



'''simple docstring'''

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

class      __magic_name__						(   _UpperCamelCase       ):







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







	def __iter__(							self :	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					:				int				=							self.tokenizer(_lowercase     ,padding=_lowercase     ,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      __magic_name__						(   _UpperCamelCase       ):







	def __init__(							self :	Optional[int]     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	Optional[int]     ,_UpperCAmelCase :	Tuple		):
			_a					:				List[str]				=							start_length
			_a					:				Optional[Any]				=							eof_strings
			_a					:				Optional[Any]				=							tokenizer







	def __call__(							self :	Dict     ,_UpperCAmelCase :	Optional[int]     ,_UpperCAmelCase :	Optional[Any]     ,**_UpperCAmelCase :	List[Any]		):
			_a					:				List[Any]				=							self.tokenizer.batch_decode(input_ids[:, self.start_length :]		)
			_a					:				Optional[int]				=							[]
			for decoded_generation in decoded_generations:
					done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings		)		)
			return all(_lowercase		)



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Any:
		_a					:				Union[str, Any]				=							re.split('(%s)' % '|'.join(SCREAMING_SNAKE_CASE_  )						,      SCREAMING_SNAKE_CASE_  )
		# last string should be ""
		return "".join(string_list[:-2]  )



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

						_a	, _a					:				Any				=							accelerator.gather((generated_tokens, generated_tasks)  )
						_a					:				str				=							generated_tokens.cpu().numpy()
						_a					:				Any				=							generated_tasks.cpu().numpy()

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

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



def 					__lowerCamelCase					(				)      ->				Union[str, Any]:
		_a					:				Dict				=							HfArgumentParser(SCREAMING_SNAKE_CASE_  )
		_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[Any]				=							'false'

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

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

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

		# Generation settings
		_a					:				Optional[int]				=							{
		    '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						,      SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_  )]  ),
		}

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

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

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

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

		_a	, _a					:				List[str]				=							accelerator.prepare(SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_  )

		_a					:				Tuple				=							complete_code(
		    SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_						,      n_tasks=SCREAMING_SNAKE_CASE_						,      batch_size=args.batch_size						,      **SCREAMING_SNAKE_CASE_						,      )

		if accelerator.is_main_process:
				_a					:				Any				=							[]

				for task in tqdm(range(SCREAMING_SNAKE_CASE_  )  ):
						_a					:				Dict				=							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					:				Tuple				=							code_eval_metric.compute(
				    references=SCREAMING_SNAKE_CASE_						,      predictions=SCREAMING_SNAKE_CASE_						,      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(SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_  )


# 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 __future__ import annotations

import collections
import pprint
from pathlib import Path



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				str:
		return "".join(sorted(lowerCAmelCase_  )  )



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				list[str]:
		return word_by_signature[signature(lowerCAmelCase_  )]


__lowerCAmelCase						      =       Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
__lowerCAmelCase						      =       sorted({word.strip().lower() for word in data.splitlines()})

__lowerCAmelCase						      =       collections.defaultdict(list)
for word in word_list:
							word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
							__lowerCAmelCase						      =       {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

							with open('''anagrams.txt''', '''w''') as file:
														file.write('''all_anagrams = \n ''')
														file.write(pprint.pformat(all_anagrams))




