


'''simple docstring'''


from random import randint
from tempfile import TemporaryFile

import numpy as np






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   ):
		_UpperCAmelCase		:       Optional[Any]								=      0
		if start < end:
				_UpperCAmelCase		:       Tuple								=      randint(__lowerCAmelCase      , __lowerCAmelCase   )
				_UpperCAmelCase		:       Union[str, Any]								=      a[end]
				_UpperCAmelCase		:       List[Any]								=      a[pivot]
				_UpperCAmelCase		:       int								=      temp

				_UpperCAmelCase					,    _UpperCAmelCase		:       Union[str, Any]								=      _in_place_partition(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   )
				count += _in_place_quick_sort(__lowerCAmelCase      , __lowerCAmelCase      , p - 1   )
				count += _in_place_quick_sort(__lowerCAmelCase      , p + 1      , __lowerCAmelCase   )
		return count






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   ):
		_UpperCAmelCase		:       Tuple								=      0
		_UpperCAmelCase		:       Tuple								=      randint(__lowerCAmelCase      , __lowerCAmelCase   )
		_UpperCAmelCase		:       Optional[Any]								=      a[end]
		_UpperCAmelCase		:       int								=      a[pivot]
		_UpperCAmelCase		:       Optional[Any]								=      temp
		_UpperCAmelCase		:       Union[str, Any]								=      start - 1
		for index in range(__lowerCAmelCase      , __lowerCAmelCase   ):
				count += 1
				if a[index] < a[end]:  # check if current val is less than pivot value
						_UpperCAmelCase		:       Optional[int]								=      new_pivot_index + 1
						_UpperCAmelCase		:       Any								=      a[new_pivot_index]
						_UpperCAmelCase		:       int								=      a[index]
						_UpperCAmelCase		:       int								=      temp

		_UpperCAmelCase		:       Optional[Any]								=      a[new_pivot_index + 1]
		_UpperCAmelCase		:       Optional[Any]								=      a[end]
		_UpperCAmelCase		:       Tuple								=      temp
		return new_pivot_index + 1, count


lowerCamelCase__    	=     TemporaryFile()
lowerCamelCase__    	=     100  # 1000 elements are to be sorted


lowerCamelCase__	,lowerCamelCase__    	=     0, 1  # mean and standard deviation
lowerCamelCase__    	=     np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
lowerCamelCase__    	=     np.load(outfile)
lowerCamelCase__    	=     len(M) - 1
lowerCamelCase__    	=     _in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)





print(z)







'''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


lowerCamelCase__    	=     ['\nclass', '\ndef', '\n#', '\n@', '\nprint', '\nif']






class 	lowerCAmelCase__			(			UpperCAmelCase__	):




			def __init__(		self  :     Tuple	,  lowerCamelCase__  :     List[Any]	,  lowerCamelCase__  :     Optional[int]	,  lowerCamelCase__  :     Optional[Any]=None	,  lowerCamelCase__  :     int=1 )   ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      tokenizer
					_UpperCAmelCase		:       Tuple								=      dataset
					_UpperCAmelCase		:       Union[str, Any]								=      len(lowerCamelCase__ ) if n_tasks is None else n_tasks
					_UpperCAmelCase		:       Any								=      n_copies






			def __iter__(		self  :     Any )   ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      []
					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() )
					_UpperCAmelCase		:       Optional[Any]								=      self.tokenizer(lowerCamelCase__	,  padding=lowerCamelCase__	,  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 	lowerCAmelCase__			(			UpperCAmelCase__	):




			def __init__(		self  :     Optional[int]	,  lowerCamelCase__  :     Tuple	,  lowerCamelCase__  :     Any	,  lowerCamelCase__  :     Dict )   ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:       List[str]								=      start_length
					_UpperCAmelCase		:       Union[str, Any]								=      eof_strings
					_UpperCAmelCase		:       Union[str, Any]								=      tokenizer






			def __call__(		self  :     Tuple	,  lowerCamelCase__  :     Union[str, Any]	,  lowerCamelCase__  :     List[str]	,  **lowerCamelCase__  :     Optional[int] )   ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Tuple								=      self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
					_UpperCAmelCase		:       Optional[int]								=      []
					for decoded_generation in decoded_generations:
							done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
					return all(lowerCamelCase__ )






def  __lowerCAmelCase (__lowerCAmelCase   ):
		_UpperCAmelCase		:       Tuple								=      re.split("(%s)" % "|".join(__lowerCAmelCase   )      , __lowerCAmelCase   )
		# last string should be ""
		return "".join(string_list[:-2]   )






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

						_UpperCAmelCase					,    _UpperCAmelCase		:       int								=      accelerator.gather((generated_tokens, generated_tasks)   )
						_UpperCAmelCase		:       Dict								=      generated_tokens.cpu().numpy()
						_UpperCAmelCase		:       Dict								=      generated_tasks.cpu().numpy()

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

		_UpperCAmelCase		:       int								=      [[] for _ in range(__lowerCAmelCase   )]
		for task, generated_tokens in gen_token_dict.items():
				for s in generated_tokens:
						_UpperCAmelCase		:       List[Any]								=      tokenizer.decode(__lowerCAmelCase      , skip_special_tokens=__lowerCAmelCase      , clean_up_tokenization_spaces=__lowerCAmelCase   )
						code_gens[task].append(remove_last_block(__lowerCAmelCase   )   )
		return code_gens






def  __lowerCAmelCase ():
		# Setup configuration
		_UpperCAmelCase		:       List[str]								=      HfArgumentParser(__lowerCAmelCase   )
		_UpperCAmelCase		:       Tuple								=      parser.parse_args()

		transformers.logging.set_verbosity_error()
		# enables code execution in code_eval metric
		_UpperCAmelCase		:       Any								=      args.HF_ALLOW_CODE_EVAL
		# make sure tokenizer plays nice with multiprocessing
		_UpperCAmelCase		:       List[str]								=      "false"

		if args.num_workers is None:
				_UpperCAmelCase		:       List[str]								=      multiprocessing.cpu_count()

		# Use dataset load to feed to accelerate
		_UpperCAmelCase		:       List[Any]								=      Accelerator()
		set_seed(args.seed      , device_specific=__lowerCAmelCase   )

		# Load model and tokenizer
		_UpperCAmelCase		:       Tuple								=      AutoTokenizer.from_pretrained(args.model_ckpt   )
		_UpperCAmelCase		:       List[str]								=      tokenizer.eos_token
		_UpperCAmelCase		:       str								=      AutoModelForCausalLM.from_pretrained(args.model_ckpt   )

		# Generation settings
		_UpperCAmelCase		:       Tuple								=      {
		    "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      , __lowerCAmelCase      , __lowerCAmelCase   )]   ),
		}

		# Load evaluation dataset and metric
		_UpperCAmelCase		:       Union[str, Any]								=      load_dataset("openai_humaneval"   )
		_UpperCAmelCase		:       List[Any]								=      load_metric("code_eval"   )

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

		_UpperCAmelCase		:       Tuple								=      TokenizedDataset(__lowerCAmelCase      , human_eval["test"]      , n_copies=__lowerCAmelCase      , n_tasks=__lowerCAmelCase   )
		# do not confuse args.batch_size, which is actually the num_return_sequences
		_UpperCAmelCase		:       List[str]								=      DataLoader(__lowerCAmelCase      , batch_size=1   )

		# Run a quick test to see if code evaluation is enabled
		try:
				_UpperCAmelCase		:       Optional[int]								=      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

		_UpperCAmelCase					,    _UpperCAmelCase		:       Any								=      accelerator.prepare(__lowerCAmelCase      , __lowerCAmelCase   )

		_UpperCAmelCase		:       Dict								=      complete_code(
		    __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , n_tasks=__lowerCAmelCase      , batch_size=args.batch_size      , **__lowerCAmelCase      , )

		if accelerator.is_main_process:
				_UpperCAmelCase		:       List[Any]								=      []

				for task in tqdm(range(__lowerCAmelCase   )   ):
						_UpperCAmelCase		:       str								=      human_eval["test"][task]["test"]
						_UpperCAmelCase		:       Union[str, Any]								=      F"""check({human_eval['test'][task]['entry_point']})"""
						references.append("\n" + test_func + "\n" + entry_point   )

				# Evaluate completions with "code_eval" metric
				_UpperCAmelCase					,    _UpperCAmelCase		:       str								=      code_eval_metric.compute(
				    references=__lowerCAmelCase      , predictions=__lowerCAmelCase      , 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(__lowerCAmelCase      , __lowerCAmelCase   )


# 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()



