
from math import log

from scipy.constants import Boltzmann, physical_constants

A     :	str    = 3_0_0  # TEMPERATURE (unit = K)


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    )       ->  float:

				if donor_conc <= 0:
								raise ValueError('''Donor concentration should be positive'''  )
				elif acceptor_conc <= 0:
								raise ValueError('''Acceptor concentration should be positive'''  )
				elif intrinsic_conc <= 0:
								raise ValueError('''Intrinsic concentration should be positive'''  )
				elif donor_conc <= intrinsic_conc:
								raise ValueError(
								    '''Donor concentration should be greater than intrinsic concentration'''  )
				elif acceptor_conc <= intrinsic_conc:
								raise ValueError(
								    '''Acceptor concentration should be greater than intrinsic concentration'''  )
				else:
								return (
								    Boltzmann
								    * T
								    * log((donor_conc * acceptor_conc) / intrinsic_conc**2  )
								    / physical_constants["electron volt"][0]
								)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



'''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,
						)



