


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							:       Optional[int]										=	logging.get_logger(__name__)
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						List[Any]=False		):




      """simple docstring"""





      a		: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"
            a		:Tuple									=			[(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 							__lowerCamelCase		(					UpperCAmelCase_		:						Optional[Any]					,    UpperCAmelCase_		:						Optional[int]					,    UpperCAmelCase_		:						str=False		):




      """simple docstring"""





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




      """simple docstring"""





      a		:str									=			dct.pop(UpperCAmelCase_		)
      a		:int									=			val
def 							__lowerCamelCase		(					):




      """simple docstring"""





      a		:str									=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      a		:List[str]									=			Image.open(requests.get(UpperCAmelCase_					,    stream=UpperCAmelCase_		).raw		)
      return im
@torch.no_grad()
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Optional[int]					,    UpperCAmelCase_		:						Any		):




      """simple docstring"""





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

      # load original model from timm
      a		:List[str]									=			timm.create_model(UpperCAmelCase_					,    pretrained=UpperCAmelCase_		)
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      a		:Tuple									=			timm_model.state_dict()
      a		: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
      a		:List[Any]									=			DeiTForImageClassificationWithTeacher(UpperCAmelCase_		).eval()
      model.load_state_dict(UpperCAmelCase_		)

      # Check outputs on an image, prepared by DeiTImageProcessor
      a		:Optional[int]									=			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
      a		:Any									=			DeiTImageProcessor(size=UpperCAmelCase_					,    crop_size=config.image_size		)
      a		:int									=			image_processor(images=prepare_img()					,    return_tensors='''pt'''		)
      a		:int									=			encoding['''pixel_values''']
      a		:int									=			model(UpperCAmelCase_		)

      a		: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__":
     snake_case							:       Optional[Any]										=	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							:       List[Any]										=	parser.parse_args()
     convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)









import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 						_snake_case    ( unittest.TestCase				):




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       torch.manual_seed(0       )
       a		:List[Any]									=			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 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Union[str, Any]									=			self.dummy_uncond_unet
       a		:Any									=			PNDMScheduler()

       a		:Any									=			PNDMPipeline(unet=_lowerCamelCase   ,    scheduler=_lowerCamelCase       )
       pndm.to(_lowerCamelCase       )
       pndm.set_progress_bar_config(disable=_lowerCamelCase       )

       a		:Any									=			torch.manual_seed(0       )
       a		:List[str]									=			pndm(generator=_lowerCamelCase   ,    num_inference_steps=20   ,    output_type='''numpy'''       ).images

       a		:int									=			torch.manual_seed(0       )
       a		:str									=			pndm(generator=_lowerCamelCase   ,    num_inference_steps=20   ,    output_type='''numpy'''   ,    return_dict=_lowerCamelCase       )[0]

       a		:int									=			image[0, -3:, -3:, -1]
       a		:List[str]									=			image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       a		:str									=			np.array([1.0, 1.0, 0.0, 1.0, 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 						_snake_case    ( unittest.TestCase				):




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			'''google/ddpm-cifar10-32'''

       a		:str									=			UNetaDModel.from_pretrained(_lowerCamelCase       )
       a		:Optional[Any]									=			PNDMScheduler()

       a		:Union[str, Any]									=			PNDMPipeline(unet=_lowerCamelCase   ,    scheduler=_lowerCamelCase       )
       pndm.to(_lowerCamelCase       )
       pndm.set_progress_bar_config(disable=_lowerCamelCase       )
       a		:Dict									=			torch.manual_seed(0       )
       a		:List[str]									=			pndm(generator=_lowerCamelCase   ,    output_type='''numpy'''       ).images

       a		:List[Any]									=			image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       a		:int									=			np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]       )

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





