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   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):







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







							def __iter__(				self     :    Tuple		):
										lowerCamelCase						: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()		)
										lowerCamelCase						:List[str]						  =     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   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):







							def __init__(				self     :    Optional[int]	,					__snake_case     :    Union[str, Any]	,					__snake_case     :    str	,					__snake_case     :    int		):
										lowerCamelCase						:Dict						  =     start_length
										lowerCamelCase						:Any						  =     eof_strings
										lowerCamelCase						:int						  =     tokenizer







							def __call__(				self     :    Any	,					__snake_case     :    str	,					__snake_case     :    Dict	,					**__snake_case     :    List[str]		):
										lowerCamelCase						:Union[str, Any]						  =     self.tokenizer.batch_decode(input_ids[:, self.start_length :]		)
										lowerCamelCase						:Optional[Any]						  =     []
										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					(   a_				: Tuple):
			lowerCamelCase						:Tuple						  =     re.split('''(%s)''' % '''|'''.join(a_)						,			a_)
			# last string should be ""
			return "".join(string_list[:-2])





def  _lowerCamelCase					(   a_				: List[str]						,			a_				: str						,			a_				: Optional[int]						,			a_				: str						,			a_				: Optional[int]						,			a_				: Tuple=20						,			**a_				: int):
			lowerCamelCase						:Dict						  =     defaultdict(a_)  # dict of list of generated tokens
			for step, batch in tqdm(enumerate(a_)):
						with torch.no_grad():
									lowerCamelCase						:Optional[int]						  =     batch['''ids'''].shape[-1]
									lowerCamelCase						:Union[str, Any]						  =     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						:Any						  =     batch['''task_id'''].repeat(a_)
									lowerCamelCase						:Tuple						  =     accelerator.pad_across_processes(
									    a_						,			dim=1						,			pad_index=tokenizer.pad_token_id)

									lowerCamelCase  ,    lowerCamelCase						:List[str]						  =     accelerator.gather((generated_tokens, generated_tasks))
									lowerCamelCase						:Tuple						  =     generated_tokens.cpu().numpy()
									lowerCamelCase						:Tuple						  =     generated_tasks.cpu().numpy()

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

			lowerCamelCase						:Optional[Any]						  =     [[] for _ in range(a_)]
			for task, generated_tokens in gen_token_dict.items():
						for s in generated_tokens:
									lowerCamelCase						:List[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  _lowerCamelCase					(   ):
			# Setup configuration
			lowerCamelCase						:int						  =     HfArgumentParser(a_)
			lowerCamelCase						:Union[str, Any]						  =     parser.parse_args()

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

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

			# Use dataset load to feed to accelerate
			lowerCamelCase						:Union[str, Any]						  =     Accelerator()
			set_seed(args.seed						,			device_specific=a_)

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

			# Generation settings
			lowerCamelCase						: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
			lowerCamelCase						:int						  =     load_dataset('''openai_humaneval''')
			lowerCamelCase						:Dict						  =     load_metric('''code_eval''')

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

			lowerCamelCase						:Union[str, 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
			lowerCamelCase						:Dict						  =     DataLoader(a_						,			batch_size=1)

			# Run a quick test to see if code evaluation is enabled
			try:
						lowerCamelCase						: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

			lowerCamelCase  ,    lowerCamelCase						:Union[str, Any]						  =     accelerator.prepare(a_						,			a_)

			lowerCamelCase						:int						  =     complete_code(
			    a_						,			a_						,			a_						,			a_						,			n_tasks=a_						,			batch_size=args.batch_size						,			**a_						,			)

			if accelerator.is_main_process:
						lowerCamelCase						:Dict						  =     []

						for task in tqdm(range(a_)):
									lowerCamelCase						:Tuple						  =     human_eval['''test'''][task]['''test''']
									lowerCamelCase						:int						  =     F"check({human_eval['test'][task]['entry_point']})"
									references.append('''\n''' + test_func + '''\n''' + entry_point)

						# Evaluate completions with "code_eval" metric
						lowerCamelCase  ,    lowerCamelCase						:Any						  =     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()




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__           =							logging.get_logger(__name__)

A__           =							{
    """andreasmadsen/efficient_mlm_m0.40""": (
        """https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"""
    ),
}
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'roberta-prelayernorm'







							def __init__(				self     :    str	,					__snake_case     :    List[str]=50265	,					__snake_case     :    Union[str, Any]=768	,					__snake_case     :    Tuple=12	,					__snake_case     :    int=12	,					__snake_case     :    Any=3072	,					__snake_case     :    Optional[int]="gelu"	,					__snake_case     :    List[Any]=0.1	,					__snake_case     :    int=0.1	,					__snake_case     :    Union[str, Any]=512	,					__snake_case     :    Dict=2	,					__snake_case     :    int=0.0_2	,					__snake_case     :    Any=1e-1_2	,					__snake_case     :    Optional[int]=1	,					__snake_case     :    Dict=0	,					__snake_case     :    Optional[int]=2	,					__snake_case     :    Any="absolute"	,					__snake_case     :    Union[str, Any]=True	,					__snake_case     :    List[str]=None	,					**__snake_case     :    Optional[int]	,					):
										super().__init__(pad_token_id=__snake_case	,					bos_token_id=__snake_case	,					eos_token_id=__snake_case	,					**__snake_case		)

										lowerCamelCase						:Optional[int]						  =     vocab_size
										lowerCamelCase						:Dict						  =     hidden_size
										lowerCamelCase						:Tuple						  =     num_hidden_layers
										lowerCamelCase						:Optional[int]						  =     num_attention_heads
										lowerCamelCase						:Any						  =     hidden_act
										lowerCamelCase						:List[Any]						  =     intermediate_size
										lowerCamelCase						:Union[str, Any]						  =     hidden_dropout_prob
										lowerCamelCase						:str						  =     attention_probs_dropout_prob
										lowerCamelCase						:Tuple						  =     max_position_embeddings
										lowerCamelCase						:int						  =     type_vocab_size
										lowerCamelCase						:Optional[Any]						  =     initializer_range
										lowerCamelCase						:Union[str, Any]						  =     layer_norm_eps
										lowerCamelCase						:Dict						  =     position_embedding_type
										lowerCamelCase						:List[Any]						  =     use_cache
										lowerCamelCase						:Optional[int]						  =     classifier_dropout


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):







							@property
							def 				snake_case  (				self     :    Any		):
										if self.task == "multiple-choice":
													lowerCamelCase						:Optional[Any]						  =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
													lowerCamelCase						:List[str]						  =     {0: '''batch''', 1: '''sequence'''}
										return OrderedDict(
										    [
										        ('''input_ids''', dynamic_axis),
										        ('''attention_mask''', dynamic_axis),
										    ]		)



