'''simple docstring'''




import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed

import numpy as np

import datasets

from .execute import check_correctness


_lowercase		:				Dict	       =		"\\n@misc{chen2021evaluating,\n      title={Evaluating Large Language Models Trained on Code},\n      author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \\nand Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \\nand Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \\nand Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \\nand Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \\nand Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \\nand Mohammad Bavarian and Clemens Winter and Philippe Tillet \\nand Felipe Petroski Such and Dave Cummings and Matthias Plappert \\nand Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \\nand William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \\nand Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \\nand William Saunders and Christopher Hesse and Andrew N. Carr \\nand Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \\nand Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \\nand Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \\nand Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},\n      year={2021},\n      eprint={2107.03374},\n      archivePrefix={arXiv},\n      primaryClass={cs.LG}\n}\n"

_lowercase		:				Dict	       =		"\\nThis metric implements the evaluation harness for the HumanEval problem solving dataset\ndescribed in the paper \"Evaluating Large Language Models Trained on Code\"\n(https://arxiv.org/abs/2107.03374).\n"


_lowercase		:				Optional[int]	       =		"\nCalculates how good are predictions given some references, using certain scores\nArgs:\n    predictions: list of candidates to evaluate. Each candidates should be a list\n        of strings with several code candidates to solve the problem.\n    references: a list with a test for each prediction. Each test should evaluate the\n        correctness of a code candidate.\n    k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])\n    num_workers: number of workers used to evaluate the canidate programs (Default: 4).\n    timeout:\nReturns:\n    pass_at_k: dict with pass rates for each k\n    results: dict with granular results of each unittest\nExamples:\n    >>> code_eval = datasets.load_metric(\"code_eval\")\n    >>> test_cases = [\"assert add(2,3)==5\"]\n    >>> candidates = [[\"def add(a,b): return a*b\", \"def add(a, b): return a+b\"]]\n    >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])\n    >>> print(pass_at_k)\n    {'pass@1': 0.5, 'pass@2': 1.0}\n"


_lowercase		:				List[Any]	       =		"\n################################################################################\n                                  !!!WARNING!!!\n################################################################################\nThe \"code_eval\" metric executes untrusted model-generated code in Python.\nAlthough it is highly unlikely that model-generated code will do something\novertly malicious in response to this test suite, model-generated code may act\ndestructively due to a lack of model capability or alignment.\nUsers are strongly encouraged to sandbox this evaluation suite so that it\ndoes not perform destructive actions on their host or network. For more\ninformation on how OpenAI sandboxes its code, see the paper \"Evaluating Large\nLanguage Models Trained on Code\" (https://arxiv.org/abs/2107.03374).\n\nOnce you have read this disclaimer and taken appropriate precautions,\nset the environment variable HF_ALLOW_CODE_EVAL=\"1\". Within Python you can to this\nwith:\n\n>>> import os\n>>> os.environ[\"HF_ALLOW_CODE_EVAL\"] = \"1\"\n\n################################################################################\\n"

_lowercase		:				Any	       =		"The MIT License\n\nCopyright (c) OpenAI (https://openai.com)\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE."





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,  _KWARGS_DESCRIPTION)
class 					__magic_name__		(					datasets.Metric):







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]					):
										return datasets.MetricInfo(
										    # This is the description that will appear on the metrics page.
										    description=_DESCRIPTION       ,    citation=_CITATION       ,    inputs_description=_KWARGS_DESCRIPTION       ,    features=datasets.Features(
										        {
										            """predictions""": datasets.Sequence(datasets.Value("""string"""					)					),
										            """references""": datasets.Value("""string"""					),
										        }					)       ,    homepage="""https://github.com/openai/human-eval"""       ,    codebase_urls=["""https://github.com/openai/human-eval"""]       ,    reference_urls=["""https://github.com/openai/human-eval"""]       ,    license=_LICENSE       ,    )







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]       ,    lowercase_       :						Optional[Any]       ,    lowercase_       :						int       ,    lowercase_       :						Optional[int]=[1, 10, 100]       ,    lowercase_       :						List[str]=4       ,    lowercase_       :						Any=3.0					):

										if os.getenv("""HF_ALLOW_CODE_EVAL"""       ,    0					) != "1":
																	raise ValueError(_WARNING					)

										if os.name == "nt":
																	raise NotImplementedError("""This metric is currently not supported on Windows."""					)

										with ThreadPoolExecutor(max_workers=lowercase_					) as executor:
																	lowercase_      :    Optional[Any]       		= []
																	lowercase_      :    List[str]       		= Counter()
																	lowercase_      :    Union[str, Any]       		= 0
																	lowercase_      :    List[str]       		= defaultdict(lowercase_					)

																	for task_id, (candidates, test_case) in enumerate(zip(lowercase_       ,    lowercase_					)					):
																								for candidate in candidates:
																															lowercase_      :    Any       		= candidate + """\n""" + test_case
																															lowercase_      :    Dict       		= (test_program, timeout, task_id, completion_id[task_id])
																															lowercase_      :    Union[str, Any]       		= executor.submit(lowercase_       ,    *lowercase_					)
																															futures.append(lowercase_					)
																															completion_id[task_id] += 1
																															n_samples += 1

																	for future in as_completed(lowercase_					):
																								lowercase_      :    Optional[Any]       		= future.result()
																								results[result["task_id"]].append((result["""completion_id"""], result)					)

										lowercase_      :    int       		= [], []
										for result in results.values():
																	result.sort()
																	lowercase_      :    str       		= [r[1]["""passed"""] for r in result]
																	total.append(len(lowercase_					)					)
																	correct.append(sum(lowercase_					)					)
										lowercase_      :    List[str]       		= np.array(lowercase_					)
										lowercase_      :    List[Any]       		= np.array(lowercase_					)

										lowercase_      :    Optional[int]       		= k
										lowercase_      :    List[Any]       		= {f'''pass@{k}''': estimate_pass_at_k(lowercase_       ,    lowercase_       ,    lowercase_					).mean() for k in ks if (total >= k).all()}

										return pass_at_k, results




def 							lowerCamelCase    (   UpperCAmelCase__ :      Any   ,   UpperCAmelCase__ :      Union[str, Any]   ,   UpperCAmelCase__ :      str				)    ->   int:

							def estimator(UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int				) -> float:
														if n - c < k:
																					return 1.0
														return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1   ,   n + 1				)				)

							if isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				):
														lowercase_      :    Any       		= itertools.repeat(UpperCAmelCase__   ,   len(UpperCAmelCase__				)				)
							else:
														assert len(UpperCAmelCase__				) == len(UpperCAmelCase__				)
														lowercase_      :    Union[str, Any]       		= iter(UpperCAmelCase__				)

							return np.array([estimator(int(UpperCAmelCase__				)   ,   int(UpperCAmelCase__				)   ,   UpperCAmelCase__				) for n, c in zip(UpperCAmelCase__   ,   UpperCAmelCase__				)]				)






'''simple docstring'''




import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


_lowercase		:				str	       =		datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
						import pyspark





@dataclass
class 					__magic_name__		(					datasets.BuilderConfig):
			UpperCamelCase__       = None




def 							lowerCamelCase			(   UpperCAmelCase__ :      "pyspark.sql.DataFrame"   ,   UpperCAmelCase__ :      List[int]   ,   )    ->   str:
							import pyspark

							def generate_fn():
														lowercase_      :    List[str]       		= df.select("""*"""   ,   pyspark.sql.functions.spark_partition_id().alias("""part_id"""				)				)
														for partition_id in partition_order:
																					lowercase_      :    int       		= df_with_partition_id.select("""*"""				).where(F'''part_id = {partition_id}'''				).drop("""part_id"""				)
																					lowercase_      :    Any       		= partition_df.collect()
																					lowercase_      :    Dict       		= 0
																					for row in rows:
																												yield F'''{partition_id}_{row_id}''', row.asDict()
																												row_id += 1

							return generate_fn





class 					__magic_name__		(					_BaseExamplesIterable):







			def __init__(			self       :						int       ,    lowercase_       :						"pyspark.sql.DataFrame"       ,    lowercase_       :						Optional[int]=None       ,    ):
										lowercase_      :    Dict       		= df
										lowercase_      :    Optional[Any]       		= partition_order or range(self.df.rdd.getNumPartitions()					)
										lowercase_      :    Optional[Any]       		= _generate_iterable_examples(self.df       ,    self.partition_order					)







			def __iter__(			self       :						List[Any]					):
										yield from self.generate_examples_fn()







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    lowercase_       :						np.random.Generator					):
										lowercase_      :    str       		= list(range(self.df.rdd.getNumPartitions()					)					)
										generator.shuffle(lowercase_					)
										return SparkExamplesIterable(self.df       ,    partition_order=lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    lowercase_       :						int       ,    lowercase_       :						int					):
										lowercase_      :    str       		= self.split_shard_indices_by_worker(lowercase_       ,    lowercase_					)
										return SparkExamplesIterable(self.df       ,    partition_order=lowercase_					)







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
										return len(self.partition_order					)





class 					__magic_name__		(					datasets.DatasetBuilder):
			UpperCamelCase__       = SparkConfig







			def __init__(			self       :						Tuple       ,    lowercase_       :						"pyspark.sql.DataFrame"       ,    lowercase_       :						str = None       ,    lowercase_       :						str = None       ,    **lowercase_       :						str       ,    ):
										import pyspark

										lowercase_      :    str       		= pyspark.sql.SparkSession.builder.getOrCreate()
										lowercase_      :    Optional[int]       		= df
										lowercase_      :    List[str]       		= working_dir

										super().__init__(
										    cache_dir=lowercase_       ,    config_name=str(self.df.semanticHash()					)       ,    **lowercase_       ,    )







			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										# Returns the path of the created file.
										def create_cache_and_write_probe(lowercase_       :						str					):
																	# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
																	# already exist.
																	os.makedirs(self._cache_dir       ,    exist_ok=lowercase_					)
																	lowercase_      :    List[str]       		= os.path.join(self._cache_dir       ,    """fs_test""" + uuid.uuida().hex					)
																	# Opening the file in append mode will create a new file unless it already exists, in which case it will not
																	# change the file contents.
																	open(lowercase_       ,    """a"""					)
																	return [probe_file]

										if self._spark.conf.get("""spark.master"""       ,    """"""					).startswith("""local"""					):
																	return

										# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
										# accessible to the driver.
										# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
										if self._cache_dir:
																	lowercase_      :    str       		= (
																	    self._spark.sparkContext.parallelize(range(1					)       ,    1					).mapPartitions(lowercase_					).collect()
																	)
																	if os.path.isfile(probe[0]					):
																								return

										raise ValueError(
										    """When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir"""					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
										return datasets.DatasetInfo(features=self.config.features					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]       ,    lowercase_       :						datasets.download.download_manager.DownloadManager					):
										return [datasets.SplitGenerator(name=datasets.Split.TRAIN					)]







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]       ,    lowercase_       :						Union[str, Any]					):
										import pyspark

										def get_arrow_batch_size(lowercase_       :						Any					):
																	for batch in it:
																								yield pa.RecordBatch.from_pydict({"""batch_bytes""": [batch.nbytes]}					)

										lowercase_      :    Union[str, Any]       		= self.df.count()
										lowercase_      :    Union[str, Any]       		= df_num_rows if df_num_rows <= 100 else 100
										# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
										lowercase_      :    Any       		= (
										    self.df.limit(lowercase_					)
										    .repartition(1					)
										    .mapInArrow(lowercase_       ,    """batch_bytes: long"""					)
										    .agg(pyspark.sql.functions.sum("""batch_bytes"""					).alias("""sample_bytes"""					)					)
										    .collect()[0]
										    .sample_bytes
										    / sample_num_rows
										)
										lowercase_      :    List[Any]       		= approx_bytes_per_row * df_num_rows
										if approx_total_size > max_shard_size:
																	# Make sure there is at least one row per partition.
																	lowercase_      :    Any       		= min(lowercase_       ,    int(approx_total_size / max_shard_size					)					)
																	lowercase_      :    Any       		= self.df.repartition(lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any       ,    lowercase_       :						str       ,    lowercase_       :						str       ,    lowercase_       :						int       ,    ):
										import pyspark

										lowercase_      :    Any       		= ParquetWriter if file_format == """parquet""" else ArrowWriter
										lowercase_      :    Dict       		= os.path.join(self._working_dir       ,    os.path.basename(lowercase_					)					) if self._working_dir else fpath
										lowercase_      :    Optional[Any]       		= file_format == """parquet"""

										# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
										# pickling the SparkContext.
										lowercase_      :    Tuple       		= self.config.features
										lowercase_      :    Any       		= self._writer_batch_size
										lowercase_      :    List[str]       		= self._fs.storage_options

										def write_arrow(lowercase_       :						str					):
																	# Within the same SparkContext, no two task attempts will share the same attempt ID.
																	lowercase_      :    List[str]       		= pyspark.TaskContext().taskAttemptId()
																	lowercase_      :    Dict       		= next(lowercase_       ,    lowercase_					)
																	if first_batch is None:
																								# Some partitions might not receive any data.
																								return pa.RecordBatch.from_arrays(
																								    [[task_id], [0], [0]]       ,    names=["""task_id""", """num_examples""", """num_bytes"""]       ,    )
																	lowercase_      :    int       		= 0
																	lowercase_      :    List[Any]       		= writer_class(
																	    features=lowercase_       ,    path=working_fpath.replace("""SSSSS"""       ,    f'''{shard_id:05d}'''					).replace("""TTTTT"""       ,    f'''{task_id:05d}'''					)       ,    writer_batch_size=lowercase_       ,    storage_options=lowercase_       ,    embed_local_files=lowercase_       ,    )
																	lowercase_      :    Optional[Any]       		= pa.Table.from_batches([first_batch]					)
																	writer.write_table(lowercase_					)
																	for batch in it:
																								if max_shard_size is not None and writer._num_bytes >= max_shard_size:
																															lowercase_		,		lowercase_      :    Dict       		= writer.finalize()
																															writer.close()
																															yield pa.RecordBatch.from_arrays(
																															    [[task_id], [num_examples], [num_bytes]]       ,    names=["""task_id""", """num_examples""", """num_bytes"""]       ,    )
																															shard_id += 1
																															lowercase_      :    Any       		= writer_class(
																															    features=writer._features       ,    path=working_fpath.replace("""SSSSS"""       ,    f'''{shard_id:05d}'''					).replace("""TTTTT"""       ,    f'''{task_id:05d}'''					)       ,    writer_batch_size=lowercase_       ,    storage_options=lowercase_       ,    embed_local_files=lowercase_       ,    )
																								lowercase_      :    List[str]       		= pa.Table.from_batches([batch]					)
																								writer.write_table(lowercase_					)

																	if writer._num_bytes > 0:
																								lowercase_		,		lowercase_      :    str       		= writer.finalize()
																								writer.close()
																								yield pa.RecordBatch.from_arrays(
																								    [[task_id], [num_examples], [num_bytes]]       ,    names=["""task_id""", """num_examples""", """num_bytes"""]       ,    )

																	if working_fpath != fpath:
																								for file in os.listdir(os.path.dirname(lowercase_					)					):
																															lowercase_      :    Optional[Any]       		= os.path.join(os.path.dirname(lowercase_					)       ,    os.path.basename(lowercase_					)					)
																															shutil.move(lowercase_       ,    lowercase_					)

										lowercase_      :    Union[str, Any]       		= (
										    self.df.mapInArrow(lowercase_       ,    """task_id: long, num_examples: long, num_bytes: long"""					)
										    .groupBy("""task_id"""					)
										    .agg(
										        pyspark.sql.functions.sum("""num_examples"""					).alias("""total_num_examples"""					)       ,    pyspark.sql.functions.sum("""num_bytes"""					).alias("""total_num_bytes"""					)       ,    pyspark.sql.functions.count("""num_bytes"""					).alias("""num_shards"""					)       ,    pyspark.sql.functions.collect_list("""num_examples"""					).alias("""shard_lengths"""					)       ,    )
										    .collect()
										)
										for row in stats:
																	yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict       ,    lowercase_       :						"datasets.SplitGenerator"       ,    lowercase_       :						str = "arrow"       ,    lowercase_       :						Optional[Union[str, int]] = None       ,    lowercase_       :						Optional[int] = None       ,    **lowercase_       :						List[str]       ,    ):
										self._validate_cache_dir()

										lowercase_      :    int       		= convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE					)
										self._repartition_df_if_needed(lowercase_					)
										lowercase_      :    Tuple       		= not is_remote_filesystem(self._fs					)
										lowercase_      :    int       		= os.path.join if is_local else posixpath.join

										lowercase_      :    Dict       		= """-TTTTT-SSSSS-of-NNNNN"""
										lowercase_      :    Dict       		= f'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
										lowercase_      :    Optional[int]       		= path_join(self._output_dir       ,    lowercase_					)

										lowercase_      :    Any       		= 0
										lowercase_      :    Tuple       		= 0
										lowercase_      :    int       		= 0
										lowercase_      :    Dict       		= []
										lowercase_      :    Union[str, Any]       		= []

										for task_id, content in self._prepare_split_single(lowercase_       ,    lowercase_       ,    lowercase_					):
																	(
																	    (
																	    lowercase_
																	)		,		(
																	    lowercase_
																	)		,		(
																	    lowercase_
																	)		,		(
																	    lowercase_
																	)		,		
																	)      :    Union[str, Any]       		= content
																	if num_bytes > 0:
																								total_num_examples += num_examples
																								total_num_bytes += num_bytes
																								total_shards += num_shards
																								task_id_and_num_shards.append((task_id, num_shards)					)
																								all_shard_lengths.extend(lowercase_					)

										lowercase_      :    List[str]       		= total_num_examples
										lowercase_      :    int       		= total_num_bytes

										# should rename everything at the end
										logger.debug(f'''Renaming {total_shards} shards.'''					)
										if total_shards > 1:
																	lowercase_      :    Tuple       		= all_shard_lengths

																	# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
																	# pickling error due to pickling the SparkContext.
																	lowercase_      :    Dict       		= self._fs

																	# use the -SSSSS-of-NNNNN pattern
																	def _rename_shard(
																	    lowercase_       :						int       ,    lowercase_       :						int       ,    lowercase_       :						int       ,    ):
																								rename(
																								    lowercase_       ,    fpath.replace("""SSSSS"""       ,    f'''{shard_id:05d}'''					).replace("""TTTTT"""       ,    f'''{task_id:05d}'''					)       ,    fpath.replace("""TTTTT-SSSSS"""       ,    f'''{global_shard_id:05d}'''					).replace("""NNNNN"""       ,    f'''{total_shards:05d}'''					)       ,    )

																	lowercase_      :    Union[str, Any]       		= []
																	lowercase_      :    Tuple       		= 0
																	for i in range(len(lowercase_					)					):
																								lowercase_		,		lowercase_      :    List[Any]       		= task_id_and_num_shards[i]
																								for shard_id in range(lowercase_					):
																															args.append([task_id, shard_id, global_shard_id]					)
																															global_shard_id += 1
																	self._spark.sparkContext.parallelize(lowercase_       ,    len(lowercase_					)					).map(lambda lowercase_					: _rename_shard(*lowercase_					)					).collect()
										else:
																	# don't use any pattern
																	lowercase_      :    List[str]       		= 0
																	lowercase_      :    Optional[Any]       		= task_id_and_num_shards[0][0]
																	self._rename(
																	    fpath.replace("""SSSSS"""       ,    f'''{shard_id:05d}'''					).replace("""TTTTT"""       ,    f'''{task_id:05d}'''					)       ,    fpath.replace(lowercase_       ,    """"""					)       ,    )







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]       ,    lowercase_       :						"datasets.SplitGenerator"       ,    ):
										return SparkExamplesIterable(self.df					)





