

'''simple docstring'''
import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD







class 							A__    (       nn.Module  ):
  A__		 =				42
  A__		 =				42
  A__		 =				0.0
  A__		 =				1
  A__		 =				1
  A__		 =				True
  A__		 =				False
  A__		 =				False
  A__		 =				False
  A__		 =				jnp.floataa





  def 						A  (		self      :	str )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[]
       _SCREAMING_SNAKE_CASE							    =[]

       for i in range(self.num_layers ):
            _SCREAMING_SNAKE_CASE							    =self.in_channels if i == 0 else self.out_channels

            _SCREAMING_SNAKE_CASE							    =FlaxResnetBlockaD(
                in_channels=_a							,					out_channels=self.out_channels							,					dropout_prob=self.dropout							,					dtype=self.dtype							,					)
            resnets.append(_a )

            _SCREAMING_SNAKE_CASE							    =FlaxTransformeraDModel(
                in_channels=self.out_channels							,					n_heads=self.num_attention_heads							,					d_head=self.out_channels // self.num_attention_heads							,					depth=1							,					use_linear_projection=self.use_linear_projection							,					only_cross_attention=self.only_cross_attention							,					use_memory_efficient_attention=self.use_memory_efficient_attention							,					dtype=self.dtype							,					)
            attentions.append(_a )

       _SCREAMING_SNAKE_CASE							    =resnets
       _SCREAMING_SNAKE_CASE							    =attentions

       if self.add_downsample:
            _SCREAMING_SNAKE_CASE							    =FlaxDownsampleaD(self.out_channels							,					dtype=self.dtype )





  def __call__(		self      :	Dict							,					_a      :	Union[str, Any]							,					_a      :	Union[str, Any]							,					_a      :	str							,					_a      :	Optional[Any]=True )	->    List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =()

       for resnet, attn in zip(self.resnets							,					self.attentions ):
            _SCREAMING_SNAKE_CASE							    =resnet(_a							,					_a							,					deterministic=_a )
            _SCREAMING_SNAKE_CASE							    =attn(_a							,					_a							,					deterministic=_a )
            output_states += (hidden_states,)

       if self.add_downsample:
            _SCREAMING_SNAKE_CASE							    =self.downsamplers_a(_a )
            output_states += (hidden_states,)

       return hidden_states, output_states







class 							A__    (       nn.Module  ):
  A__		 =				42
  A__		 =				42
  A__		 =				0.0
  A__		 =				1
  A__		 =				True
  A__		 =				jnp.floataa





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[]

       for i in range(self.num_layers ):
            _SCREAMING_SNAKE_CASE							    =self.in_channels if i == 0 else self.out_channels

            _SCREAMING_SNAKE_CASE							    =FlaxResnetBlockaD(
                in_channels=_a							,					out_channels=self.out_channels							,					dropout_prob=self.dropout							,					dtype=self.dtype							,					)
            resnets.append(_a )
       _SCREAMING_SNAKE_CASE							    =resnets

       if self.add_downsample:
            _SCREAMING_SNAKE_CASE							    =FlaxDownsampleaD(self.out_channels							,					dtype=self.dtype )





  def __call__(		self      :	Dict							,					_a      :	int							,					_a      :	Tuple							,					_a      :	Union[str, Any]=True )	->    int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =()

       for resnet in self.resnets:
            _SCREAMING_SNAKE_CASE							    =resnet(_a							,					_a							,					deterministic=_a )
            output_states += (hidden_states,)

       if self.add_downsample:
            _SCREAMING_SNAKE_CASE							    =self.downsamplers_a(_a )
            output_states += (hidden_states,)

       return hidden_states, output_states







class 							A__    (       nn.Module  ):
  A__		 =				42
  A__		 =				42
  A__		 =				42
  A__		 =				0.0
  A__		 =				1
  A__		 =				1
  A__		 =				True
  A__		 =				False
  A__		 =				False
  A__		 =				False
  A__		 =				jnp.floataa





  def 						A  (		self      :	int )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[]
       _SCREAMING_SNAKE_CASE							    =[]

       for i in range(self.num_layers ):
            _SCREAMING_SNAKE_CASE							    =self.in_channels if (i == self.num_layers - 1) else self.out_channels
            _SCREAMING_SNAKE_CASE							    =self.prev_output_channel if i == 0 else self.out_channels

            _SCREAMING_SNAKE_CASE							    =FlaxResnetBlockaD(
                in_channels=resnet_in_channels + res_skip_channels							,					out_channels=self.out_channels							,					dropout_prob=self.dropout							,					dtype=self.dtype							,					)
            resnets.append(_a )

            _SCREAMING_SNAKE_CASE							    =FlaxTransformeraDModel(
                in_channels=self.out_channels							,					n_heads=self.num_attention_heads							,					d_head=self.out_channels // self.num_attention_heads							,					depth=1							,					use_linear_projection=self.use_linear_projection							,					only_cross_attention=self.only_cross_attention							,					use_memory_efficient_attention=self.use_memory_efficient_attention							,					dtype=self.dtype							,					)
            attentions.append(_a )

       _SCREAMING_SNAKE_CASE							    =resnets
       _SCREAMING_SNAKE_CASE							    =attentions

       if self.add_upsample:
            _SCREAMING_SNAKE_CASE							    =FlaxUpsampleaD(self.out_channels							,					dtype=self.dtype )





  def __call__(		self      :	Optional[Any]							,					_a      :	Optional[Any]							,					_a      :	Dict							,					_a      :	Union[str, Any]							,					_a      :	str							,					_a      :	List[str]=True )	->    int:





       '''simple docstring'''






       for resnet, attn in zip(self.resnets							,					self.attentions ):
            # pop res hidden states
            _SCREAMING_SNAKE_CASE							    =res_hidden_states_tuple[-1]
            _SCREAMING_SNAKE_CASE							    =res_hidden_states_tuple[:-1]
            _SCREAMING_SNAKE_CASE							    =jnp.concatenate((hidden_states, res_hidden_states)							,					axis=-1 )

            _SCREAMING_SNAKE_CASE							    =resnet(_a							,					_a							,					deterministic=_a )
            _SCREAMING_SNAKE_CASE							    =attn(_a							,					_a							,					deterministic=_a )

       if self.add_upsample:
            _SCREAMING_SNAKE_CASE							    =self.upsamplers_a(_a )

       return hidden_states







class 							A__    (       nn.Module  ):
  A__		 =				42
  A__		 =				42
  A__		 =				42
  A__		 =				0.0
  A__		 =				1
  A__		 =				True
  A__		 =				jnp.floataa





  def 						A  (		self      :	Optional[int] )	->    Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[]

       for i in range(self.num_layers ):
            _SCREAMING_SNAKE_CASE							    =self.in_channels if (i == self.num_layers - 1) else self.out_channels
            _SCREAMING_SNAKE_CASE							    =self.prev_output_channel if i == 0 else self.out_channels

            _SCREAMING_SNAKE_CASE							    =FlaxResnetBlockaD(
                in_channels=resnet_in_channels + res_skip_channels							,					out_channels=self.out_channels							,					dropout_prob=self.dropout							,					dtype=self.dtype							,					)
            resnets.append(_a )

       _SCREAMING_SNAKE_CASE							    =resnets

       if self.add_upsample:
            _SCREAMING_SNAKE_CASE							    =FlaxUpsampleaD(self.out_channels							,					dtype=self.dtype )





  def __call__(		self      :	str							,					_a      :	Dict							,					_a      :	Dict							,					_a      :	Optional[Any]							,					_a      :	str=True )	->    Optional[int]:





       '''simple docstring'''






       for resnet in self.resnets:
            # pop res hidden states
            _SCREAMING_SNAKE_CASE							    =res_hidden_states_tuple[-1]
            _SCREAMING_SNAKE_CASE							    =res_hidden_states_tuple[:-1]
            _SCREAMING_SNAKE_CASE							    =jnp.concatenate((hidden_states, res_hidden_states)							,					axis=-1 )

            _SCREAMING_SNAKE_CASE							    =resnet(_a							,					_a							,					deterministic=_a )

       if self.add_upsample:
            _SCREAMING_SNAKE_CASE							    =self.upsamplers_a(_a )

       return hidden_states







class 							A__    (       nn.Module  ):
  A__		 =				42
  A__		 =				0.0
  A__		 =				1
  A__		 =				1
  A__		 =				False
  A__		 =				False
  A__		 =				jnp.floataa





  def 						A  (		self      :	List[str] )	->    Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[
           FlaxResnetBlockaD(
               in_channels=self.in_channels							,					out_channels=self.in_channels							,					dropout_prob=self.dropout							,					dtype=self.dtype							,					)
       ]

       _SCREAMING_SNAKE_CASE							    =[]

       for _ in range(self.num_layers ):
            _SCREAMING_SNAKE_CASE							    =FlaxTransformeraDModel(
                in_channels=self.in_channels							,					n_heads=self.num_attention_heads							,					d_head=self.in_channels // self.num_attention_heads							,					depth=1							,					use_linear_projection=self.use_linear_projection							,					use_memory_efficient_attention=self.use_memory_efficient_attention							,					dtype=self.dtype							,					)
            attentions.append(_a )

            _SCREAMING_SNAKE_CASE							    =FlaxResnetBlockaD(
                in_channels=self.in_channels							,					out_channels=self.in_channels							,					dropout_prob=self.dropout							,					dtype=self.dtype							,					)
            resnets.append(_a )

       _SCREAMING_SNAKE_CASE							    =resnets
       _SCREAMING_SNAKE_CASE							    =attentions





  def __call__(		self      :	Union[str, Any]							,					_a      :	List[Any]							,					_a      :	Tuple							,					_a      :	Optional[Any]							,					_a      :	str=True )	->    Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.resnets[0](_a							,					_a )
       for attn, resnet in zip(self.attentions							,					self.resnets[1:] ):
            _SCREAMING_SNAKE_CASE							    =attn(_a							,					_a							,					deterministic=_a )
            _SCREAMING_SNAKE_CASE							    =resnet(_a							,					_a							,					deterministic=_a )

       return hidden_states


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()
snake_case :		Optional[Any]							      =  logging.get_logger(__name__)






def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]      ,   _snake_case      :  Union[str, Any]=False   )				->		List[str]:







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     []

	# 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"
		__magic_name__		:		int	   =     [(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   lowerCAmelCase_						(					_snake_case      :  Any      ,   _snake_case      :  Any      ,   _snake_case      :  Dict=False   )				->		int:







	'''simple docstring'''


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






def   lowerCAmelCase_						(					_snake_case      :  List[str]   )				->		List[str]:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     ["head.weight", "head.bias"]
	for k in ignore_keys:
		state_dict.pop(_snake_case      ,   _snake_case   )






def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  int      ,   _snake_case      :  Union[str, Any]   )				->		Optional[int]:







	'''simple docstring'''


	__magic_name__		:		int	   =     dct.pop(_snake_case   )
	__magic_name__		:		List[Any]	   =     val






def   lowerCAmelCase_						(					)				->		Dict:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     "http://images.cocodataset.org/val2017/000000039769.jpg"
	__magic_name__		:		List[str]	   =     Image.open(requests.get(_snake_case      ,   stream=_snake_case   ).raw   )
	return im






@torch.no_grad()
def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  Any      ,   _snake_case      :  int=False   )				->		Dict:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     BitConfig(
	    global_padding="same"      ,   layer_type="bottleneck"      ,   depths=(3, 4, 9)      ,   out_features=["stage3"]      ,   embedding_dynamic_padding=_snake_case      ,   )
	__magic_name__		:		List[str]	   =     ViTHybridConfig(backbone_config=_snake_case      ,   image_size=384      ,   num_labels=1000   )
	__magic_name__		:		str	   =     False

	# load original model from timm
	__magic_name__		:		Union[str, Any]	   =     timm.create_model(_snake_case      ,   pretrained=_snake_case   )
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	__magic_name__		:		List[Any]	   =     timm_model.state_dict()
	if base_model:
		remove_classification_head_(_snake_case   )
	__magic_name__		:		Tuple	   =     create_rename_keys(_snake_case      ,   _snake_case   )
	for src, dest in rename_keys:
		rename_key(_snake_case      ,   _snake_case      ,   _snake_case   )
	read_in_q_k_v(_snake_case      ,   _snake_case      ,   _snake_case   )

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

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		__magic_name__		:		List[str]	   =     ViTHybridModel(_snake_case   ).eval()
	else:
		__magic_name__		:		str	   =     ViTHybridForImageClassification(_snake_case   ).eval()
	model.load_state_dict(_snake_case   )

	# create image processor
	__magic_name__		:		List[Any]	   =     create_transform(**resolve_data_config({}      ,   model=_snake_case   )   )
	__magic_name__		:		int	   =     transform.transforms

	__magic_name__		:		List[str]	   =     {
	    "bilinear": PILImageResampling.BILINEAR,
	    "bicubic": PILImageResampling.BICUBIC,
	    "nearest": PILImageResampling.NEAREST,
	}

	__magic_name__		:		int	   =     ViTHybridImageProcessor(
	    do_resize=_snake_case      ,   size={"shortest_edge": timm_transforms[0].size}      ,   resample=pillow_resamplings[timm_transforms[0].interpolation.value]      ,   do_center_crop=_snake_case      ,   crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}      ,   do_normalize=_snake_case      ,   image_mean=timm_transforms[-1].mean.tolist()      ,   image_std=timm_transforms[-1].std.tolist()      ,   )

	__magic_name__		:		List[Any]	   =     prepare_img()
	__magic_name__		:		Any	   =     transform(_snake_case   ).unsqueeze(0   )
	__magic_name__		:		Tuple	   =     processor(_snake_case      ,   return_tensors="pt"   ).pixel_values

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

	# verify logits
	with torch.no_grad():
		__magic_name__		:		Optional[int]	   =     model(_snake_case   )
		__magic_name__		:		List[str]	   =     outputs.logits

	print("Predicted class:"      ,   logits.argmax(-1   ).item()   )
	if base_model:
		__magic_name__		:		List[str]	   =     timm_model.forward_features(_snake_case   )
		assert timm_pooled_output.shape == outputs.pooler_output.shape
		assert torch.allclose(_snake_case      ,   outputs.pooler_output      ,   atol=1E-3   )
	else:
		__magic_name__		:		Any	   =     timm_model(_snake_case   )
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(_snake_case      ,   outputs.logits      ,   atol=1E-3   )
	print("Looks ok!"   )

	if pytorch_dump_folder_path is not None:
		Path(_snake_case   ).mkdir(exist_ok=_snake_case   )
		print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}'''   )
		model.save_pretrained(_snake_case   )
		print(F'''Saving processor to {pytorch_dump_folder_path}'''   )
		processor.save_pretrained(_snake_case   )

	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__":
				snake_case :		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."
				)

				snake_case :		List[Any]							      =  parser.parse_args()
				convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
