


'''simple docstring'''


import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase=()					,       __lowerCAmelCase=None					,       __lowerCAmelCase="no"					,       __lowerCAmelCase="29500"      ):
		_UpperCAmelCase						:				List[str]	    =					False
		_UpperCAmelCase						:				Dict	    =					False
		if any(key.startswith("KAGGLE"      ) for key in os.environ.keys()      ):
				_UpperCAmelCase						:				Dict	    =					True
		elif "IPython" in sys.modules:
				_UpperCAmelCase						:				Optional[int]	    =					"google.colab" in str(sys.modules["IPython"].get_ipython()      )

		try:
				_UpperCAmelCase						:				List[str]	    =					PrecisionType(mixed_precision.lower()      )
		except ValueError:
				raise ValueError(
				    F"""Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}."""      )

		if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME"					,       __lowerCAmelCase      ) is not None):
				# TPU launch
				import torch_xla.distributed.xla_multiprocessing as xmp

				if len(AcceleratorState._shared_state      ) > 0:
						raise ValueError(
						    "To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
						    "your training function. Restart your notebook and make sure no cells initializes an "
						    "`Accelerator`."      )
				if num_processes is None:
						_UpperCAmelCase						:				Union[str, Any]	    =					8

				_UpperCAmelCase						:				int	    =					PrepareForLaunch(__lowerCAmelCase					,       distributed_type="TPU"      )
				print(F"""Launching a training on {num_processes} TPU cores."""      )
				xmp.spawn(__lowerCAmelCase					,       args=__lowerCAmelCase					,       nprocs=__lowerCAmelCase					,       start_method="fork"      )
		elif in_colab:
				# No need for a distributed launch otherwise as it's either CPU or one GPU.
				if torch.cuda.is_available():
						print("Launching training on one GPU."      )
				else:
						print("Launching training on one CPU."      )
				function(*__lowerCAmelCase      )
		else:
				if num_processes is None:
						raise ValueError(
						    "You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."      )

				if num_processes > 1:
						# Multi-GPU launch
						from torch.multiprocessing import start_processes
						from torch.multiprocessing.spawn import ProcessRaisedException

						if len(AcceleratorState._shared_state      ) > 0:
								raise ValueError(
								    "To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
								    "inside your training function. Restart your notebook and make sure no cells initializes an "
								    "`Accelerator`."      )

						if torch.cuda.is_initialized():
								raise ValueError(
								    "To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
								    "using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
								    "function."      )

						# torch.distributed will expect a few environment variable to be here. We set the ones common to each
						# process here (the other ones will be set be the launcher).
						with patch_environment(
						    world_size=__lowerCAmelCase					,       master_addr="127.0.01"					,       master_port=__lowerCAmelCase					,       mixed_precision=__lowerCAmelCase      ):
								_UpperCAmelCase						:				Dict	    =					PrepareForLaunch(__lowerCAmelCase					,       distributed_type="MULTI_GPU"      )
								print(F"""Launching training on {num_processes} GPUs."""      )
								try:
										start_processes(__lowerCAmelCase					,       args=__lowerCAmelCase					,       nprocs=__lowerCAmelCase					,       start_method="fork"      )
								except ProcessRaisedException as e:
										if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
												raise RuntimeError(
												    "CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
												    "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
												    "Please review your imports and test them when running the `notebook_launcher()` to identify "
												    "which one is problematic."      ) from e

				else:
						# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
						if is_mps_available():
								_UpperCAmelCase						:				int	    =					"1"
								print("Launching training on MPS."      )
						elif torch.cuda.is_available():
								print("Launching training on one GPU."      )
						else:
								print("Launching training on CPU."      )
						function(*__lowerCAmelCase      )






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase=()					,       __lowerCAmelCase=2      ):
		from torch.multiprocessing import start_processes

		with tempfile.NamedTemporaryFile() as tmp_file:
				# torch.distributed will expect a few environment variable to be here. We set the ones common to each
				# process here (the other ones will be set be the launcher).
				with patch_environment(
				    world_size=__lowerCAmelCase					,       master_addr="127.0.01"					,       master_port="29500"					,       accelerate_mixed_precision="no"					,       accelerate_debug_rdv_file=tmp_file.name					,       accelerate_use_cpu="yes"					,       ):
						_UpperCAmelCase						:				Dict	    =					PrepareForLaunch(__lowerCAmelCase					,       debug=__lowerCAmelCase      )
						start_processes(__lowerCAmelCase					,       args=__lowerCAmelCase					,       nprocs=__lowerCAmelCase					,       start_method="fork"      )







'''simple docstring'''


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        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				Union[str, Any]	    =					old_name

		if "patch_embed" in old_name:
				_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase						:				Dict	    =					old_name.split("."      )

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

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

						_UpperCAmelCase						:				Optional[Any]	    =					"last_stage." + trimmed_name

		elif "network" in old_name and re.search(R".\d."					,       __lowerCAmelCase      ):
				_UpperCAmelCase						:				Optional[Any]	    =					old_name.replace("network"					,       "intermediate_stages"      )

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

		return new_name






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		for key in checkpoint.copy().keys():
				_UpperCAmelCase						:				List[Any]	    =					checkpoint.pop(__lowerCAmelCase      )
				_UpperCAmelCase						:				List[Any]	    =					val

		return checkpoint






def        __lowerCAmelCase       ():
		_UpperCAmelCase						:				Optional[int]	    =					"http://images.cocodataset.org/val2017/000000039769.jpg"
		_UpperCAmelCase						:				Tuple	    =					Image.open(requests.get(__lowerCAmelCase					,       stream=__lowerCAmelCase      ).raw      )

		return image






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				Union[str, Any]	    =					torch.load(__lowerCAmelCase					,       map_location="cpu"      )["model"]
		_UpperCAmelCase						:				Dict	    =					EfficientFormerConfig.from_json_file(__lowerCAmelCase      )
		_UpperCAmelCase						:				Optional[Any]	    =					EfficientFormerForImageClassificationWithTeacher(__lowerCAmelCase      )
		_UpperCAmelCase						:				Tuple	    =					"_".join(checkpoint_path.split("/"      )[-1].split("."      )[0].split("_"      )[:-1]      )

		_UpperCAmelCase						:				Union[str, Any]	    =					config.depths[-1] - config.num_metaad_blocks + 1
		_UpperCAmelCase						:				Optional[int]	    =					convert_torch_checkpoint(__lowerCAmelCase					,       __lowerCAmelCase      )

		model.load_state_dict(__lowerCAmelCase      )
		model.eval()

		_UpperCAmelCase						:				Optional[Any]	    =					{
		    "bilinear": PILImageResampling.BILINEAR,
		    "bicubic": PILImageResampling.BICUBIC,
		    "nearest": PILImageResampling.NEAREST,
		}

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

		# original processing pipeline
		_UpperCAmelCase						:				int	    =					Compose(
		    [
		        Resize(__lowerCAmelCase					,       interpolation=pillow_resamplings["bicubic"]      ),
		        CenterCrop(__lowerCAmelCase      ),
		        ToTensor(),
		        Normalize(__lowerCAmelCase					,       __lowerCAmelCase      ),
		    ]      )
		_UpperCAmelCase						:				Any	    =					image_transforms(__lowerCAmelCase      ).unsqueeze(0      )

		assert torch.allclose(__lowerCAmelCase					,       __lowerCAmelCase      )

		_UpperCAmelCase						:				Union[str, Any]	    =					model(__lowerCAmelCase      )
		_UpperCAmelCase						:				Dict	    =					outputs.logits

		_UpperCAmelCase						:				Optional[int]	    =					(1, 1_000)

		if "l1" in model_name:
				_UpperCAmelCase						:				List[Any]	    =					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:
				_UpperCAmelCase						:				List[Any]	    =					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:
				_UpperCAmelCase						:				List[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__":
						lowerCamelCase__					=   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)

						lowerCamelCase__					=   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,
						)



