

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


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

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							def __init__(     self			,				_a			,				_a			,				_a=None			,				_a=1			)       ->       Optional[int]:
												_a      :       List[Any]   =						tokenizer
												_a      :       Dict   =						dataset
												_a      :       str   =						len(_a			) if n_tasks is None else n_tasks
												_a      :       Any   =						n_copies




							def __iter__(     self			)       ->       Optional[int]:
												_a      :       Dict   =						[]
												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()			)
												_a      :       Dict   =						self.tokenizer(_a			,				padding=_a			,				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_						(     __lowercase  ):
							"""simple docstring"""







							def __init__(     self			,				_a			,				_a			,				_a			)       ->       List[Any]:
												_a      :       Optional[int]   =						start_length
												_a      :       Optional[int]   =						eof_strings
												_a      :       Dict   =						tokenizer




							def __call__(     self			,				_a			,				_a			,				**_a			)       ->       Union[str, Any]:
												_a      :       Any   =						self.tokenizer.batch_decode(input_ids[:, self.start_length :]			)
												_a      :       Tuple   =						[]
												for decoded_generation in decoded_generations:
																	done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings			)			)
												return all(_a			)



def   __UpperCAmelCase					(      __a :     Tuple      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       int   =						re.split('''(%s)''' % '''|'''.join(__a      )						,__a      )
					# last string should be ""
					return "".join(string_list[:-2]      )



def   __UpperCAmelCase					(      __a :     Dict						,__a :     List[Any]						,__a :     int						,__a :     List[Any]						,__a :     List[Any]						,__a :     Union[str, Any]=20						,**__a :     Union[str, Any]      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       int   =						defaultdict(__a      )  # dict of list of generated tokens
					for step, batch in tqdm(enumerate(__a      )      ):
										with torch.no_grad():
															_a      :       List[Any]   =						batch['''ids'''].shape[-1]
															_a      :       List[str]   =						accelerator.unwrap_model(__a      ).generate(
															    input_ids=batch['''ids'''][:, : batch['''input_len''']]						,num_return_sequences=__a						,**__a      )
															# each task is generated batch_size times
															_a      :       str   =						batch['''task_id'''].repeat(__a      )
															_a      :       Dict   =						accelerator.pad_across_processes(
															    __a						,dim=1						,pad_index=tokenizer.pad_token_id      )

															_a ,   _a      :       Tuple   =						accelerator.gather((generated_tokens, generated_tasks)      )
															_a      :       List[Any]   =						generated_tokens.cpu().numpy()
															_a      :       Dict   =						generated_tasks.cpu().numpy()

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

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



def   __UpperCAmelCase					(      )     ->	str:

					"""simple docstring"""

					_a      :       Union[str, Any]   =						HfArgumentParser(__a      )
					_a      :       List[Any]   =						parser.parse_args()

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

					if args.num_workers is None:
										_a      :       Optional[Any]   =						multiprocessing.cpu_count()

					# Use dataset load to feed to accelerate
					_a      :       str   =						Accelerator()
					set_seed(args.seed						,device_specific=__a      )

					# Load model and tokenizer
					_a      :       Union[str, Any]   =						AutoTokenizer.from_pretrained(args.model_ckpt      )
					_a      :       Dict   =						tokenizer.eos_token
					_a      :       Tuple   =						AutoModelForCausalLM.from_pretrained(args.model_ckpt      )

					# Generation settings
					_a      :       Any   =						{
					    '''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						,__a						,__a      )]      ),
					}

					# Load evaluation dataset and metric
					_a      :       Optional[Any]   =						load_dataset('''openai_humaneval'''      )
					_a      :       int   =						load_metric('''code_eval'''      )

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

					_a      :       List[Any]   =						TokenizedDataset(__a						,human_eval['''test''']						,n_copies=__a						,n_tasks=__a      )
					# do not confuse args.batch_size, which is actually the num_return_sequences
					_a      :       Union[str, Any]   =						DataLoader(__a						,batch_size=1      )

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

					_a ,   _a      :       Optional[int]   =						accelerator.prepare(__a						,__a      )

					_a      :       Dict   =						complete_code(
					    __a						,__a						,__a						,__a						,n_tasks=__a						,batch_size=args.batch_size						,**__a						,)

					if accelerator.is_main_process:
										_a      :       Union[str, Any]   =						[]

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

										# Evaluate completions with "code_eval" metric
										_a ,   _a      :       Optional[int]   =						code_eval_metric.compute(
										    references=__a						,predictions=__a						,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(__a						,__a      )


# 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 .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


lowerCamelCase_							      = logging.get_logger(__name__)







class      UpperCamelCase_			:
	__magic_name__       =							42
	__magic_name__       =							None





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( )		->     Optional[int]:
				raise NotImplementedError





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					Union[str, Any]		)		->     Optional[Any]:
				raise NotImplementedError





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					Union[str, Any]		)		->     str:
				raise NotImplementedError





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     Dict:
				if not self.is_available():
							raise RuntimeError(
							    f"""You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}."""		)





	@classmethod
	def 	_SCREAMING_SNAKE_CASE ( cls   :					Tuple		)		->     List[str]:
				return f"""`pip install {cls.pip_package or cls.name}`"""







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''optuna'''





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( )		->     List[str]:
				return is_optuna_available()





	def 	_SCREAMING_SNAKE_CASE ( self   :					int      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					Tuple		)		->     int:
				return run_hp_search_optuna(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]      ,      lowerCAmelCase_   :					Dict		)		->     Optional[Any]:
				return default_hp_space_optuna(lowerCAmelCase_		)







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''ray'''
	__magic_name__       =							'''\'ray[tune]\''''





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( )		->     Optional[int]:
				return is_ray_available()





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]      ,      lowerCAmelCase_   :					Union[str, Any]      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					Tuple		)		->     List[str]:
				return run_hp_search_ray(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str      ,      lowerCAmelCase_   :					Tuple		)		->     Optional[int]:
				return default_hp_space_ray(lowerCAmelCase_		)







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''sigopt'''





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( )		->     List[Any]:
				return is_sigopt_available()





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					str		)		->     str:
				return run_hp_search_sigopt(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					Any		)		->     Any:
				return default_hp_space_sigopt(lowerCAmelCase_		)







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''wandb'''





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( )		->     int:
				return is_wandb_available()





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict      ,      lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					Optional[Any]		)		->     Optional[Any]:
				return run_hp_search_wandb(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					List[str]		)		->     Any:
				return default_hp_space_wandb(lowerCAmelCase_		)





lowerCamelCase_							      = {
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}

def 							snake_case (  ):
			UpperCAmelCase_	:					List[str]     	=   [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
			if len(A__		) > 0:
						UpperCAmelCase_	:					Optional[Any]     	=   available_backends[0].name
						if len(A__		) > 1:
									logger.info(
									    F"""{len(A__		)} hyperparameter search backends available. Using {name} as the default."""		)
						return name
			raise RuntimeError(
			    "No hyperparameter search backend available.\n"
			    + "\n".join(
			        F""" - To install {backend.name} run {backend.pip_install()}"""
			        for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()		)		)



