





'''simple docstring'''

import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


if is_flax_available():
  import jax
  import jax.numpy as jnp
  from flax.jax_utils import replicate
  from flax.training.common_utils import shard

  from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline






@require_flax
class        UpperCAmelCase     (	unittest.TestCase    ):
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
     with tempfile.TemporaryDirectory() as tmpdirname:
         # pipeline has Flax weights
         A__    =      FlaxDiffusionPipeline.from_pretrained(
             "hf-internal-testing/tiny-stable-diffusion-pipe"						,	safety_checker=lowercase_						,	cache_dir=lowercase_							)

         A__    =      [t[-1] for t in os.walk(os.path.join(lowercase_						,	os.listdir(lowercase_							)[0]						,	"snapshots"							)							)]
         A__    =      [item for sublist in all_root_files for item in sublist]

         # None of the downloaded files should be a PyTorch file even if we have some here:
         # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
         assert not any(f.endswith(".bin"							) for f in files							)






@slow
@require_flax
class        UpperCAmelCase     (	unittest.TestCase    ):
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Any:
     A__,     A__    =      FlaxStableDiffusionPipeline.from_pretrained(
         "hf-internal-testing/tiny-stable-diffusion-pipe"						,	safety_checker=lowercase_							)

     A__    =      (
         "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
         " field, close up, split lighting, cinematic"
     )

     A__    =      jax.random.PRNGKey(0							)
     A__    =      4

     A__    =      jax.device_count()
     A__    =      num_samples * [prompt]
     A__    =      pipeline.prepare_inputs(lowercase_							)

     # shard inputs and rng
     A__    =      replicate(lowercase_							)
     A__    =      jax.random.split(lowercase_						,	lowercase_							)
     A__    =      shard(lowercase_							)

     A__    =      pipeline(lowercase_						,	lowercase_						,	lowercase_						,	lowercase_						,	jit=lowercase_							).images

     assert images.shape == (num_samples, 1, 64, 64, 3)
     if jax.device_count() == 8:
         assert np.abs(np.abs(images[0, 0, :2, :2, -2:]						,	dtype=np.floataa							).sum() - 4.1_5_1_4_7_4_5							) < 1E-3
         assert np.abs(np.abs(lowercase_						,	dtype=np.floataa							).sum() - 4_9_9_4_7.8_7_5							) < 5E-1

     A__    =      pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]							)							)							)
     assert len(lowercase_							) == num_samples
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							str:
     A__,     A__    =      FlaxStableDiffusionPipeline.from_pretrained(
         "CompVis/stable-diffusion-v1-4"						,	revision="flax"						,	safety_checker=lowercase_							)

     A__    =      (
         "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
         " field, close up, split lighting, cinematic"
     )

     A__    =      jax.random.PRNGKey(0							)
     A__    =      50

     A__    =      jax.device_count()
     A__    =      num_samples * [prompt]
     A__    =      pipeline.prepare_inputs(lowercase_							)

     # shard inputs and rng
     A__    =      replicate(lowercase_							)
     A__    =      jax.random.split(lowercase_						,	lowercase_							)
     A__    =      shard(lowercase_							)

     A__    =      pipeline(lowercase_						,	lowercase_						,	lowercase_						,	lowercase_						,	jit=lowercase_							).images

     assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
     if jax.device_count() == 8:
         assert np.abs((np.abs(images[0, 0, :2, :2, -2:]						,	dtype=np.floataa							).sum() - 0.0_5_6_5_2_4_0_1)							) < 1E-3
         assert np.abs((np.abs(lowercase_						,	dtype=np.floataa							).sum() - 2_3_8_3_8_0_8.2)							) < 5E-1
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							int:
     A__,     A__    =      FlaxStableDiffusionPipeline.from_pretrained(
         "CompVis/stable-diffusion-v1-4"						,	revision="bf16"						,	dtype=jnp.bfloataa						,	safety_checker=lowercase_							)

     A__    =      (
         "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
         " field, close up, split lighting, cinematic"
     )

     A__    =      jax.random.PRNGKey(0							)
     A__    =      50

     A__    =      jax.device_count()
     A__    =      num_samples * [prompt]
     A__    =      pipeline.prepare_inputs(lowercase_							)

     # shard inputs and rng
     A__    =      replicate(lowercase_							)
     A__    =      jax.random.split(lowercase_						,	lowercase_							)
     A__    =      shard(lowercase_							)

     A__    =      pipeline(lowercase_						,	lowercase_						,	lowercase_						,	lowercase_						,	jit=lowercase_							).images

     assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
     if jax.device_count() == 8:
         assert np.abs((np.abs(images[0, 0, :2, :2, -2:]						,	dtype=np.floataa							).sum() - 0.0_4_0_0_3_9_0_6)							) < 1E-3
         assert np.abs((np.abs(lowercase_						,	dtype=np.floataa							).sum() - 2_3_7_3_5_1_6.7_5)							) < 5E-1
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Tuple:
     A__,     A__    =      FlaxStableDiffusionPipeline.from_pretrained(
         "CompVis/stable-diffusion-v1-4"						,	revision="bf16"						,	dtype=jnp.bfloataa							)

     A__    =      (
         "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
         " field, close up, split lighting, cinematic"
     )

     A__    =      jax.random.PRNGKey(0							)
     A__    =      50

     A__    =      jax.device_count()
     A__    =      num_samples * [prompt]
     A__    =      pipeline.prepare_inputs(lowercase_							)

     # shard inputs and rng
     A__    =      replicate(lowercase_							)
     A__    =      jax.random.split(lowercase_						,	lowercase_							)
     A__    =      shard(lowercase_							)

     A__    =      pipeline(lowercase_						,	lowercase_						,	lowercase_						,	lowercase_						,	jit=lowercase_							).images

     assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
     if jax.device_count() == 8:
         assert np.abs((np.abs(images[0, 0, :2, :2, -2:]						,	dtype=np.floataa							).sum() - 0.0_4_0_0_3_9_0_6)							) < 1E-3
         assert np.abs((np.abs(lowercase_						,	dtype=np.floataa							).sum() - 2_3_7_3_5_1_6.7_5)							) < 5E-1
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Dict:
     A__    =      FlaxDDIMScheduler(
         beta_start=0.0_0_0_8_5						,	beta_end=0.0_1_2						,	beta_schedule="scaled_linear"						,	set_alpha_to_one=lowercase_						,	steps_offset=1						,	)

     A__,     A__    =      FlaxStableDiffusionPipeline.from_pretrained(
         "CompVis/stable-diffusion-v1-4"						,	revision="bf16"						,	dtype=jnp.bfloataa						,	scheduler=lowercase_						,	safety_checker=lowercase_						,	)
     A__    =      scheduler.create_state()

     A__    =      scheduler_state

     A__    =      (
         "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
         " field, close up, split lighting, cinematic"
     )

     A__    =      jax.random.PRNGKey(0							)
     A__    =      50

     A__    =      jax.device_count()
     A__    =      num_samples * [prompt]
     A__    =      pipeline.prepare_inputs(lowercase_							)

     # shard inputs and rng
     A__    =      replicate(lowercase_							)
     A__    =      jax.random.split(lowercase_						,	lowercase_							)
     A__    =      shard(lowercase_							)

     A__    =      pipeline(lowercase_						,	lowercase_						,	lowercase_						,	lowercase_						,	jit=lowercase_							).images

     assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
     if jax.device_count() == 8:
         assert np.abs((np.abs(images[0, 0, :2, :2, -2:]						,	dtype=np.floataa							).sum() - 0.0_4_5_0_4_3_9_4_5)							) < 1E-3
         assert np.abs((np.abs(lowercase_						,	dtype=np.floataa							).sum() - 2_3_4_7_6_9_3.5)							) < 5E-1



 def 					UpperCAmelCase_							(    self				:Any							)->							Any:
     A__    =      (
         "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
         " field, close up, split lighting, cinematic"
     )

     A__    =      jax.device_count()
     A__    =      num_samples * [prompt]
     A__    =      jax.random.split(jax.random.PRNGKey(0							)						,	lowercase_							)

     A__,     A__    =      FlaxStableDiffusionPipeline.from_pretrained(
         "CompVis/stable-diffusion-v1-4"						,	revision="bf16"						,	dtype=jnp.bfloataa						,	safety_checker=lowercase_						,	)

     A__    =      replicate(lowercase_							)
     A__    =      pipeline.prepare_inputs(lowercase_							)
     A__    =      shard(lowercase_							)
     A__    =      pipeline(lowercase_						,	lowercase_						,	lowercase_						,	jit=lowercase_							).images
     assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
     A__    =      images[2, 0, 2_56, 10:17, 1]

     # With memory efficient attention
     A__,     A__    =      FlaxStableDiffusionPipeline.from_pretrained(
         "CompVis/stable-diffusion-v1-4"						,	revision="bf16"						,	dtype=jnp.bfloataa						,	safety_checker=lowercase_						,	use_memory_efficient_attention=lowercase_						,	)

     A__    =      replicate(lowercase_							)
     A__    =      pipeline.prepare_inputs(lowercase_							)
     A__    =      shard(lowercase_							)
     A__    =      pipeline(lowercase_						,	lowercase_						,	lowercase_						,	jit=lowercase_							).images
     assert images_eff.shape == (num_samples, 1, 5_12, 5_12, 3)
     A__    =      images[2, 0, 2_56, 10:17, 1]

     # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
     # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
     assert abs(slice_eff - slice							).max() < 1E-2










'''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							:     Any		     =logging.get_logger(__name__)





def 						UpperCamelCase    (							_lowerCamelCase : List[str]						,	_lowerCamelCase : Tuple=False	):
    A__    =      []

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





def 						UpperCamelCase    (							_lowerCamelCase : Any	):
    A__    =      ["head.weight", "head.bias"]
    for k in ignore_keys:
        state_dict.pop(_lowerCamelCase						,	_lowerCamelCase	)





def 						UpperCamelCase    (							_lowerCamelCase : str						,	_lowerCamelCase : Optional[int]						,	_lowerCamelCase : Union[str, Any]	):
    A__    =      dct.pop(_lowerCamelCase	)
    A__    =      val





def 						UpperCamelCase    (							):
    A__    =      "http://images.cocodataset.org/val2017/000000039769.jpg"
    A__    =      Image.open(requests.get(_lowerCamelCase						,	stream=_lowerCamelCase	).raw	)
    return im





@torch.no_grad()
def 						UpperCamelCase    (							_lowerCamelCase : Dict						,	_lowerCamelCase : int						,	_lowerCamelCase : int=False	):
    A__    =      BitConfig(
        global_padding="same"						,	layer_type="bottleneck"						,	depths=(3, 4, 9)						,	out_features=["stage3"]						,	embedding_dynamic_padding=_lowerCamelCase						,	)
    A__    =      ViTHybridConfig(backbone_config=_lowerCamelCase						,	image_size=3_84						,	num_labels=10_00	)
    A__    =      False

    # load original model from timm
    A__    =      timm.create_model(_lowerCamelCase						,	pretrained=_lowerCamelCase	)
    timm_model.eval()

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

    A__    =      "huggingface/label-files"
    A__    =      "imagenet-1k-id2label.json"
    A__    =      json.load(open(hf_hub_download(_lowerCamelCase						,	_lowerCamelCase						,	repo_type="dataset"	)						,	"r"	)	)
    A__    =      {int(_lowerCamelCase	): v for k, v in idalabel.items()}
    A__    =      idalabel
    A__    =      {v: k for k, v in idalabel.items()}

    # load HuggingFace model
    if vit_name[-5:] == "in21k":
        A__    =      ViTHybridModel(_lowerCamelCase	).eval()
    else:
        A__    =      ViTHybridForImageClassification(_lowerCamelCase	).eval()
    model.load_state_dict(_lowerCamelCase	)

    # create image processor
    A__    =      create_transform(**resolve_data_config({}						,	model=_lowerCamelCase	)	)
    A__    =      transform.transforms

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

    A__    =      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()						,	)

    A__    =      prepare_img()
    A__    =      transform(_lowerCamelCase	).unsqueeze(0	)
    A__    =      processor(_lowerCamelCase						,	return_tensors="pt"	).pixel_values

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

    # verify logits
    with torch.no_grad():
        A__    =      model(_lowerCamelCase	)
        A__    =      outputs.logits

    print("Predicted class:"						,	logits.argmax(-1	).item()	)
    if base_model:
        A__    =      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:
        A__    =      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__":
  __lowerCAmelCase							:     Dict		     =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							:     Optional[Any]		     =parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)



