






import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
a_							=			logging.get_logger()


@dataclass
class    lowercase__   :
	a_   			=42
	a_   			=field(default_factory=__snake_case		)
	a_   			=field(default_factory=__snake_case		)

	def 						UpperCAmelCase     (							self						,			__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase				)->    Any:







							'''simple docstring'''





							lowerCAmelCase__       					=  len(list(m.modules()				)				) == 1 or isinstance(__SCREAMING_SNAKE_CASE						,			nn.Convad				) or isinstance(__SCREAMING_SNAKE_CASE						,			nn.BatchNormad				)
							if has_not_submodules:
													self.traced.append(__SCREAMING_SNAKE_CASE				)

	def __call__(							self						,			__UpperCAmelCase				)->    Union[str, Any]:







							'''simple docstring'''





							for m in self.module.modules():
													self.handles.append(m.register_forward_hook(self._forward_hook				)				)
							self.module(__SCREAMING_SNAKE_CASE				)
							[x.remove() for x in self.handles]
							return self


	@property
	def 						UpperCAmelCase     (							self				)->    List[str]:







							'''simple docstring'''





							return list(filter(lambda __UpperCAmelCase				: len(list(x.state_dict().keys()				)				) > 0						,			self.traced				)				)





@dataclass
class    lowercase__   :
	a_   			=42
	a_   			=42
	a_   			=1
	a_   			=field(default_factory=__snake_case		)
	a_   			=field(default_factory=__snake_case		)
	a_   			=True

	def __call__(							self						,			__UpperCAmelCase				)->    Tuple:







							'''simple docstring'''





							lowerCAmelCase__       					=  Tracker(self.dest				)(__SCREAMING_SNAKE_CASE				).parametrized
							lowerCAmelCase__       					=  Tracker(self.src				)(__SCREAMING_SNAKE_CASE				).parametrized

							lowerCAmelCase__       					=  list(filter(lambda __UpperCAmelCase				: type(__SCREAMING_SNAKE_CASE				) not in self.src_skip						,			__SCREAMING_SNAKE_CASE				)				)
							lowerCAmelCase__       					=  list(filter(lambda __UpperCAmelCase				: type(__SCREAMING_SNAKE_CASE				) not in self.dest_skip						,			__SCREAMING_SNAKE_CASE				)				)

							if len(__SCREAMING_SNAKE_CASE				) != len(__SCREAMING_SNAKE_CASE				) and self.raise_if_mismatch:
													raise Exception(
													    F"Numbers of operations are different. Source module has {len(__SCREAMING_SNAKE_CASE				)} operations while"
													    F" destination module has {len(__SCREAMING_SNAKE_CASE				)}."				)

							for dest_m, src_m in zip(__SCREAMING_SNAKE_CASE						,			__SCREAMING_SNAKE_CASE				):
													dest_m.load_state_dict(src_m.state_dict()				)
													if self.verbose == 1:
																			print(F"Transfered from={src_m} to={dest_m}"				)





class    lowercase__   (	nn.Module		):

	def __init__(							self						,			__UpperCAmelCase				)->    Tuple:







							'''simple docstring'''





							super().__init__()

							lowerCAmelCase__       					=  []
							# - get the stem
							feature_blocks.append(("conv1", model.stem)				)
							# - get all the feature blocks
							for k, v in model.trunk_output.named_children():
													assert k.startswith("block"				), F"Unexpected layer name {k}"
													lowerCAmelCase__       					=  len(__SCREAMING_SNAKE_CASE				) + 1
													feature_blocks.append((F"res{block_index}", v)				)

							lowerCAmelCase__       					=  nn.ModuleDict(__SCREAMING_SNAKE_CASE				)


	def 						UpperCAmelCase     (							self						,			__UpperCAmelCase				)->    int:







							'''simple docstring'''





							return get_trunk_forward_outputs(
							    __SCREAMING_SNAKE_CASE						,			out_feat_keys=__SCREAMING_SNAKE_CASE						,			feature_blocks=self._feature_blocks						,			)





class    lowercase__   (	__snake_case		):

	def 						UpperCAmelCase     (							self						,			__UpperCAmelCase				)->    str:







							'''simple docstring'''





							lowerCAmelCase__       					=  x.split("-"				)
							return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]				)


	def __getitem__(							self						,			__UpperCAmelCase				)->    Callable[[], Tuple[nn.Module, Dict]]:







							'''simple docstring'''





							if x not in self:
													lowerCAmelCase__       					=  self.convert_name_to_timm(__SCREAMING_SNAKE_CASE				)
													lowerCAmelCase__       					=  partial(lambda: (timm.create_model(__SCREAMING_SNAKE_CASE						,			pretrained=__SCREAMING_SNAKE_CASE				).eval(), None)				)

							else:
													lowerCAmelCase__       					=  super().__getitem__(__SCREAMING_SNAKE_CASE				)

							return val





class    lowercase__   (	__snake_case		):

	def __getitem__(							self						,			__UpperCAmelCase				)->    Callable[[], nn.Module]:







							'''simple docstring'''





							if "seer" in x and "in1k" not in x:
													lowerCAmelCase__       					=  RegNetModel
							else:
													lowerCAmelCase__       					=  RegNetForImageClassification
							return val





def 				_a     (   UpperCamelCase_    : str			,      UpperCamelCase_    : List[Any]			,      UpperCamelCase_    : List[Tuple[str, str]]      )       ->      Tuple:
						"""simple docstring"""







						for from_key, to_key in keys:
												lowerCAmelCase__       					=  from_state_dict[from_key].clone()
												print(F"Copied key={from_key} to={to_key}"      )
						return to_state_dict


def 				_a     (   UpperCamelCase_    : str			,      UpperCamelCase_    : Callable[[], nn.Module]			,      UpperCamelCase_    : Callable[[], nn.Module]			,      UpperCamelCase_    : RegNetConfig			,      UpperCamelCase_    : Path			,      UpperCamelCase_    : bool = True			,      )       ->      Dict:
						"""simple docstring"""







						print(F"Converting {name}..."      )
						with torch.no_grad():
												lowerCAmelCase__					,							lowerCAmelCase__       					=  from_model_func()
												lowerCAmelCase__       					=  our_model_func(_lowerCamelCase      ).eval()
												lowerCAmelCase__       					=  ModuleTransfer(src=_lowerCamelCase			,      dest=_lowerCamelCase			,      raise_if_mismatch=_lowerCamelCase      )
												lowerCAmelCase__       					=  torch.randn((1, 3, 224, 224)      )
												module_transfer(_lowerCamelCase      )

						if from_state_dict is not None:
												lowerCAmelCase__       					=  []
												# for seer - in1k finetuned we have to manually copy the head
												if "seer" in name and "in1k" in name:
																		lowerCAmelCase__       					=  [("0.clf.0.weight", "classifier.1.weight"), ("0.clf.0.bias", "classifier.1.bias")]
												lowerCAmelCase__       					=  manually_copy_vissl_head(_lowerCamelCase			,      our_model.state_dict()			,      _lowerCamelCase      )
												our_model.load_state_dict(_lowerCamelCase      )

						lowerCAmelCase__       					=  our_model(_lowerCamelCase			,      output_hidden_states=_lowerCamelCase      )
						lowerCAmelCase__       					=  (
						    our_outputs.logits if isinstance(_lowerCamelCase			,      _lowerCamelCase      ) else our_outputs.last_hidden_state
						)

						lowerCAmelCase__       					=  from_model(_lowerCamelCase      )
						lowerCAmelCase__       					=  from_output[-1] if type(_lowerCamelCase      ) is list else from_output

						# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
						if "seer" in name and "in1k" in name:
												lowerCAmelCase__       					=  our_outputs.hidden_states[-1]

						assert torch.allclose(_lowerCamelCase			,      _lowerCamelCase      ), "The model logits don't match the original one."

						if push_to_hub:
												our_model.push_to_hub(
												    repo_path_or_name=save_directory / name			,      commit_message="Add model"			,      use_temp_dir=_lowerCamelCase			,      )

												lowerCAmelCase__       					=  224 if "seer" not in name else 384
												# we can use the convnext one
												lowerCAmelCase__       					=  AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"			,      size=_lowerCamelCase      )
												image_processor.push_to_hub(
												    repo_path_or_name=save_directory / name			,      commit_message="Add image processor"			,      use_temp_dir=_lowerCamelCase			,      )

												print(F"Pushed {name}"      )


def 				_a     (   UpperCamelCase_    : Path			,      UpperCamelCase_    : str = None			,      UpperCamelCase_    : bool = True      )       ->      Tuple:
						"""simple docstring"""







						lowerCAmelCase__       					=  "imagenet-1k-id2label.json"
						lowerCAmelCase__       					=  1_000
						lowerCAmelCase__       					=  (1, num_labels)

						lowerCAmelCase__       					=  "huggingface/label-files"
						lowerCAmelCase__       					=  num_labels
						lowerCAmelCase__       					=  json.load(open(cached_download(hf_hub_url(_lowerCamelCase			,      _lowerCamelCase			,      repo_type="dataset"      )      )			,      "r"      )      )
						lowerCAmelCase__       					=  {int(_lowerCamelCase      ): v for k, v in idalabel.items()}

						lowerCAmelCase__       					=  idalabel
						lowerCAmelCase__       					=  {v: k for k, v in idalabel.items()}

						lowerCAmelCase__       					=  partial(_lowerCamelCase			,      num_labels=_lowerCamelCase			,      idalabel=_lowerCamelCase			,      labelaid=_lowerCamelCase      )

						lowerCAmelCase__       					=  {
						    "regnet-x-002": ImageNetPreTrainedConfig(
						        depths=[1, 1, 4, 7]			,      hidden_sizes=[24, 56, 152, 368]			,      groups_width=8			,      layer_type="x"      ),
						    "regnet-x-004": ImageNetPreTrainedConfig(
						        depths=[1, 2, 7, 12]			,      hidden_sizes=[32, 64, 160, 384]			,      groups_width=16			,      layer_type="x"      ),
						    "regnet-x-006": ImageNetPreTrainedConfig(
						        depths=[1, 3, 5, 7]			,      hidden_sizes=[48, 96, 240, 528]			,      groups_width=24			,      layer_type="x"      ),
						    "regnet-x-008": ImageNetPreTrainedConfig(
						        depths=[1, 3, 7, 5]			,      hidden_sizes=[64, 128, 288, 672]			,      groups_width=16			,      layer_type="x"      ),
						    "regnet-x-016": ImageNetPreTrainedConfig(
						        depths=[2, 4, 10, 2]			,      hidden_sizes=[72, 168, 408, 912]			,      groups_width=24			,      layer_type="x"      ),
						    "regnet-x-032": ImageNetPreTrainedConfig(
						        depths=[2, 6, 15, 2]			,      hidden_sizes=[96, 192, 432, 1_008]			,      groups_width=48			,      layer_type="x"      ),
						    "regnet-x-040": ImageNetPreTrainedConfig(
						        depths=[2, 5, 14, 2]			,      hidden_sizes=[80, 240, 560, 1_360]			,      groups_width=40			,      layer_type="x"      ),
						    "regnet-x-064": ImageNetPreTrainedConfig(
						        depths=[2, 4, 10, 1]			,      hidden_sizes=[168, 392, 784, 1_624]			,      groups_width=56			,      layer_type="x"      ),
						    "regnet-x-080": ImageNetPreTrainedConfig(
						        depths=[2, 5, 15, 1]			,      hidden_sizes=[80, 240, 720, 1_920]			,      groups_width=120			,      layer_type="x"      ),
						    "regnet-x-120": ImageNetPreTrainedConfig(
						        depths=[2, 5, 11, 1]			,      hidden_sizes=[224, 448, 896, 2_240]			,      groups_width=112			,      layer_type="x"      ),
						    "regnet-x-160": ImageNetPreTrainedConfig(
						        depths=[2, 6, 13, 1]			,      hidden_sizes=[256, 512, 896, 2_048]			,      groups_width=128			,      layer_type="x"      ),
						    "regnet-x-320": ImageNetPreTrainedConfig(
						        depths=[2, 7, 13, 1]			,      hidden_sizes=[336, 672, 1_344, 2_520]			,      groups_width=168			,      layer_type="x"      ),
						    # y variant
						    "regnet-y-002": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]			,      hidden_sizes=[24, 56, 152, 368]			,      groups_width=8      ),
						    "regnet-y-004": ImageNetPreTrainedConfig(
						        depths=[1, 3, 6, 6]			,      hidden_sizes=[48, 104, 208, 440]			,      groups_width=8      ),
						    "regnet-y-006": ImageNetPreTrainedConfig(
						        depths=[1, 3, 7, 4]			,      hidden_sizes=[48, 112, 256, 608]			,      groups_width=16      ),
						    "regnet-y-008": ImageNetPreTrainedConfig(
						        depths=[1, 3, 8, 2]			,      hidden_sizes=[64, 128, 320, 768]			,      groups_width=16      ),
						    "regnet-y-016": ImageNetPreTrainedConfig(
						        depths=[2, 6, 17, 2]			,      hidden_sizes=[48, 120, 336, 888]			,      groups_width=24      ),
						    "regnet-y-032": ImageNetPreTrainedConfig(
						        depths=[2, 5, 13, 1]			,      hidden_sizes=[72, 216, 576, 1_512]			,      groups_width=24      ),
						    "regnet-y-040": ImageNetPreTrainedConfig(
						        depths=[2, 6, 12, 2]			,      hidden_sizes=[128, 192, 512, 1_088]			,      groups_width=64      ),
						    "regnet-y-064": ImageNetPreTrainedConfig(
						        depths=[2, 7, 14, 2]			,      hidden_sizes=[144, 288, 576, 1_296]			,      groups_width=72      ),
						    "regnet-y-080": ImageNetPreTrainedConfig(
						        depths=[2, 4, 10, 1]			,      hidden_sizes=[168, 448, 896, 2_016]			,      groups_width=56      ),
						    "regnet-y-120": ImageNetPreTrainedConfig(
						        depths=[2, 5, 11, 1]			,      hidden_sizes=[224, 448, 896, 2_240]			,      groups_width=112      ),
						    "regnet-y-160": ImageNetPreTrainedConfig(
						        depths=[2, 4, 11, 1]			,      hidden_sizes=[224, 448, 1_232, 3_024]			,      groups_width=112      ),
						    "regnet-y-320": ImageNetPreTrainedConfig(
						        depths=[2, 5, 12, 1]			,      hidden_sizes=[232, 696, 1_392, 3_712]			,      groups_width=232      ),
						    # models created by SEER -> https://arxiv.org/abs/2202.08360
						    "regnet-y-320-seer": RegNetConfig(depths=[2, 5, 12, 1]			,      hidden_sizes=[232, 696, 1_392, 3_712]			,      groups_width=232      ),
						    "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 12, 1]			,      hidden_sizes=[328, 984, 1_968, 4_920]			,      groups_width=328      ),
						    "regnet-y-1280-seer": RegNetConfig(
						        depths=[2, 7, 17, 1]			,      hidden_sizes=[528, 1_056, 2_904, 7_392]			,      groups_width=264      ),
						    "regnet-y-2560-seer": RegNetConfig(
						        depths=[3, 7, 16, 1]			,      hidden_sizes=[640, 1_696, 2_544, 5_088]			,      groups_width=640      ),
						    "regnet-y-10b-seer": ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]			,      hidden_sizes=[2_020, 4_040, 11_110, 28_280]			,      groups_width=1_010      ),
						    # finetuned on imagenet
						    "regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
						        depths=[2, 5, 12, 1]			,      hidden_sizes=[232, 696, 1_392, 3_712]			,      groups_width=232      ),
						    "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
						        depths=[2, 5, 12, 1]			,      hidden_sizes=[328, 984, 1_968, 4_920]			,      groups_width=328      ),
						    "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]			,      hidden_sizes=[528, 1_056, 2_904, 7_392]			,      groups_width=264      ),
						    "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
						        depths=[3, 7, 16, 1]			,      hidden_sizes=[640, 1_696, 2_544, 5_088]			,      groups_width=640      ),
						    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]			,      hidden_sizes=[2_020, 4_040, 11_110, 28_280]			,      groups_width=1_010      ),
						}

						lowerCAmelCase__       					=  NameToOurModelFuncMap()
						lowerCAmelCase__       					=  NameToFromModelFuncMap()
						# add seer weights logic

						def load_using_classy_vision(UpperCamelCase_    : str			,      UpperCamelCase_    : Callable[[], nn.Module]      ) -> Tuple[nn.Module, Dict]:
												lowerCAmelCase__       					=  torch.hub.load_state_dict_from_url(_lowerCamelCase			,      model_dir=str(_lowerCamelCase      )			,      map_location="cpu"      )
												lowerCAmelCase__       					=  model_func()
												# check if we have a head, if yes add it
												lowerCAmelCase__       					=  files["classy_state_dict"]["base_model"]["model"]
												lowerCAmelCase__       					=  model_state_dict["trunk"]
												model.load_state_dict(_lowerCamelCase      )
												return model.eval(), model_state_dict["heads"]

						# pretrained
						lowerCAmelCase__       					=  partial(
						    _lowerCamelCase			,      "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"			,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )			,      )

						lowerCAmelCase__       					=  partial(
						    _lowerCamelCase			,      "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"			,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )			,      )

						lowerCAmelCase__       					=  partial(
						    _lowerCamelCase			,      "https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"			,      lambda: FakeRegNetVisslWrapper(RegNetYaaagf()      )			,      )

						lowerCAmelCase__       					=  partial(
						    _lowerCamelCase			,      "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"			,      lambda: FakeRegNetVisslWrapper(
						        RegNet(RegNetParams(depth=27			,      group_width=1_010			,      w_a=1_744			,      w_a=620.83			,      w_m=2.52      )      )      )			,      )

						# IN1K finetuned
						lowerCAmelCase__       					=  partial(
						    _lowerCamelCase			,      "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"			,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )			,      )

						lowerCAmelCase__       					=  partial(
						    _lowerCamelCase			,      "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"			,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )			,      )

						lowerCAmelCase__       					=  partial(
						    _lowerCamelCase			,      "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"			,      lambda: FakeRegNetVisslWrapper(RegNetYaaagf()      )			,      )

						lowerCAmelCase__       					=  partial(
						    _lowerCamelCase			,      "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"			,      lambda: FakeRegNetVisslWrapper(
						        RegNet(RegNetParams(depth=27			,      group_width=1_010			,      w_a=1_744			,      w_a=620.83			,      w_m=2.52      )      )      )			,      )

						if model_name:
												convert_weight_and_push(
												    _lowerCamelCase			,      names_to_from_model_map[model_name]			,      names_to_ours_model_map[model_name]			,      names_to_config[model_name]			,      _lowerCamelCase			,      _lowerCamelCase			,      )
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(
																		    _lowerCamelCase			,      names_to_from_model_map[model_name]			,      names_to_ours_model_map[model_name]			,      _lowerCamelCase			,      _lowerCamelCase			,      _lowerCamelCase			,      )
						return config, expected_shape


if __name__ == "__main__":
	a_							=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--model_name''',
	    default=None,
	    type=str,
	    help=(
	        '''The name of the model you wish to convert, it must be one of the supported regnet* architecture,'''
	        ''' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'''
	    ),
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''',
	    default=None,
	    type=Path,
	    required=True,
	    help='''Path to the output PyTorch model directory.''',
	)
	parser.add_argument(
	    '''--push_to_hub''',
	    default=True,
	    type=bool,
	    required=False,
	    help='''If True, push model and image processor to the hub.''',
	)

	a_							=			parser.parse_args()

	a_							=			args.pytorch_dump_folder_path
	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
	convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)














"""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


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






class      a						(					__snake_case		):






    def __init__(       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							Any       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							Any=None       ,						__SCREAMING_SNAKE_CASE     :							Tuple=1					)     ->		str:
      lowerCamelCase_         =						tokenizer
      lowerCamelCase_         =						dataset
      lowerCamelCase_         =						len(__SCREAMING_SNAKE_CASE					) if n_tasks is None else n_tasks
      lowerCamelCase_         =						n_copies






    def __iter__(       self     :							Dict					)     ->		Any:
      lowerCamelCase_         =						[]
      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()					)
      lowerCamelCase_         =						self.tokenizer(__SCREAMING_SNAKE_CASE       ,						padding=__SCREAMING_SNAKE_CASE       ,						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      a						(					__snake_case		):






    def __init__(       self     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							str       ,						__SCREAMING_SNAKE_CASE     :							int       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		List[Any]:
      lowerCamelCase_         =						start_length
      lowerCamelCase_         =						eof_strings
      lowerCamelCase_         =						tokenizer






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







def      lowerCamelCase__       (							_lowerCamelCase       :		List[Any] )      ->				Tuple:
  lowerCamelCase_         =						re.split('(%s)' % '|'.join(_lowerCamelCase )		,				_lowerCamelCase )
  # last string should be ""
  return "".join(string_list[:-2] )







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

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

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

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







def      lowerCamelCase__       (							)      ->				Tuple:
  # Setup configuration
  lowerCamelCase_         =						HfArgumentParser(_lowerCamelCase )
  lowerCamelCase_         =						parser.parse_args()

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

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

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

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

  # Generation settings
  lowerCamelCase_         =						{
      '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
  lowerCamelCase_         =						load_dataset('openai_humaneval' )
  lowerCamelCase_         =						load_metric('code_eval' )

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

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

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

  lowerCamelCase_     ,	lowerCamelCase_         =						accelerator.prepare(_lowerCamelCase		,				_lowerCamelCase )

  lowerCamelCase_         =						complete_code(
      _lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase		,				n_tasks=_lowerCamelCase		,				batch_size=args.batch_size		,				**_lowerCamelCase		,				)

  if accelerator.is_main_process:
    lowerCamelCase_         =						[]

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

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




