array(TypedSequence(["foo", "bar"]       ,			type=Value("int64")))

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       Dict:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       pa.array(TypedSequence([1, 2, 3]       ,			try_type=Value("int32")))
				self.assertEqual(arr.type       ,			pa.intaa())

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Union[str, Any]												=       pa.array(TypedSequence(["foo", "bar"]       ,			try_type=Value("int64")))
				self.assertEqual(arr.type       ,			pa.string())

	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       pa.array(TypedSequence([[[1, 2, 3]]]       ,			type=ArrayaD((1, 3)       ,			"int64")))
				self.assertEqual(arr.type       ,			ArrayaDExtensionType((1, 3)       ,			"int64"))

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       Any:

				"""simple docstring"""

				with self.assertRaises((TypeError, pa.lib.ArrowInvalid)):
							__lowerCAmelCase      :  int												=       pa.array(TypedSequence(["foo", "bar"]       ,			type=ArrayaD((1, 3)       ,			"int64")))

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  int												=       pa.array(TypedSequence([[[1, 2, 3]]]       ,			try_type=ArrayaD((1, 3)       ,			"int64")))
				self.assertEqual(arr.type       ,			ArrayaDExtensionType((1, 3)       ,			"int64"))

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       List[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Union[str, Any]												=       pa.array(TypedSequence(["foo", "bar"]       ,			try_type=ArrayaD((1, 3)       ,			"int64")))
				self.assertEqual(arr.type       ,			pa.string())







	@require_pil
	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       Tuple:

				"""simple docstring"""

				import PIL.Image

				__lowerCAmelCase      :  Dict												=       PIL.Image.fromarray(np.arange(10       ,			dtype=np.uinta).reshape(2       ,			5))
				with patch(
				    "datasets.arrow_writer.cast_to_python_objects"       ,			side_effect=_SCREAMING_SNAKE_CASE) as mock_cast_to_python_objects:
							__lowerCAmelCase      :  Union[str, Any]												=       pa.array(TypedSequence([{"path": None, "bytes": b"image_bytes"}, pil_image]       ,			type=Image()))
							__lowerCAmelCase						,	__lowerCAmelCase      :  Optional[int]												=       mock_cast_to_python_objects.call_args_list[-1]
							self.assertIn("optimize_list_casting"       ,			_SCREAMING_SNAKE_CASE)
							self.assertFalse(kwargs["optimize_list_casting"])






def 			_lowercase				(  __snake_case					,__snake_case	)						->   Dict:
			__lowerCAmelCase      :  Dict												=       pa.BufferReader(__snake_case	) if isinstance(__snake_case					,pa.Buffer	) else pa.memory_map(__snake_case	)
			__lowerCAmelCase      :  Union[str, Any]												=       pa.ipc.open_stream(__snake_case	)
			__lowerCAmelCase      :  pa.Table												=       f.read_all()
			assert len(pa_table.to_batches()	) == expected_num_chunks
			assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
			del pa_table


@pytest.mark.parametrize("writer_batch_size"					,[None, 1, 10]	)
@pytest.mark.parametrize(
    "fields"					,[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]	)
def 			_lowercase				(  __snake_case					,__snake_case	)						->   Optional[Any]:
			__lowerCAmelCase      :  List[str]												=       pa.BufferOutputStream()
			__lowerCAmelCase      :  Optional[int]												=       pa.schema(__snake_case	) if fields else None
			with ArrowWriter(stream=__snake_case					,schema=__snake_case					,writer_batch_size=__snake_case	) as writer:
						writer.write({"col_1": "foo", "col_2": 1}	)
						writer.write({"col_1": "bar", "col_2": 2}	)
						__lowerCAmelCase						,	__lowerCAmelCase      :  List[Any]												=       writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			if not fields:
						__lowerCAmelCase      :  List[Any]												=       {"col_1": pa.string(), "col_2": pa.intaa()}
			assert writer._schema == pa.schema(__snake_case					,metadata=writer._schema.metadata	)
			_check_output(output.getvalue()					,expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)


def 			_lowercase				(  )						->   Optional[Any]:
			__lowerCAmelCase      :  Any												=       pa.BufferOutputStream()
			__lowerCAmelCase      :  str												=       Features({"labels": ClassLabel(names=["neg", "pos"]	)}	)
			with ArrowWriter(stream=__snake_case					,features=__snake_case	) as writer:
						writer.write({"labels": 0}	)
						writer.write({"labels": 1}	)
						__lowerCAmelCase						,	__lowerCAmelCase      :  Tuple												=       writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			assert writer._schema == features.arrow_schema
			assert writer._schema.metadata == features.arrow_schema.metadata
			__lowerCAmelCase      :  Tuple												=       pa.BufferReader(output.getvalue()	)
			__lowerCAmelCase      :  List[Any]												=       pa.ipc.open_stream(__snake_case	)
			__lowerCAmelCase      :  pa.Table												=       f.read_all()
			__lowerCAmelCase      :  Any												=       pa_table.schema
			assert pa_table.num_rows == 2
			assert schema == features.arrow_schema
			assert schema.metadata == features.arrow_schema.metadata
			assert features == Features.from_arrow_schema(__snake_case	)


@pytest.mark.parametrize("writer_batch_size"					,[None, 1, 10]	)
def 			_lowercase				(  __snake_case	)						->   int:
			__lowerCAmelCase      :  Union[str, Any]												=       pa.BufferOutputStream()
			with ArrowWriter(
			    stream=__snake_case					,writer_batch_size=__snake_case					,hash_salt="split_name"					,check_duplicates=__snake_case					,) as writer:
						with pytest.raises(__snake_case	):
									writer.write({"col_1": "foo", "col_2": 1}					,key=[1, 2]	)
									__lowerCAmelCase						,	__lowerCAmelCase      :  Optional[Any]												=       writer.finalize()


@pytest.mark.parametrize("writer_batch_size"					,[None, 2, 10]	)
def 			_lowercase				(  __snake_case	)						->   Dict:
			__lowerCAmelCase      :  Tuple												=       pa.BufferOutputStream()
			with ArrowWriter(
			    stream=__snake_case					,writer_batch_size=__snake_case					,hash_salt="split_name"					,check_duplicates=__snake_case					,) as writer:
						with pytest.raises(__snake_case	):
									writer.write({"col_1": "foo", "col_2": 1}					,key=10	)
									writer.write({"col_1": "bar", "col_2": 2}					,key=10	)
									__lowerCAmelCase						,	__lowerCAmelCase      :  Optional[Any]												=       writer.finalize()


@pytest.mark.parametrize("writer_batch_size"					,[None, 2, 10]	)
def 			_lowercase				(  __snake_case	)						->   Tuple:
			__lowerCAmelCase      :  List[str]												=       pa.BufferOutputStream()
			with ArrowWriter(
			    stream=__snake_case					,writer_batch_size=__snake_case					,hash_salt="split_name"					,check_duplicates=__snake_case					,) as writer:
						writer.write({"col_1": "foo", "col_2": 1}					,key=1	)
						writer.write({"col_1": "bar", "col_2": 2}					,key=2	)
						__lowerCAmelCase						,	__lowerCAmelCase      :  Union[str, Any]												=       writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			_check_output(output.getvalue()					,expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)


@pytest.mark.parametrize("writer_batch_size"					,[None, 1, 10]	)
@pytest.mark.parametrize(
    "fields"					,[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]	)
def 			_lowercase				(  __snake_case					,__snake_case	)						->   Tuple:
			__lowerCAmelCase      :  Dict												=       pa.BufferOutputStream()
			__lowerCAmelCase      :  Union[str, Any]												=       pa.schema(__snake_case	) if fields else None
			with ArrowWriter(stream=__snake_case					,schema=__snake_case					,writer_batch_size=__snake_case	) as writer:
						writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}	)
						writer.write_batch({"col_1": [], "col_2": []}	)
						__lowerCAmelCase						,	__lowerCAmelCase      :  str												=       writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			if not fields:
						__lowerCAmelCase      :  List[str]												=       {"col_1": pa.string(), "col_2": pa.intaa()}
			assert writer._schema == pa.schema(__snake_case					,metadata=writer._schema.metadata	)
			_check_output(output.getvalue()					,expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)


@pytest.mark.parametrize("writer_batch_size"					,[None, 1, 10]	)
@pytest.mark.parametrize(
    "fields"					,[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]	)
def 			_lowercase				(  __snake_case					,__snake_case	)						->   List[Any]:
			__lowerCAmelCase      :  int												=       pa.BufferOutputStream()
			__lowerCAmelCase      :  Tuple												=       pa.schema(__snake_case	) if fields else None
			with ArrowWriter(stream=__snake_case					,schema=__snake_case					,writer_batch_size=__snake_case	) as writer:
						writer.write_table(pa.Table.from_pydict({"col_1": ["foo", "bar"], "col_2": [1, 2]}	)	)
						__lowerCAmelCase						,	__lowerCAmelCase      :  Dict												=       writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			if not fields:
						__lowerCAmelCase      :  int												=       {"col_1": pa.string(), "col_2": pa.intaa()}
			assert writer._schema == pa.schema(__snake_case					,metadata=writer._schema.metadata	)
			_check_output(output.getvalue()					,expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)


@pytest.mark.parametrize("writer_batch_size"					,[None, 1, 10]	)
@pytest.mark.parametrize(
    "fields"					,[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]	)
def 			_lowercase				(  __snake_case					,__snake_case	)						->   int:
			__lowerCAmelCase      :  str												=       pa.BufferOutputStream()
			__lowerCAmelCase      :  Dict												=       pa.schema(__snake_case	) if fields else None
			with ArrowWriter(stream=__snake_case					,schema=__snake_case					,writer_batch_size=__snake_case	) as writer:
						writer.write_row(pa.Table.from_pydict({"col_1": ["foo"], "col_2": [1]}	)	)
						writer.write_row(pa.Table.from_pydict({"col_1": ["bar"], "col_2": [2]}	)	)
						__lowerCAmelCase						,	__lowerCAmelCase      :  Tuple												=       writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			if not fields:
						__lowerCAmelCase      :  int												=       {"col_1": pa.string(), "col_2": pa.intaa()}
			assert writer._schema == pa.schema(__snake_case					,metadata=writer._schema.metadata	)
			_check_output(output.getvalue()					,expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)


def 			_lowercase				(  )						->   Union[str, Any]:
			with tempfile.TemporaryDirectory() as tmp_dir:
						__lowerCAmelCase      :  int												=       {"col_1": pa.string(), "col_2": pa.intaa()}
						__lowerCAmelCase      :  Tuple												=       os.path.join(__snake_case					,"test.arrow"	)
						with ArrowWriter(path=__snake_case					,schema=pa.schema(__snake_case	)	) as writer:
									writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}	)
									__lowerCAmelCase						,	__lowerCAmelCase      :  Any												=       writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						assert writer._schema == pa.schema(__snake_case					,metadata=writer._schema.metadata	)
						_check_output(__snake_case					,1	)


def 			_lowercase				(  __snake_case	)						->   Dict:
			if pa.types.is_list(__snake_case	):
						return get_base_dtype(arr_type.value_type	)
			else:
						return arr_type


def 			_lowercase				(  __snake_case					,__snake_case	)						->   Tuple:
			if isinstance(lst[0]					,__snake_case	):
						change_first_primitive_element_in_list(lst[0]					,__snake_case	)
			else:
						__lowerCAmelCase      :  Tuple												=       value


@pytest.mark.parametrize("optimized_int_type, expected_dtype"					,[(None, pa.intaa()), (Value("int32"	), pa.intaa())]	)
@pytest.mark.parametrize("sequence"					,[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]	)
def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case	)						->   Any:
			__lowerCAmelCase      :  str												=       pa.array(TypedSequence(__snake_case					,optimized_int_type=__snake_case	)	)
			assert get_base_dtype(arr.type	) == expected_dtype


@pytest.mark.parametrize(
    "col, expected_dtype"					,[
        ("attention_mask", pa.inta()),
        ("special_tokens_mask", pa.inta()),
        ("token_type_ids", pa.inta()),
        ("input_ids", pa.intaa()),
        ("other", pa.intaa()),
    ]					,)
@pytest.mark.parametrize("sequence"					,[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]	)
def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case	)						->   List[Any]:
			# in range
			__lowerCAmelCase      :  Dict												=       pa.array(OptimizedTypedSequence(__snake_case					,col=__snake_case	)	)
			assert get_base_dtype(arr.type	) == expected_dtype

			# not in range
			if col != "other":
						# avoids errors due to in-place modifications
						__lowerCAmelCase      :  List[str]												=       copy.deepcopy(__snake_case	)
						__lowerCAmelCase      :  str												=       np.iinfo(expected_dtype.to_pandas_dtype()	).max + 1
						change_first_primitive_element_in_list(__snake_case					,__snake_case	)
						__lowerCAmelCase      :  Optional[int]												=       pa.array(OptimizedTypedSequence(__snake_case					,col=__snake_case	)	)
						assert get_base_dtype(arr.type	) == pa.intaa()


@pytest.mark.parametrize("raise_exception"					,[False, True]	)
def 			_lowercase				(  __snake_case					,__snake_case	)						->   Union[str, Any]:
			__lowerCAmelCase      :  str												=       str(tmp_path / "dataset-train.arrow"	)
			try:
						with ArrowWriter(path=__snake_case	) as writer:
									if raise_exception:
												raise pa.lib.ArrowInvalid()
									else:
												writer.stream.close()
			except pa.lib.ArrowInvalid:
						pass
			finally:
						assert writer.stream.closed


def 			_lowercase				(  __snake_case	)						->   Optional[int]:
			__lowerCAmelCase      :  List[Any]												=       "mock://dataset-train.arrow"
			with ArrowWriter(path=__snake_case					,storage_options=mockfs.storage_options	) as writer:
						assert isinstance(writer._fs					,type(__snake_case	)	)
						assert writer._fs.storage_options == mockfs.storage_options
						writer.write({"col_1": "foo", "col_2": 1}	)
						writer.write({"col_1": "bar", "col_2": 2}	)
						__lowerCAmelCase						,	__lowerCAmelCase      :  int												=       writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			assert mockfs.exists(__snake_case	)


def 			_lowercase				(  )						->   str:
			__lowerCAmelCase      :  Tuple												=       pa.BufferOutputStream()
			with ParquetWriter(stream=__snake_case	) as writer:
						writer.write({"col_1": "foo", "col_2": 1}	)
						writer.write({"col_1": "bar", "col_2": 2}	)
						__lowerCAmelCase						,	__lowerCAmelCase      :  Union[str, Any]												=       writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			__lowerCAmelCase      :  str												=       pa.BufferReader(output.getvalue()	)
			__lowerCAmelCase      :  pa.Table												=       pq.read_table(__snake_case	)
			assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}


@require_pil
@pytest.mark.parametrize("embed_local_files"					,[False, True]	)
def 			_lowercase				(  __snake_case					,__snake_case	)						->   Dict:
			import PIL.Image

			__lowerCAmelCase      :  List[Any]												=       str(tmp_path / "test_image_rgb.jpg"	)
			PIL.Image.fromarray(np.zeros((5, 5)					,dtype=np.uinta	)	).save(__snake_case					,format="png"	)
			__lowerCAmelCase      :  Any												=       pa.BufferOutputStream()
			with ParquetWriter(
			    stream=__snake_case					,features=Features({"image": Image()}	)					,embed_local_files=__snake_case	) as writer:
						writer.write({"image": image_path}	)
						writer.finalize()
			__lowerCAmelCase      :  int												=       pa.BufferReader(output.getvalue()	)
			__lowerCAmelCase      :  pa.Table												=       pq.read_table(__snake_case	)
			__lowerCAmelCase      :  List[Any]												=       pa_table.to_pydict()
			if embed_local_files:
						assert isinstance(out["image"][0]["path"]					,__snake_case	)
						with open(__snake_case					,"rb"	) as f:
									assert out["image"][0]["bytes"] == f.read()
			else:
						assert out["image"][0]["path"] == image_path
						assert out["image"][0]["bytes"] is None





def 			_lowercase				(  )						->   Optional[int]:
			__lowerCAmelCase      :  Dict												=       pa.schema([pa.field("col_1"					,pa.string()					,nullable=__snake_case	)]	)
			__lowerCAmelCase      :  List[str]												=       pa.BufferOutputStream()
			with ArrowWriter(stream=__snake_case	) as writer:
						writer._build_writer(inferred_schema=__snake_case	)
						assert writer._schema == pa.schema([pa.field("col_1"					,pa.string()	)]	)




"""simple docstring"""






import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import 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.g4dn.xlarge',
            'results': {'train_runtime': 6_5_0, 'eval_accuracy': 0.6, 'eval_loss': 0.9},
        },
        {
            'framework': 'tensorflow',
            'script': 'run_tf.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.g4dn.xlarge',
            'results': {'train_runtime': 6_0_0, 'eval_accuracy': 0.3, 'eval_loss': 0.9},
        },
    ]							)
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       Tuple:

				"""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=_SCREAMING_SNAKE_CASE       ,			)
				assert hasattr(self       ,			"env")

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any]       ,			_SCREAMING_SNAKE_CASE:		Any=1)				->       Dict:

				"""simple docstring"""

				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}-single"""       ,			instance_count=_SCREAMING_SNAKE_CASE       ,			instance_type=self.instance_type       ,			debugger_hook_config=_SCREAMING_SNAKE_CASE       ,			hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}       ,			metric_definitions=self.env.metric_definitions       ,			py_version="py36"       ,			)

	def 							_SCREAMING_SNAKE_CASE   (     self:		str       ,			_SCREAMING_SNAKE_CASE:		List[Any])				->       Optional[Any]:

				"""simple docstring"""

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







	def 							_SCREAMING_SNAKE_CASE   (     self:		str)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       self.create_estimator()

				# run training
				estimator.fit()

				# result dataframe
				__lowerCAmelCase      :  Tuple												=       TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

				# extract kpis
				__lowerCAmelCase      :  Optional[Any]												=       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      :  Tuple												=       (
				    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}       ,			_SCREAMING_SNAKE_CASE)