




from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
__lowerCAmelCase	:       Dict     =	numpy.array([0, 0])
__lowerCAmelCase	:       Optional[int]     =	numpy.array([0.5, 0.8_6_6_0_2_5_4])
__lowerCAmelCase	:       Optional[int]     =	numpy.array([1, 0])
__lowerCAmelCase	:       Any     =	[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]




def     _UpperCamelCase       (				A	:      str,						A	:      List[Any]     ):






			'''simple docstring'''







			a					=    initial_vectors
			for _ in range(lowerCAmelCase__     ):
						a					=    iteration_step(lowerCAmelCase__     )
			return vectors




def     _UpperCamelCase       (				A	:      Dict     ):






			'''simple docstring'''







			a					=    []
			for i, start_vector in enumerate(vectors[:-1]     ):
						a					=    vectors[i + 1]
						new_vectors.append(lowerCAmelCase__     )
						a					=    end_vector - start_vector
						new_vectors.append(start_vector + difference_vector / 3     )
						new_vectors.append(
						    start_vector + difference_vector / 3 + rotate(difference_vector / 3,						60     )     )
						new_vectors.append(start_vector + difference_vector * 2 / 3     )
			new_vectors.append(vectors[-1]     )
			return new_vectors




def     _UpperCamelCase       (				A	:      List[str],						A	:      Tuple     ):






			'''simple docstring'''







			a					=    numpy.radians(lowerCAmelCase__     )
			a					,	a					=    numpy.cos(lowerCAmelCase__     ), numpy.sin(lowerCAmelCase__     )
			a					=    numpy.array(((c, -s), (s, c))     )
			return numpy.dot(lowerCAmelCase__,						lowerCAmelCase__     )




def     _UpperCamelCase       (				A	:      Any     ):






			'''simple docstring'''







			a					=    plt.gca()
			axes.set_aspect("equal"     )

			# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
			# y-coordinates as inputs, which are constructed from the vector-list using
			# zip()
			a					,	a					=    zip(*lowerCAmelCase__     )
			plt.plot(lowerCAmelCase__,						lowerCAmelCase__     )
			plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		__lowerCAmelCase	:       Union[str, Any]     =	iterate(INITIAL_VECTORS, 5)
		plot(processed_vectors)












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	:       Union[str, Any]     =	['\nclass', '\ndef', '\n#', '\n@', '\nprint', '\nif']




class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				def __init__(   self       :  Any      ,   __lowerCamelCase       :  List[str]      ,   __lowerCamelCase       :  Union[str, Any]      ,   __lowerCamelCase       :  Union[str, Any]=None      ,   __lowerCamelCase       :  Any=1							)      ->  Union[str, Any]:
							a					=    tokenizer
							a					=    dataset
							a					=    len(__lowerCamelCase							) if n_tasks is None else n_tasks
							a					=    n_copies





				def __iter__(   self       :  Tuple							)      ->  str:
							a					=    []
							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					=    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 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				def __init__(   self       :  int      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  Tuple							)      ->  Optional[Any]:
							a					=    start_length
							a					=    eof_strings
							a					=    tokenizer





				def __call__(   self       :  int      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  List[str]      ,   **__lowerCamelCase       :  Optional[int]							)      ->  Optional[Any]:
							a					=    self.tokenizer.batch_decode(input_ids[:, self.start_length :]							)
							a					=    []
							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     __magic_name__       (				A	:      List[Any]     ):






			'''simple docstring'''







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




def     __magic_name__       (				A	:      Union[str, Any],						A	:      Optional[Any],						A	:      List[Any],						A	:      Optional[Any],						A	:      List[str],						A	:      List[Any]=20,						**A	:      Union[str, Any]     ):






			'''simple docstring'''







			a					=    defaultdict(A     )  # dict of list of generated tokens
			for step, batch in tqdm(enumerate(A     )     ):
						with torch.no_grad():
									a					=    batch["ids"].shape[-1]
									a					=    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					=    batch["task_id"].repeat(A     )
									a					=    accelerator.pad_across_processes(
									    A,						dim=1,						pad_index=tokenizer.pad_token_id     )

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

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

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




def     __magic_name__       (				):






			'''simple docstring'''







			a					=    HfArgumentParser(A     )
			a					=    parser.parse_args()

			transformers.logging.set_verbosity_error()
			# enables code execution in code_eval metric
			a					=    args.HF_ALLOW_CODE_EVAL
			# make sure tokenizer plays nice with multiprocessing
			a					=    "false"

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

			# Use dataset load to feed to accelerate
			a					=    Accelerator()
			set_seed(args.seed,						device_specific=A     )

			# Load model and tokenizer
			a					=    AutoTokenizer.from_pretrained(args.model_ckpt     )
			a					=    tokenizer.eos_token
			a					=    AutoModelForCausalLM.from_pretrained(args.model_ckpt     )

			# Generation settings
			a					=    {
			    "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					=    load_dataset("openai_humaneval"     )
			a					=    load_metric("code_eval"     )

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

			a					=    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					=    DataLoader(A,						batch_size=1     )

			# Run a quick test to see if code evaluation is enabled
			try:
						a					=    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					=    accelerator.prepare(A,						A     )

			a					=    complete_code(
			    A,						A,						A,						A,						n_tasks=A,						batch_size=args.batch_size,						**A,						)

			if accelerator.is_main_process:
						a					=    []

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

						# Evaluate completions with "code_eval" metric
						a					,	a					=    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()






