


'''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()
__snake_case    						=logging.get_logger(__name__)





def 	a_					( lowerCamelCase	:     Union[str, Any]	, lowerCamelCase	:     List[str]=False				):
   lowerCAmelCase     =			[]
   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"
      lowerCAmelCase     =			[(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_					( lowerCamelCase	:     Tuple	, lowerCamelCase	:     Union[str, Any]	, lowerCamelCase	:     Union[str, Any]=False				):
   for i in range(config.num_hidden_layers				):
      if base_model:
         lowerCAmelCase     =			''
      else:
         lowerCAmelCase     =			'deit.'
      # 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 	a_					( lowerCamelCase	:     Dict	, lowerCamelCase	:     List[Any]	, lowerCamelCase	:     str				):
   lowerCAmelCase     =			dct.pop(lowerCamelCase				)
   lowerCAmelCase     =			val





def 	a_					( ):
   lowerCAmelCase     =			'http://images.cocodataset.org/val2017/000000039769.jpg'
   lowerCAmelCase     =			Image.open(requests.get(lowerCamelCase	, stream=lowerCamelCase				).raw				)
   return im





@torch.no_grad()
def 	a_					( lowerCamelCase	:     List[str]	, lowerCamelCase	:     Dict				):
   lowerCAmelCase     =			DeiTConfig()
   # all deit models have fine-tuned heads
   lowerCAmelCase     =			False
   # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
   lowerCAmelCase     =			1000
   lowerCAmelCase     =			'huggingface/label-files'
   lowerCAmelCase     =			'imagenet-1k-id2label.json'
   lowerCAmelCase     =			json.load(open(hf_hub_download(lowerCamelCase	, lowerCamelCase	, repo_type='dataset'				)	, 'r'				)				)
   lowerCAmelCase     =			{int(lowerCamelCase				): v for k, v in idalabel.items()}
   lowerCAmelCase     =			idalabel
   lowerCAmelCase     =			{v: k for k, v in idalabel.items()}
   lowerCAmelCase     =			int(deit_name[-6:-4]				)
   lowerCAmelCase     =			int(deit_name[-3:]				)
   # size of the architecture
   if deit_name[9:].startswith('tiny'				):
      lowerCAmelCase     =			192
      lowerCAmelCase     =			768
      lowerCAmelCase     =			12
      lowerCAmelCase     =			3
   elif deit_name[9:].startswith('small'				):
      lowerCAmelCase     =			384
      lowerCAmelCase     =			1536
      lowerCAmelCase     =			12
      lowerCAmelCase     =			6
   if deit_name[9:].startswith('base'				):
      pass
   elif deit_name[4:].startswith('large'				):
      lowerCAmelCase     =			1024
      lowerCAmelCase     =			4096
      lowerCAmelCase     =			24
      lowerCAmelCase     =			16

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

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

   # load HuggingFace model
   lowerCAmelCase     =			DeiTForImageClassificationWithTeacher(lowerCamelCase				).eval()
   model.load_state_dict(lowerCamelCase				)

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

   lowerCAmelCase     =			timm_model(lowerCamelCase				)
   assert timm_logits.shape == outputs.logits.shape
   assert torch.allclose(lowerCamelCase	, outputs.logits	, atol=1e-3				)

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


if __name__ == "__main__":
       __snake_case    						=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."""
       )

       __snake_case    						=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 		UpperCAmelCase_      (  unittest.TestCase		):





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





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  Any:
        lowerCAmelCase     =			StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'    )
        lowerCAmelCase     =			sd_pipe.to(UpperCAmelCase__    )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__    )

        sd_pipe.set_scheduler('sample_euler'    )

        lowerCAmelCase     =			'A painting of a squirrel eating a burger'
        lowerCAmelCase     =			torch.manual_seed(0    )
        lowerCAmelCase     =			sd_pipe([prompt]    ,      generator=UpperCAmelCase__    ,      guidance_scale=9.0    ,      num_inference_steps=2_0    ,      output_type='np'    )

        lowerCAmelCase     =			output.images

        lowerCAmelCase     =			image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        lowerCAmelCase     =			np.array([0.0_447, 0.0_492, 0.0_468, 0.0_408, 0.0_383, 0.0_408, 0.0_354, 0.0_380, 0.0_339]    )

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





     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  Dict:
        lowerCAmelCase     =			StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'    )
        lowerCAmelCase     =			sd_pipe.to(UpperCAmelCase__    )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__    )

        sd_pipe.set_scheduler('sample_euler'    )

        lowerCAmelCase     =			'A painting of a squirrel eating a burger'
        lowerCAmelCase     =			torch.manual_seed(0    )
        lowerCAmelCase     =			sd_pipe([prompt]    ,      generator=UpperCAmelCase__    ,      guidance_scale=9.0    ,      num_inference_steps=2_0    ,      output_type='np'    )

        lowerCAmelCase     =			output.images

        lowerCAmelCase     =			image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        lowerCAmelCase     =			np.array([0.1_237, 0.1_320, 0.1_438, 0.1_359, 0.1_390, 0.1_132, 0.1_277, 0.1_175, 0.1_112]    )

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





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  List[str]:
        lowerCAmelCase     =			StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'    )
        lowerCAmelCase     =			sd_pipe.to(UpperCAmelCase__    )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__    )

        sd_pipe.set_scheduler('sample_dpmpp_2m'    )

        lowerCAmelCase     =			'A painting of a squirrel eating a burger'
        lowerCAmelCase     =			torch.manual_seed(0    )
        lowerCAmelCase     =			sd_pipe(
            [prompt]    ,      generator=UpperCAmelCase__    ,      guidance_scale=7.5    ,      num_inference_steps=1_5    ,      output_type='np'    ,      use_karras_sigmas=UpperCAmelCase__    ,      )

        lowerCAmelCase     =			output.images

        lowerCAmelCase     =			image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        lowerCAmelCase     =			np.array(
            [0.11_381_689, 0.12_112_921, 0.1_389_457, 0.12_549_606, 0.1_244_964, 0.10_831_517, 0.11_562_866, 0.10_867_816, 0.10_499_048]    )

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





