
"""simple docstring"""







from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
							import torch

if is_tf_available():
							import tensorflow as tf




class 				A__ (			_lowerCamelCase):
		A_		: str            =			['image_processor']
		A_		: Any            =			'SamImageProcessor'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE				):
			super().__init__(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Union[str, Any]		    =   self.image_processor
			__lowerCAmelCase    :		Any		    =   -10
			__lowerCAmelCase    :		Optional[int]		    =   self.image_processor.size['longest_edge']

		def __call__(   self ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE = None ,	**_SCREAMING_SNAKE_CASE ,	):
			__lowerCAmelCase    :		Dict		    =   self.image_processor(
			    _SCREAMING_SNAKE_CASE ,	return_tensors=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE ,	)

			# pop arguments that are not used in the foward but used nevertheless
			__lowerCAmelCase    :		List[str]		    =   encoding_image_processor['original_sizes']

			if hasattr(_SCREAMING_SNAKE_CASE ,	'numpy'				):  # Checks if Torch or TF tensor
				__lowerCAmelCase    :		int		    =   original_sizes.numpy()

			__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		str		    =   self._check_and_preprocess_points(
			    input_points=_SCREAMING_SNAKE_CASE ,	input_labels=_SCREAMING_SNAKE_CASE ,	input_boxes=_SCREAMING_SNAKE_CASE ,	)

			__lowerCAmelCase    :		Union[str, Any]		    =   self._normalize_and_convert(
			    _SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	input_points=_SCREAMING_SNAKE_CASE ,	input_labels=_SCREAMING_SNAKE_CASE ,	input_boxes=_SCREAMING_SNAKE_CASE ,	return_tensors=_SCREAMING_SNAKE_CASE ,	)

			return encoding_image_processor

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE="pt" ,	):
			if input_points is not None:
				if len(_SCREAMING_SNAKE_CASE				) != len(_SCREAMING_SNAKE_CASE				):
					__lowerCAmelCase    :		Tuple		    =   [
					    self._normalize_coordinates(self.target_size ,	_SCREAMING_SNAKE_CASE ,	original_sizes[0]				) for point in input_points
					]
				else:
					__lowerCAmelCase    :		List[str]		    =   [
					    self._normalize_coordinates(self.target_size ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
					    for point, original_size in zip(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
					]
				# check that all arrays have the same shape
				if not all(point.shape == input_points[0].shape for point in input_points				):
					if input_labels is not None:
						__lowerCAmelCase    ,     __lowerCAmelCase    :		str		    =   self._pad_points_and_labels(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

				__lowerCAmelCase    :		List[str]		    =   np.array(_SCREAMING_SNAKE_CASE				)

			if input_labels is not None:
				__lowerCAmelCase    :		Dict		    =   np.array(_SCREAMING_SNAKE_CASE				)

			if input_boxes is not None:
				if len(_SCREAMING_SNAKE_CASE				) != len(_SCREAMING_SNAKE_CASE				):
					__lowerCAmelCase    :		Dict		    =   [
					    self._normalize_coordinates(self.target_size ,	_SCREAMING_SNAKE_CASE ,	original_sizes[0] ,	is_bounding_box=_SCREAMING_SNAKE_CASE				)
					    for box in input_boxes
					]
				else:
					__lowerCAmelCase    :		List[Any]		    =   [
					    self._normalize_coordinates(self.target_size ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	is_bounding_box=_SCREAMING_SNAKE_CASE				)
					    for box, original_size in zip(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
					]
				__lowerCAmelCase    :		Optional[int]		    =   np.array(_SCREAMING_SNAKE_CASE				)

			if input_boxes is not None:
				if return_tensors == "pt":
					__lowerCAmelCase    :		List[str]		    =   torch.from_numpy(_SCREAMING_SNAKE_CASE				)
					# boxes batch size of 1 by default
					__lowerCAmelCase    :		int		    =   input_boxes.unsqueeze(1				) if len(input_boxes.shape				) != 3 else input_boxes
				elif return_tensors == "tf":
					__lowerCAmelCase    :		Optional[Any]		    =   tf.convert_to_tensor(_SCREAMING_SNAKE_CASE				)
					# boxes batch size of 1 by default
					__lowerCAmelCase    :		Optional[int]		    =   tf.expand_dims(_SCREAMING_SNAKE_CASE ,	1				) if len(input_boxes.shape				) != 3 else input_boxes
				encoding_image_processor.update({'input_boxes': input_boxes}				)
			if input_points is not None:
				if return_tensors == "pt":
					__lowerCAmelCase    :		Optional[int]		    =   torch.from_numpy(_SCREAMING_SNAKE_CASE				)
					# point batch size of 1 by default
					__lowerCAmelCase    :		Tuple		    =   input_points.unsqueeze(1				) if len(input_points.shape				) != 4 else input_points
				elif return_tensors == "tf":
					__lowerCAmelCase    :		int		    =   tf.convert_to_tensor(_SCREAMING_SNAKE_CASE				)
					# point batch size of 1 by default
					__lowerCAmelCase    :		int		    =   tf.expand_dims(_SCREAMING_SNAKE_CASE ,	1				) if len(input_points.shape				) != 4 else input_points
				encoding_image_processor.update({'input_points': input_points}				)
			if input_labels is not None:
				if return_tensors == "pt":
					__lowerCAmelCase    :		List[Any]		    =   torch.from_numpy(_SCREAMING_SNAKE_CASE				)
					# point batch size of 1 by default
					__lowerCAmelCase    :		Optional[int]		    =   input_labels.unsqueeze(1				) if len(input_labels.shape				) != 3 else input_labels
				elif return_tensors == "tf":
					__lowerCAmelCase    :		Tuple		    =   tf.convert_to_tensor(_SCREAMING_SNAKE_CASE				)
					# point batch size of 1 by default
					__lowerCAmelCase    :		str		    =   tf.expand_dims(_SCREAMING_SNAKE_CASE ,	1				) if len(input_labels.shape				) != 3 else input_labels
				encoding_image_processor.update({'input_labels': input_labels}				)

			return encoding_image_processor

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Optional[int]		    =   max([point.shape[0] for point in input_points]				)
			__lowerCAmelCase    :		Any		    =   []
			for i, point in enumerate(_SCREAMING_SNAKE_CASE				):
				if point.shape[0] != expected_nb_points:
					__lowerCAmelCase    :		Any		    =   np.concatenate(
					    [point, np.zeros((expected_nb_points - point.shape[0], 2)				) + self.point_pad_value] ,	axis=0				)
					__lowerCAmelCase    :		Dict		    =   np.append(input_labels[i] ,	[self.point_pad_value]				)
				processed_input_points.append(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Tuple		    =   processed_input_points
			return input_points, input_labels

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=False				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		str		    =   original_size
			__lowerCAmelCase    ,     __lowerCAmelCase    :		Optional[Any]		    =   self.image_processor._get_preprocess_shape(_SCREAMING_SNAKE_CASE ,	longest_edge=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		str		    =   deepcopy(_SCREAMING_SNAKE_CASE				).astype(_SCREAMING_SNAKE_CASE				)

			if is_bounding_box:
				__lowerCAmelCase    :		Optional[Any]		    =   coords.reshape(-1 ,	2 ,	2				)

			__lowerCAmelCase    :		Union[str, Any]		    =   coords[..., 0] * (new_w / old_w)
			__lowerCAmelCase    :		str		    =   coords[..., 1] * (new_h / old_h)

			if is_bounding_box:
				__lowerCAmelCase    :		Union[str, Any]		    =   coords.reshape(-1 ,	4				)

			return coords

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	):
			if input_points is not None:
				if hasattr(_SCREAMING_SNAKE_CASE ,	'numpy'				):  # Checks for TF or Torch tensor
					__lowerCAmelCase    :		Optional[Any]		    =   input_points.numpy().tolist()

				if not isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) or not isinstance(input_points[0] ,	_SCREAMING_SNAKE_CASE				):
					raise ValueError('Input points must be a list of list of floating points.'				)
				__lowerCAmelCase    :		List[Any]		    =   [np.array(_SCREAMING_SNAKE_CASE				) for input_point in input_points]
			else:
				__lowerCAmelCase    :		Any		    =   None

			if input_labels is not None:
				if hasattr(_SCREAMING_SNAKE_CASE ,	'numpy'				):
					__lowerCAmelCase    :		Optional[int]		    =   input_labels.numpy().tolist()

				if not isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) or not isinstance(input_labels[0] ,	_SCREAMING_SNAKE_CASE				):
					raise ValueError('Input labels must be a list of list integers.'				)
				__lowerCAmelCase    :		List[str]		    =   [np.array(_SCREAMING_SNAKE_CASE				) for label in input_labels]
			else:
				__lowerCAmelCase    :		Tuple		    =   None

			if input_boxes is not None:
				if hasattr(_SCREAMING_SNAKE_CASE ,	'numpy'				):
					__lowerCAmelCase    :		Optional[int]		    =   input_boxes.numpy().tolist()

				if (
				    not isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
				    or not isinstance(input_boxes[0] ,	_SCREAMING_SNAKE_CASE				)
				    or not isinstance(input_boxes[0][0] ,	_SCREAMING_SNAKE_CASE				)
				):
					raise ValueError('Input boxes must be a list of list of list of floating points.'				)
				__lowerCAmelCase    :		Tuple		    =   [np.array(_SCREAMING_SNAKE_CASE				).astype(np.floataa				) for box in input_boxes]
			else:
				__lowerCAmelCase    :		Union[str, Any]		    =   None

			return input_points, input_labels, input_boxes

		@property
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   self.image_processor.model_input_names
			return list(dict.fromkeys(_SCREAMING_SNAKE_CASE				)				)







		def 							__lowerCamelCase							(   self ,	*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			return self.image_processor.post_process_masks(*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)

"""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()
lowerCamelCase__           =	logging.get_logger(__name__)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=False	):
	__lowerCAmelCase    :		List[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"
		__lowerCAmelCase    :		Optional[Any]		    =   [(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       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase=False	):
	for i in range(config.num_hidden_layers	):
		if base_model:
			__lowerCAmelCase    :		Tuple		    =   ''
		else:
			__lowerCAmelCase    :		int		    =   'vit.'
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		__lowerCAmelCase    :		int		    =   state_dict.pop(F"blocks.{i}.attn.qkv.weight"	)
		__lowerCAmelCase    :		Dict		    =   state_dict.pop(F"blocks.{i}.attn.qkv.bias"	)
		# next, add query, keys and values (in that order) to the state dict
		__lowerCAmelCase    :		Tuple		    =   in_proj_weight[
		    : config.hidden_size, :
		]
		__lowerCAmelCase    :		Union[str, Any]		    =   in_proj_bias[: config.hidden_size]
		__lowerCAmelCase    :		List[Any]		    =   in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		__lowerCAmelCase    :		str		    =   in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		__lowerCAmelCase    :		Optional[int]		    =   in_proj_weight[
		    -config.hidden_size :, :
		]
		__lowerCAmelCase    :		Union[str, Any]		    =   in_proj_bias[-config.hidden_size :]
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		List[str]		    =   ['head.weight', 'head.bias']
	for k in ignore_keys:
		state_dict.pop(_UpperCamelCase ,						_UpperCamelCase	)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		int		    =   dct.pop(_UpperCamelCase	)
	__lowerCAmelCase    :		List[Any]		    =   val
def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		Optional[int]		    =   'http://images.cocodataset.org/val2017/000000039769.jpg'
	__lowerCAmelCase    :		Optional[Any]		    =   Image.open(requests.get(_UpperCamelCase ,						stream=_UpperCamelCase	).raw	)
	return im
@torch.no_grad()
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase=False	):
	__lowerCAmelCase    :		List[str]		    =   BitConfig(
	    global_padding='same' ,						layer_type='bottleneck' ,						depths=(3, 4, 9) ,						out_features=['stage3'] ,						embedding_dynamic_padding=_UpperCamelCase ,						)
	__lowerCAmelCase    :		Tuple		    =   ViTHybridConfig(backbone_config=_UpperCamelCase ,						image_size=384 ,						num_labels=1000	)
	__lowerCAmelCase    :		Optional[Any]		    =   False

	# load original model from timm
	__lowerCAmelCase    :		Tuple		    =   timm.create_model(_UpperCamelCase ,						pretrained=_UpperCamelCase	)
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	__lowerCAmelCase    :		str		    =   timm_model.state_dict()
	if base_model:
		remove_classification_head_(_UpperCamelCase	)
	__lowerCAmelCase    :		Dict		    =   create_rename_keys(_UpperCamelCase ,						_UpperCamelCase	)
	for src, dest in rename_keys:
		rename_key(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)
	read_in_q_k_v(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)

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

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		__lowerCAmelCase    :		str		    =   ViTHybridModel(_UpperCamelCase	).eval()
	else:
		__lowerCAmelCase    :		Optional[Any]		    =   ViTHybridForImageClassification(_UpperCamelCase	).eval()
	model.load_state_dict(_UpperCamelCase	)

	# create image processor
	__lowerCAmelCase    :		Union[str, Any]		    =   create_transform(**resolve_data_config({} ,						model=_UpperCamelCase	)	)
	__lowerCAmelCase    :		Optional[int]		    =   transform.transforms

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

	__lowerCAmelCase    :		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() ,						)

	__lowerCAmelCase    :		Optional[Any]		    =   prepare_img()
	__lowerCAmelCase    :		Union[str, Any]		    =   transform(_UpperCamelCase	).unsqueeze(0	)
	__lowerCAmelCase    :		Union[str, Any]		    =   processor(_UpperCamelCase ,						return_tensors='pt'	).pixel_values

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

	# verify logits
	with torch.no_grad():
		__lowerCAmelCase    :		Any		    =   model(_UpperCamelCase	)
		__lowerCAmelCase    :		List[Any]		    =   outputs.logits

	print('Predicted class:' ,						logits.argmax(-1	).item()	)
	if base_model:
		__lowerCAmelCase    :		Optional[int]		    =   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:
		__lowerCAmelCase    :		List[str]		    =   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__":
							lowerCamelCase__           =	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."""
							)

							lowerCamelCase__           =	parser.parse_args()
							convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)