

"""simple docstring"""




import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class 			a					(		UpperCAmelCase			):
						_lowercase        =     (UniPCMultistepScheduler,)
						_lowercase        =     (("num_inference_steps", 2_5),)


						def     _UpperCAmelCase			(    self  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      {
										    "num_train_timesteps": 1000,
										    "beta_start": 0.00_01,
										    "beta_end": 0.02,
										    "beta_schedule": "linear",
										    "solver_order": 2,
										    "solver_type": "bh2",
										}

										config.update(**A_		)
										return config


						def     _UpperCAmelCase			(    self  ,   A_=0  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      dict(self.forward_default_kwargs		)
										_UpperCAmelCase    :						str							=      kwargs.pop("num_inference_steps"  ,   A_		)
										_UpperCAmelCase    :						Union[str, Any]							=      self.dummy_sample
										_UpperCAmelCase    :						Optional[int]							=      0.1 * sample
										_UpperCAmelCase    :						Tuple							=      [residual + 0.2, residual + 0.15, residual + 0.10]

										for scheduler_class in self.scheduler_classes:
														_UpperCAmelCase    :						Dict							=      self.get_scheduler_config(**A_		)
														_UpperCAmelCase    :						str							=      scheduler_class(**A_		)
														scheduler.set_timesteps(A_		)
														# copy over dummy past residuals
														_UpperCAmelCase    :						Union[str, Any]							=      dummy_past_residuals[: scheduler.config.solver_order]

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(A_		)
																		_UpperCAmelCase    :						Dict							=      scheduler_class.from_pretrained(A_		)
																		new_scheduler.set_timesteps(A_		)
																		# copy over dummy past residuals
																		_UpperCAmelCase    :						str							=      dummy_past_residuals[: new_scheduler.config.solver_order]

														_UpperCAmelCase    :						List[Any]							=      sample, sample
														for t in range(A_  ,   time_step + scheduler.config.solver_order + 1		):
																		_UpperCAmelCase    :						Dict							=      scheduler.step(A_  ,   A_  ,   A_  ,   **A_		).prev_sample
																		_UpperCAmelCase    :						Optional[Any]							=      new_scheduler.step(A_  ,   A_  ,   A_  ,   **A_		).prev_sample

																		assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"


						def     _UpperCAmelCase			(    self  ,   A_=0  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      dict(self.forward_default_kwargs		)
										_UpperCAmelCase    :						Tuple							=      kwargs.pop("num_inference_steps"  ,   A_		)
										_UpperCAmelCase    :						int							=      self.dummy_sample
										_UpperCAmelCase    :						str							=      0.1 * sample
										_UpperCAmelCase    :						Tuple							=      [residual + 0.2, residual + 0.15, residual + 0.10]

										for scheduler_class in self.scheduler_classes:
														_UpperCAmelCase    :						Dict							=      self.get_scheduler_config()
														_UpperCAmelCase    :						List[str]							=      scheduler_class(**A_		)
														scheduler.set_timesteps(A_		)

														# copy over dummy past residuals (must be after setting timesteps)
														_UpperCAmelCase    :						List[str]							=      dummy_past_residuals[: scheduler.config.solver_order]

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(A_		)
																		_UpperCAmelCase    :						Any							=      scheduler_class.from_pretrained(A_		)
																		# copy over dummy past residuals
																		new_scheduler.set_timesteps(A_		)

																		# copy over dummy past residual (must be after setting timesteps)
																		_UpperCAmelCase    :						Optional[int]							=      dummy_past_residuals[: new_scheduler.config.solver_order]

														_UpperCAmelCase    :						List[Any]							=      scheduler.step(A_  ,   A_  ,   A_  ,   **A_		).prev_sample
														_UpperCAmelCase    :						Optional[int]							=      new_scheduler.step(A_  ,   A_  ,   A_  ,   **A_		).prev_sample

														assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"


						def     _UpperCAmelCase			(    self  ,   A_=None  ,   **A_		):







										'''simple docstring'''


										if scheduler is None:
														_UpperCAmelCase    :						List[Any]							=      self.scheduler_classes[0]
														_UpperCAmelCase    :						Optional[int]							=      self.get_scheduler_config(**A_		)
														_UpperCAmelCase    :						List[Any]							=      scheduler_class(**A_		)

										_UpperCAmelCase    :						Optional[Any]							=      self.scheduler_classes[0]
										_UpperCAmelCase    :						Any							=      self.get_scheduler_config(**A_		)
										_UpperCAmelCase    :						Union[str, Any]							=      scheduler_class(**A_		)

										_UpperCAmelCase    :						List[str]							=      10
										_UpperCAmelCase    :						Dict							=      self.dummy_model()
										_UpperCAmelCase    :						Optional[int]							=      self.dummy_sample_deter
										scheduler.set_timesteps(A_		)

										for i, t in enumerate(scheduler.timesteps		):
														_UpperCAmelCase    :						int							=      model(A_  ,   A_		)
														_UpperCAmelCase    :						Dict							=      scheduler.step(A_  ,   A_  ,   A_		).prev_sample

										return sample


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      dict(self.forward_default_kwargs		)

										_UpperCAmelCase    :						Union[str, Any]							=      kwargs.pop("num_inference_steps"  ,   A_		)

										for scheduler_class in self.scheduler_classes:
														_UpperCAmelCase    :						List[str]							=      self.get_scheduler_config()
														_UpperCAmelCase    :						Tuple							=      scheduler_class(**A_		)

														_UpperCAmelCase    :						Optional[int]							=      self.dummy_sample
														_UpperCAmelCase    :						Optional[Any]							=      0.1 * sample

														if num_inference_steps is not None and hasattr(A_  ,   "set_timesteps"		):
																		scheduler.set_timesteps(A_		)
														elif num_inference_steps is not None and not hasattr(A_  ,   "set_timesteps"		):
																		_UpperCAmelCase    :						int							=      num_inference_steps

														# copy over dummy past residuals (must be done after set_timesteps)
														_UpperCAmelCase    :						List[Any]							=      [residual + 0.2, residual + 0.15, residual + 0.10]
														_UpperCAmelCase    :						Optional[int]							=      dummy_past_residuals[: scheduler.config.solver_order]

														_UpperCAmelCase    :						Dict							=      scheduler.timesteps[5]
														_UpperCAmelCase    :						Dict							=      scheduler.timesteps[6]

														_UpperCAmelCase    :						Tuple							=      scheduler.step(A_  ,   A_  ,   A_  ,   **A_		).prev_sample
														_UpperCAmelCase    :						str							=      scheduler.step(A_  ,   A_  ,   A_  ,   **A_		).prev_sample

														self.assertEqual(output_a.shape  ,   sample.shape		)
														self.assertEqual(output_a.shape  ,   output_a.shape		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      UniPCMultistepScheduler(**self.get_scheduler_config()		)
										_UpperCAmelCase    :						Dict							=      self.full_loop(scheduler=A_		)
										_UpperCAmelCase    :						Union[str, Any]							=      torch.mean(torch.abs(A_		)		)

										assert abs(result_mean.item() - 0.24_64		) < 1e-3

										_UpperCAmelCase    :						Tuple							=      DPMSolverSinglestepScheduler.from_config(scheduler.config		)
										_UpperCAmelCase    :						List[str]							=      DEISMultistepScheduler.from_config(scheduler.config		)
										_UpperCAmelCase    :						Dict							=      DPMSolverMultistepScheduler.from_config(scheduler.config		)
										_UpperCAmelCase    :						List[str]							=      UniPCMultistepScheduler.from_config(scheduler.config		)

										_UpperCAmelCase    :						int							=      self.full_loop(scheduler=A_		)
										_UpperCAmelCase    :						Union[str, Any]							=      torch.mean(torch.abs(A_		)		)

										assert abs(result_mean.item() - 0.24_64		) < 1e-3


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for timesteps in [25, 50, 100, 999, 1000]:
														self.check_over_configs(num_train_timesteps=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										self.check_over_configs(thresholding=A_		)
										for order in [1, 2, 3]:
														for solver_type in ["bh1", "bh2"]:
																		for threshold in [0.5, 1.0, 2.0]:
																						for prediction_type in ["epsilon", "sample"]:
																										self.check_over_configs(
																										    thresholding=A_  ,   prediction_type=A_  ,   sample_max_value=A_  ,   solver_order=A_  ,   solver_type=A_  ,   )


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(prediction_type=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for solver_type in ["bh1", "bh2"]:
														for order in [1, 2, 3]:
																		for prediction_type in ["epsilon", "sample"]:
																						self.check_over_configs(
																						    solver_order=A_  ,   solver_type=A_  ,   prediction_type=A_  ,   )
																						_UpperCAmelCase    :						str							=      self.full_loop(
																						    solver_order=A_  ,   solver_type=A_  ,   prediction_type=A_  ,   )
																						assert not torch.isnan(A_		).any(), "Samples have nan numbers"


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										self.check_over_configs(lower_order_final=A_		)
										self.check_over_configs(lower_order_final=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
														self.check_over_forward(num_inference_steps=A_  ,   time_step=0		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      self.full_loop()
										_UpperCAmelCase    :						Any							=      torch.mean(torch.abs(A_		)		)

										assert abs(result_mean.item() - 0.24_64		) < 1e-3


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      self.full_loop(prediction_type="v_prediction"		)
										_UpperCAmelCase    :						List[Any]							=      torch.mean(torch.abs(A_		)		)

										assert abs(result_mean.item() - 0.10_14		) < 1e-3


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      self.scheduler_classes[0]
										_UpperCAmelCase    :						List[Any]							=      self.get_scheduler_config(thresholding=A_  ,   dynamic_thresholding_ratio=0		)
										_UpperCAmelCase    :						List[str]							=      scheduler_class(**A_		)

										_UpperCAmelCase    :						List[Any]							=      10
										_UpperCAmelCase    :						int							=      self.dummy_model()
										_UpperCAmelCase    :						int							=      self.dummy_sample_deter.half()
										scheduler.set_timesteps(A_		)

										for i, t in enumerate(scheduler.timesteps		):
														_UpperCAmelCase    :						Optional[Any]							=      model(A_  ,   A_		)
														_UpperCAmelCase    :						str							=      scheduler.step(A_  ,   A_  ,   A_		).prev_sample

										assert sample.dtype == torch.floataa





						def     _UpperCAmelCase			(    self  ,   **A_		):







										'''simple docstring'''


										for scheduler_class in self.scheduler_classes:
														_UpperCAmelCase    :						Union[str, Any]							=      self.get_scheduler_config(**A_		)
														_UpperCAmelCase    :						List[Any]							=      scheduler_class(**A_		)

														scheduler.set_timesteps(scheduler.config.num_train_timesteps		)
														assert len(scheduler.timesteps.unique()		) == scheduler.num_inference_steps





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()
SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

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

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int	)				->    Optional[int]:
				_UpperCAmelCase    :						Dict							=      ["head.weight", "head.bias"]
				for k in ignore_keys:
								state_dict.pop(lowerCAmelCase					, lowerCAmelCase	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Any					, lowerCAmelCase:					Optional[int]					, lowerCAmelCase:					Dict	)				->    Tuple:
				_UpperCAmelCase    :						str							=      dct.pop(lowerCAmelCase	)
				_UpperCAmelCase    :						Any							=      val

def 							__SCREAMING_SNAKE_CASE							(			)				->    List[Any]:
				_UpperCAmelCase    :						Tuple							=      "http://images.cocodataset.org/val2017/000000039769.jpg"
				_UpperCAmelCase    :						Tuple							=      Image.open(requests.get(lowerCAmelCase					, stream=lowerCAmelCase	).raw	)
				return im

@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Tuple					, lowerCAmelCase:					int					, lowerCAmelCase:					List[Any]=False	)				->    Any:
				_UpperCAmelCase    :						List[Any]							=      BitConfig(
				    global_padding="same"					, layer_type="bottleneck"					, depths=(3, 4, 9)					, out_features=["stage3"]					, embedding_dynamic_padding=lowerCAmelCase					, )
				_UpperCAmelCase    :						Optional[Any]							=      ViTHybridConfig(backbone_config=lowerCAmelCase					, image_size=384					, num_labels=1000	)
				_UpperCAmelCase    :						str							=      False

				# load original model from timm
				_UpperCAmelCase    :						Optional[Any]							=      timm.create_model(lowerCAmelCase					, pretrained=lowerCAmelCase	)
				timm_model.eval()

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

				_UpperCAmelCase    :						str							=      "huggingface/label-files"
				_UpperCAmelCase    :						Tuple							=      "imagenet-1k-id2label.json"
				_UpperCAmelCase    :						Union[str, Any]							=      json.load(open(hf_hub_download(lowerCAmelCase					, lowerCAmelCase					, repo_type="dataset"	)					, "r"	)	)
				_UpperCAmelCase    :						Any							=      {int(lowerCAmelCase	): v for k, v in idalabel.items()}
				_UpperCAmelCase    :						Dict							=      idalabel
				_UpperCAmelCase    :						Dict							=      {v: k for k, v in idalabel.items()}

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

				# create image processor
				_UpperCAmelCase    :						Any							=      create_transform(**resolve_data_config({}					, model=lowerCAmelCase	)	)
				_UpperCAmelCase    :						Tuple							=      transform.transforms

				_UpperCAmelCase    :						Tuple							=      {
				    "bilinear": PILImageResampling.BILINEAR,
				    "bicubic": PILImageResampling.BICUBIC,
				    "nearest": PILImageResampling.NEAREST,
				}

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

				_UpperCAmelCase    :						List[str]							=      prepare_img()
				_UpperCAmelCase    :						List[Any]							=      transform(lowerCAmelCase	).unsqueeze(0	)
				_UpperCAmelCase    :						Optional[Any]							=      processor(lowerCAmelCase					, return_tensors="pt"	).pixel_values

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

				# verify logits
				with torch.no_grad():
								_UpperCAmelCase    :						Union[str, Any]							=      model(lowerCAmelCase	)
								_UpperCAmelCase    :						Optional[Any]							=      outputs.logits

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

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

				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__":
					SCREAMING_SNAKE_CASE_            = 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.'
					)

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


