
"""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:
     __snake_case:	nn.Module
     __snake_case:	List[nn.Module]						=     field(default_factory=_lowercase       )
     __snake_case:	list						=     field(default_factory=_lowercase       )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     Union[str, Any]:







         """simple docstring"""



         a__		=			len(list(m.modules()   )   ) == 1 or isinstance(__SCREAMING_SNAKE_CASE     ,				nn.Convad   ) or isinstance(__SCREAMING_SNAKE_CASE     ,				nn.BatchNormad   )
         if has_not_submodules:
             self.traced.append(__SCREAMING_SNAKE_CASE   )



     def __call__(							self     ,				__SCREAMING_SNAKE_CASE   )		->     str:







         """simple docstring"""



         for m in self.module.modules():
             self.handles.append(m.register_forward_hook(self._forward_hook   )   )
         self.module(__SCREAMING_SNAKE_CASE   )
         [x.remove() for x in self.handles]
         return self




     @property
     def 			lowercase__  (							self   )		->     int:







         """simple docstring"""



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







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



     def __call__(							self     ,				__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         a__		=			Tracker(self.dest   )(__SCREAMING_SNAKE_CASE   ).parametrized
         a__		=			Tracker(self.src   )(__SCREAMING_SNAKE_CASE   ).parametrized

         a__		=			list(filter(lambda __SCREAMING_SNAKE_CASE   : type(__SCREAMING_SNAKE_CASE   ) not in self.src_skip     ,				__SCREAMING_SNAKE_CASE   )   )
         a__		=			list(filter(lambda __SCREAMING_SNAKE_CASE   : type(__SCREAMING_SNAKE_CASE   ) not in self.dest_skip     ,				__SCREAMING_SNAKE_CASE   )   )

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

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




def     __magic_name__		(					UpperCamelCase     :		str							,		UpperCamelCase     :		ResNetConfig							,		UpperCamelCase     :		Path							,		UpperCamelCase     :		bool = True     )     ->						Dict:
    print(f'Converting {name}...'     )
    with torch.no_grad():
        a__		=			timm.create_model(UpperCamelCase							,		pretrained=UpperCamelCase     ).eval()
        a__		=			ResNetForImageClassification(UpperCamelCase     ).eval()
        a__		=			ModuleTransfer(src=UpperCamelCase							,		dest=UpperCamelCase     )
        a__		=			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."

    a__		=			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
        a__		=			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     __magic_name__		(					UpperCamelCase     :		Path							,		UpperCamelCase     :		str = None							,		UpperCamelCase     :		bool = True     )     ->						str:
    a__		=			'imagenet-1k-id2label.json'
    a__		=			1000
    a__		=			(1, num_labels)

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

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

    a__		=			partial(UpperCamelCase							,		num_labels=UpperCamelCase							,		idalabel=UpperCamelCase							,		labelaid=UpperCamelCase     )

    a__		=			{
        '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[int]     						=       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"""




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

import numpy as np
import torch

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		:       int     						=       logging.get_logger(__name__)







class     lowercase(_lowercase       ):
     __snake_case:	Any						=     ['input_features', 'is_longer']



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE=6_4     ,				__SCREAMING_SNAKE_CASE=4_8_0_0_0     ,				__SCREAMING_SNAKE_CASE=4_8_0     ,				__SCREAMING_SNAKE_CASE=1_0     ,				__SCREAMING_SNAKE_CASE=1_0_2_4     ,				__SCREAMING_SNAKE_CASE=0.0     ,				__SCREAMING_SNAKE_CASE=False     ,				__SCREAMING_SNAKE_CASE = 0     ,				__SCREAMING_SNAKE_CASE = 1_4_0_0_0     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = "fusion"     ,				__SCREAMING_SNAKE_CASE = "repeatpad"     ,				**__SCREAMING_SNAKE_CASE     ,				)		->     Union[str, Any]:







         """simple docstring"""



         super().__init__(
             feature_size=__SCREAMING_SNAKE_CASE     ,				sampling_rate=__SCREAMING_SNAKE_CASE     ,				padding_value=__SCREAMING_SNAKE_CASE     ,				return_attention_mask=__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE     ,				)
         a__		=			top_db
         a__		=			truncation
         a__		=			padding
         a__		=			fft_window_size
         a__		=			(fft_window_size >> 1) + 1
         a__		=			hop_length
         a__		=			max_length_s
         a__		=			max_length_s * sampling_rate
         a__		=			sampling_rate
         a__		=			frequency_min
         a__		=			frequency_max
         a__		=			mel_filter_bank(
             num_frequency_bins=self.nb_frequency_bins     ,				num_mel_filters=__SCREAMING_SNAKE_CASE     ,				min_frequency=__SCREAMING_SNAKE_CASE     ,				max_frequency=__SCREAMING_SNAKE_CASE     ,				sampling_rate=__SCREAMING_SNAKE_CASE     ,				norm=__SCREAMING_SNAKE_CASE     ,				mel_scale='htk'     ,				)
         a__		=			mel_filter_bank(
             num_frequency_bins=self.nb_frequency_bins     ,				num_mel_filters=__SCREAMING_SNAKE_CASE     ,				min_frequency=__SCREAMING_SNAKE_CASE     ,				max_frequency=__SCREAMING_SNAKE_CASE     ,				sampling_rate=__SCREAMING_SNAKE_CASE     ,				norm='slaney'     ,				mel_scale='slaney'     ,				)



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







         """simple docstring"""



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



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = None   )		->     np.ndarray:







         """simple docstring"""



         a__		=			spectrogram(
             __SCREAMING_SNAKE_CASE     ,				window_function(self.fft_window_size     ,				'hann'   )     ,				frame_length=self.fft_window_size     ,				hop_length=self.hop_length     ,				power=2.0     ,				mel_filters=__SCREAMING_SNAKE_CASE     ,				log_mel='dB'     ,				)
         return log_mel_spectrogram.T



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     List[Any]:







         """simple docstring"""



         a__		=			np.array_split(list(range(0     ,				total_frames - chunk_frames + 1   )   )     ,				3   )
         if len(ranges[1]   ) == 0:
             # if the audio is too short, we just use the first chunk
             a__		=			[0]
         if len(ranges[2]   ) == 0:
             # if the audio is too short, we just use the first chunk
             a__		=			[0]
         # randomly choose index for each part
         a__		=			np.random.choice(ranges[0]   )
         a__		=			np.random.choice(ranges[1]   )
         a__		=			np.random.choice(ranges[2]   )

         a__		=			mel[idx_front : idx_front + chunk_frames, :]
         a__		=			mel[idx_middle : idx_middle + chunk_frames, :]
         a__		=			mel[idx_back : idx_back + chunk_frames, :]

         a__		=			torch.tensor(mel[None, None, :]   )
         a__		=			torch.nn.functional.interpolate(
             __SCREAMING_SNAKE_CASE     ,				size=[chunk_frames, 6_4]     ,				mode='bilinear'     ,				align_corners=__SCREAMING_SNAKE_CASE   )
         a__		=			mel_shrink[0][0].numpy()
         a__		=			np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]     ,				axis=0   )
         return mel_fusion



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     np.array:







         """simple docstring"""



         if waveform.shape[0] > max_length:
             if truncation == "rand_trunc":
                 a__		=			True
                 # random crop to max_length (for compatibility) -> this should be handled by self.pad
                 a__		=			len(__SCREAMING_SNAKE_CASE   ) - max_length
                 a__		=			np.random.randint(0     ,				overflow + 1   )
                 a__		=			waveform[idx : idx + max_length]
                 a__		=			self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE     ,				self.mel_filters_slaney   )[None, :]
             elif truncation == "fusion":
                 a__		=			self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE     ,				self.mel_filters   )
                 a__		=			max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                 a__		=			mel.shape[0]
                 if chunk_frames == total_frames:
                     # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                     # In this case, we just use the whole audio.
                     a__		=			np.stack([mel, mel, mel, mel]     ,				axis=0   )
                     a__		=			False
                 else:
                     a__		=			self._random_mel_fusion(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
                     a__		=			True
             else:
                 raise NotImplementedError(f'data_truncating {truncation} not implemented'   )

         else:
             a__		=			False
             # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
             if waveform.shape[0] < max_length:
                 if padding == "repeat":
                     a__		=			int(max_length / len(__SCREAMING_SNAKE_CASE   )   )
                     a__		=			np.stack(np.tile(__SCREAMING_SNAKE_CASE     ,				n_repeat + 1   )   )[:max_length]
                 if padding == "repeatpad":
                     a__		=			int(max_length / len(__SCREAMING_SNAKE_CASE   )   )
                     a__		=			np.stack(np.tile(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )   )
                 a__		=			np.pad(__SCREAMING_SNAKE_CASE     ,				(0, max_length - waveform.shape[0])     ,				mode='constant'     ,				constant_values=0   )

             if truncation == "fusion":
                 a__		=			self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE     ,				self.mel_filters   )
                 a__		=			np.stack([input_mel, input_mel, input_mel, input_mel]     ,				axis=0   )
             else:
                 a__		=			self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE     ,				self.mel_filters_slaney   )[None, :]

         return input_mel, longer




     def __call__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				**__SCREAMING_SNAKE_CASE     ,				)		->     BatchFeature:







         """simple docstring"""



         a__		=			truncation if truncation is not None else self.truncation
         a__		=			padding if padding else self.padding

         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.'   )

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

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

         # always return batch
         if not is_batched:
             a__		=			[np.asarray(__SCREAMING_SNAKE_CASE   )]

         # convert to mel spectrogram, truncate and pad if needed.
         a__		=			[
             self._get_input_mel(__SCREAMING_SNAKE_CASE     ,				max_length if max_length else self.nb_max_samples     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
             for waveform in raw_speech
         ]

         a__		=			[]
         a__		=			[]
         for mel, longer in padded_inputs:
             input_mel.append(__SCREAMING_SNAKE_CASE   )
             is_longer.append(__SCREAMING_SNAKE_CASE   )

         if truncation == "fusion" and sum(__SCREAMING_SNAKE_CASE   ) == 0:
             # if no audio is longer than 10s, then randomly select one audio to be longer
             a__		=			np.random.randint(0     ,				len(__SCREAMING_SNAKE_CASE   )   )
             a__		=			True

         if isinstance(input_mel[0]     ,				__SCREAMING_SNAKE_CASE   ):
             a__		=			[np.asarray(__SCREAMING_SNAKE_CASE     ,				dtype=np.floataa   ) for feature in input_mel]

         # is_longer is a list of bool
         a__		=			[[longer] for longer in is_longer]

         a__		=			{'input_features': input_mel, 'is_longer': is_longer}
         a__		=			BatchFeature(__SCREAMING_SNAKE_CASE   )

         if return_tensors is not None:
             a__		=			input_features.convert_to_tensors(__SCREAMING_SNAKE_CASE   )

         return input_features


