
"""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()
UpperCamelCase_       :    Union[str, Any]						      =			logging.get_logger(__name__)
def 				A_  (__a					, __a=False					):

					'''simple docstring'''





					A_								=    []
					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_								=    [(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_  (__a					, __a					, __a=False					):

					'''simple docstring'''





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

					'''simple docstring'''





					A_								=    dct.pop(_A					)
					A_								=    val
def 				A_  ():

					'''simple docstring'''





					A_								=    'http://images.cocodataset.org/val2017/000000039769.jpg'
					A_								=    Image.open(requests.get(_A					, stream=_A					).raw					)
					return im
@torch.no_grad()
def 				A_  (__a					, __a					):

					'''simple docstring'''





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

					# load original model from timm
					A_								=    timm.create_model(_A					, pretrained=_A					)
					timm_model.eval()

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

					# load HuggingFace model
					A_								=    DeiTForImageClassificationWithTeacher(_A					).eval()
					model.load_state_dict(_A					)

					# Check outputs on an image, prepared by DeiTImageProcessor
					A_								=    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_								=    DeiTImageProcessor(size=_A					, crop_size=config.image_size					)
					A_								=    image_processor(images=prepare_img()					, return_tensors="pt"					)
					A_								=    encoding['pixel_values']
					A_								=    model(_A					)

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

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


if __name__ == "__main__":
		UpperCamelCase_       :    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.'''
		)

		UpperCamelCase_       :    int						      =			parser.parse_args()
		convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)








'''simple docstring'''







from __future__ import annotations

from collections.abc import Iterator
class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							value
            _lowerCAmelCase     :				Node | None       =							None
            _lowerCAmelCase     :				Node | None       =							None
class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							tree







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            if node is None:
                   return 0
            return node.value + (
                self.depth_first_search(node.left ) + self.depth_first_search(node.right )
            )







     def __iter__(			self ):

            '''simple docstring'''

            yield self.depth_first_search(self.tree )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

