
'''simple docstring'''



import json
import multiprocessing
import os
import re
from collections import defaultdict

import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList


_lowercase    =		["""\nclass""", """\ndef""", """\n#""", """\n@""", """\nprint""", """\nif"""]




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						def __init__( self   ,       _lowercase   ,       _lowercase   ,       _lowercase=None   ,       _lowercase=1				):

												"""simple docstring"""



												_lowerCAmelCase  						=			tokenizer
												_lowerCAmelCase  						=			dataset
												_lowerCAmelCase  						=			len(_lowercase				) if n_tasks is None else n_tasks
												_lowerCAmelCase  						=			n_copies




						def __iter__( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			[]
												for task in range(self.n_tasks				):
																		# without strip, the model generate commented codes ...
																		prompts.append(self.tokenizer.eos_token + self.dataset[task]["""prompt"""].strip()				)
												_lowerCAmelCase  						=			self.tokenizer(_lowercase   ,       padding=_lowercase   ,       return_tensors="""pt"""				)
												for task in range(self.n_tasks				):
																		for _ in range(self.n_copies				):
																								yield {
																								    "ids": outputs.input_ids[task],
																								    "task_id": task,
																								    "input_len": outputs.attention_mask[task].sum(),
																								}






class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						def __init__( self   ,       _lowercase   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			start_length
												_lowerCAmelCase  						=			eof_strings
												_lowerCAmelCase  						=			tokenizer




						def __call__( self   ,       _lowercase   ,       _lowercase   ,       **_lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.tokenizer.batch_decode(input_ids[:, self.start_length :]				)
												_lowerCAmelCase  						=			[]
												for decoded_generation in decoded_generations:
																		done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings				)				)
												return all(_lowercase				)






def A				(__lowerCamelCase				:Optional[int]		):
						_lowerCAmelCase  						=			re.split("""(%s)""" % """|""".join(SCREAMING_SNAKE_CASE_		)			,     SCREAMING_SNAKE_CASE_		)
						# last string should be ""
						return "".join(string_list[:-2]		)






def A				(__lowerCamelCase				:Optional[Any]			,     __lowerCamelCase				:Optional[int]			,     __lowerCamelCase				:Dict			,     __lowerCamelCase				:Tuple			,     __lowerCamelCase				:List[Any]			,     __lowerCamelCase				:Tuple=20			,     **__lowerCamelCase				:Tuple		):
						_lowerCAmelCase  						=			defaultdict(SCREAMING_SNAKE_CASE_		)  # dict of list of generated tokens
						for step, batch in tqdm(enumerate(SCREAMING_SNAKE_CASE_		)		):
												with torch.no_grad():
																		_lowerCAmelCase  						=			batch["""ids"""].shape[-1]
																		_lowerCAmelCase  						=			accelerator.unwrap_model(SCREAMING_SNAKE_CASE_		).generate(
																		    input_ids=batch["""ids"""][:, : batch["""input_len"""]]			,     num_return_sequences=SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_		)
																		# each task is generated batch_size times
																		_lowerCAmelCase  						=			batch["""task_id"""].repeat(SCREAMING_SNAKE_CASE_		)
																		_lowerCAmelCase  						=			accelerator.pad_across_processes(
																		    SCREAMING_SNAKE_CASE_			,     dim=1			,     pad_index=tokenizer.pad_token_id		)

																		_lowerCAmelCase						,   _lowerCAmelCase  						=			accelerator.gather((generated_tokens, generated_tasks)		)
																		_lowerCAmelCase  						=			generated_tokens.cpu().numpy()
																		_lowerCAmelCase  						=			generated_tasks.cpu().numpy()

																		for task, generated_tokens in zip(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_		):
																								gen_token_dict[task].append(SCREAMING_SNAKE_CASE_		)

						_lowerCAmelCase  						=			[[] for _ in range(SCREAMING_SNAKE_CASE_		)]
						for task, generated_tokens in gen_token_dict.items():
												for s in generated_tokens:
																		_lowerCAmelCase  						=			tokenizer.decode(SCREAMING_SNAKE_CASE_			,     skip_special_tokens=SCREAMING_SNAKE_CASE_			,     clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_		)
																		code_gens[task].append(remove_last_block(SCREAMING_SNAKE_CASE_		)		)
						return code_gens






def A				():
						_lowerCAmelCase  						=			HfArgumentParser(SCREAMING_SNAKE_CASE_		)
						_lowerCAmelCase  						=			parser.parse_args()

						transformers.logging.set_verbosity_error()
						# enables code execution in code_eval metric
						_lowerCAmelCase  						=			args.HF_ALLOW_CODE_EVAL
						# make sure tokenizer plays nice with multiprocessing
						_lowerCAmelCase  						=			"""false"""

						if args.num_workers is None:
												_lowerCAmelCase  						=			multiprocessing.cpu_count()

						# Use dataset load to feed to accelerate
						_lowerCAmelCase  						=			Accelerator()
						set_seed(args.seed			,     device_specific=SCREAMING_SNAKE_CASE_		)

						# Load model and tokenizer
						_lowerCAmelCase  						=			AutoTokenizer.from_pretrained(args.model_ckpt		)
						_lowerCAmelCase  						=			tokenizer.eos_token
						_lowerCAmelCase  						=			AutoModelForCausalLM.from_pretrained(args.model_ckpt		)

						# Generation settings
						_lowerCAmelCase  						=			{
						    """do_sample""": args.do_sample,
						    """temperature""": args.temperature,
						    """max_new_tokens""": args.max_new_tokens,
						    """top_p""": args.top_p,
						    """top_k""": args.top_k,
						    """stopping_criteria""": StoppingCriteriaList([EndOfFunctionCriteria(0			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_		)]		),
						}

						# Load evaluation dataset and metric
						_lowerCAmelCase  						=			load_dataset("""openai_humaneval"""		)
						_lowerCAmelCase  						=			load_metric("""code_eval"""		)

						_lowerCAmelCase  						=			args.num_tasks if args.num_tasks is not None else len(human_eval["""test"""]		)
						_lowerCAmelCase  						=			args.n_samples // args.batch_size

						_lowerCAmelCase  						=			TokenizedDataset(SCREAMING_SNAKE_CASE_			,     human_eval["""test"""]			,     n_copies=SCREAMING_SNAKE_CASE_			,     n_tasks=SCREAMING_SNAKE_CASE_		)
						# do not confuse args.batch_size, which is actually the num_return_sequences
						_lowerCAmelCase  						=			DataLoader(SCREAMING_SNAKE_CASE_			,     batch_size=1		)

						# Run a quick test to see if code evaluation is enabled
						try:
												_lowerCAmelCase  						=			code_eval_metric.compute(references=[""""""]			,     predictions=[[""""""]]		)
						except ValueError as exception:
												print(
												    """Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL=\"1\"`"""
												    """ flag to enable code evaluation."""		)
												raise exception

						_lowerCAmelCase						,   _lowerCAmelCase  						=			accelerator.prepare(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_		)

						_lowerCAmelCase  						=			complete_code(
						    SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     n_tasks=SCREAMING_SNAKE_CASE_			,     batch_size=args.batch_size			,     **SCREAMING_SNAKE_CASE_			,     )

						if accelerator.is_main_process:
												_lowerCAmelCase  						=			[]

												for task in tqdm(range(SCREAMING_SNAKE_CASE_		)		):
																		_lowerCAmelCase  						=			human_eval["""test"""][task]["""test"""]
																		_lowerCAmelCase  						=			f'check({human_eval["test"][task]["entry_point"]})'
																		references.append("""\n""" + test_func + """\n""" + entry_point		)

												# Evaluate completions with "code_eval" metric
												_lowerCAmelCase						,   _lowerCAmelCase  						=			code_eval_metric.compute(
												    references=SCREAMING_SNAKE_CASE_			,     predictions=SCREAMING_SNAKE_CASE_			,     num_workers=args.num_workers		)
												print(f'Results: {pass_at_k}'		)

												# Save results to json file
												with open(args.output_file			,     """w"""		) as fp:
																		json.dump(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_		)


# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
						main()


'''simple docstring'''



from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_lowercase    =		10


def A				(__lowerCamelCase				:int			,     __lowerCamelCase				:int			,     __lowerCamelCase				:list[int]			,     __lowerCamelCase				:int		):
						for i in range(__lowerCamelCase			,     __lowerCamelCase		):
												if array[i] == target:
																		return i
						return -1


def A				(__lowerCamelCase				:list[int]			,     __lowerCamelCase				:int		):
						_lowerCAmelCase  						=			0
						_lowerCAmelCase  						=			len(__lowerCamelCase		)
						while left <= right:
												if right - left < precision:
																		return lin_search(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase		)

												_lowerCAmelCase  						=			(left + right) // 3 + 1
												_lowerCAmelCase  						=			2 * (left + right) // 3 + 1

												if array[one_third] == target:
																		return one_third
												elif array[two_third] == target:
																		return two_third

												elif target < array[one_third]:
																		_lowerCAmelCase  						=			one_third - 1
												elif array[two_third] < target:
																		_lowerCAmelCase  						=			two_third + 1

												else:
																		_lowerCAmelCase  						=			one_third + 1
																		_lowerCAmelCase  						=			two_third - 1
						else:
												return -1


def A				(__lowerCamelCase				:int			,     __lowerCamelCase				:int			,     __lowerCamelCase				:list[int]			,     __lowerCamelCase				:int		):
						if left < right:
												if right - left < precision:
																		return lin_search(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase		)
												_lowerCAmelCase  						=			(left + right) // 3 + 1
												_lowerCAmelCase  						=			2 * (left + right) // 3 + 1

												if array[one_third] == target:
																		return one_third
												elif array[two_third] == target:
																		return two_third

												elif target < array[one_third]:
																		return rec_ternary_search(__lowerCamelCase			,     one_third - 1			,     __lowerCamelCase			,     __lowerCamelCase		)
												elif array[two_third] < target:
																		return rec_ternary_search(two_third + 1			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase		)
												else:
																		return rec_ternary_search(one_third + 1			,     two_third - 1			,     __lowerCamelCase			,     __lowerCamelCase		)
						else:
												return -1


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						_lowercase    =		input("""Enter numbers separated by comma:\n""").strip()
						_lowercase    =		[int(item.strip()) for item in user_input.split(""",""")]
						assert collection == sorted(collection), F"List must be ordered.\n{collection}."
						_lowercase    =		int(input("""Enter the number to be found in the list:\n""").strip())
						_lowercase    =		ite_ternary_search(collection, target)
						_lowercase    =		rec_ternary_search(0, len(collection) - 1, collection, target)
						if resulta != -1:
												print(F"""Iterative search: {target} found at positions: {resulta}""")
												print(F"""Recursive search: {target} found at positions: {resulta}""")
						else:
												print("""Not found""")
