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


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





class      lowerCAmelCase						(					_a			):



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__=None				,      lowerCAmelCase__=1       ):
            _A=	tokenizer
            _A=	dataset
            _A=	len(lowerCAmelCase__       ) if n_tasks is None else n_tasks
            _A=	n_copies





     def __iter__(					self       ):
            _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      lowerCAmelCase						(					_a			):



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	start_length
            _A=	eof_strings
            _A=	tokenizer





     def __call__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      **lowerCAmelCase__       ):
            _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 							UpperCamelCase				(    lowerCAmelCase_   )		->						Any:




       '''simple docstring'''






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

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_=20       ,					**lowerCAmelCase_   )		->						int:




       '''simple docstring'''






       _A=	defaultdict(lowerCAmelCase_   )  # dict of list of generated tokens
       for step, batch in tqdm(enumerate(lowerCAmelCase_   )   ):
              with torch.no_grad():
                     _A=	batch['ids'].shape[-1]
                     _A=	accelerator.unwrap_model(lowerCAmelCase_   ).generate(
                         input_ids=batch['ids'][:, : batch['input_len']]       ,					num_return_sequences=lowerCAmelCase_       ,					**lowerCAmelCase_   )
                     # each task is generated batch_size times
                     _A=	batch['task_id'].repeat(lowerCAmelCase_   )
                     _A=	accelerator.pad_across_processes(
                         lowerCAmelCase_       ,					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(lowerCAmelCase_       ,					lowerCAmelCase_   ):
                            gen_token_dict[task].append(lowerCAmelCase_   )

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

def 							UpperCamelCase				(    )		->						Tuple:




       '''simple docstring'''






       _A=	HfArgumentParser(lowerCAmelCase_   )
       _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=lowerCAmelCase_   )

       # 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       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )]   ),
       }

       # 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(lowerCAmelCase_       ,					human_eval['test']       ,					n_copies=lowerCAmelCase_       ,					n_tasks=lowerCAmelCase_   )
       # do not confuse args.batch_size, which is actually the num_return_sequences
       _A=	DataLoader(lowerCAmelCase_       ,					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(lowerCAmelCase_       ,					lowerCAmelCase_   )

       _A=	complete_code(
           lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					n_tasks=lowerCAmelCase_       ,					batch_size=args.batch_size       ,					**lowerCAmelCase_       ,					)

       if accelerator.is_main_process:
              _A=	[]

              for task in tqdm(range(lowerCAmelCase_   )   ):
                     _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=lowerCAmelCase_       ,					predictions=lowerCAmelCase_       ,					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(lowerCAmelCase_       ,					lowerCAmelCase_   )


# 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()
import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


UpperCAmelCase_								=							logging.get_logger(__name__)





class      lowerCAmelCase						(					_a			):



     def __init__(					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            warnings.warn(
                'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
                ' Please use PerceiverImageProcessor instead.'				,      lowerCAmelCase__				,      )
            super().__init__(*lowerCAmelCase__				,      **lowerCAmelCase__       )