
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


lowerCamelCase__	=							logging.get_logger(__name__)







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Tuple	=		["input_features"]

						def __init__(			self       ,							a=80       ,							a=1_60_00       ,							a=1_60       ,							a=30       ,							a=4_00       ,							a=0.0       ,							a=False       ,							**a       ,							)					->      List[Any]:


								'''simple docstring'''

								super().__init__(
								    feature_size=a       ,							sampling_rate=a       ,							padding_value=a       ,							return_attention_mask=a       ,							**a       ,							)
								_UpperCamelCase						    =	n_fft
								_UpperCamelCase						    =	hop_length
								_UpperCamelCase						    =	chunk_length
								_UpperCamelCase						    =	chunk_length * sampling_rate
								_UpperCamelCase						    =	self.n_samples // hop_length
								_UpperCamelCase						    =	sampling_rate
								_UpperCamelCase						    =	mel_filter_bank(
								    num_frequency_bins=1 + n_fft // 2       ,							num_mel_filters=a       ,							min_frequency=0.0       ,							max_frequency=8000.0       ,							sampling_rate=a       ,							norm="""slaney"""       ,							mel_scale="""slaney"""       ,							)

						def  A_				(			self       ,							a				)					->      np.ndarray:


								'''simple docstring'''

								_UpperCamelCase						    =	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"""       ,							)
								_UpperCamelCase						    =	log_spec[:, :-1]
								_UpperCamelCase						    =	np.maximum(a       ,							log_spec.max() - 8.0				)
								_UpperCamelCase						    =	(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  A_				(			a       ,							a       ,							a = 0.0				)					->      List[np.ndarray]:


								'''simple docstring'''

								if attention_mask is not None:
										_UpperCamelCase						    =	np.array(a       ,							np.intaa				)
										_UpperCamelCase						    =	[]

										for vector, length in zip(a       ,							attention_mask.sum(-1				)				):
												_UpperCamelCase						    =	(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7				)
												if length < normed_slice.shape[0]:
														_UpperCamelCase						    =	padding_value

												normed_input_values.append(a				)
								else:
										_UpperCamelCase						    =	[(x - x.mean()) / np.sqrt(x.var() + 1e-7				) for x in input_values]

								return normed_input_values

						def __call__(			self       ,							a       ,							a = True       ,							a = None       ,							a = None       ,							a = None       ,							a = "max_length"       ,							a = None       ,							a = None       ,							a = None       ,							**a       ,							)					->      BatchFeature:


								'''simple docstring'''

								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."""				)

								_UpperCamelCase						    =	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}'				)
								_UpperCamelCase						    =	is_batched_numpy or (
								    isinstance(a       ,							(list, tuple)				) and (isinstance(raw_speech[0]       ,							(np.ndarray, tuple, list)				))
								)

								if is_batched:
										_UpperCamelCase						    =	[np.asarray([speech]       ,							dtype=np.floataa				).T for speech in raw_speech]
								elif not is_batched and not isinstance(a       ,							np.ndarray				):
										_UpperCamelCase						    =	np.asarray(a       ,							dtype=np.floataa				)
								elif isinstance(a       ,							np.ndarray				) and raw_speech.dtype is np.dtype(np.floataa				):
										_UpperCamelCase						    =	raw_speech.astype(np.floataa				)

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

								_UpperCamelCase						    =	BatchFeature({"""input_features""": raw_speech}				)

								# convert into correct format for padding

								_UpperCamelCase						    =	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:
										_UpperCamelCase						    =	self.zero_mean_unit_var_norm(
										    padded_inputs["""input_features"""]       ,							attention_mask=padded_inputs["""attention_mask"""]       ,							padding_value=self.padding_value       ,							)
										_UpperCamelCase						    =	np.stack(padded_inputs["""input_features"""]       ,							axis=0				)

								# make sure list is in array format
								_UpperCamelCase						    =	padded_inputs.get("""input_features"""				).transpose(2       ,							0       ,							1				)

								_UpperCamelCase						    =	[self._np_extract_fbank_features(a				) for waveform in input_features[0]]

								if isinstance(input_features[0]       ,							a				):
										_UpperCamelCase						    =	[np.asarray(a       ,							dtype=np.floataa				) for feature in input_features]
								else:
										_UpperCamelCase						    =	input_features

								if return_attention_mask:
										# rescale from sample (48000) to feature (3000)
										_UpperCamelCase						    =	padded_inputs["""attention_mask"""][:, :: self.hop_length]

								if return_tensors is not None:
										_UpperCamelCase						    =	padded_inputs.convert_to_tensors(a				)

								return padded_inputs







						def  A_				(			self				)					->      Dict[str, Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	copy.deepcopy(self.__dict__				)
								_UpperCamelCase						    =	self.__class__.__name__
								if "mel_filters" in output:
										del output["mel_filters"]
								return output





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()
lowerCamelCase__	=							logging.get_logger()







@dataclass
class        lowerCAmelCase__      :
						UpperCamelCase_							: nn.Module
						UpperCamelCase_							: List[nn.Module]	=		field(default_factory=__lowercase	)
						UpperCamelCase_							: list	=		field(default_factory=__lowercase	)

						def  A_				(			self       ,							a       ,							a       ,							a				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	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       ,							a				)					->      Optional[int]:


								'''simple docstring'''

								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  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								return list(filter(lambda a				: len(list(x.state_dict().keys()				)				) > 0       ,							self.traced				)				)







@dataclass
class        lowerCAmelCase__      :
						UpperCamelCase_							: nn.Module
						UpperCamelCase_							: nn.Module
						UpperCamelCase_							: int	=		0
						UpperCamelCase_							: List	=		field(default_factory=__lowercase	)
						UpperCamelCase_							: List	=		field(default_factory=__lowercase	)

						def __call__(			self       ,							a				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	Tracker(self.dest				)(a				).parametrized
								_UpperCamelCase						    =	Tracker(self.src				)(a				).parametrized

								_UpperCamelCase						    =	list(filter(lambda a				: type(a				) not in self.src_skip       ,							a				)				)
								_UpperCamelCase						    =	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 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase = True )		->				Optional[Any]:

		"""simple docstring"""






		print(F'Converting {name}...' )
		with torch.no_grad():
				_UpperCamelCase						    =	timm.create_model(lowerCAmelCase						,  pretrained=lowerCAmelCase ).eval()
				_UpperCamelCase						    =	ResNetForImageClassification(lowerCAmelCase ).eval()
				_UpperCamelCase						    =	ModuleTransfer(src=lowerCAmelCase						,  dest=lowerCAmelCase )
				_UpperCamelCase						    =	torch.randn((1, 3, 2_2_4, 2_2_4) )
				module_transfer(lowerCAmelCase )

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

		_UpperCamelCase						    =	F'resnet{"-".join(name.split("resnet" ) )}'
		print(lowerCAmelCase )

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

				# we can use the convnext one
				_UpperCamelCase						    =	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=lowerCAmelCase						,  )

				print(F'Pushed {checkpoint_name}' )


def 							__A(lowerCAmelCase						,  lowerCAmelCase = None						,  lowerCAmelCase = True )		->				Tuple:

		"""simple docstring"""






		_UpperCamelCase						    =	"""imagenet-1k-id2label.json"""
		_UpperCamelCase						    =	1_0_0_0
		_UpperCamelCase						    =	(1, num_labels)

		_UpperCamelCase						    =	"""huggingface/label-files"""
		_UpperCamelCase						    =	num_labels
		_UpperCamelCase						    =	json.load(open(hf_hub_download(lowerCAmelCase						,  lowerCAmelCase						,  repo_type="""dataset""" )						,  """r""" ) )
		_UpperCamelCase						    =	{int(lowerCAmelCase ): v for k, v in idalabel.items()}

		_UpperCamelCase						    =	idalabel
		_UpperCamelCase						    =	{v: k for k, v in idalabel.items()}

		_UpperCamelCase						    =	partial(lowerCAmelCase						,  num_labels=lowerCAmelCase						,  idalabel=lowerCAmelCase						,  labelaid=lowerCAmelCase )

		_UpperCamelCase						    =	{
		    """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(lowerCAmelCase						,  names_to_config[model_name]						,  lowerCAmelCase						,  lowerCAmelCase )
		else:
				for model_name, config in names_to_config.items():
						convert_weight_and_push(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )
		return config, expected_shape


if __name__ == "__main__":
		lowerCamelCase__	=							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.",
		)

		lowerCamelCase__	=							parser.parse_args()
		lowerCamelCase__	=							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)



