



'''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()
a_						:     List[str]	     =		logging.get_logger(__name__)

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__=False ):


      """simple docstring"""



      lowerCamelCase								=		[]

      # 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								=		[(pair[0], pair[1][4:]) if pair[1].startswith("vit" ) else pair for pair in rename_keys]
      else:
            # layernorm + classification head
            rename_keys.extend(
                [
                    ("norm.weight", "vit.layernorm.weight"),
                    ("norm.bias", "vit.layernorm.bias"),
                    ("head.weight", "classifier.weight"),
                    ("head.bias", "classifier.bias"),
                ] )
      # fmt: on

      return rename_keys

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__=False ):


      """simple docstring"""



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

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		["head.weight", "head.bias"]
      for k in ignore_keys:
            state_dict.pop(UpperCAmelCase__   ,   UpperCAmelCase__ )

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		dct.pop(UpperCAmelCase__ )
      lowerCamelCase								=		val

def     __lowercase(	):


      """simple docstring"""



      lowerCamelCase								=		"http://images.cocodataset.org/val2017/000000039769.jpg"
      lowerCamelCase								=		Image.open(requests.get(UpperCAmelCase__   ,   stream=UpperCAmelCase__ ).raw )
      return im

@torch.no_grad()
def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__=False ):


      """simple docstring"""



      lowerCamelCase								=		BitConfig(
          global_padding="same"   ,   layer_type="bottleneck"   ,   depths=(3, 4, 9)   ,   out_features=["stage3"]   ,   embedding_dynamic_padding=UpperCAmelCase__   ,   )
      lowerCamelCase								=		ViTHybridConfig(backbone_config=UpperCAmelCase__   ,   image_size=384   ,   num_labels=1000 )
      lowerCamelCase								=		False

      # load original model from timm
      lowerCamelCase								=		timm.create_model(UpperCAmelCase__   ,   pretrained=UpperCAmelCase__ )
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      lowerCamelCase								=		timm_model.state_dict()
      if base_model:
            remove_classification_head_(UpperCAmelCase__ )
      lowerCamelCase								=		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								=		"huggingface/label-files"
      lowerCamelCase								=		"imagenet-1k-id2label.json"
      lowerCamelCase								=		json.load(open(hf_hub_download(UpperCAmelCase__   ,   UpperCAmelCase__   ,   repo_type="dataset" )   ,   "r" ) )
      lowerCamelCase								=		{int(UpperCAmelCase__ ): v for k, v in idalabel.items()}
      lowerCamelCase								=		idalabel
      lowerCamelCase								=		{v: k for k, v in idalabel.items()}

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

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

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

      lowerCamelCase								=		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								=		prepare_img()
      lowerCamelCase								=		transform(UpperCAmelCase__ ).unsqueeze(0 )
      lowerCamelCase								=		processor(UpperCAmelCase__   ,   return_tensors="pt" ).pixel_values

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

      # verify logits
      with torch.no_grad():
            lowerCamelCase								=		model(UpperCAmelCase__ )
            lowerCamelCase								=		outputs.logits

      print("Predicted class:"   ,   logits.argmax(-1 ).item() )
      if base_model:
            lowerCamelCase								=		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								=		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__":
     a_						:     int	     =		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.'
     )

     a_						:     Any	     =		parser.parse_args()
     convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     @property
     def 						_a      (self    ):






           '''simple docstring'''



           torch.manual_seed(0    )
           lowerCamelCase								=		UNetaDModel(
               block_out_channels=(32, 64)				,			layers_per_block=2				,			sample_size=32				,			in_channels=3				,			out_channels=3				,			down_block_types=("DownBlock2D", "AttnDownBlock2D")				,			up_block_types=("AttnUpBlock2D", "UpBlock2D")				,			)
           return model







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.dummy_uncond_unet
           lowerCamelCase								=		KarrasVeScheduler()

           lowerCamelCase								=		KarrasVePipeline(unet=__a				,			scheduler=__a    )
           pipe.to(__a    )
           pipe.set_progress_bar_config(disable=__a    )

           lowerCamelCase								=		torch.manual_seed(0    )
           lowerCamelCase								=		pipe(num_inference_steps=2				,			generator=__a				,			output_type="numpy"    ).images

           lowerCamelCase								=		torch.manual_seed(0    )
           lowerCamelCase								=		pipe(num_inference_steps=2				,			generator=__a				,			output_type="numpy"				,			return_dict=__a    )[0]

           lowerCamelCase								=		image[0, -3:, -3:, -1]
           lowerCamelCase								=		image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           lowerCamelCase								=		np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2





@slow
@require_torch
class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		"google/ncsnpp-celebahq-256"
           lowerCamelCase								=		UNetaDModel.from_pretrained(__a    )
           lowerCamelCase								=		KarrasVeScheduler()

           lowerCamelCase								=		KarrasVePipeline(unet=__a				,			scheduler=__a    )
           pipe.to(__a    )
           pipe.set_progress_bar_config(disable=__a    )

           lowerCamelCase								=		torch.manual_seed(0    )
           lowerCamelCase								=		pipe(num_inference_steps=20				,			generator=__a				,			output_type="numpy"    ).images

           lowerCamelCase								=		image[0, -3:, -3:, -1]
           assert image.shape == (1, 2_56, 2_56, 3)
           lowerCamelCase								=		np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2