






'''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 transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
a_					=	logging.get_logger(__name__)





def 	_a(					UpperCamelCase__			:  Optional[Any],		UpperCamelCase__			:  List[Any]=False							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=[]
      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"deit.encoder.layer.{i}.layernorm_before.weight")							)
            rename_keys.append((f"blocks.{i}.norm1.bias", f"deit.encoder.layer.{i}.layernorm_before.bias")							)
            rename_keys.append((f"blocks.{i}.attn.proj.weight", f"deit.encoder.layer.{i}.attention.output.dense.weight")							)
            rename_keys.append((f"blocks.{i}.attn.proj.bias", f"deit.encoder.layer.{i}.attention.output.dense.bias")							)
            rename_keys.append((f"blocks.{i}.norm2.weight", f"deit.encoder.layer.{i}.layernorm_after.weight")							)
            rename_keys.append((f"blocks.{i}.norm2.bias", f"deit.encoder.layer.{i}.layernorm_after.bias")							)
            rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"deit.encoder.layer.{i}.intermediate.dense.weight")							)
            rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"deit.encoder.layer.{i}.intermediate.dense.bias")							)
            rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"deit.encoder.layer.{i}.output.dense.weight")							)
            rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"deit.encoder.layer.{i}.output.dense.bias")							)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ('''cls_token''', '''deit.embeddings.cls_token'''),
              ('''dist_token''', '''deit.embeddings.distillation_token'''),
              ('''patch_embed.proj.weight''', '''deit.embeddings.patch_embeddings.projection.weight'''),
              ('''patch_embed.proj.bias''', '''deit.embeddings.patch_embeddings.projection.bias'''),
              ('''pos_embed''', '''deit.embeddings.position_embeddings'''),
          ]							)

      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 "deit" from all keys that start with "deit"
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=[(pair[0], pair[1][4:]) if pair[1].startswith('''deit'''							) else pair for pair in rename_keys]
      else:
            # layernorm + classification heads
            rename_keys.extend(
                [
                    ('''norm.weight''', '''deit.layernorm.weight'''),
                    ('''norm.bias''', '''deit.layernorm.bias'''),
                    ('''head.weight''', '''cls_classifier.weight'''),
                    ('''head.bias''', '''cls_classifier.bias'''),
                    ('''head_dist.weight''', '''distillation_classifier.weight'''),
                    ('''head_dist.bias''', '''distillation_classifier.bias'''),
                ]							)

      return rename_keys





def 	_a(					UpperCamelCase__			:  Dict,		UpperCamelCase__			:  List[str],		UpperCamelCase__			:  Any=False							):




      '''simple docstring'''






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





def 	_a(					UpperCamelCase__			:  List[Any],		UpperCamelCase__			:  Optional[Any],		UpperCamelCase__			:  List[str]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Dict													=dct.pop(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			int													=val





def 	_a(					):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			str													='''http://images.cocodataset.org/val2017/000000039769.jpg'''
      SCREAMING_SNAKE_CASE__       :			Any													=Image.open(requests.get(UpperCamelCase__,		stream=UpperCamelCase__							).raw							)
      return im





@torch.no_grad()
def 	_a(					UpperCamelCase__			:  str,		UpperCamelCase__			:  Any							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Dict													=DeiTConfig()
      # all deit models have fine-tuned heads
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=False
      # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=1_0_0_0
      SCREAMING_SNAKE_CASE__       :			List[str]													='''huggingface/label-files'''
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													='''imagenet-1k-id2label.json'''
      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=json.load(open(hf_hub_download(UpperCamelCase__,		UpperCamelCase__,		repo_type='''dataset'''							),		'''r'''							)							)
      SCREAMING_SNAKE_CASE__       :			str													={int(UpperCamelCase__							): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE__       :			Dict													=idalabel
      SCREAMING_SNAKE_CASE__       :			List[Any]													={v: k for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE__       :			Any													=int(deit_name[-6:-4]							)
      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=int(deit_name[-3:]							)
      # size of the architecture
      if deit_name[9:].startswith('''tiny'''							):
            SCREAMING_SNAKE_CASE__       :			Dict													=1_9_2
            SCREAMING_SNAKE_CASE__       :			int													=7_6_8
            SCREAMING_SNAKE_CASE__       :			str													=1_2
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=3
      elif deit_name[9:].startswith('''small'''							):
            SCREAMING_SNAKE_CASE__       :			int													=3_8_4
            SCREAMING_SNAKE_CASE__       :			List[str]													=1_5_3_6
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=1_2
            SCREAMING_SNAKE_CASE__       :			Any													=6
      if deit_name[9:].startswith('''base'''							):
            pass
      elif deit_name[4:].startswith('''large'''							):
            SCREAMING_SNAKE_CASE__       :			List[str]													=1_0_2_4
            SCREAMING_SNAKE_CASE__       :			str													=4_0_9_6
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=2_4
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=1_6

      # load original model from timm
      SCREAMING_SNAKE_CASE__       :			Any													=timm.create_model(UpperCamelCase__,		pretrained=UpperCamelCase__							)
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=timm_model.state_dict()
      SCREAMING_SNAKE_CASE__       :			List[str]													=create_rename_keys(UpperCamelCase__,		UpperCamelCase__							)
      for src, dest in rename_keys:
            rename_key(UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__							)
      read_in_q_k_v(UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__							)

      # load HuggingFace model
      SCREAMING_SNAKE_CASE__       :			Dict													=DeiTForImageClassificationWithTeacher(UpperCamelCase__							).eval()
      model.load_state_dict(UpperCamelCase__							)

      # Check outputs on an image, prepared by DeiTImageProcessor
      SCREAMING_SNAKE_CASE__       :			Dict													=int(
          (2_5_6 / 2_2_4) * config.image_size							)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=DeiTImageProcessor(size=UpperCamelCase__,		crop_size=config.image_size							)
      SCREAMING_SNAKE_CASE__       :			List[Any]													=image_processor(images=prepare_img(),		return_tensors='''pt'''							)
      SCREAMING_SNAKE_CASE__       :			Any													=encoding['''pixel_values''']
      SCREAMING_SNAKE_CASE__       :			List[str]													=model(UpperCamelCase__							)

      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=timm_model(UpperCamelCase__							)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(UpperCamelCase__,		outputs.logits,		atol=1e-3							)

      Path(UpperCamelCase__							).mkdir(exist_ok=UpperCamelCase__							)
      print(f"Saving model {deit_name} to {pytorch_dump_folder_path}"							)
      model.save_pretrained(UpperCamelCase__							)
      print(f"Saving image processor to {pytorch_dump_folder_path}"							)
      image_processor.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  a_					=	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--deit_name',
      default='vit_deit_base_distilled_patch16_224',
      type=str,
      help='Name of the DeiT 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.'
  )

  a_					=	parser.parse_args()
  convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)







'''simple docstring'''
import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






@slow
@require_torch_gpu
class        __SCREAMING_SNAKE_CASE (     unittest.TestCase			):




 def        __magic_name__		( self		:							List[str] )		->  Optional[Any]:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def        __magic_name__		( self		:							Union[str, Any] )		->  Dict:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' )
       SCREAMING_SNAKE_CASE__       :			str													=sd_pipe.to(__lowercase )
       sd_pipe.set_progress_bar_config(disable=__lowercase )

       sd_pipe.set_scheduler('''sample_euler''' )

       SCREAMING_SNAKE_CASE__       :			List[Any]													='''A painting of a squirrel eating a burger'''
       SCREAMING_SNAKE_CASE__       :			List[str]													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=sd_pipe([prompt]   , generator=__lowercase   , guidance_scale=9.0   , num_inference_steps=20   , output_type='''np''' )

       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=output.images

       SCREAMING_SNAKE_CASE__       :			int													=image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       SCREAMING_SNAKE_CASE__       :			Any													=np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )

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




 def        __magic_name__		( self		:							List[str] )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			List[str]													=StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
       SCREAMING_SNAKE_CASE__       :			int													=sd_pipe.to(__lowercase )
       sd_pipe.set_progress_bar_config(disable=__lowercase )

       sd_pipe.set_scheduler('''sample_euler''' )

       SCREAMING_SNAKE_CASE__       :			Any													='''A painting of a squirrel eating a burger'''
       SCREAMING_SNAKE_CASE__       :			Dict													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			Tuple													=sd_pipe([prompt]   , generator=__lowercase   , guidance_scale=9.0   , num_inference_steps=20   , output_type='''np''' )

       SCREAMING_SNAKE_CASE__       :			Dict													=output.images

       SCREAMING_SNAKE_CASE__       :			Dict													=image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       SCREAMING_SNAKE_CASE__       :			List[str]													=np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-1




 def        __magic_name__		( self		:							List[str] )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			Any													=StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
       SCREAMING_SNAKE_CASE__       :			Tuple													=sd_pipe.to(__lowercase )
       sd_pipe.set_progress_bar_config(disable=__lowercase )

       sd_pipe.set_scheduler('''sample_dpmpp_2m''' )

       SCREAMING_SNAKE_CASE__       :			Tuple													='''A painting of a squirrel eating a burger'''
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=sd_pipe(
           [prompt]   , generator=__lowercase   , guidance_scale=7.5   , num_inference_steps=15   , output_type='''np'''   , use_karras_sigmas=__lowercase   , )

       SCREAMING_SNAKE_CASE__       :			Optional[int]													=output.images

       SCREAMING_SNAKE_CASE__       :			Dict													=image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=np.array(
           [0.11381689, 0.12112921, 0.1389457, 0.12549606, 0.1244964, 0.10831517, 0.11562866, 0.10867816, 0.10499048] )

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