'''simple docstring'''







from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter


def    lowerCamelCase       (			UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  float = 1 / sqrt(2		)		)			->	IIRFilter:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Tuple						    = tau * frequency / samplerate
							SCREAMING_SNAKE_CASE__     :str						    = sin(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = cos(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :int						    = _sin / (2 * q_factor)

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

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

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


def    lowerCamelCase       (			UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  float = 1 / sqrt(2		)		)			->	IIRFilter:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Dict						    = tau * frequency / samplerate
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = sin(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :List[Any]						    = cos(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = _sin / (2 * q_factor)

							SCREAMING_SNAKE_CASE__     :Optional[int]						    = (1 + _cos) / 2
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = -1 - _cos

							SCREAMING_SNAKE_CASE__     :List[Any]						    = 1 + alpha
							SCREAMING_SNAKE_CASE__     :int						    = -2 * _cos
							SCREAMING_SNAKE_CASE__     :Tuple						    = 1 - alpha

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


def    lowerCamelCase       (			UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  float = 1 / sqrt(2		)		)			->	IIRFilter:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :List[str]						    = tau * frequency / samplerate
							SCREAMING_SNAKE_CASE__     :List[Any]						    = sin(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :str						    = cos(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = _sin / (2 * q_factor)

							SCREAMING_SNAKE_CASE__     :Dict						    = _sin / 2
							SCREAMING_SNAKE_CASE__     :List[Any]						    = 0
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = -ba

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

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


def    lowerCamelCase       (			UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  float = 1 / sqrt(2		)		)			->	IIRFilter:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Dict						    = tau * frequency / samplerate
							SCREAMING_SNAKE_CASE__     :Dict						    = sin(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = cos(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Tuple						    = _sin / (2 * q_factor)

							SCREAMING_SNAKE_CASE__     :Tuple						    = 1 - alpha
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = -2 * _cos
							SCREAMING_SNAKE_CASE__     :Tuple						    = 1 + alpha

							SCREAMING_SNAKE_CASE__     :List[Any]						    = IIRFilter(2		)
							filt.set_coefficients([ba, ba, ba]				,						[ba, ba, ba]		)
							return filt


def    lowerCamelCase       (			UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  float				,						UpperCAmelCase__				:  float = 1 / sqrt(2		)				,						)			->	IIRFilter:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Optional[int]						    = tau * frequency / samplerate
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = sin(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Dict						    = cos(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Dict						    = _sin / (2 * q_factor)
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = 1_0 ** (gain_db / 4_0)

							SCREAMING_SNAKE_CASE__     :Optional[int]						    = 1 + alpha * big_a
							SCREAMING_SNAKE_CASE__     :Dict						    = -2 * _cos
							SCREAMING_SNAKE_CASE__     :List[str]						    = 1 - alpha * big_a
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 1 + alpha / big_a
							SCREAMING_SNAKE_CASE__     :Any						    = -2 * _cos
							SCREAMING_SNAKE_CASE__     :str						    = 1 - alpha / big_a

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


def    lowerCamelCase       (			UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  float				,						UpperCAmelCase__				:  float = 1 / sqrt(2		)				,						)			->	IIRFilter:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :str						    = tau * frequency / samplerate
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = sin(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = cos(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = _sin / (2 * q_factor)
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 1_0 ** (gain_db / 4_0)
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = (big_a + 1) - (big_a - 1) * _cos
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = (big_a + 1) + (big_a - 1) * _cos
							SCREAMING_SNAKE_CASE__     :Dict						    = (big_a - 1) - (big_a + 1) * _cos
							SCREAMING_SNAKE_CASE__     :int						    = (big_a - 1) + (big_a + 1) * _cos
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 2 * sqrt(UpperCAmelCase__		) * alpha

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

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





def    lowerCamelCase       (			UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  float				,						UpperCAmelCase__				:  float = 1 / sqrt(2		)				,						)			->	IIRFilter:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :int						    = tau * frequency / samplerate
							SCREAMING_SNAKE_CASE__     :Any						    = sin(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = cos(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = _sin / (2 * q_factor)
							SCREAMING_SNAKE_CASE__     :Tuple						    = 1_0 ** (gain_db / 4_0)
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = (big_a + 1) - (big_a - 1) * _cos
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = (big_a + 1) + (big_a - 1) * _cos
							SCREAMING_SNAKE_CASE__     :str						    = (big_a - 1) - (big_a + 1) * _cos
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = (big_a - 1) + (big_a + 1) * _cos
							SCREAMING_SNAKE_CASE__     :List[str]						    = 2 * sqrt(UpperCAmelCase__		) * alpha

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

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







'''simple docstring'''







import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase_      	=	logging.get_logger()




@dataclass
class _SCREAMING_SNAKE_CASE:
						A_			:							nn.Module
						A_			:							List[nn.Module]     		= field(default_factory=_SCREAMING_SNAKE_CASE					)
						A_			:							list     		= field(default_factory=_SCREAMING_SNAKE_CASE					)




						def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       Optional[Any]							,							UpperCamelCase_ :       Tensor							,							UpperCamelCase_ :       Tensor      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = len(list(m.modules()      )      ) == 1 or isinstance(UpperCamelCase_							,							nn.Convad      ) or isinstance(UpperCamelCase_							,							nn.BatchNormad      )
													if has_not_submodules:
																				self.traced.append(UpperCamelCase_      )




						def __call__(    self :       Tuple							,							UpperCamelCase_ :       Tensor      )     ->				int:
													for m in self.module.modules():
																				self.handles.append(m.register_forward_hook(self._forward_hook      )      )
													self.module(UpperCamelCase_      )
													[x.remove() for x in self.handles]
													return self




						@property
						def      __lowerCamelCase       (    self :       Tuple      )     ->				str:
													# check the len of the state_dict keys to see if we have learnable params
													return list(filter(lambda UpperCamelCase_      : len(list(x.state_dict().keys()      )      ) > 0							,							self.traced      )      )




@dataclass
class _SCREAMING_SNAKE_CASE:
						A_			:							nn.Module
						A_			:							nn.Module
						A_			:							int     		= 0
						A_			:							List     		= field(default_factory=_SCREAMING_SNAKE_CASE					)
						A_			:							List     		= field(default_factory=_SCREAMING_SNAKE_CASE					)




						def __call__(    self :       List[Any]							,							UpperCamelCase_ :       Tensor      )     ->				int:
													SCREAMING_SNAKE_CASE__     :List[str]						    = Tracker(self.dest      )(UpperCamelCase_      ).parametrized
													SCREAMING_SNAKE_CASE__     :Any						    = Tracker(self.src      )(UpperCamelCase_      ).parametrized

													SCREAMING_SNAKE_CASE__     :Dict						    = list(filter(lambda UpperCamelCase_      : type(UpperCamelCase_      ) not in self.src_skip							,							UpperCamelCase_      )      )
													SCREAMING_SNAKE_CASE__     :str						    = list(filter(lambda UpperCamelCase_      : type(UpperCamelCase_      ) not in self.dest_skip							,							UpperCamelCase_      )      )

													if len(UpperCamelCase_      ) != len(UpperCamelCase_      ):
																				raise Exception(
																				    f'''Numbers of operations are different. Source module has {len(UpperCamelCase_      )} operations while'''
																				    f''' destination module has {len(UpperCamelCase_      )}.'''      )

													for dest_m, src_m in zip(UpperCamelCase_							,							UpperCamelCase_      ):
																				dest_m.load_state_dict(src_m.state_dict()      )
																				if self.verbose == 1:
																											print(f'''Transfered from={src_m} to={dest_m}'''      )


def    lowerCamelCase       (			UpperCAmelCase__				:  str				,						UpperCAmelCase__				:  ResNetConfig				,						UpperCAmelCase__				:  Path				,						UpperCAmelCase__				:  bool = True		)			->	Union[str, Any]:


							'''simple docstring'''






							print(F'''Converting {name}...'''		)
							with torch.no_grad():
														SCREAMING_SNAKE_CASE__     :str						    = timm.create_model(UpperCAmelCase__				,						pretrained=UpperCAmelCase__		).eval()
														SCREAMING_SNAKE_CASE__     :List[str]						    = ResNetForImageClassification(UpperCAmelCase__		).eval()
														SCREAMING_SNAKE_CASE__     :Dict						    = ModuleTransfer(src=UpperCAmelCase__				,						dest=UpperCAmelCase__		)
														SCREAMING_SNAKE_CASE__     :List[Any]						    = torch.randn((1, 3, 2_2_4, 2_2_4)		)
														module_transfer(UpperCAmelCase__		)

							assert torch.allclose(from_model(UpperCAmelCase__		)				,						our_model(UpperCAmelCase__		).logits		), "The model logits don't match the original one."

							SCREAMING_SNAKE_CASE__     :List[str]						    = F'''resnet{"-".join(name.split("resnet"		)		)}'''
							print(UpperCAmelCase__		)

							if push_to_hub:
														our_model.push_to_hub(
														    repo_path_or_name=save_directory / checkpoint_name				,						commit_message='Add model'				,						use_temp_dir=UpperCAmelCase__				,						)

														# we can use the convnext one
														SCREAMING_SNAKE_CASE__     :int						    = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'		)
														image_processor.push_to_hub(
														    repo_path_or_name=save_directory / checkpoint_name				,						commit_message='Add image processor'				,						use_temp_dir=UpperCAmelCase__				,						)

														print(F'''Pushed {checkpoint_name}'''		)


def    lowerCamelCase       (			UpperCAmelCase__				:  Path				,						UpperCAmelCase__				:  str = None				,						UpperCAmelCase__				:  bool = True		)			->	int:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :List[str]						    = 'imagenet-1k-id2label.json'
							SCREAMING_SNAKE_CASE__     :Any						    = 1_0_0_0
							SCREAMING_SNAKE_CASE__     :List[Any]						    = (1, num_labels)

							SCREAMING_SNAKE_CASE__     :List[str]						    = 'huggingface/label-files'
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = num_labels
							SCREAMING_SNAKE_CASE__     :Tuple						    = json.load(open(hf_hub_download(UpperCAmelCase__				,						UpperCAmelCase__				,						repo_type='dataset'		)				,						'r'		)		)
							SCREAMING_SNAKE_CASE__     :int						    = {int(UpperCAmelCase__		): v for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE__     :str						    = idalabel
							SCREAMING_SNAKE_CASE__     :Any						    = {v: k for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = partial(UpperCAmelCase__				,						num_labels=UpperCAmelCase__				,						idalabel=UpperCAmelCase__				,						labelaid=UpperCAmelCase__		)

							SCREAMING_SNAKE_CASE__     :List[str]						    = {
							    'resnet18': ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]				,						hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]				,						layer_type='basic'		),
							    'resnet26': ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]				,						hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,						layer_type='bottleneck'		),
							    'resnet34': ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]				,						hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]				,						layer_type='basic'		),
							    'resnet50': ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]				,						hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,						layer_type='bottleneck'		),
							    'resnet101': ImageNetPreTrainedConfig(
							        depths=[3, 4, 2_3, 3]				,						hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,						layer_type='bottleneck'		),
							    'resnet152': ImageNetPreTrainedConfig(
							        depths=[3, 8, 3_6, 3]				,						hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,						layer_type='bottleneck'		),
							}

							if model_name:
														convert_weight_and_push(UpperCAmelCase__				,						names_to_config[model_name]				,						UpperCAmelCase__				,						UpperCAmelCase__		)
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__		)
							return config, expected_shape


if __name__ == "__main__":
		UpperCamelCase_      	=	argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--model_name''',
		    default=None,
		    type=str,
		    help=(
		        '''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
		        ''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default=None,
		    type=Path,
		    required=True,
		    help='''Path to the output PyTorch model directory.''',
		)
		parser.add_argument(
		    '''--push_to_hub''',
		    default=True,
		    type=bool,
		    required=False,
		    help='''If True, push model and image processor to the hub.''',
		)

		UpperCamelCase_      	=	parser.parse_args()
		UpperCamelCase_      	=	args.pytorch_dump_folder_path
		pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
		convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)






