"""simple docstring"""







import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


_a					:					Optional[int]=			logging.get_logger(__name__)

class 		UpperCamelCase      (   lowercase						):
				UpperCAmelCase    :				Any   =		["""input_features"""]



				def __init__(self    :				int     ,  _A    :				Any=80     ,  _A    :				Any=1_60_00     ,  _A    :				Union[str, Any]=1_60     ,  _A    :				int=30     ,  _A    :				str=4_00     ,  _A    :				Any=0.0     ,  _A    :				Optional[int]=False     ,  **_A    :				List[str]     ,  )   ->     Optional[int]:
					super().__init__(
					    feature_size=_A     ,  sampling_rate=_A     ,  padding_value=_A     ,  return_attention_mask=_A     ,  **_A     ,  )
					__snake_case      :							List[str]						=			n_fft
					__snake_case      :							str						=			hop_length
					__snake_case      :							Optional[Any]						=			chunk_length
					__snake_case      :							int						=			chunk_length * sampling_rate
					__snake_case      :							List[str]						=			self.n_samples // hop_length
					__snake_case      :							Any						=			sampling_rate
					__snake_case      :							Optional[Any]						=			mel_filter_bank(
					    num_frequency_bins=1 + n_fft // 2     ,  num_mel_filters=_A     ,  min_frequency=0.0     ,  max_frequency=8_000.0     ,  sampling_rate=_A     ,  norm='slaney'     ,  mel_scale='slaney'     ,  )



				def  _lowercase					(self    :				str     ,  _A    :				np.array)   ->     np.ndarray:
					__snake_case      :							Optional[int]						=			spectrogram(
					    _A     ,  window_function(self.n_fft     ,  'hann')     ,  frame_length=self.n_fft     ,  hop_length=self.hop_length     ,  power=2.0     ,  mel_filters=self.mel_filters     ,  log_mel='log10'     ,  )
					__snake_case      :							List[str]						=			log_spec[:, :-1]
					__snake_case      :							str						=			np.maximum(_A     ,  log_spec.max() - 8.0)
					__snake_case      :							Any						=			(log_spec + 4.0) / 4.0
					return log_spec



				@staticmethod
				# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
				def  _lowercase					(_A    :				List[np.ndarray]     ,  _A    :				List[np.ndarray]     ,  _A    :				float = 0.0)   ->     List[np.ndarray]:
					if attention_mask is not None:
						__snake_case      :							Optional[Any]						=			np.array(_A     ,  np.intaa)
						__snake_case      :							Tuple						=			[]

						for vector, length in zip(_A     ,  attention_mask.sum(-1)):
							__snake_case      :							Dict						=			(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7)
							if length < normed_slice.shape[0]:
								__snake_case      :							List[Any]						=			padding_value

							normed_input_values.append(_A)
					else:
						__snake_case      :							int						=			[(x - x.mean()) / np.sqrt(x.var() + 1E-7) for x in input_values]

					return normed_input_values



				def __call__(self    :				Any     ,  _A    :				Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,  _A    :				bool = True     ,  _A    :				Optional[int] = None     ,  _A    :				Optional[Union[str, TensorType]] = None     ,  _A    :				Optional[bool] = None     ,  _A    :				Optional[str] = "max_length"     ,  _A    :				Optional[int] = None     ,  _A    :				Optional[int] = None     ,  _A    :				Optional[bool] = None     ,  **_A    :				Any     ,  )   ->     BatchFeature:

					if sampling_rate is not None:
						if sampling_rate != self.sampling_rate:
							raise ValueError(
							    f"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
							    f" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
							    f" was sampled with {self.sampling_rate} and not {sampling_rate}.")
					else:
						logger.warning(
						    'It is strongly recommended to pass the `sampling_rate` argument to this function. '
						    'Failing to do so can result in silent errors that might be hard to debug.')

					__snake_case      :							Dict						=			isinstance(_A     ,  np.ndarray) and len(raw_speech.shape) > 1
					if is_batched_numpy and len(raw_speech.shape) > 2:
						raise ValueError(f"Only mono-channel audio is supported for input to {self}")
					__snake_case      :							Dict						=			is_batched_numpy or (
					    isinstance(_A     ,  (list, tuple)) and (isinstance(raw_speech[0]     ,  (np.ndarray, tuple, list)))
					)

					if is_batched:
						__snake_case      :							List[str]						=			[np.asarray([speech]     ,  dtype=np.floataa).T for speech in raw_speech]
					elif not is_batched and not isinstance(_A     ,  np.ndarray):
						__snake_case      :							Tuple						=			np.asarray(_A     ,  dtype=np.floataa)
					elif isinstance(_A     ,  np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
						__snake_case      :							str						=			raw_speech.astype(np.floataa)

					# always return batch
					if not is_batched:
						__snake_case      :							int						=			[np.asarray([raw_speech]).T]

					__snake_case      :							Any						=			BatchFeature({'input_features': raw_speech})

					# convert into correct format for padding

					__snake_case      :							Any						=			self.pad(
					    _A     ,  padding=_A     ,  max_length=max_length if max_length else self.n_samples     ,  truncation=_A     ,  pad_to_multiple_of=_A     ,  return_attention_mask=return_attention_mask or do_normalize     ,  )

					# zero-mean and unit-variance normalization
					if do_normalize:
						__snake_case      :							Optional[Any]						=			self.zero_mean_unit_var_norm(
						    padded_inputs['input_features']     ,  attention_mask=padded_inputs['attention_mask']     ,  padding_value=self.padding_value     ,  )
						__snake_case      :							Optional[Any]						=			np.stack(padded_inputs['input_features']     ,  axis=0)

					# make sure list is in array format
					__snake_case      :							Union[str, Any]						=			padded_inputs.get('input_features').transpose(2     ,  0     ,  1)

					__snake_case      :							List[Any]						=			[self._np_extract_fbank_features(_A) for waveform in input_features[0]]

					if isinstance(input_features[0]     ,  _A):
						__snake_case      :							Union[str, Any]						=			[np.asarray(_A     ,  dtype=np.floataa) for feature in input_features]
					else:
						__snake_case      :							Optional[int]						=			input_features

					if return_attention_mask:
						# rescale from sample (48000) to feature (3000)
						__snake_case      :							Union[str, Any]						=			padded_inputs['attention_mask'][:, :: self.hop_length]

					if return_tensors is not None:
						__snake_case      :							Dict						=			padded_inputs.convert_to_tensors(_A)

					return padded_inputs






				def  _lowercase					(self    :				Optional[int])   ->     Dict[str, Any]:
					__snake_case      :							List[Any]						=			copy.deepcopy(self.__dict__)
					__snake_case      :							Union[str, Any]						=			self.__class__.__name__
					if "mel_filters" in output:
						del output["mel_filters"]
					return output






"""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()
_a					:					Optional[int]=			logging.get_logger()

@dataclass
class 		UpperCamelCase      :
				UpperCAmelCase    :				nn.Module
				UpperCAmelCase    :				List[nn.Module]   =		field(default_factory=lowercase						)
				UpperCAmelCase    :				list   =		field(default_factory=lowercase						)



				def  _lowercase					(self    :				str     ,  _A    :				Optional[Any]     ,  _A    :				Tensor     ,  _A    :				Tensor)   ->     Any:
					__snake_case      :							str						=			len(list(m.modules())) == 1 or isinstance(_A     ,  nn.Convad) or isinstance(_A     ,  nn.BatchNormad)
					if has_not_submodules:
						self.traced.append(_A)



				def __call__(self    :				Dict     ,  _A    :				Tensor)   ->     Optional[Any]:
					for m in self.module.modules():
						self.handles.append(m.register_forward_hook(self._forward_hook))
					self.module(_A)
					[x.remove() for x in self.handles]
					return self






				@property
				def  _lowercase					(self    :				Union[str, Any])   ->     List[str]:
					# check the len of the state_dict keys to see if we have learnable params
					return list(filter(lambda _A: len(list(x.state_dict().keys())) > 0     ,  self.traced))







@dataclass
class 		UpperCamelCase      :
				UpperCAmelCase    :				nn.Module
				UpperCAmelCase    :				nn.Module
				UpperCAmelCase    :				int   =		0
				UpperCAmelCase    :				List   =		field(default_factory=lowercase						)
				UpperCAmelCase    :				List   =		field(default_factory=lowercase						)



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

					__snake_case      :							List[Any]						=			list(filter(lambda _A: type(_A) not in self.src_skip     ,  _A))
					__snake_case      :							Any						=			list(filter(lambda _A: type(_A) not in self.dest_skip     ,  _A))

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

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







def   __UpperCAmelCase    (   UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    ResNetConfig      ,							UpperCAmelCase_   :    Path      ,							UpperCAmelCase_   :    bool = True  )							->      List[str]:

	'''simple docstring'''





	print(F"Converting {name}..."  )
	with torch.no_grad():
		__snake_case      :							Dict						=			timm.create_model(UpperCAmelCase_      ,							pretrained=UpperCAmelCase_  ).eval()
		__snake_case      :							List[Any]						=			ResNetForImageClassification(UpperCAmelCase_  ).eval()
		__snake_case      :							int						=			ModuleTransfer(src=UpperCAmelCase_      ,							dest=UpperCAmelCase_  )
		__snake_case      :							Optional[Any]						=			torch.randn((1, 3, 2_24, 2_24)  )
		module_transfer(UpperCAmelCase_  )

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

	__snake_case      :							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
		__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   __UpperCAmelCase    (   UpperCAmelCase_   :    Path      ,							UpperCAmelCase_   :    str = None      ,							UpperCAmelCase_   :    bool = True  )							->      Union[str, Any]:

	'''simple docstring'''





	__snake_case      :							str						=			'imagenet-1k-id2label.json'
	__snake_case      :							Optional[Any]						=			10_00
	__snake_case      :							Any						=			(1, num_labels)

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

	__snake_case      :							Optional[Any]						=			idalabel
	__snake_case      :							Optional[Any]						=			{v: k for k, v in idalabel.items()}

	__snake_case      :							Optional[int]						=			partial(UpperCAmelCase_      ,							num_labels=UpperCAmelCase_      ,							idalabel=UpperCAmelCase_      ,							labelaid=UpperCAmelCase_  )

	__snake_case      :							str						=			{
	    'resnet18': ImageNetPreTrainedConfig(
	        depths=[2, 2, 2, 2]      ,							hidden_sizes=[64, 1_28, 2_56, 5_12]      ,							layer_type='basic'  ),
	    'resnet26': ImageNetPreTrainedConfig(
	        depths=[2, 2, 2, 2]      ,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]      ,							layer_type='bottleneck'  ),
	    'resnet34': ImageNetPreTrainedConfig(
	        depths=[3, 4, 6, 3]      ,							hidden_sizes=[64, 1_28, 2_56, 5_12]      ,							layer_type='basic'  ),
	    'resnet50': ImageNetPreTrainedConfig(
	        depths=[3, 4, 6, 3]      ,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]      ,							layer_type='bottleneck'  ),
	    'resnet101': ImageNetPreTrainedConfig(
	        depths=[3, 4, 23, 3]      ,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]      ,							layer_type='bottleneck'  ),
	    'resnet152': ImageNetPreTrainedConfig(
	        depths=[3, 8, 36, 3]      ,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]      ,							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__":
	_a					:					Optional[Any]=			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.",
	)

	_a					:					Union[str, Any]=			parser.parse_args()
	_a					:					Path=			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)





