

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()






from __future__ import annotations

from dataclasses import dataclass

@dataclass
class 							UpperCAmelCase_						:
							"""simple docstring"""







							UpperCAmelCase__			:		float
							UpperCAmelCase__			:		TreeNode | None             =       None
							UpperCAmelCase__			:		TreeNode | None             =       None



def   __UpperCAmelCase					(      __a :     TreeNode | None      )     ->	bool:

					"""simple docstring"""

					def is_valid_tree(__a :     TreeNode | None      ) -> bool:
										if node is None:
															return True

										if not isinstance(__a						,__a      ):
															return False

										try:
															float(node.data      )
										except (TypeError, ValueError):
															return False

										return is_valid_tree(node.left      ) and is_valid_tree(node.right      )

					if not is_valid_tree(__a      ):
										raise ValueError(
										    '''Each node should be type of TreeNode and data should be float.'''      )

					def is_binary_search_tree_recursive_check(
					    __a :     TreeNode | None						,__a :     float						,__a :     float      ) -> bool:

										if node is None:
															return True

										return (
										    left_bound < node.data < right_bound
										    and is_binary_search_tree_recursive_check(node.left						,__a						,node.data      )
										    and is_binary_search_tree_recursive_check(
										        node.right						,node.data						,__a      )
										)

					return is_binary_search_tree_recursive_check(__a						,-float('''inf'''      )						,float('''inf'''      )      )


if __name__ == "__main__":
			import doctest

			doctest.testmod()



