


'''simple docstring'''
import argparse
import json
from pathlib import Path

import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download

from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_ :     Dict								=      logging.get_logger(__name__)


def 			_lowercase       (				UpperCamelCase__       :					Optional[int]			):
 __A  :		Tuple       =			ASTConfig()

 if "10-10" in model_name:
  pass
 elif "speech-commands" in model_name:
  __A  :		List[str]       =			128
 elif "12-12" in model_name:
  __A  :		Any       =			12
  __A  :		Dict       =			12
 elif "14-14" in model_name:
  __A  :		Any       =			14
  __A  :		Optional[int]       =			14
 elif "16-16" in model_name:
  __A  :		Dict       =			16
  __A  :		Optional[int]       =			16
 else:
  raise ValueError('Model not supported'			)

 __A  :		Union[str, Any]       =			'huggingface/label-files'
 if "speech-commands" in model_name:
  __A  :		List[str]       =			35
  __A  :		Tuple       =			'speech-commands-v2-id2label.json'
 else:
  __A  :		Dict       =			527
  __A  :		List[Any]       =			'audioset-id2label.json'

 __A  :		int       =			json.load(open(hf_hub_download(UpperCamelCase__,				UpperCamelCase__,				repo_type='dataset'			),				'r'			)			)
 __A  :		List[str]       =			{int(UpperCamelCase__			): v for k, v in idalabel.items()}
 __A  :		int       =			idalabel
 __A  :		str       =			{v: k for k, v in idalabel.items()}

 return config


def 			_lowercase       (				UpperCamelCase__       :					List[str]			):
 if "module.v" in name:
  __A  :		List[str]       =			name.replace('module.v',				'audio_spectrogram_transformer'			)
 if "cls_token" in name:
  __A  :		Union[str, Any]       =			name.replace('cls_token',				'embeddings.cls_token'			)
 if "dist_token" in name:
  __A  :		int       =			name.replace('dist_token',				'embeddings.distillation_token'			)
 if "pos_embed" in name:
  __A  :		Dict       =			name.replace('pos_embed',				'embeddings.position_embeddings'			)
 if "patch_embed.proj" in name:
  __A  :		Union[str, Any]       =			name.replace('patch_embed.proj',				'embeddings.patch_embeddings.projection'			)
 # transformer blocks
 if "blocks" in name:
  __A  :		int       =			name.replace('blocks',				'encoder.layer'			)
 if "attn.proj" in name:
  __A  :		Tuple       =			name.replace('attn.proj',				'attention.output.dense'			)
 if "attn" in name:
  __A  :		str       =			name.replace('attn',				'attention.self'			)
 if "norm1" in name:
  __A  :		Dict       =			name.replace('norm1',				'layernorm_before'			)
 if "norm2" in name:
  __A  :		Optional[Any]       =			name.replace('norm2',				'layernorm_after'			)
 if "mlp.fc1" in name:
  __A  :		Optional[int]       =			name.replace('mlp.fc1',				'intermediate.dense'			)
 if "mlp.fc2" in name:
  __A  :		List[str]       =			name.replace('mlp.fc2',				'output.dense'			)
 # final layernorm
 if "audio_spectrogram_transformer.norm" in name:
  __A  :		Optional[Any]       =			name.replace('audio_spectrogram_transformer.norm',				'audio_spectrogram_transformer.layernorm'			)
 # classifier head
 if "module.mlp_head.0" in name:
  __A  :		Union[str, Any]       =			name.replace('module.mlp_head.0',				'classifier.layernorm'			)
 if "module.mlp_head.1" in name:
  __A  :		Any       =			name.replace('module.mlp_head.1',				'classifier.dense'			)

 return name


def 			_lowercase       (				UpperCamelCase__       :					Optional[int],				UpperCamelCase__       :					int			):
 for key in orig_state_dict.copy().keys():
  __A  :		str       =			orig_state_dict.pop(UpperCamelCase__			)

  if "qkv" in key:
   __A  :		Any       =			key.split('.'			)
   __A  :		str       =			int(key_split[3]			)
   __A  :		Optional[int]       =			config.hidden_size
   if "weight" in key:
    __A  :		str       =			val[:dim, :]
    __A  :		str       =			val[dim : dim * 2, :]
    __A  :		Tuple       =			val[-dim:, :]
   else:
    __A  :		Tuple       =			val[:dim]
    __A  :		List[Any]       =			val[dim : dim * 2]
    __A  :		Tuple       =			val[-dim:]
  else:
   __A  :		List[str]       =			val

 return orig_state_dict


def 			_lowercase       (				UpperCamelCase__       :					List[Any]			):
 __A  :		str       =			[
     'module.v.head.weight',
     'module.v.head.bias',
     'module.v.head_dist.weight',
     'module.v.head_dist.bias',
 ]
 for k in ignore_keys:
  state_dict.pop(UpperCamelCase__,				UpperCamelCase__			)


@torch.no_grad()
def 			_lowercase       (				UpperCamelCase__       :					Optional[int],				UpperCamelCase__       :					Any,				UpperCamelCase__       :					Optional[int]=False			):
 __A  :		str       =			get_audio_spectrogram_transformer_config(UpperCamelCase__			)

 __A  :		Dict       =			{
     'ast-finetuned-audioset-10-10-0.4593': (
         'https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1'
     ),
     'ast-finetuned-audioset-10-10-0.450': (
         'https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1'
     ),
     'ast-finetuned-audioset-10-10-0.448': (
         'https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1'
     ),
     'ast-finetuned-audioset-10-10-0.448-v2': (
         'https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1'
     ),
     'ast-finetuned-audioset-12-12-0.447': (
         'https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1'
     ),
     'ast-finetuned-audioset-14-14-0.443': (
         'https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1'
     ),
     'ast-finetuned-audioset-16-16-0.442': (
         'https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1'
     ),
     'ast-finetuned-speech-commands-v2': (
         'https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1'
     ),
 }

 # load original state_dict
 __A  :		Tuple       =			model_name_to_url[model_name]
 __A  :		str       =			torch.hub.load_state_dict_from_url(UpperCamelCase__,				map_location='cpu'			)
 # remove some keys
 remove_keys(UpperCamelCase__			)
 # rename some keys
 __A  :		Tuple       =			convert_state_dict(UpperCamelCase__,				UpperCamelCase__			)

 # load 🤗 model
 __A  :		List[Any]       =			ASTForAudioClassification(UpperCamelCase__			)
 model.eval()

 model.load_state_dict(UpperCamelCase__			)

 # verify outputs on dummy input
 # source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
 __A  :		Optional[int]       =			-4.2677393 if 'speech-commands' not in model_name else -6.845978
 __A  :		Any       =			4.5689974 if 'speech-commands' not in model_name else 5.5654526
 __A  :		List[str]       =			1024 if 'speech-commands' not in model_name else 128
 __A  :		List[str]       =			ASTFeatureExtractor(mean=UpperCamelCase__,				std=UpperCamelCase__,				max_length=UpperCamelCase__			)

 if "speech-commands" in model_name:
  __A  :		List[Any]       =			load_dataset('speech_commands',				'v0.02',				split='validation'			)
  __A  :		Union[str, Any]       =			dataset[0]['audio']['array']
 else:
  __A  :		Optional[Any]       =			hf_hub_download(
      repo_id='nielsr/audio-spectogram-transformer-checkpoint',				filename='sample_audio.flac',				repo_type='dataset',				)

  __A     ,__A  :		Dict       =			torchaudio.load(UpperCamelCase__			)
  __A  :		str       =			waveform.squeeze().numpy()

 __A  :		Any       =			feature_extractor(UpperCamelCase__,				sampling_rate=16000,				return_tensors='pt'			)

 # forward pass
 __A  :		List[Any]       =			model(**UpperCamelCase__			)
 __A  :		List[Any]       =			outputs.logits

 if model_name == "ast-finetuned-audioset-10-10-0.4593":
  __A  :		Any       =			torch.tensor([-0.8760, -7.0042, -8.6602]			)
 elif model_name == "ast-finetuned-audioset-10-10-0.450":
  __A  :		Union[str, Any]       =			torch.tensor([-1.1986, -7.0903, -8.2718]			)
 elif model_name == "ast-finetuned-audioset-10-10-0.448":
  __A  :		Dict       =			torch.tensor([-2.6128, -8.0080, -9.4344]			)
 elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
  __A  :		Dict       =			torch.tensor([-1.5080, -7.4534, -8.8917]			)
 elif model_name == "ast-finetuned-audioset-12-12-0.447":
  __A  :		List[str]       =			torch.tensor([-0.5050, -6.5833, -8.0843]			)
 elif model_name == "ast-finetuned-audioset-14-14-0.443":
  __A  :		Optional[Any]       =			torch.tensor([-0.3826, -7.0336, -8.2413]			)
 elif model_name == "ast-finetuned-audioset-16-16-0.442":
  __A  :		Any       =			torch.tensor([-1.2113, -6.9101, -8.3470]			)
 elif model_name == "ast-finetuned-speech-commands-v2":
  __A  :		Tuple       =			torch.tensor([6.1589, -8.0566, -8.7984]			)
 else:
  raise ValueError('Unknown model name'			)
 if not torch.allclose(logits[0, :3],				UpperCamelCase__,				atol=1E-4			):
  raise ValueError('Logits don\'t match'			)
 print('Looks ok!'			)

 if pytorch_dump_folder_path is not None:
  Path(UpperCamelCase__			).mkdir(exist_ok=UpperCamelCase__			)
  print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""			)
  model.save_pretrained(UpperCamelCase__			)
  print(f"""Saving feature extractor to {pytorch_dump_folder_path}"""			)
  feature_extractor.save_pretrained(UpperCamelCase__			)

 if push_to_hub:
  print('Pushing model and feature extractor to the hub...'			)
  model.push_to_hub(f"""MIT/{model_name}"""			)
  feature_extractor.push_to_hub(f"""MIT/{model_name}"""			)


if __name__ == "__main__":
 UpperCAmelCase_ :     str								=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--model_name',
     default='ast-finetuned-audioset-10-10-0.4593',
     type=str,
     help='Name of the Audio Spectrogram Transformer model you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
 )
 parser.add_argument(
     '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
 )

 UpperCAmelCase_ :     List[str]								=      parser.parse_args()
 convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










'''simple docstring'''
import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_ :     Tuple								=      logging.get_logger(__name__)


def 			_lowercase       (				UpperCamelCase__       :					Optional[Any],				UpperCamelCase__       :					Tuple=False			):
 __A  :		List[str]       =			[]

 # fmt: off
 # stem:
 rename_keys.append(('cls_token', 'vit.embeddings.cls_token')			)
 rename_keys.append(('pos_embed', 'vit.embeddings.position_embeddings')			)

 rename_keys.append(('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight')			)
 rename_keys.append(('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias')			)

 # backbone
 rename_keys.append(('patch_embed.backbone.stem.conv.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight')			)
 rename_keys.append(('patch_embed.backbone.stem.norm.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight')			)
 rename_keys.append(('patch_embed.backbone.stem.norm.bias', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias')			)

 for stage_idx in range(len(config.backbone_config.depths			)			):
  for layer_idx in range(config.backbone_config.depths[stage_idx]			):
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""")			)

  rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""")			)
  rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""")			)
  rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""")			)

 # transformer encoder
 for i in range(config.num_hidden_layers			):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")			)
  rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")			)
  rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")			)
  rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")			)
  rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")			)
  rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")			)
  rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")			)
  rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")			)
  rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")			)
  rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")			)

 if base_model:
  # layernorm + pooler
  rename_keys.extend(
      [
          ('norm.weight', 'layernorm.weight'),
          ('norm.bias', 'layernorm.bias'),
          ('pre_logits.fc.weight', 'pooler.dense.weight'),
          ('pre_logits.fc.bias', 'pooler.dense.bias'),
      ]			)

  # if just the base model, we should remove "vit" from all keys that start with "vit"
  __A  :		List[str]       =			[(pair[0], pair[1][4:]) if pair[1].startswith('vit'			) else pair for pair in rename_keys]
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ('norm.weight', 'vit.layernorm.weight'),
          ('norm.bias', 'vit.layernorm.bias'),
          ('head.weight', 'classifier.weight'),
          ('head.bias', 'classifier.bias'),
      ]			)
 # fmt: on

 return rename_keys


def 			_lowercase       (				UpperCamelCase__       :					Any,				UpperCamelCase__       :					int,				UpperCamelCase__       :					List[Any]=False			):
 for i in range(config.num_hidden_layers			):
  if base_model:
   __A  :		Optional[Any]       =			''
  else:
   __A  :		int       =			'vit.'
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  __A  :		str       =			state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""			)
  __A  :		str       =			state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""			)
  # next, add query, keys and values (in that order) to the state dict
  __A  :		List[Any]       =			in_proj_weight[
      : config.hidden_size, :
  ]
  __A  :		List[Any]       =			in_proj_bias[: config.hidden_size]
  __A  :		Optional[int]       =			in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __A  :		int       =			in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  __A  :		Dict       =			in_proj_weight[
      -config.hidden_size :, :
  ]
  __A  :		Optional[Any]       =			in_proj_bias[-config.hidden_size :]


def 			_lowercase       (				UpperCamelCase__       :					Optional[Any]			):
 __A  :		Any       =			['head.weight', 'head.bias']
 for k in ignore_keys:
  state_dict.pop(UpperCamelCase__,				UpperCamelCase__			)


def 			_lowercase       (				UpperCamelCase__       :					Any,				UpperCamelCase__       :					Any,				UpperCamelCase__       :					Union[str, Any]			):
 __A  :		Dict       =			dct.pop(UpperCamelCase__			)
 __A  :		Optional[Any]       =			val


def 			_lowercase       (				):
 __A  :		Tuple       =			'http://images.cocodataset.org/val2017/000000039769.jpg'
 __A  :		Optional[Any]       =			Image.open(requests.get(UpperCamelCase__,				stream=UpperCamelCase__			).raw			)
 return im


@torch.no_grad()
def 			_lowercase       (				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					int,				UpperCamelCase__       :					Any=False			):
 __A  :		Optional[Any]       =			BitConfig(
     global_padding='same',				layer_type='bottleneck',				depths=(3, 4, 9),				out_features=['stage3'],				embedding_dynamic_padding=UpperCamelCase__,				)
 __A  :		str       =			ViTHybridConfig(backbone_config=UpperCamelCase__,				image_size=384,				num_labels=1000			)
 __A  :		Union[str, Any]       =			False

 # load original model from timm
 __A  :		List[Any]       =			timm.create_model(UpperCamelCase__,				pretrained=UpperCamelCase__			)
 timm_model.eval()

 # load state_dict of original model, remove and rename some keys
 __A  :		Optional[int]       =			timm_model.state_dict()
 if base_model:
  remove_classification_head_(UpperCamelCase__			)
 __A  :		List[str]       =			create_rename_keys(UpperCamelCase__,				UpperCamelCase__			)
 for src, dest in rename_keys:
  rename_key(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
 read_in_q_k_v(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)

 __A  :		List[str]       =			'huggingface/label-files'
 __A  :		Optional[Any]       =			'imagenet-1k-id2label.json'
 __A  :		Optional[int]       =			json.load(open(hf_hub_download(UpperCamelCase__,				UpperCamelCase__,				repo_type='dataset'			),				'r'			)			)
 __A  :		Optional[int]       =			{int(UpperCamelCase__			): v for k, v in idalabel.items()}
 __A  :		List[Any]       =			idalabel
 __A  :		Tuple       =			{v: k for k, v in idalabel.items()}

 # load HuggingFace model
 if vit_name[-5:] == "in21k":
  __A  :		List[Any]       =			ViTHybridModel(UpperCamelCase__			).eval()
 else:
  __A  :		int       =			ViTHybridForImageClassification(UpperCamelCase__			).eval()
 model.load_state_dict(UpperCamelCase__			)

 # create image processor
 __A  :		Tuple       =			create_transform(**resolve_data_config({},				model=UpperCamelCase__			)			)
 __A  :		Union[str, Any]       =			transform.transforms

 __A  :		str       =			{
     'bilinear': PILImageResampling.BILINEAR,
     'bicubic': PILImageResampling.BICUBIC,
     'nearest': PILImageResampling.NEAREST,
 }

 __A  :		List[str]       =			ViTHybridImageProcessor(
     do_resize=UpperCamelCase__,				size={'shortest_edge': timm_transforms[0].size},				resample=pillow_resamplings[timm_transforms[0].interpolation.value],				do_center_crop=UpperCamelCase__,				crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]},				do_normalize=UpperCamelCase__,				image_mean=timm_transforms[-1].mean.tolist(),				image_std=timm_transforms[-1].std.tolist(),				)

 __A  :		Optional[Any]       =			prepare_img()
 __A  :		Any       =			transform(UpperCamelCase__			).unsqueeze(0			)
 __A  :		Dict       =			processor(UpperCamelCase__,				return_tensors='pt'			).pixel_values

 # verify pixel values
 assert torch.allclose(UpperCamelCase__,				UpperCamelCase__			)

 # verify logits
 with torch.no_grad():
  __A  :		Union[str, Any]       =			model(UpperCamelCase__			)
  __A  :		Dict       =			outputs.logits

 print('Predicted class:',				logits.argmax(-1			).item()			)
 if base_model:
  __A  :		Tuple       =			timm_model.forward_features(UpperCamelCase__			)
  assert timm_pooled_output.shape == outputs.pooler_output.shape
  assert torch.allclose(UpperCamelCase__,				outputs.pooler_output,				atol=1E-3			)
 else:
  __A  :		Any       =			timm_model(UpperCamelCase__			)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(UpperCamelCase__,				outputs.logits,				atol=1E-3			)
 print('Looks ok!'			)

 if pytorch_dump_folder_path is not None:
  Path(UpperCamelCase__			).mkdir(exist_ok=UpperCamelCase__			)
  print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}"""			)
  model.save_pretrained(UpperCamelCase__			)
  print(f"""Saving processor to {pytorch_dump_folder_path}"""			)
  processor.save_pretrained(UpperCamelCase__			)

 if push_to_hub:
  print(f"""Pushing model and processor to the hub {vit_name}"""			)
  model.push_to_hub(f"""ybelkada/{vit_name}"""			)
  processor.push_to_hub(f"""ybelkada/{vit_name}"""			)


if __name__ == "__main__":
 UpperCAmelCase_ :     Optional[Any]								=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--vit_name',
     default='vit_base_r50_s16_384',
     type=str,
     help='Name of the hybrid ViT timm model you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
 )
 parser.add_argument(
     '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
 )

 UpperCAmelCase_ :     Union[str, Any]								=      parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)






