





import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__lowercase       :Any       =  {
    "sample_size": 32,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 1_000,
    "block_out_channels": [32, 64],
    "attention_head_dim": 8,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

__lowercase       :Any       =  {
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1_000,
    "block_out_channels": [192, 192 * 2, 192 * 3, 192 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

__lowercase       :Dict       =  {
    "sample_size": 256,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": None,
    "block_out_channels": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "default",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

__lowercase       :Union[str, Any]       =  {
    "num_train_timesteps": 40,
    "sigma_min": 0.0_0_2,
    "sigma_max": 8_0.0,
}

__lowercase       :List[str]       =  {
    "num_train_timesteps": 201,
    "sigma_min": 0.0_0_2,
    "sigma_max": 8_0.0,
}

__lowercase       :int       =  {
    "num_train_timesteps": 151,
    "sigma_min": 0.0_0_2,
    "sigma_max": 8_0.0,
}







def       UpperCAmelCase							(     _lowerCamelCase      :  List[str]     ):





						'''simple docstring'''


						if isinstance(__lowerCAmelCase			,   __lowerCAmelCase     ):
												return v
						if v.lower() in ("yes", "true", "t", "y", "1"):
												return True
						elif v.lower() in ("no", "false", "f", "n", "0"):
												return False
						else:
												raise argparse.ArgumentTypeError("boolean value expected"     )







def       UpperCAmelCase							(     _lowerCamelCase      :  Optional[int]			,   _lowerCamelCase      :  Dict			,   _lowerCamelCase      :  Optional[int]			,   _lowerCamelCase      :  Optional[int]			,   _lowerCamelCase      :  Any=False     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Any   =					checkpoint[f"""{old_prefix}.in_layers.0.weight"""]
						SCREAMING_SNAKE_CASE__     :				str   =					checkpoint[f"""{old_prefix}.in_layers.0.bias"""]
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					checkpoint[f"""{old_prefix}.in_layers.2.weight"""]
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					checkpoint[f"""{old_prefix}.in_layers.2.bias"""]
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					checkpoint[f"""{old_prefix}.emb_layers.1.weight"""]
						SCREAMING_SNAKE_CASE__     :				str   =					checkpoint[f"""{old_prefix}.emb_layers.1.bias"""]
						SCREAMING_SNAKE_CASE__     :				str   =					checkpoint[f"""{old_prefix}.out_layers.0.weight"""]
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					checkpoint[f"""{old_prefix}.out_layers.0.bias"""]
						SCREAMING_SNAKE_CASE__     :				Tuple   =					checkpoint[f"""{old_prefix}.out_layers.3.weight"""]
						SCREAMING_SNAKE_CASE__     :				Tuple   =					checkpoint[f"""{old_prefix}.out_layers.3.bias"""]

						if has_skip:
												SCREAMING_SNAKE_CASE__     :				Tuple   =					checkpoint[f"""{old_prefix}.skip_connection.weight"""]
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					checkpoint[f"""{old_prefix}.skip_connection.bias"""]

						return new_checkpoint







def       UpperCAmelCase							(     _lowerCamelCase      :  int			,   _lowerCamelCase      :  Optional[int]			,   _lowerCamelCase      :  Union[str, Any]			,   _lowerCamelCase      :  Optional[int]			,   _lowerCamelCase      :  Optional[int]=None     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Any   =					checkpoint[f"""{old_prefix}.qkv.weight"""].chunk(3			,   dim=0     )
						SCREAMING_SNAKE_CASE__     :				Dict   =					checkpoint[f"""{old_prefix}.qkv.bias"""].chunk(3			,   dim=0     )

						SCREAMING_SNAKE_CASE__     :				Dict   =					checkpoint[f"""{old_prefix}.norm.weight"""]
						SCREAMING_SNAKE_CASE__     :				Any   =					checkpoint[f"""{old_prefix}.norm.bias"""]

						SCREAMING_SNAKE_CASE__     :				str   =					weight_q.squeeze(-1     ).squeeze(-1     )
						SCREAMING_SNAKE_CASE__     :				Tuple   =					bias_q.squeeze(-1     ).squeeze(-1     )
						SCREAMING_SNAKE_CASE__     :				Dict   =					weight_k.squeeze(-1     ).squeeze(-1     )
						SCREAMING_SNAKE_CASE__     :				int   =					bias_k.squeeze(-1     ).squeeze(-1     )
						SCREAMING_SNAKE_CASE__     :				str   =					weight_v.squeeze(-1     ).squeeze(-1     )
						SCREAMING_SNAKE_CASE__     :				Dict   =					bias_v.squeeze(-1     ).squeeze(-1     )

						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					(
						    checkpoint[f"""{old_prefix}.proj_out.weight"""].squeeze(-1     ).squeeze(-1     )
						)
						SCREAMING_SNAKE_CASE__     :				Dict   =					checkpoint[f"""{old_prefix}.proj_out.bias"""].squeeze(-1     ).squeeze(-1     )

						return new_checkpoint







def       UpperCAmelCase							(     _lowerCamelCase      :  Optional[Any]			,   _lowerCamelCase      :  str     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					torch.load(__lowerCAmelCase			,   map_location="cpu"     )
						SCREAMING_SNAKE_CASE__     :				Dict   =					{}

						SCREAMING_SNAKE_CASE__     :				Any   =					checkpoint["""time_embed.0.weight"""]
						SCREAMING_SNAKE_CASE__     :				str   =					checkpoint["""time_embed.0.bias"""]
						SCREAMING_SNAKE_CASE__     :				int   =					checkpoint["""time_embed.2.weight"""]
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					checkpoint["""time_embed.2.bias"""]

						if unet_config["num_class_embeds"] is not None:
												SCREAMING_SNAKE_CASE__     :				Dict   =					checkpoint["""label_emb.weight"""]

						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					checkpoint["""input_blocks.0.0.weight"""]
						SCREAMING_SNAKE_CASE__     :				Dict   =					checkpoint["""input_blocks.0.0.bias"""]

						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					unet_config["""down_block_types"""]
						SCREAMING_SNAKE_CASE__     :				List[str]   =					unet_config["""layers_per_block"""]
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					unet_config["""attention_head_dim"""]
						SCREAMING_SNAKE_CASE__     :				str   =					unet_config["""block_out_channels"""]
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					1
						SCREAMING_SNAKE_CASE__     :				Any   =					channels_list[0]

						for i, layer_type in enumerate(__lowerCAmelCase     ):
												SCREAMING_SNAKE_CASE__     :				Optional[int]   =					channels_list[i]
												SCREAMING_SNAKE_CASE__     :				List[Any]   =					current_channels != prev_channels
												if layer_type == "ResnetDownsampleBlock2D":
																		for j in range(__lowerCAmelCase     ):
																								SCREAMING_SNAKE_CASE__     :				Any   =					f"""down_blocks.{i}.resnets.{j}"""
																								SCREAMING_SNAKE_CASE__     :				Tuple   =					f"""input_blocks.{current_layer}.0"""
																								SCREAMING_SNAKE_CASE__     :				str   =					True if j == 0 and downsample_block_has_skip else False
																								SCREAMING_SNAKE_CASE__     :				Any   =					convert_resnet(__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			,   has_skip=__lowerCAmelCase     )
																								current_layer += 1

												elif layer_type == "AttnDownBlock2D":
																		for j in range(__lowerCAmelCase     ):
																								SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					f"""down_blocks.{i}.resnets.{j}"""
																								SCREAMING_SNAKE_CASE__     :				Optional[int]   =					f"""input_blocks.{current_layer}.0"""
																								SCREAMING_SNAKE_CASE__     :				int   =					True if j == 0 and downsample_block_has_skip else False
																								SCREAMING_SNAKE_CASE__     :				Optional[int]   =					convert_resnet(__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			,   has_skip=__lowerCAmelCase     )
																								SCREAMING_SNAKE_CASE__     :				Tuple   =					f"""down_blocks.{i}.attentions.{j}"""
																								SCREAMING_SNAKE_CASE__     :				Optional[int]   =					f"""input_blocks.{current_layer}.1"""
																								SCREAMING_SNAKE_CASE__     :				List[str]   =					convert_attention(
																								    __lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase     )
																								current_layer += 1

												if i != len(__lowerCAmelCase     ) - 1:
																		SCREAMING_SNAKE_CASE__     :				str   =					f"""down_blocks.{i}.downsamplers.0"""
																		SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					f"""input_blocks.{current_layer}.0"""
																		SCREAMING_SNAKE_CASE__     :				List[Any]   =					convert_resnet(__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase     )
																		current_layer += 1

												SCREAMING_SNAKE_CASE__     :				Tuple   =					current_channels

						# hardcoded the mid-block for now
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					"""mid_block.resnets.0"""
						SCREAMING_SNAKE_CASE__     :				List[str]   =					"""middle_block.0"""
						SCREAMING_SNAKE_CASE__     :				Any   =					convert_resnet(__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase     )
						SCREAMING_SNAKE_CASE__     :				str   =					"""mid_block.attentions.0"""
						SCREAMING_SNAKE_CASE__     :				Tuple   =					"""middle_block.1"""
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					convert_attention(__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase     )
						SCREAMING_SNAKE_CASE__     :				Any   =					"""mid_block.resnets.1"""
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					"""middle_block.2"""
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					convert_resnet(__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase     )

						SCREAMING_SNAKE_CASE__     :				Dict   =					0
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					unet_config["""up_block_types"""]

						for i, layer_type in enumerate(__lowerCAmelCase     ):
												if layer_type == "ResnetUpsampleBlock2D":
																		for j in range(layers_per_block + 1     ):
																								SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					f"""up_blocks.{i}.resnets.{j}"""
																								SCREAMING_SNAKE_CASE__     :				str   =					f"""output_blocks.{current_layer}.0"""
																								SCREAMING_SNAKE_CASE__     :				Tuple   =					convert_resnet(__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			,   has_skip=__lowerCAmelCase     )
																								current_layer += 1

																		if i != len(__lowerCAmelCase     ) - 1:
																								SCREAMING_SNAKE_CASE__     :				Any   =					f"""up_blocks.{i}.upsamplers.0"""
																								SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					f"""output_blocks.{current_layer-1}.1"""
																								SCREAMING_SNAKE_CASE__     :				Any   =					convert_resnet(__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase     )
												elif layer_type == "AttnUpBlock2D":
																		for j in range(layers_per_block + 1     ):
																								SCREAMING_SNAKE_CASE__     :				Dict   =					f"""up_blocks.{i}.resnets.{j}"""
																								SCREAMING_SNAKE_CASE__     :				Dict   =					f"""output_blocks.{current_layer}.0"""
																								SCREAMING_SNAKE_CASE__     :				int   =					convert_resnet(__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			,   has_skip=__lowerCAmelCase     )
																								SCREAMING_SNAKE_CASE__     :				Dict   =					f"""up_blocks.{i}.attentions.{j}"""
																								SCREAMING_SNAKE_CASE__     :				List[str]   =					f"""output_blocks.{current_layer}.1"""
																								SCREAMING_SNAKE_CASE__     :				str   =					convert_attention(
																								    __lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase     )
																								current_layer += 1

																		if i != len(__lowerCAmelCase     ) - 1:
																								SCREAMING_SNAKE_CASE__     :				List[Any]   =					f"""up_blocks.{i}.upsamplers.0"""
																								SCREAMING_SNAKE_CASE__     :				List[str]   =					f"""output_blocks.{current_layer-1}.2"""
																								SCREAMING_SNAKE_CASE__     :				Optional[int]   =					convert_resnet(__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase     )

						SCREAMING_SNAKE_CASE__     :				Any   =					checkpoint["""out.0.weight"""]
						SCREAMING_SNAKE_CASE__     :				Dict   =					checkpoint["""out.0.bias"""]
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					checkpoint["""out.2.weight"""]
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					checkpoint["""out.2.bias"""]

						return new_checkpoint


if __name__ == "__main__":
				__lowercase       :List[str]       =  argparse.ArgumentParser()

				parser.add_argument("--unet_path", default=None, type=str, required=True, help="Path to the unet.pt to convert.")
				parser.add_argument(
				    "--dump_path", default=None, type=str, required=True, help="Path to output the converted UNet model."
				)
				parser.add_argument("--class_cond", default=True, type=str, help="Whether the model is class-conditional.")

				__lowercase       :Union[str, Any]       =  parser.parse_args()
				__lowercase       :int       =  strabool(args.class_cond)

				__lowercase       :int       =  os.path.basename(args.unet_path)
				print(F"Checkpoint: {ckpt_name}")

				# Get U-Net config
				if "imagenet64" in ckpt_name:
								__lowercase       :Any       =  IMAGENET_64_UNET_CONFIG
				elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
								__lowercase       :int       =  LSUN_256_UNET_CONFIG
				elif "test" in ckpt_name:
								__lowercase       :Optional[int]       =  TEST_UNET_CONFIG
				else:
								raise ValueError(F"Checkpoint type {ckpt_name} is not currently supported.")

				if not args.class_cond:
								__lowercase       :Any       =  None

				__lowercase       :Optional[Any]       =  con_pt_to_diffuser(args.unet_path, unet_config)

				__lowercase       :Dict       =  UNetaDModel(**unet_config)
				image_unet.load_state_dict(converted_unet_ckpt)

				# Get scheduler config
				if "cd" in ckpt_name or "test" in ckpt_name:
								__lowercase       :Optional[int]       =  CD_SCHEDULER_CONFIG
				elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
								__lowercase       :int       =  CT_IMAGENET_64_SCHEDULER_CONFIG
				elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
								__lowercase       :List[str]       =  CT_LSUN_256_SCHEDULER_CONFIG
				else:
								raise ValueError(F"Checkpoint type {ckpt_name} is not currently supported.")

				__lowercase       :List[Any]       =  CMStochasticIterativeScheduler(**scheduler_config)

				__lowercase       :str       =  ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
				consistency_model.save_pretrained(args.dump_path)






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


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



class    _a     (				lowercase__							):






							"""simple docstring"""

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






							def __iter__(      self				:     str    )	->Tuple:
													SCREAMING_SNAKE_CASE__     :				str   =					[]
													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__     :				int   =					self.tokenizer(a							,	padding=a							,	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     (				lowercase__							):






							"""simple docstring"""

							def __init__(      self				:     Dict							,	a				:     int							,	a				:     int							,	a				:     Tuple    )	->Dict:
													SCREAMING_SNAKE_CASE__     :				Dict   =					start_length
													SCREAMING_SNAKE_CASE__     :				Any   =					eof_strings
													SCREAMING_SNAKE_CASE__     :				Any   =					tokenizer






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







def       UpperCAmelCase							(     _lowerCamelCase      :  Dict     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				List[Any]   =					re.split("(%s)" % "|".join(_lowerCamelCase     )			,   _lowerCamelCase     )
						# last string should be ""
						return "".join(string_list[:-2]     )







def       UpperCAmelCase							(     _lowerCamelCase      :  Dict			,   _lowerCamelCase      :  Optional[Any]			,   _lowerCamelCase      :  Tuple			,   _lowerCamelCase      :  str			,   _lowerCamelCase      :  int			,   _lowerCamelCase      :  str=20			,   **_lowerCamelCase      :  Dict     ):





						'''simple docstring'''


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

																		SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Any   =					accelerator.gather((generated_tokens, generated_tasks)     )
																		SCREAMING_SNAKE_CASE__     :				Dict   =					generated_tokens.cpu().numpy()
																		SCREAMING_SNAKE_CASE__     :				Any   =					generated_tasks.cpu().numpy()

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

						SCREAMING_SNAKE_CASE__     :				List[Any]   =					[[] for _ in range(_lowerCamelCase     )]
						for task, generated_tokens in gen_token_dict.items():
												for s in generated_tokens:
																		SCREAMING_SNAKE_CASE__     :				List[Any]   =					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							(     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					HfArgumentParser(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					parser.parse_args()

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

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

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

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

						# Generation settings
						SCREAMING_SNAKE_CASE__     :				List[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			,   _lowerCamelCase			,   _lowerCamelCase     )]     ),
						}

						# Load evaluation dataset and metric
						SCREAMING_SNAKE_CASE__     :				str   =					load_dataset("openai_humaneval"     )
						SCREAMING_SNAKE_CASE__     :				Any   =					load_metric("code_eval"     )

						SCREAMING_SNAKE_CASE__     :				Dict   =					args.num_tasks if args.num_tasks is not None else len(human_eval["test"]     )
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					args.n_samples // args.batch_size

						SCREAMING_SNAKE_CASE__     :				Dict   =					TokenizedDataset(_lowerCamelCase			,   human_eval["test"]			,   n_copies=_lowerCamelCase			,   n_tasks=_lowerCamelCase     )
						# do not confuse args.batch_size, which is actually the num_return_sequences
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					DataLoader(_lowerCamelCase			,   batch_size=1     )

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

						SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[int]   =					accelerator.prepare(_lowerCamelCase			,   _lowerCamelCase     )

						SCREAMING_SNAKE_CASE__     :				Tuple   =					complete_code(
						    _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   n_tasks=_lowerCamelCase			,   batch_size=args.batch_size			,   **_lowerCamelCase			,   )

						if accelerator.is_main_process:
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					[]

												for task in tqdm(range(_lowerCamelCase     )     ):
																		SCREAMING_SNAKE_CASE__     :				List[Any]   =					human_eval["test"][task]["test"]
																		SCREAMING_SNAKE_CASE__     :				List[Any]   =					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__     :				Dict   =					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()