





import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling







def       UpperCAmelCase							(     _lowerCamelCase      :  int			,   _lowerCamelCase      :  Dict     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					old_name

						if "patch_embed" in old_name:
												SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					old_name.split("."     )

												if layer == "0":
																		SCREAMING_SNAKE_CASE__     :				Tuple   =					old_name.replace("0"			,   "convolution1"     )
												elif layer == "1":
																		SCREAMING_SNAKE_CASE__     :				int   =					old_name.replace("1"			,   "batchnorm_before"     )
												elif layer == "3":
																		SCREAMING_SNAKE_CASE__     :				List[str]   =					old_name.replace("3"			,   "convolution2"     )
												else:
																		SCREAMING_SNAKE_CASE__     :				List[str]   =					old_name.replace("4"			,   "batchnorm_after"     )

						if "network" in old_name and re.search(r"\d\.\d"			,   _lowerCamelCase     ):
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					r"\b\d{2}\b"
												if bool(re.search(_lowerCamelCase			,   _lowerCamelCase     )     ):
																		SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					re.search(r"\d\.\d\d."			,   _lowerCamelCase     ).group()
												else:
																		SCREAMING_SNAKE_CASE__     :				str   =					re.search(r"\d\.\d."			,   _lowerCamelCase     ).group()
												if int(match[0]     ) < 6:
																		SCREAMING_SNAKE_CASE__     :				Tuple   =					old_name.replace(_lowerCamelCase			,   ""     )
																		SCREAMING_SNAKE_CASE__     :				List[Any]   =					trimmed_name.replace("network"			,   match[0] + ".meta4D_layers.blocks." + match[2:-1]     )
																		SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					"intermediate_stages." + trimmed_name
												else:
																		SCREAMING_SNAKE_CASE__     :				str   =					old_name.replace(_lowerCamelCase			,   ""     )
																		if int(match[2]     ) < num_meta4D_last_stage:
																								SCREAMING_SNAKE_CASE__     :				str   =					trimmed_name.replace("network"			,   "meta4D_layers.blocks." + match[2]     )
																		else:
																								SCREAMING_SNAKE_CASE__     :				Optional[int]   =					str(int(match[2]     ) - num_meta4D_last_stage     )
																								SCREAMING_SNAKE_CASE__     :				List[str]   =					trimmed_name.replace("network"			,   "meta3D_layers.blocks." + layer_index     )
																								if "norm1" in old_name:
																														SCREAMING_SNAKE_CASE__     :				Optional[int]   =					trimmed_name.replace("norm1"			,   "layernorm1"     )
																								elif "norm2" in old_name:
																														SCREAMING_SNAKE_CASE__     :				List[Any]   =					trimmed_name.replace("norm2"			,   "layernorm2"     )
																								elif "fc1" in old_name:
																														SCREAMING_SNAKE_CASE__     :				Dict   =					trimmed_name.replace("fc1"			,   "linear_in"     )
																								elif "fc2" in old_name:
																														SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					trimmed_name.replace("fc2"			,   "linear_out"     )

																		SCREAMING_SNAKE_CASE__     :				Any   =					"last_stage." + trimmed_name

						elif "network" in old_name and re.search(r".\d."			,   _lowerCamelCase     ):
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					old_name.replace("network"			,   "intermediate_stages"     )

						if "fc" in new_name:
												SCREAMING_SNAKE_CASE__     :				Dict   =					new_name.replace("fc"			,   "convolution"     )
						elif ("norm1" in new_name) and ("layernorm1" not in new_name):
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					new_name.replace("norm1"			,   "batchnorm_before"     )
						elif ("norm2" in new_name) and ("layernorm2" not in new_name):
												SCREAMING_SNAKE_CASE__     :				Optional[int]   =					new_name.replace("norm2"			,   "batchnorm_after"     )
						if "proj" in new_name:
												SCREAMING_SNAKE_CASE__     :				Dict   =					new_name.replace("proj"			,   "projection"     )
						if "dist_head" in new_name:
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					new_name.replace("dist_head"			,   "distillation_classifier"     )
						elif "head" in new_name:
												SCREAMING_SNAKE_CASE__     :				int   =					new_name.replace("head"			,   "classifier"     )
						elif "patch_embed" in new_name:
												SCREAMING_SNAKE_CASE__     :				List[Any]   =					"efficientformer." + new_name
						elif new_name == "norm.weight" or new_name == "norm.bias":
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					new_name.replace("norm"			,   "layernorm"     )
												SCREAMING_SNAKE_CASE__     :				Dict   =					"efficientformer." + new_name
						else:
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					"efficientformer.encoder." + new_name

						return new_name







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





						'''simple docstring'''


						for key in checkpoint.copy().keys():
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					checkpoint.pop(_lowerCamelCase     )
												SCREAMING_SNAKE_CASE__     :				List[str]   =					val

						return checkpoint







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Tuple   =					"http://images.cocodataset.org/val2017/000000039769.jpg"
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					Image.open(requests.get(_lowerCamelCase			,   stream=_lowerCamelCase     ).raw     )

						return image







def       UpperCAmelCase							(     _lowerCamelCase      :  Path			,   _lowerCamelCase      :  Path			,   _lowerCamelCase      :  Path			,   _lowerCamelCase      :  bool     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Tuple   =					torch.load(_lowerCamelCase			,   map_location="cpu"     )["model"]
						SCREAMING_SNAKE_CASE__     :				Tuple   =					EfficientFormerConfig.from_json_file(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Tuple   =					EfficientFormerForImageClassificationWithTeacher(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					"_".join(checkpoint_path.split("/"     )[-1].split("."     )[0].split("_"     )[:-1]     )

						SCREAMING_SNAKE_CASE__     :				Dict   =					config.depths[-1] - config.num_metaad_blocks + 1
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					convert_torch_checkpoint(_lowerCamelCase			,   _lowerCamelCase     )

						model.load_state_dict(_lowerCamelCase     )
						model.eval()

						SCREAMING_SNAKE_CASE__     :				str   =					{
						    "bilinear": PILImageResampling.BILINEAR,
						    "bicubic": PILImageResampling.BICUBIC,
						    "nearest": PILImageResampling.NEAREST,
						}

						# prepare image
						SCREAMING_SNAKE_CASE__     :				int   =					prepare_img()
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					256
						SCREAMING_SNAKE_CASE__     :				int   =					224
						SCREAMING_SNAKE_CASE__     :				Dict   =					EfficientFormerImageProcessor(
						    size={"shortest_edge": image_size}			,   crop_size={"height": crop_size, "width": crop_size}			,   resample=pillow_resamplings["bicubic"]			,   )
						SCREAMING_SNAKE_CASE__     :				List[str]   =					processor(images=_lowerCamelCase			,   return_tensors="pt"     ).pixel_values

						# original processing pipeline
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					Compose(
						    [
						        Resize(_lowerCamelCase			,   interpolation=pillow_resamplings["bicubic"]     ),
						        CenterCrop(_lowerCamelCase     ),
						        ToTensor(),
						        Normalize(_lowerCamelCase			,   _lowerCamelCase     ),
						    ]     )
						SCREAMING_SNAKE_CASE__     :				str   =					image_transforms(_lowerCamelCase     ).unsqueeze(0     )

						assert torch.allclose(_lowerCamelCase			,   _lowerCamelCase     )

						SCREAMING_SNAKE_CASE__     :				str   =					model(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				int   =					outputs.logits

						SCREAMING_SNAKE_CASE__     :				Tuple   =					(1, 1_000)

						if "l1" in model_name:
												SCREAMING_SNAKE_CASE__     :				Dict   =					torch.Tensor(
												    [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]     )
												assert torch.allclose(logits[0, :10]			,   _lowerCamelCase			,   atol=1E-3     )
												assert logits.shape == expected_shape
						elif "l3" in model_name:
												SCREAMING_SNAKE_CASE__     :				int   =					torch.Tensor(
												    [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]     )
												assert torch.allclose(logits[0, :10]			,   _lowerCamelCase			,   atol=1E-3     )
												assert logits.shape == expected_shape
						elif "l7" in model_name:
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					torch.Tensor(
												    [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]     )
												assert logits.shape == expected_shape
						else:
												raise ValueError(
												    f"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""     )

						# Save Checkpoints
						Path(_lowerCamelCase     ).mkdir(exist_ok=_lowerCamelCase     )
						model.save_pretrained(_lowerCamelCase     )
						print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""     )
						processor.save_pretrained(_lowerCamelCase     )
						print(f"""Processor successfuly saved at {pytorch_dump_path}"""     )

						if push_to_hub:
												print("Pushing model to the hub..."     )

												model.push_to_hub(
												    repo_id=f"""Bearnardd/{pytorch_dump_path}"""			,   commit_message="Add model"			,   use_temp_dir=_lowerCamelCase			,   )
												processor.push_to_hub(
												    repo_id=f"""Bearnardd/{pytorch_dump_path}"""			,   commit_message="Add image processor"			,   use_temp_dir=_lowerCamelCase			,   )


if __name__ == "__main__":
				__lowercase       :int       =  argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--pytorch_model_path",
				    default=None,
				    type=str,
				    required=True,
				    help="Path to EfficientFormer pytorch checkpoint.",
				)
				parser.add_argument(
				    "--config_file",
				    default=None,
				    type=str,
				    required=True,
				    help="The json file for EfficientFormer model config.",
				)
				parser.add_argument(
				    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)

				parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
				parser.add_argument(
				    "--no-push_to_hub",
				    dest="push_to_hub",
				    action="store_false",
				    help="Do not push model and image processor to the hub",
				)
				parser.set_defaults(push_to_hub=True)

				__lowercase       :List[Any]       =  parser.parse_args()
				convert_efficientformer_checkpoint(
				    checkpoint_path=args.pytorch_model_path,
				    efficientformer_config_file=args.config_file,
				    pytorch_dump_path=args.pytorch_dump_path,
				    push_to_hub=args.push_to_hub,
				)






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()