

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





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




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





   def __iter__(       self					:			Dict			):
       SCREAMING_SNAKE_CASE										=   []
       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()			)
       SCREAMING_SNAKE_CASE										=   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 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




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





   def __call__(       self					:			Dict     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			Union[str, Any]     , **__lowerCamelCase					:			Any			):
       SCREAMING_SNAKE_CASE										=   self.tokenizer.batch_decode(input_ids[:, self.start_length :]			)
       SCREAMING_SNAKE_CASE										=   []
       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 	__a     (      A__			:  Optional[Any]       ):
    SCREAMING_SNAKE_CASE										=   re.split("(%s)" % "|".join(A__       )			,  A__       )
    # last string should be ""
    return "".join(string_list[:-2]       )





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

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

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

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





def 	__a     (      ):
    # Setup configuration
    SCREAMING_SNAKE_CASE										=   HfArgumentParser(A__       )
    SCREAMING_SNAKE_CASE										=   parser.parse_args()

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

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

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

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

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

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

    SCREAMING_SNAKE_CASE										=   TokenizedDataset(A__			,  human_eval["test"]			,  n_copies=A__			,  n_tasks=A__       )
    # do not confuse args.batch_size, which is actually the num_return_sequences
    SCREAMING_SNAKE_CASE										=   DataLoader(A__			,  batch_size=1       )

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

    SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   accelerator.prepare(A__			,  A__       )

    SCREAMING_SNAKE_CASE										=   complete_code(
        A__			,  A__			,  A__			,  A__			,  n_tasks=A__			,  batch_size=args.batch_size			,  **A__			,  )

    if accelerator.is_main_process:
        SCREAMING_SNAKE_CASE										=   []

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

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



'''simple docstring'''
def 	_A  (       A							)  ->				list:
	lowercase						:		str   					=		int(A							)
	if n_element < 1:
		lowercase						:		List[Any]   					=		ValueError("a should be a positive number"							)
		raise my_error

	lowercase						:		List[Any]   					=		[1]
	lowercase					,		lowercase					,		lowercase						:		Tuple   					=		(0, 0, 0)
	lowercase						:		Dict   					=		1
	while index < n_element:
		while hamming_list[i] * 2 <= hamming_list[-1]:
			i += 1
		while hamming_list[j] * 3 <= hamming_list[-1]:
			j += 1
		while hamming_list[k] * 5 <= hamming_list[-1]:
			k += 1
		hamming_list.append(
		    min(hamming_list[i] * 2							,hamming_list[j] * 3							,hamming_list[k] * 5							)							)
		index += 1
	return hamming_list


if __name__ == "__main__":
					lowerCAmelCase   : Union[str, Any]             =				input("""Enter the last number (nth term) of the Hamming Number Series: """)
					print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
					lowerCAmelCase   : Tuple             =				hamming(int(n))
					print("""-----------------------------------------------------""")
					print(F'''The list with nth numbers is: {hamming_numbers}''')
					print("""-----------------------------------------------------""")


