



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 						__a (     __SCREAMING_SNAKE_CASE				):



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



	def __iter__(						self							:    Tuple)->							Dict:
						__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(__snake_case				,	padding=__snake_case				,	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 						__a (     __SCREAMING_SNAKE_CASE				):



	def __init__(						self							:    Optional[int]				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    str				,	snake_case_							:    int)->							Optional[Any]:
						__lowerCAmelCase	   =       start_length
						__lowerCAmelCase	   =       eof_strings
						__lowerCAmelCase	   =       tokenizer



	def __call__(						self							:    Any				,	snake_case_							:    str				,	snake_case_							:    Dict				,	**snake_case_							:    List[str])->							Optional[int]:
						__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(__snake_case)






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






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

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

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

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






def 					__lowerCAmelCase		(  )  ->						str:
					# Setup configuration
					__lowerCAmelCase	   =       HfArgumentParser(a_	)
					__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=a_	)

					# 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				,   a_				,   a_	)]	),
					}

					# 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(a_				,   human_eval["""test"""]				,   n_copies=a_				,   n_tasks=a_	)
					# do not confuse args.batch_size, which is actually the num_return_sequences
					__lowerCAmelCase	   =       DataLoader(a_				,   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	   =       accelerator.prepare(a_				,   a_	)

					__lowerCAmelCase	   =       complete_code(
					    a_				,   a_				,   a_				,   a_				,   n_tasks=a_				,   batch_size=args.batch_size				,   **a_				,   )

					if accelerator.is_main_process:
										__lowerCAmelCase	   =       []

										for task in tqdm(range(a_	)	):
															__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	   =       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()









def 					__lowerCAmelCase		(  __lowerCamelCase	:    List[Any]	)  ->						Any:
					__lowerCAmelCase	   =[]
					__lowerCAmelCase	   =set({"""(""", """[""", """{"""}	)
					__lowerCAmelCase	   =set({""")""", """]""", """}"""}	)
					__lowerCAmelCase	   ={"""{""": """}""", """[""": """]""", """(""": """)"""}

					for i in range(len(__lowerCamelCase	)	):
										if s[i] in open_brackets:
															stack.append(s[i]	)

										elif s[i] in closed_brackets and (
										    len(__lowerCamelCase	) == 0 or (len(__lowerCamelCase	) > 0 and open_to_closed[stack.pop()] != s[i])
										):
															return False

					return len(__lowerCamelCase	) == 0






def 					__lowerCAmelCase		(  )  ->						List[str]:
					__lowerCAmelCase	   =input("""Enter sequence of brackets: """	)
					if is_balanced(__lowerCamelCase	):
										print(__lowerCamelCase				,   """is balanced"""	)
					else:
										print(__lowerCamelCase				,   """is not balanced"""	)


if __name__ == "__main__":
				main()




