

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

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_									=    logging.get_logger(__name__)


@dataclass
class 					A		:







    """simple docstring"""





    lowerCamelCase						      =				42
    lowerCamelCase						      =				field(default_factory=_UpperCAmelCase					)
    lowerCamelCase						      =				field(default_factory=_UpperCAmelCase					)


    def 		snake_case__	( self					:			List[Any],lowercase_					:			Tuple,lowercase_					:			Tensor,lowercase_					:			Tensor						)->       Tuple:





        '''simple docstring'''







        A__        =  len(list(m.modules()						)						) == 1 or isinstance(lowercase_,nn.Convad						) or isinstance(lowercase_,nn.BatchNormad						)
        if has_not_submodules:
            if not isinstance(lowercase_,lowercase_						):
                self.traced.append(lowercase_						)


    def __call__( self					:			Tuple,lowercase_					:			Tensor						)->       Union[str, Any]:





        '''simple docstring'''







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


    @property
    def 		snake_case__	( self					:			int						)->       int:





        '''simple docstring'''







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




@dataclass
class 					A		:







    """simple docstring"""





    lowerCamelCase						      =				42
    lowerCamelCase						      =				42
    lowerCamelCase						      =				0
    lowerCamelCase						      =				field(default_factory=_UpperCAmelCase					)
    lowerCamelCase						      =				field(default_factory=_UpperCAmelCase					)


    def __call__( self					:			int,lowercase_					:			Tensor						)->       Union[str, Any]:





        '''simple docstring'''







        A__        =  Tracker(self.dest						)(lowercase_						).parametrized
        A__        =  Tracker(self.src						)(lowercase_						).parametrized

        A__        =  list(filter(lambda lowercase_						: type(lowercase_						) not in self.src_skip,lowercase_						)						)
        A__        =  list(filter(lambda lowercase_						: type(lowercase_						) not in self.dest_skip,lowercase_						)						)

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

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




def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      nn.Module       ,     SCREAMING_SNAKE_CASE__     :      nn.Module )				-> nn.Module:





    '''simple docstring'''






    A__        =  from_model.state_dict()
    A__        =  our_model.state_dict()
    A__        =  our_model.config
    A__        =  []
    for stage_idx in range(len(config.hidden_sizes ) ):
        for block_id in range(config.depths[stage_idx] ):
            A__        =  f'block{stage_idx + 1}.{block_id}.layer_scale_1'
            A__        =  f'van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'

            all_keys.append((from_key, to_key) )
            A__        =  f'block{stage_idx + 1}.{block_id}.layer_scale_2'
            A__        =  f'van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight'

            all_keys.append((from_key, to_key) )

    for from_key, to_key in all_keys:
        A__        =  from_state_dict.pop(SCREAMING_SNAKE_CASE__ )

    our_model.load_state_dict(SCREAMING_SNAKE_CASE__ )
    return our_model
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      str       ,     SCREAMING_SNAKE_CASE__     :      VanConfig       ,     SCREAMING_SNAKE_CASE__     :      str       ,     SCREAMING_SNAKE_CASE__     :      nn.Module       ,     SCREAMING_SNAKE_CASE__     :      Path       ,     SCREAMING_SNAKE_CASE__     :      bool = True       ,     )				-> Any:





    '''simple docstring'''






    print(f'Downloading weights for {name}...' )
    A__        =  cached_download(SCREAMING_SNAKE_CASE__ )
    print(f'Converting {name}...' )
    A__        =  torch.load(SCREAMING_SNAKE_CASE__ )['state_dict']
    from_model.load_state_dict(SCREAMING_SNAKE_CASE__ )
    from_model.eval()
    with torch.no_grad():
        A__        =  VanForImageClassification(SCREAMING_SNAKE_CASE__ ).eval()
        A__        =  ModuleTransfer(src=SCREAMING_SNAKE_CASE__       ,     dest=SCREAMING_SNAKE_CASE__ )
        A__        =  torch.randn((1, 3, 224, 224) )
        module_transfer(SCREAMING_SNAKE_CASE__ )
        A__        =  copy_parameters(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__ )

    if not torch.allclose(from_model(SCREAMING_SNAKE_CASE__ )       ,     our_model(SCREAMING_SNAKE_CASE__ ).logits ):
        raise ValueError('The model logits don\'t match the original one.' )

    A__        =  name
    print(SCREAMING_SNAKE_CASE__ )

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

        # 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=SCREAMING_SNAKE_CASE__       ,     )

        print(f'Pushed {checkpoint_name}' )
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Path       ,     SCREAMING_SNAKE_CASE__     :      str = None       ,     SCREAMING_SNAKE_CASE__     :      bool = True )				-> Tuple:





    '''simple docstring'''






    A__        =  'imagenet-1k-id2label.json'
    A__        =  1000

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

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

    A__        =  partial(SCREAMING_SNAKE_CASE__       ,     num_labels=SCREAMING_SNAKE_CASE__       ,     idalabel=SCREAMING_SNAKE_CASE__       ,     labelaid=SCREAMING_SNAKE_CASE__ )

    A__        =  {
        'van-tiny': ImageNetPreTrainedConfig(
            hidden_sizes=[32, 64, 160, 256]       ,     depths=[3, 3, 5, 2]       ,     mlp_ratios=[8, 8, 4, 4]       ,     ),
        'van-small': ImageNetPreTrainedConfig(
            hidden_sizes=[64, 128, 320, 512]       ,     depths=[2, 2, 4, 2]       ,     mlp_ratios=[8, 8, 4, 4]       ,     ),
        'van-base': ImageNetPreTrainedConfig(
            hidden_sizes=[64, 128, 320, 512]       ,     depths=[3, 3, 12, 3]       ,     mlp_ratios=[8, 8, 4, 4]       ,     ),
        'van-large': ImageNetPreTrainedConfig(
            hidden_sizes=[64, 128, 320, 512]       ,     depths=[3, 5, 27, 3]       ,     mlp_ratios=[8, 8, 4, 4]       ,     ),
    }

    A__        =  {
        'van-tiny': van_tiny,
        'van-small': van_small,
        'van-base': van_base,
        'van-large': van_large,
    }

    A__        =  {
        'van-tiny': (
            'https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar'
        ),
        'van-small': (
            'https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar'
        ),
        'van-base': (
            'https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar'
        ),
        'van-large': (
            'https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar'
        ),
    }

    if model_name:
        convert_weight_and_push(
            SCREAMING_SNAKE_CASE__       ,     names_to_config[model_name]       ,     checkpoint=names_to_original_checkpoints[model_name]       ,     from_model=names_to_original_models[model_name]()       ,     save_directory=SCREAMING_SNAKE_CASE__       ,     push_to_hub=SCREAMING_SNAKE_CASE__       ,     )
    else:
        for model_name, config in names_to_config.items():
            convert_weight_and_push(
                SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     checkpoint=names_to_original_checkpoints[model_name]       ,     from_model=names_to_original_models[model_name]()       ,     save_directory=SCREAMING_SNAKE_CASE__       ,     push_to_hub=SCREAMING_SNAKE_CASE__       ,     )


if __name__ == "__main__":
     lowercase_									=    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: van-tiny/small/base/large. 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(
         "--van_dir",
         required=True,
         type=Path,
         help=(
             "A path to VAN's original implementation directory. You can download from here:"
             " https://github.com/Visual-Attention-Network/VAN-Classification"
         ),
     )
     parser.add_argument(
         "--push_to_hub",
         default=True,
         type=bool,
         required=False,
         help="If True, push model and image processor to the hub.",
     )

     lowercase_									=    parser.parse_args()
     lowercase_									=    args.pytorch_dump_folder_path
     pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
     lowercase_									=    args.van_dir
     # append the path to the parents to maskformer dir
     sys.path.append(str(van_dir.parent))
     from van.models.van import van_base, van_large, van_small, van_tiny

     convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowercase_									=    logging.get_logger(__name__)


class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    lowerCamelCase						      =				['input_values', 'attention_mask']


    def __init__( self					:			Tuple,lowercase_					:			Optional[Any]=1,lowercase_					:			Union[str, Any]=1_6_0_0_0,lowercase_					:			str=1_2_8,lowercase_					:			Optional[Any]=1_0_2_4,lowercase_					:			Union[str, Any]=0.0,lowercase_					:			Any=True,lowercase_					:			List[Any]=-4.2_677_393,lowercase_					:			Union[str, Any]=4.5_689_974,lowercase_					:			Dict=False,**lowercase_					:			Dict,)->       int:





        '''simple docstring'''







        super().__init__(feature_size=lowercase_,sampling_rate=lowercase_,padding_value=lowercase_,**lowercase_						)
        A__        =  num_mel_bins
        A__        =  max_length
        A__        =  do_normalize
        A__        =  mean
        A__        =  std
        A__        =  return_attention_mask


    def 		snake_case__	( self					:			Any,lowercase_					:			np.ndarray,lowercase_					:			int,)->       np.ndarray:





        '''simple docstring'''







        A__        =  torch.from_numpy(lowercase_						).unsqueeze(0						)
        A__        =  ta_kaldi.fbank(
            lowercase_,htk_compat=lowercase_,sample_frequency=self.sampling_rate,use_energy=lowercase_,window_type='hanning',num_mel_bins=self.num_mel_bins,dither=0.0,frame_shift=1_0,)

        A__        =  fbank.shape[0]
        A__        =  max_length - n_frames

        # pad or truncate, depending on difference
        if difference > 0:
            A__        =  torch.nn.ZeroPadad((0, 0, 0, difference)						)
            A__        =  pad_module(lowercase_						)
        elif difference < 0:
            A__        =  fbank[0:max_length, :]

        A__        =  fbank.numpy()

        return fbank


    def 		snake_case__	( self					:			int,lowercase_					:			np.ndarray						)->       np.ndarray:





        '''simple docstring'''







        return (input_values - (self.mean)) / (self.std * 2)


    def __call__( self					:			str,lowercase_					:			Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],lowercase_					:			Optional[int] = None,lowercase_					:			Optional[Union[str, TensorType]] = None,**lowercase_					:			str,)->       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} 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.'						)

        A__        =  isinstance(lowercase_,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(lowercase_,(list, tuple)						) and (isinstance(raw_speech[0],(np.ndarray, tuple, list)						))
        )

        if is_batched:
            A__        =  [np.asarray(lowercase_,dtype=np.floataa						) for speech in raw_speech]
        elif not is_batched and not isinstance(lowercase_,np.ndarray						):
            A__        =  np.asarray(lowercase_,dtype=np.floataa						)
        elif isinstance(lowercase_,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__        =  [raw_speech]

        # extract fbank features and pad/truncate to max_length
        A__        =  [self._extract_fbank_features(lowercase_,max_length=self.max_length						) for waveform in raw_speech]

        # convert into BatchFeature
        A__        =  BatchFeature({'input_values': features}						)

        # make sure list is in array format
        A__        =  padded_inputs.get('input_values'						)
        if isinstance(input_values[0],lowercase_						):
            A__        =  [np.asarray(lowercase_,dtype=np.floataa						) for feature in input_values]

        # normalization
        if self.do_normalize:
            A__        =  [self.normalize(lowercase_						) for feature in input_values]

        if return_tensors is not None:
            A__        =  padded_inputs.convert_to_tensors(lowercase_						)

        return padded_inputs

