
import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


A		: Dict								=     datasets.utils.logging.get_logger(__name__)



@dataclass
class    a_ (			datasets.BuilderConfig    ):
			a	:			int					   =							1_0_0_0_0
			a	:			Optional[List[str]]					   =							None
			a	:			Optional[datasets.Features]					   =							None







class    a_ (			datasets.ArrowBasedBuilder    ):
			a	:			List[Any]					   =							ParquetConfig






			def    UpperCamelCase_				(      self  ):
									return datasets.DatasetInfo(features=self.config.features  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									if not self.config.data_files:
															raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""  )
									_lowercase					       =					dl_manager.download_and_extract(self.config.data_files  )
									if isinstance(__UpperCamelCase				,			(str, list, tuple)  ):
															_lowercase					       =					data_files
															if isinstance(__UpperCamelCase				,			__UpperCamelCase  ):
																					_lowercase					       =					[files]
															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
															_lowercase					       =					[dl_manager.iter_files(__UpperCamelCase  ) for file in files]
															return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,			gen_kwargs={"""files""": files}  )]
									_lowercase					       =					[]
									for split_name, files in data_files.items():
															if isinstance(__UpperCamelCase				,			__UpperCamelCase  ):
																					_lowercase					       =					[files]
															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
															_lowercase					       =					[dl_manager.iter_files(__UpperCamelCase  ) for file in files]
															# Infer features is they are stoed in the arrow schema
															if self.info.features is None:
																					for file in itertools.chain.from_iterable(__UpperCamelCase  ):
																											with open(__UpperCamelCase				,			"""rb"""  ) as f:
																																	_lowercase					       =					datasets.Features.from_arrow_schema(pq.read_schema(__UpperCamelCase  )  )
																											break
															splits.append(datasets.SplitGenerator(name=__UpperCamelCase				,			gen_kwargs={"""files""": files}  )  )
									return splits






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									if self.info.features is not None:
															# more expensive cast to support nested features with keys in a different order
															# allows str <-> int/float or str to Audio for example
															_lowercase					       =					table_cast(__UpperCamelCase				,			self.info.features.arrow_schema  )
									return pa_table






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									_lowercase					       =					self.info.features.arrow_schema if self.info.features is not None else None
									if self.info.features is not None and self.config.columns is not None:
															if sorted(field.name for field in schema  ) != sorted(self.config.columns  ):
																					raise ValueError(
																					    f"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"""  )
									for file_idx, file in enumerate(itertools.chain.from_iterable(__UpperCamelCase  )  ):
															with open(__UpperCamelCase				,			"""rb"""  ) as f:
																					_lowercase					       =					pq.ParquetFile(__UpperCamelCase  )
																					try:
																											for batch_idx, record_batch in enumerate(
																											    parquet_file.iter_batches(batch_size=self.config.batch_size				,			columns=self.config.columns  )  ):
																																	_lowercase					       =					pa.Table.from_batches([record_batch]  )
																																	# Uncomment for debugging (will print the Arrow table size and elements)
																																	# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																	# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																	yield f"""{file_idx}_{batch_idx}""", self._cast_table(__UpperCamelCase  )
																					except ValueError as e:
																											logger.error(f"""Failed to read file '{file}' with error {type(__UpperCamelCase  )}: {e}"""  )
																											raise

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''': 1_6_0_0, '''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''': 1_6_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
    ]    )
class    a_ (			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=__UpperCamelCase				,			)
									assert hasattr(self				,			"""env"""  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									# configuration for running training on smdistributed Model Parallel
									_lowercase					       =					{
									    """enabled""": True,
									    """processes_per_host""": 8,
									}
									_lowercase					       =					{
									    """enabled""": True,
									    """parameters""": {
									        """microbatches""": 4,
									        """placement_strategy""": """spread""",
									        """pipeline""": """interleaved""",
									        """optimize""": """speed""",
									        """partitions""": 4,
									        """ddp""": True,
									    },
									}

									_lowercase					       =					{"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

									_lowercase					       =					"""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=__UpperCamelCase				,			instance_type=self.instance_type				,			debugger_hook_config=__UpperCamelCase				,			hyperparameters={
									        **self.env.hyperparameters,
									        """model_name_or_path""": self.model_name_or_path,
									        """max_steps""": 500,
									    }				,			metric_definitions=self.env.metric_definitions				,			distribution=__UpperCamelCase				,			py_version="""py36"""				,			)






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






			@parameterized.expand([(1,)]  )
			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									# create estimator
									_lowercase					       =					self.create_estimator(__UpperCamelCase  )

									# run training
									estimator.fit()

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

									# extract kpis
									_lowercase					       =					list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]  )
									_lowercase					       =					list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]  )
									# get train time from SageMaker job, this includes starting, preprocessing, stopping
									_lowercase					       =					(
									    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}				,			__UpperCamelCase  )