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()
SCREAMING_SNAKE_CASE__		:       Union[str, Any]       						=       logging.get_logger(__name__)







def      _a					(   lowercase__	:      List[str]  ,	lowercase__	:      List[str]=False			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					int      =		[]
     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__					:					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      _a					(   lowercase__	:      Optional[Any]  ,	lowercase__	:      Union[str, Any]  ,	lowercase__	:      Optional[int]=False			):







     '''simple docstring'''


     for i in range(config.num_hidden_layers			):
          if base_model:
               SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		''
          else:
               SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		'deit.'
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          SCREAMING_SNAKE_CASE__					:					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__					:					Optional[int]      =		in_proj_weight[
              : config.hidden_size, :
          ]
          SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		in_proj_bias[: config.hidden_size]
          SCREAMING_SNAKE_CASE__					:					str      =		in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          SCREAMING_SNAKE_CASE__					:					int      =		in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          SCREAMING_SNAKE_CASE__					:					int      =		in_proj_weight[
              -config.hidden_size :, :
          ]
          SCREAMING_SNAKE_CASE__					:					List[str]      =		in_proj_bias[-config.hidden_size :]







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







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					int      =		dct.pop(lowercase__			)
     SCREAMING_SNAKE_CASE__					:					str      =		val







def      _a					(   ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Optional[int]      =		'http://images.cocodataset.org/val2017/000000039769.jpg'
     SCREAMING_SNAKE_CASE__					:					Any      =		Image.open(requests.get(lowercase__  ,	stream=lowercase__			).raw			)
     return im







@torch.no_grad()
def      _a					(   lowercase__	:      Any  ,	lowercase__	:      Union[str, Any]			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Tuple      =		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__					:					List[Any]      =		10_00
     SCREAMING_SNAKE_CASE__					:					str      =		'huggingface/label-files'
     SCREAMING_SNAKE_CASE__					:					Optional[int]      =		'imagenet-1k-id2label.json'
     SCREAMING_SNAKE_CASE__					:					Dict      =		json.load(open(hf_hub_download(lowercase__  ,	lowercase__  ,	repo_type='dataset'			)  ,	'r'			)			)
     SCREAMING_SNAKE_CASE__					:					Any      =		{int(lowercase__			): v for k, v in idalabel.items()}
     SCREAMING_SNAKE_CASE__					:					int      =		idalabel
     SCREAMING_SNAKE_CASE__					:					Any      =		{v: k for k, v in idalabel.items()}
     SCREAMING_SNAKE_CASE__					:					Any      =		int(deit_name[-6:-4]			)
     SCREAMING_SNAKE_CASE__					:					List[Any]      =		int(deit_name[-3:]			)
     # size of the architecture
     if deit_name[9:].startswith('tiny'			):
          SCREAMING_SNAKE_CASE__					:					Optional[int]      =		1_92
          SCREAMING_SNAKE_CASE__					:					str      =		7_68
          SCREAMING_SNAKE_CASE__					:					Optional[int]      =		12
          SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		3
     elif deit_name[9:].startswith('small'			):
          SCREAMING_SNAKE_CASE__					:					List[Any]      =		3_84
          SCREAMING_SNAKE_CASE__					:					Dict      =		15_36
          SCREAMING_SNAKE_CASE__					:					Dict      =		12
          SCREAMING_SNAKE_CASE__					:					List[str]      =		6
     if deit_name[9:].startswith('base'			):
          pass
     elif deit_name[4:].startswith('large'			):
          SCREAMING_SNAKE_CASE__					:					List[str]      =		10_24
          SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		40_96
          SCREAMING_SNAKE_CASE__					:					List[str]      =		24
          SCREAMING_SNAKE_CASE__					:					str      =		16

     # load original model from timm
     SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		timm.create_model(lowercase__  ,	pretrained=lowercase__			)
     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__					:					Any      =		create_rename_keys(lowercase__  ,	lowercase__			)
     for src, dest in rename_keys:
          rename_key(lowercase__  ,	lowercase__  ,	lowercase__			)
     read_in_q_k_v(lowercase__  ,	lowercase__  ,	lowercase__			)

     # load HuggingFace model
     SCREAMING_SNAKE_CASE__					:					str      =		DeiTForImageClassificationWithTeacher(lowercase__			).eval()
     model.load_state_dict(lowercase__			)

     # Check outputs on an image, prepared by DeiTImageProcessor
     SCREAMING_SNAKE_CASE__					:					Tuple      =		int(
         (2_56 / 2_24) * 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[Any]      =		DeiTImageProcessor(size=lowercase__  ,	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__					:					Tuple      =		model(lowercase__			)

     SCREAMING_SNAKE_CASE__					:					List[str]      =		timm_model(lowercase__			)
     assert timm_logits.shape == outputs.logits.shape
     assert torch.allclose(lowercase__  ,	outputs.logits  ,	atol=1E-3			)

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


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:       int       						=       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."
      )

      SCREAMING_SNAKE_CASE__		:       Dict       						=       parser.parse_args()
      convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)










import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


UpperCamelCase__           =			False

class 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):
							pass






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






							def 					_lowerCamelCase		(    self	):
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      VersatileDiffusionTextToImagePipeline.from_pretrained("""shi-labs/versatile-diffusion"""	)
														# remove text_unet
														pipe.remove_unused_weights()
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														UpperCamelCase__					  =      """A painting of a squirrel eating a burger """
														UpperCamelCase__					  =      torch.manual_seed(0	)
														UpperCamelCase__					  =      pipe(
														    prompt=__lowerCAmelCase	,			generator=__lowerCAmelCase	,			guidance_scale=7.5	,			num_inference_steps=2	,			output_type="""numpy"""	).images

														with tempfile.TemporaryDirectory() as tmpdirname:
																					pipe.save_pretrained(__lowerCAmelCase	)
																					UpperCamelCase__					  =      VersatileDiffusionTextToImagePipeline.from_pretrained(__lowerCAmelCase	)
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														UpperCamelCase__					  =      generator.manual_seed(0	)
														UpperCamelCase__					  =      pipe(
														    prompt=__lowerCAmelCase	,			generator=__lowerCAmelCase	,			guidance_scale=7.5	,			num_inference_steps=2	,			output_type="""numpy"""	).images

														assert np.abs(image - new_image	).sum() < 1E-5, "Models don't have the same forward pass"







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      VersatileDiffusionTextToImagePipeline.from_pretrained(
														    """shi-labs/versatile-diffusion"""	,			torch_dtype=torch.floataa	)
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														UpperCamelCase__					  =      """A painting of a squirrel eating a burger """
														UpperCamelCase__					  =      torch.manual_seed(0	)
														UpperCamelCase__					  =      pipe(
														    prompt=__lowerCAmelCase	,			generator=__lowerCAmelCase	,			guidance_scale=7.5	,			num_inference_steps=50	,			output_type="""numpy"""	).images

														UpperCamelCase__					  =      image[0, 253:256, 253:256, -1]

														assert image.shape == (1, 512, 512, 3)
														UpperCamelCase__					  =      np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]	)

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




