



'''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  snake_case_     (					_lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						int  )      ->   Optional[Any]:
							UpperCAmelCase :       Dict 							=		old_name

							if "patch_embed" in old_name:
														UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       List[str] 							=		old_name.split('''.'''  )

														if layer == "0":
																					UpperCAmelCase :       Dict 							=		old_name.replace('''0'''   ,      '''convolution1'''  )
														elif layer == "1":
																					UpperCAmelCase :       List[Any] 							=		old_name.replace('''1'''   ,      '''batchnorm_before'''  )
														elif layer == "3":
																					UpperCAmelCase :       Any 							=		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 :       int 							=		R'''\b\d{2}\b'''
														if bool(re.search(_lowerCAmelCase   ,      _lowerCAmelCase  )  ):
																					UpperCAmelCase :       Optional[int] 							=		re.search(R'''\d\.\d\d.'''   ,      _lowerCAmelCase  ).group()
														else:
																					UpperCAmelCase :       str 							=		re.search(R'''\d\.\d.'''   ,      _lowerCAmelCase  ).group()
														if int(match[0]  ) < 6:
																					UpperCAmelCase :       int 							=		old_name.replace(_lowerCAmelCase   ,      ''''''  )
																					UpperCAmelCase :       int 							=		trimmed_name.replace('''network'''   ,      match[0] + '''.meta4D_layers.blocks.''' + match[2:-1]  )
																					UpperCAmelCase :       int 							=		'''intermediate_stages.''' + trimmed_name
														else:
																					UpperCAmelCase :       str 							=		old_name.replace(_lowerCAmelCase   ,      ''''''  )
																					if int(match[2]  ) < num_meta4D_last_stage:
																												UpperCAmelCase :       Optional[int] 							=		trimmed_name.replace('''network'''   ,      '''meta4D_layers.blocks.''' + match[2]  )
																					else:
																												UpperCAmelCase :       Any 							=		str(int(match[2]  ) - num_meta4D_last_stage  )
																												UpperCAmelCase :       str 							=		trimmed_name.replace('''network'''   ,      '''meta3D_layers.blocks.''' + layer_index  )
																												if "norm1" in old_name:
																																			UpperCAmelCase :       str 							=		trimmed_name.replace('''norm1'''   ,      '''layernorm1'''  )
																												elif "norm2" in old_name:
																																			UpperCAmelCase :       Union[str, Any] 							=		trimmed_name.replace('''norm2'''   ,      '''layernorm2'''  )
																												elif "fc1" in old_name:
																																			UpperCAmelCase :       Any 							=		trimmed_name.replace('''fc1'''   ,      '''linear_in'''  )
																												elif "fc2" in old_name:
																																			UpperCAmelCase :       List[str] 							=		trimmed_name.replace('''fc2'''   ,      '''linear_out'''  )

																					UpperCAmelCase :       Tuple 							=		'''last_stage.''' + trimmed_name

							elif "network" in old_name and re.search(R'''.\d.'''   ,      _lowerCAmelCase  ):
														UpperCAmelCase :       Any 							=		old_name.replace('''network'''   ,      '''intermediate_stages'''  )

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

							return new_name


def  snake_case_     (					_lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						Dict  )      ->   List[Any]:
							for key in checkpoint.copy().keys():
														UpperCAmelCase :       str 							=		checkpoint.pop(_lowerCAmelCase  )
														UpperCAmelCase :       Tuple 							=		val

							return checkpoint


def  snake_case_     (					)      ->   Dict:
							UpperCAmelCase :       List[str] 							=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
							UpperCAmelCase :       Any 							=		Image.open(requests.get(_lowerCAmelCase   ,      stream=_lowerCAmelCase  ).raw  )

							return image


def  snake_case_     (					_lowerCAmelCase  :						Path   ,      _lowerCAmelCase  :						Path   ,      _lowerCAmelCase  :						Path   ,      _lowerCAmelCase  :						bool  )      ->   Any:
							UpperCAmelCase :       Tuple 							=		torch.load(_lowerCAmelCase   ,      map_location='''cpu'''  )['''model''']
							UpperCAmelCase :       Optional[Any] 							=		EfficientFormerConfig.from_json_file(_lowerCAmelCase  )
							UpperCAmelCase :       List[Any] 							=		EfficientFormerForImageClassificationWithTeacher(_lowerCAmelCase  )
							UpperCAmelCase :       List[Any] 							=		'''_'''.join(checkpoint_path.split('''/'''  )[-1].split('''.'''  )[0].split('''_'''  )[:-1]  )

							UpperCAmelCase :       int 							=		config.depths[-1] - config.num_metaad_blocks + 1
							UpperCAmelCase :       int 							=		convert_torch_checkpoint(_lowerCAmelCase   ,      _lowerCAmelCase  )

							model.load_state_dict(_lowerCAmelCase  )
							model.eval()

							UpperCAmelCase :       Any 							=		{
							    '''bilinear''': PILImageResampling.BILINEAR,
							    '''bicubic''': PILImageResampling.BICUBIC,
							    '''nearest''': PILImageResampling.NEAREST,
							}

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

							# original processing pipeline
							UpperCAmelCase :       Optional[Any] 							=		Compose(
							    [
							        Resize(_lowerCAmelCase   ,      interpolation=pillow_resamplings['''bicubic''']  ),
							        CenterCrop(_lowerCAmelCase  ),
							        ToTensor(),
							        Normalize(_lowerCAmelCase   ,      _lowerCAmelCase  ),
							    ]  )
							UpperCAmelCase :       Dict 							=		image_transforms(_lowerCAmelCase  ).unsqueeze(0  )

							assert torch.allclose(_lowerCAmelCase   ,      _lowerCAmelCase  )

							UpperCAmelCase :       Optional[int] 							=		model(_lowerCAmelCase  )
							UpperCAmelCase :       List[str] 							=		outputs.logits

							UpperCAmelCase :       str 							=		(1, 1000)

							if "l1" in model_name:
														UpperCAmelCase :       Union[str, 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 :       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:
														UpperCAmelCase :       Tuple 							=		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__":
						UpperCamelCase__:   str								=			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)

						UpperCamelCase__:   Tuple								=			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,
						)










'''simple docstring'''






from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter


def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						float = 1 / sqrt(2  )  )      ->   IIRFilter:
							UpperCAmelCase :       Optional[int] 							=		tau * frequency / samplerate
							UpperCAmelCase :       List[Any] 							=		sin(_lowerCAmelCase  )
							UpperCAmelCase :       Optional[Any] 							=		cos(_lowerCAmelCase  )
							UpperCAmelCase :       int 							=		_sin / (2 * q_factor)

							UpperCAmelCase :       Any 							=		(1 - _cos) / 2
							UpperCAmelCase :       List[Any] 							=		1 - _cos

							UpperCAmelCase :       Union[str, Any] 							=		1 + alpha
							UpperCAmelCase :       Any 							=		-2 * _cos
							UpperCAmelCase :       Dict 							=		1 - alpha

							UpperCAmelCase :       Union[str, Any] 							=		IIRFilter(2  )
							filt.set_coefficients([aa, aa, aa]   ,      [ba, ba, ba]  )
							return filt


def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						float = 1 / sqrt(2  )  )      ->   IIRFilter:
							UpperCAmelCase :       Any 							=		tau * frequency / samplerate
							UpperCAmelCase :       Tuple 							=		sin(_lowerCAmelCase  )
							UpperCAmelCase :       Tuple 							=		cos(_lowerCAmelCase  )
							UpperCAmelCase :       Dict 							=		_sin / (2 * q_factor)

							UpperCAmelCase :       int 							=		(1 + _cos) / 2
							UpperCAmelCase :       List[Any] 							=		-1 - _cos

							UpperCAmelCase :       Tuple 							=		1 + alpha
							UpperCAmelCase :       List[str] 							=		-2 * _cos
							UpperCAmelCase :       Optional[Any] 							=		1 - alpha

							UpperCAmelCase :       Tuple 							=		IIRFilter(2  )
							filt.set_coefficients([aa, aa, aa]   ,      [ba, ba, ba]  )
							return filt


def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						float = 1 / sqrt(2  )  )      ->   IIRFilter:
							UpperCAmelCase :       Optional[int] 							=		tau * frequency / samplerate
							UpperCAmelCase :       Optional[int] 							=		sin(_lowerCAmelCase  )
							UpperCAmelCase :       Tuple 							=		cos(_lowerCAmelCase  )
							UpperCAmelCase :       Optional[int] 							=		_sin / (2 * q_factor)

							UpperCAmelCase :       Union[str, Any] 							=		_sin / 2
							UpperCAmelCase :       Any 							=		0
							UpperCAmelCase :       int 							=		-ba

							UpperCAmelCase :       Optional[Any] 							=		1 + alpha
							UpperCAmelCase :       List[Any] 							=		-2 * _cos
							UpperCAmelCase :       Optional[Any] 							=		1 - alpha

							UpperCAmelCase :       int 							=		IIRFilter(2  )
							filt.set_coefficients([aa, aa, aa]   ,      [ba, ba, ba]  )
							return filt


def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						float = 1 / sqrt(2  )  )      ->   IIRFilter:
							UpperCAmelCase :       List[str] 							=		tau * frequency / samplerate
							UpperCAmelCase :       Union[str, Any] 							=		sin(_lowerCAmelCase  )
							UpperCAmelCase :       str 							=		cos(_lowerCAmelCase  )
							UpperCAmelCase :       Optional[Any] 							=		_sin / (2 * q_factor)

							UpperCAmelCase :       List[str] 							=		1 - alpha
							UpperCAmelCase :       Any 							=		-2 * _cos
							UpperCAmelCase :       Optional[int] 							=		1 + alpha

							UpperCAmelCase :       Union[str, Any] 							=		IIRFilter(2  )
							filt.set_coefficients([ba, ba, ba]   ,      [ba, ba, ba]  )
							return filt


def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						float   ,      _lowerCAmelCase  :						float = 1 / sqrt(2  )   ,      )      ->   IIRFilter:
							UpperCAmelCase :       Optional[Any] 							=		tau * frequency / samplerate
							UpperCAmelCase :       Union[str, Any] 							=		sin(_lowerCAmelCase  )
							UpperCAmelCase :       Optional[int] 							=		cos(_lowerCAmelCase  )
							UpperCAmelCase :       Dict 							=		_sin / (2 * q_factor)
							UpperCAmelCase :       str 							=		10 ** (gain_db / 40)

							UpperCAmelCase :       int 							=		1 + alpha * big_a
							UpperCAmelCase :       Union[str, Any] 							=		-2 * _cos
							UpperCAmelCase :       Optional[Any] 							=		1 - alpha * big_a
							UpperCAmelCase :       Union[str, Any] 							=		1 + alpha / big_a
							UpperCAmelCase :       Tuple 							=		-2 * _cos
							UpperCAmelCase :       Any 							=		1 - alpha / big_a

							UpperCAmelCase :       Optional[Any] 							=		IIRFilter(2  )
							filt.set_coefficients([aa, aa, aa]   ,      [ba, ba, ba]  )
							return filt


def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						float   ,      _lowerCAmelCase  :						float = 1 / sqrt(2  )   ,      )      ->   IIRFilter:
							UpperCAmelCase :       Any 							=		tau * frequency / samplerate
							UpperCAmelCase :       Optional[int] 							=		sin(_lowerCAmelCase  )
							UpperCAmelCase :       Union[str, Any] 							=		cos(_lowerCAmelCase  )
							UpperCAmelCase :       str 							=		_sin / (2 * q_factor)
							UpperCAmelCase :       List[str] 							=		10 ** (gain_db / 40)
							UpperCAmelCase :       Optional[int] 							=		(big_a + 1) - (big_a - 1) * _cos
							UpperCAmelCase :       int 							=		(big_a + 1) + (big_a - 1) * _cos
							UpperCAmelCase :       int 							=		(big_a - 1) - (big_a + 1) * _cos
							UpperCAmelCase :       Optional[int] 							=		(big_a - 1) + (big_a + 1) * _cos
							UpperCAmelCase :       str 							=		2 * sqrt(_lowerCAmelCase  ) * alpha

							UpperCAmelCase :       Dict 							=		big_a * (pmc + aaa)
							UpperCAmelCase :       Any 							=		2 * big_a * mpc
							UpperCAmelCase :       Union[str, Any] 							=		big_a * (pmc - aaa)
							UpperCAmelCase :       Optional[int] 							=		ppmc + aaa
							UpperCAmelCase :       Optional[Any] 							=		-2 * pmpc
							UpperCAmelCase :       Optional[Any] 							=		ppmc - aaa

							UpperCAmelCase :       int 							=		IIRFilter(2  )
							filt.set_coefficients([aa, aa, aa]   ,      [ba, ba, ba]  )
							return filt




def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						float   ,      _lowerCAmelCase  :						float = 1 / sqrt(2  )   ,      )      ->   IIRFilter:
							UpperCAmelCase :       int 							=		tau * frequency / samplerate
							UpperCAmelCase :       Union[str, Any] 							=		sin(_lowerCAmelCase  )
							UpperCAmelCase :       Union[str, Any] 							=		cos(_lowerCAmelCase  )
							UpperCAmelCase :       Any 							=		_sin / (2 * q_factor)
							UpperCAmelCase :       int 							=		10 ** (gain_db / 40)
							UpperCAmelCase :       List[str] 							=		(big_a + 1) - (big_a - 1) * _cos
							UpperCAmelCase :       Union[str, Any] 							=		(big_a + 1) + (big_a - 1) * _cos
							UpperCAmelCase :       Optional[Any] 							=		(big_a - 1) - (big_a + 1) * _cos
							UpperCAmelCase :       Union[str, Any] 							=		(big_a - 1) + (big_a + 1) * _cos
							UpperCAmelCase :       List[str] 							=		2 * sqrt(_lowerCAmelCase  ) * alpha

							UpperCAmelCase :       Any 							=		big_a * (ppmc + aaa)
							UpperCAmelCase :       str 							=		-2 * big_a * pmpc
							UpperCAmelCase :       List[Any] 							=		big_a * (ppmc - aaa)
							UpperCAmelCase :       Optional[Any] 							=		pmc + aaa
							UpperCAmelCase :       Any 							=		2 * mpc
							UpperCAmelCase :       str 							=		pmc - aaa

							UpperCAmelCase :       Union[str, Any] 							=		IIRFilter(2  )
							filt.set_coefficients([aa, aa, aa]   ,      [ba, ba, ba]  )
							return filt





