'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase__  :						Tuple					=      {
    "configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
    "configuration_maskformer_swin": ["MaskFormerSwinConfig"],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__  :						List[str]					=      ["MaskFormerFeatureExtractor"]
  UpperCamelCase__  :						int					=      ["MaskFormerImageProcessor"]


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__  :						Optional[int]					=      [
      "MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MaskFormerForInstanceSegmentation",
      "MaskFormerModel",
      "MaskFormerPreTrainedModel",
  ]
  UpperCamelCase__  :						str					=      [
      "MaskFormerSwinBackbone",
      "MaskFormerSwinModel",
      "MaskFormerSwinPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
  from .configuration_maskformer_swin import MaskFormerSwinConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_maskformer import MaskFormerFeatureExtractor
    from .image_processing_maskformer import MaskFormerImageProcessor
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_maskformer import (
        MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        MaskFormerForInstanceSegmentation,
        MaskFormerModel,
        MaskFormerPreTrainedModel,
    )
    from .modeling_maskformer_swin import (
        MaskFormerSwinBackbone,
        MaskFormerSwinModel,
        MaskFormerSwinPreTrainedModel,
    )


else:
  import sys

  UpperCamelCase__  :						List[str]					=      _LazyModule(__name__, globals()['__file__'], _import_structure)





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


SCREAMING_SNAKE_CASE		:     Optional[int]         =	["\nclass", "\ndef", "\n#", "\n@", "\nprint", "\nif"]

class        UpperCamelCase						(				__a				):





						def __init__(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase=None			,     __UpperCamelCase=1						)				->				List[str]:
												UpperCamelCase_		:							Dict  =		tokenizer
												UpperCamelCase_		:							Dict  =		dataset
												UpperCamelCase_		:							str  =		len(__UpperCamelCase						) if n_tasks is None else n_tasks
												UpperCamelCase_		:							int  =		n_copies





						def __iter__(self						)				->				Any:
												UpperCamelCase_		:							int  =		[]
												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()						)
												UpperCamelCase_		:							Any  =		self.tokenizer(__UpperCamelCase			,     padding=__UpperCamelCase			,     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						(				__a				):





						def __init__(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase						)				->				Dict:
												UpperCamelCase_		:							List[Any]  =		start_length
												UpperCamelCase_		:							List[str]  =		eof_strings
												UpperCamelCase_		:							str  =		tokenizer





						def __call__(self			,     __UpperCamelCase			,     __UpperCamelCase			,     **__UpperCamelCase						)				->				Union[str, Any]:
												UpperCamelCase_		:							List[str]  =		self.tokenizer.batch_decode(input_ids[:, self.start_length :]						)
												UpperCamelCase_		:							List[Any]  =		[]
												for decoded_generation in decoded_generations:
																		done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings						)						)
												return all(__UpperCamelCase						)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Dict			):
						UpperCamelCase_		:							int  =		re.split("""(%s)""" % """|""".join(_SCREAMING_SNAKE_CASE			)		, _SCREAMING_SNAKE_CASE			)
						# last string should be ""
						return "".join(string_list[:-2]			)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Optional[int]		, _SCREAMING_SNAKE_CASE     :				Optional[Any]		, _SCREAMING_SNAKE_CASE     :				Optional[Any]		, _SCREAMING_SNAKE_CASE     :				int		, _SCREAMING_SNAKE_CASE     :				List[str]		, _SCREAMING_SNAKE_CASE     :				Optional[Any]=20		, **_SCREAMING_SNAKE_CASE     :				List[str]			):
						UpperCamelCase_		:							Union[str, Any]  =		defaultdict(_SCREAMING_SNAKE_CASE			)  # dict of list of generated tokens
						for step, batch in tqdm(enumerate(_SCREAMING_SNAKE_CASE			)			):
												with torch.no_grad():
																		UpperCamelCase_		:							str  =		batch["""ids"""].shape[-1]
																		UpperCamelCase_		:							List[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
																		UpperCamelCase_		:							List[str]  =		batch["""task_id"""].repeat(_SCREAMING_SNAKE_CASE			)
																		UpperCamelCase_		:							Union[str, Any]  =		accelerator.pad_across_processes(
																		    _SCREAMING_SNAKE_CASE		, dim=1		, pad_index=tokenizer.pad_token_id			)

																		UpperCamelCase_,UpperCamelCase_		:							Any  =		accelerator.gather((generated_tokens, generated_tasks)			)
																		UpperCamelCase_		:							str  =		generated_tokens.cpu().numpy()
																		UpperCamelCase_		:							List[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			)

						UpperCamelCase_		:							List[str]  =		[[] for _ in range(_SCREAMING_SNAKE_CASE			)]
						for task, generated_tokens in gen_token_dict.items():
												for s in generated_tokens:
																		UpperCamelCase_		:							List[Any]  =		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_			(   ):
						# Setup configuration
						UpperCamelCase_		:							List[Any]  =		HfArgumentParser(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							Optional[Any]  =		parser.parse_args()

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

						if args.num_workers is None:
												UpperCamelCase_		:							List[str]  =		multiprocessing.cpu_count()

						# Use dataset load to feed to accelerate
						UpperCamelCase_		:							Optional[Any]  =		Accelerator()
						set_seed(args.seed		, device_specific=_SCREAMING_SNAKE_CASE			)

						# Load model and tokenizer
						UpperCamelCase_		:							str  =		AutoTokenizer.from_pretrained(args.model_ckpt			)
						UpperCamelCase_		:							Any  =		tokenizer.eos_token
						UpperCamelCase_		:							Tuple  =		AutoModelForCausalLM.from_pretrained(args.model_ckpt			)

						# Generation settings
						UpperCamelCase_		:							Union[str, 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		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)]			),
						}

						# Load evaluation dataset and metric
						UpperCamelCase_		:							Optional[int]  =		load_dataset("""openai_humaneval"""			)
						UpperCamelCase_		:							Any  =		load_metric("""code_eval"""			)

						UpperCamelCase_		:							int  =		args.num_tasks if args.num_tasks is not None else len(human_eval["""test"""]			)
						UpperCamelCase_		:							List[str]  =		args.n_samples // args.batch_size

						UpperCamelCase_		:							Tuple  =		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
						UpperCamelCase_		:							List[str]  =		DataLoader(_SCREAMING_SNAKE_CASE		, batch_size=1			)

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

						UpperCamelCase_,UpperCamelCase_		:							int  =		accelerator.prepare(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)

						UpperCamelCase_		:							Optional[int]  =		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:
												UpperCamelCase_		:							List[str]  =		[]

												for task in tqdm(range(_SCREAMING_SNAKE_CASE			)			):
																		UpperCamelCase_		:							int  =		human_eval["""test"""][task]["""test"""]
																		UpperCamelCase_		:							Tuple  =		f'''check({human_eval["test"][task]["entry_point"]})'''
																		references.append("""\n""" + test_func + """\n""" + entry_point			)

												# Evaluate completions with "code_eval" metric
												UpperCamelCase_,UpperCamelCase_		:							Any  =		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()






