






'''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[Any]   					=logging.get_logger()

@dataclass
class _lowercase     :
							a    				=	42
							a    				=	field(default_factory=_lowercase		)
							a    				=	field(default_factory=_lowercase		)




							def 		lowerCamelCase_    (							self:    int      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    Tensor      ,							UpperCamelCase__:    Tensor					):
														lowerCamelCase__ :     int	  =	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					):
														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:    List[Any]					):
														# 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 _lowercase     :
							a    				=	42
							a    				=	42
							a    				=	0
							a    				=	field(default_factory=_lowercase		)
							a    				=	field(default_factory=_lowercase		)




							def __call__(							self:    Dict      ,							UpperCamelCase__:    Tensor					):
														lowerCamelCase__ :     Union[str, Any]	  =	Tracker(self.dest					)(UpperCamelCase__					).parametrized
														lowerCamelCase__ :     Any	  =	Tracker(self.src					)(UpperCamelCase__					).parametrized

														lowerCamelCase__ :     List[str]	  =	list(filter(lambda UpperCamelCase__					: type(UpperCamelCase__					) not in self.src_skip      ,							UpperCamelCase__					)					)
														lowerCamelCase__ :     Optional[int]	  =	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 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase = True				)				->	Dict:
							print(f'''Converting {name}...'''				)
							with torch.no_grad():
														lowerCamelCase__ :     int	  =	timm.create_model(UpperCamelCase    ,      pretrained=UpperCamelCase				).eval()
														lowerCamelCase__ :     Dict	  =	ResNetForImageClassification(UpperCamelCase				).eval()
														lowerCamelCase__ :     Tuple	  =	ModuleTransfer(src=UpperCamelCase    ,      dest=UpperCamelCase				)
														lowerCamelCase__ :     Dict	  =	torch.randn((1, 3, 224, 224)				)
														module_transfer(UpperCamelCase				)

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

							lowerCamelCase__ :     Optional[Any]	  =	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
														lowerCamelCase__ :     str	  =	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 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase = None    ,      UpperCamelCase = True				)				->	List[Any]:
							lowerCamelCase__ :     Tuple	  =	"""imagenet-1k-id2label.json"""
							lowerCamelCase__ :     List[Any]	  =	1000
							lowerCamelCase__ :     Dict	  =	(1, num_labels)

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

							lowerCamelCase__ :     Union[str, Any]	  =	idalabel
							lowerCamelCase__ :     int	  =	{v: k for k, v in idalabel.items()}

							lowerCamelCase__ :     Optional[Any]	  =	partial(UpperCamelCase    ,      num_labels=UpperCamelCase    ,      idalabel=UpperCamelCase    ,      labelaid=UpperCamelCase				)

							lowerCamelCase__ :     str	  =	{
							    """resnet18""": ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]    ,      hidden_sizes=[64, 128, 256, 512]    ,      layer_type="""basic"""				),
							    """resnet26""": ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]    ,      hidden_sizes=[256, 512, 1024, 2048]    ,      layer_type="""bottleneck"""				),
							    """resnet34""": ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]    ,      hidden_sizes=[64, 128, 256, 512]    ,      layer_type="""basic"""				),
							    """resnet50""": ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]    ,      hidden_sizes=[256, 512, 1024, 2048]    ,      layer_type="""bottleneck"""				),
							    """resnet101""": ImageNetPreTrainedConfig(
							        depths=[3, 4, 23, 3]    ,      hidden_sizes=[256, 512, 1024, 2048]    ,      layer_type="""bottleneck"""				),
							    """resnet152""": ImageNetPreTrainedConfig(
							        depths=[3, 8, 36, 3]    ,      hidden_sizes=[256, 512, 1024, 2048]    ,      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     :      List[str]   					=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     :      Optional[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)









'''simple docstring'''







from typing import List, Optional, Union

import numpy as np

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


_A     :      List[str]   					=logging.get_logger(__name__)

class _lowercase     (    _lowercase		):
							a    				=	["""input_features""", """attention_mask"""]




							def __init__(							self:    Optional[int]      ,							UpperCamelCase__:    str=80      ,							UpperCamelCase__:    Union[str, Any]=16_000      ,							UpperCamelCase__:    str=0.0      ,							UpperCamelCase__:    Tuple=10      ,							UpperCamelCase__:    Dict=25      ,							UpperCamelCase__:    List[Any]="hamming_window"      ,							UpperCamelCase__:    Tuple=32_768.0      ,							UpperCamelCase__:    List[Any]=0.97      ,							UpperCamelCase__:    Optional[int]=1.0      ,							UpperCamelCase__:    str=True      ,							UpperCamelCase__:    Optional[Any]=True      ,							UpperCamelCase__:    Optional[int]=False      ,							**UpperCamelCase__:    Optional[int]      ,							):
														super().__init__(feature_size=UpperCamelCase__      ,							sampling_rate=UpperCamelCase__      ,							padding_value=UpperCamelCase__      ,							**UpperCamelCase__					)

														lowerCamelCase__ :     Dict	  =	feature_size
														lowerCamelCase__ :     Tuple	  =	sampling_rate
														lowerCamelCase__ :     Union[str, Any]	  =	padding_value
														lowerCamelCase__ :     Union[str, Any]	  =	hop_length
														lowerCamelCase__ :     Any	  =	win_length
														lowerCamelCase__ :     Tuple	  =	frame_signal_scale
														lowerCamelCase__ :     Dict	  =	preemphasis_coeff
														lowerCamelCase__ :     Optional[Any]	  =	mel_floor
														lowerCamelCase__ :     Any	  =	normalize_means
														lowerCamelCase__ :     Optional[int]	  =	normalize_vars
														lowerCamelCase__ :     Dict	  =	win_function
														lowerCamelCase__ :     Dict	  =	return_attention_mask

														lowerCamelCase__ :     Optional[Any]	  =	win_length * sampling_rate // 1_000
														lowerCamelCase__ :     Tuple	  =	hop_length * sampling_rate // 1_000

														lowerCamelCase__ :     List[Any]	  =	optimal_fft_length(self.sample_size					)
														lowerCamelCase__ :     List[str]	  =	(self.n_fft // 2) + 1




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    np.array					):
														if self.win_function == "hamming_window":
																					lowerCamelCase__ :     Dict	  =	window_function(window_length=self.sample_size      ,							name=self.win_function      ,							periodic=UpperCamelCase__					)
														else:
																					lowerCamelCase__ :     Union[str, Any]	  =	window_function(window_length=self.sample_size      ,							name=self.win_function					)

														lowerCamelCase__ :     List[Any]	  =	mel_filter_bank(
														    num_frequency_bins=self.n_freqs      ,							num_mel_filters=self.feature_size      ,							min_frequency=0.0      ,							max_frequency=self.sampling_rate / 2.0      ,							sampling_rate=self.sampling_rate      ,							)

														lowerCamelCase__ :     Tuple	  =	spectrogram(
														    one_waveform * self.frame_signal_scale      ,							window=UpperCamelCase__      ,							frame_length=self.sample_size      ,							hop_length=self.sample_stride      ,							fft_length=self.n_fft      ,							center=UpperCamelCase__      ,							preemphasis=self.preemphasis_coeff      ,							mel_filters=UpperCamelCase__      ,							mel_floor=self.mel_floor      ,							log_mel="""log"""      ,							)
														return msfc_features.T




							def 		lowerCamelCase_    (							self:    Optional[int]      ,							UpperCamelCase__:    List[str]      ,							UpperCamelCase__:    List[Any]      ,							UpperCamelCase__:    Union[str, Any]					):
														# make sure we normalize float32 arrays
														if self.normalize_means:
																					lowerCamelCase__ :     List[str]	  =	x[:input_length].mean(axis=0					)
																					lowerCamelCase__ :     List[str]	  =	np.subtract(UpperCamelCase__      ,							UpperCamelCase__					)
														if self.normalize_vars:
																					lowerCamelCase__ :     Union[str, Any]	  =	x[:input_length].std(axis=0					)
																					lowerCamelCase__ :     str	  =	np.divide(UpperCamelCase__      ,							UpperCamelCase__					)

														if input_length < x.shape[0]:
																					lowerCamelCase__ :     List[Any]	  =	padding_value

														# make sure array is in float32
														lowerCamelCase__ :     str	  =	x.astype(np.floataa					)

														return x




							def 		lowerCamelCase_    (							self:    str      ,							UpperCamelCase__:    List[np.ndarray]      ,							UpperCamelCase__:    Optional[np.ndarray] = None					):
														lowerCamelCase__ :     Optional[int]	  =	attention_mask.sum(-1					) if attention_mask is not None else [x.shape[0] for x in input_features]
														return [self._normalize_one(UpperCamelCase__      ,							UpperCamelCase__      ,							self.padding_value					) for x, n in zip(UpperCamelCase__      ,							UpperCamelCase__					)]




							def __call__(							self:    List[str]      ,							UpperCamelCase__:    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,							UpperCamelCase__:    Union[bool, str, PaddingStrategy] = False      ,							UpperCamelCase__:    Optional[int] = None      ,							UpperCamelCase__:    bool = False      ,							UpperCamelCase__:    Optional[int] = None      ,							UpperCamelCase__:    Optional[bool] = None      ,							UpperCamelCase__:    Optional[Union[str, TensorType]] = None      ,							UpperCamelCase__:    Optional[int] = None      ,							**UpperCamelCase__:    Tuple      ,							):

														if sampling_rate is not None:
																					if sampling_rate != self.sampling_rate:
																												raise ValueError(
																												    F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
																												    F''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
																												    F''' {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."""					)

														lowerCamelCase__ :     List[str]	  =	isinstance(UpperCamelCase__      ,							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}'''					)
														lowerCamelCase__ :     List[str]	  =	is_batched_numpy or (
														    isinstance(UpperCamelCase__      ,							(list, tuple)					) and (isinstance(raw_speech[0]      ,							(np.ndarray, tuple, list)					))
														)

														if is_batched:
																					lowerCamelCase__ :     str	  =	[np.asarray(UpperCamelCase__      ,							dtype=np.floataa					) for speech in raw_speech]
														elif not is_batched and not isinstance(UpperCamelCase__      ,							np.ndarray					):
																					lowerCamelCase__ :     List[Any]	  =	np.asarray(UpperCamelCase__      ,							dtype=np.floataa					)
														elif isinstance(UpperCamelCase__      ,							np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
																					lowerCamelCase__ :     Optional[Any]	  =	raw_speech.astype(np.floataa					)

														# always return batch
														if not is_batched:
																					lowerCamelCase__ :     Optional[Any]	  =	[raw_speech]

														# extract fbank features
														lowerCamelCase__ :     str	  =	[self._extract_mfsc_features(UpperCamelCase__					) for one_waveform in raw_speech]

														# convert into correct format for padding
														lowerCamelCase__ :     str	  =	BatchFeature({"""input_features""": features}					)

														lowerCamelCase__ :     str	  =	self.pad(
														    UpperCamelCase__      ,							padding=UpperCamelCase__      ,							max_length=UpperCamelCase__      ,							truncation=UpperCamelCase__      ,							pad_to_multiple_of=UpperCamelCase__      ,							return_attention_mask=UpperCamelCase__      ,							**UpperCamelCase__      ,							)
														# make sure list is in array format
														lowerCamelCase__ :     str	  =	padded_inputs.get("""input_features"""					)
														if isinstance(input_features[0]      ,							UpperCamelCase__					):
																					lowerCamelCase__ :     Dict	  =	[np.asarray(UpperCamelCase__      ,							dtype=np.floataa					) for feature in input_features]

														lowerCamelCase__ :     List[str]	  =	padded_inputs.get("""attention_mask"""					)
														if attention_mask is not None:
																					lowerCamelCase__ :     Any	  =	[np.asarray(UpperCamelCase__      ,							dtype=np.intaa					) for array in attention_mask]

														if self.normalize_means or self.normalize_vars:
																					lowerCamelCase__ :     str	  =	(
																					    np.array(UpperCamelCase__      ,							dtype=np.intaa					)
																					    if self._get_padding_strategies(UpperCamelCase__      ,							max_length=UpperCamelCase__					) is not PaddingStrategy.DO_NOT_PAD
																					    and padding
																					    else None
																					)
																					lowerCamelCase__ :     Union[str, Any]	  =	self.normalize(
																					    padded_inputs["""input_features"""]      ,							attention_mask=UpperCamelCase__					)

														if return_tensors is not None:
																					lowerCamelCase__ :     Dict	  =	padded_inputs.convert_to_tensors(UpperCamelCase__					)

														return padded_inputs

