from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)
def    A			(					_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE							:					List[Any]           =							[]
						for part_id in partition_order:
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							df.where(f"""SPARK_PARTITION_ID() = {part_id}"""		).collect()
												for row_idx, row in enumerate(_lowercase		):
																		expected_row_ids_and_row_dicts.append((f"""{part_id}_{row_idx}""", row.asDict())		)
						return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def    A			(					):
						SCREAMING_SNAKE_CASE							:					int           =							pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
						SCREAMING_SNAKE_CASE							:					Tuple           =							spark.range(100		).repartition(1		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							Spark(_lowercase		)
						# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
						# that each partition can hold 2 rows.
						spark_builder._repartition_df_if_needed(max_shard_size=16		)
						# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
						assert spark_builder.df.rdd.getNumPartitions() == 50
@require_not_windows
@require_dill_gt_0_3_2
def    A			(					):
						SCREAMING_SNAKE_CASE							:					Dict           =							pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
						SCREAMING_SNAKE_CASE							:					List[Any]           =							spark.range(10		).repartition(2		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							[1, 0]
						SCREAMING_SNAKE_CASE							:					int           =							_generate_iterable_examples(_lowercase	,						_lowercase		)  # Reverse the partitions.
						SCREAMING_SNAKE_CASE							:					str           =							_get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase	,						_lowercase		)

						for i, (row_id, row_dict) in enumerate(generate_fn()		):
												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Dict           =							expected_row_ids_and_row_dicts[i]
												assert row_id == expected_row_id
												assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def    A			(					):
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							spark.range(10		).repartition(1		)
						SCREAMING_SNAKE_CASE							:					List[Any]           =							SparkExamplesIterable(_lowercase		)
						assert it.n_shards == 1
						for i, (row_id, row_dict) in enumerate(_lowercase		):
												assert row_id == f"""0_{i}"""
												assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def    A			(					):
						SCREAMING_SNAKE_CASE							:					Tuple           =							pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
						SCREAMING_SNAKE_CASE							:					Dict           =							spark.range(30		).repartition(3		)
						# Mock the generator so that shuffle reverses the partition indices.
						with patch('''numpy.random.Generator'''		) as generator_mock:
												SCREAMING_SNAKE_CASE							:					Tuple           =							lambda _lowercase		: x.reverse()
												SCREAMING_SNAKE_CASE							:					Tuple           =							_get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase	,						[2, 1, 0]		)

												SCREAMING_SNAKE_CASE							:					str           =							SparkExamplesIterable(_lowercase		).shuffle_data_sources(_lowercase		)
												assert shuffled_it.n_shards == 3
												for i, (row_id, row_dict) in enumerate(_lowercase		):
																		SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					List[Any]           =							expected_row_ids_and_row_dicts[i]
																		assert row_id == expected_row_id
																		assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def    A			(					):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							spark.range(20		).repartition(4		)

						# Partitions 0 and 2
						SCREAMING_SNAKE_CASE							:					int           =							SparkExamplesIterable(_lowercase		).shard_data_sources(worker_id=0	,						num_workers=2		)
						assert shard_it_a.n_shards == 2
						SCREAMING_SNAKE_CASE							:					str           =							_get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase	,						[0, 2]		)
						for i, (row_id, row_dict) in enumerate(_lowercase		):
												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					str           =							expected_row_ids_and_row_dicts_a[i]
												assert row_id == expected_row_id
												assert row_dict == expected_row_dict

						# Partitions 1 and 3
						SCREAMING_SNAKE_CASE							:					Any           =							SparkExamplesIterable(_lowercase		).shard_data_sources(worker_id=1	,						num_workers=2		)
						assert shard_it_a.n_shards == 2
						SCREAMING_SNAKE_CASE							:					Dict           =							_get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase	,						[1, 3]		)
						for i, (row_id, row_dict) in enumerate(_lowercase		):
												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Any           =							expected_row_ids_and_row_dicts_a[i]
												assert row_id == expected_row_id
												assert row_dict == expected_row_dict



@require_not_windows
@require_dill_gt_0_3_2
def    A			(					):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
						SCREAMING_SNAKE_CASE							:					List[Any]           =							spark.range(100		).repartition(1		)
						SCREAMING_SNAKE_CASE							:					Dict           =							Spark(_lowercase		)
						# Choose a small max_shard_size for maximum partitioning.
						spark_builder._repartition_df_if_needed(max_shard_size=1		)
						# The new number of partitions should not be greater than the number of rows.
						assert spark_builder.df.rdd.getNumPartitions() == 100






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__		)





