






"""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()
lowerCamelCase_       			=   logging.get_logger(__name__)

def        snake_case   (     A__			,A__=False		):
			UpperCAmelCase_  :				List[str]      =      []
			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"
						UpperCAmelCase_  :				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        snake_case   (     A__			,A__			,A__=False		):






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

def        snake_case   (     A__			,A__			,A__		):
			UpperCAmelCase_  :				Any      =      dct.pop(_UpperCamelCase		)
			UpperCAmelCase_  :				List[str]      =      val

def        snake_case   (     ):
			UpperCAmelCase_  :				Tuple      =      "http://images.cocodataset.org/val2017/000000039769.jpg"
			UpperCAmelCase_  :				int      =      Image.open(requests.get(_UpperCamelCase			,stream=_UpperCamelCase		).raw		)
			return im

@torch.no_grad()
def        snake_case   (     A__			,A__		):
			UpperCAmelCase_  :				Optional[int]      =      DeiTConfig()
			# all deit models have fine-tuned heads
			UpperCAmelCase_  :				Optional[int]      =      False
			# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
			UpperCAmelCase_  :				Optional[int]      =      10_00
			UpperCAmelCase_  :				Optional[Any]      =      "huggingface/label-files"
			UpperCAmelCase_  :				Tuple      =      "imagenet-1k-id2label.json"
			UpperCAmelCase_  :				Union[str, Any]      =      json.load(open(hf_hub_download(_UpperCamelCase			,_UpperCamelCase			,repo_type="dataset"		)			,"r"		)		)
			UpperCAmelCase_  :				str      =      {int(_UpperCamelCase		): v for k, v in idalabel.items()}
			UpperCAmelCase_  :				Optional[int]      =      idalabel
			UpperCAmelCase_  :				Tuple      =      {v: k for k, v in idalabel.items()}
			UpperCAmelCase_  :				Optional[Any]      =      int(deit_name[-6:-4]		)
			UpperCAmelCase_  :				str      =      int(deit_name[-3:]		)
			# size of the architecture
			if deit_name[9:].startswith("tiny"		):
						UpperCAmelCase_  :				List[str]      =      1_92
						UpperCAmelCase_  :				Optional[int]      =      7_68
						UpperCAmelCase_  :				Dict      =      12
						UpperCAmelCase_  :				List[str]      =      3
			elif deit_name[9:].startswith("small"		):
						UpperCAmelCase_  :				Optional[int]      =      3_84
						UpperCAmelCase_  :				str      =      15_36
						UpperCAmelCase_  :				Optional[Any]      =      12
						UpperCAmelCase_  :				Tuple      =      6
			if deit_name[9:].startswith("base"		):
						pass
			elif deit_name[4:].startswith("large"		):
						UpperCAmelCase_  :				Tuple      =      10_24
						UpperCAmelCase_  :				List[str]      =      40_96
						UpperCAmelCase_  :				Union[str, Any]      =      24
						UpperCAmelCase_  :				Tuple      =      16

			# load original model from timm
			UpperCAmelCase_  :				str      =      timm.create_model(_UpperCamelCase			,pretrained=_UpperCamelCase		)
			timm_model.eval()

			# load state_dict of original model, remove and rename some keys
			UpperCAmelCase_  :				int      =      timm_model.state_dict()
			UpperCAmelCase_  :				Dict      =      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
			UpperCAmelCase_  :				Dict      =      DeiTForImageClassificationWithTeacher(_UpperCamelCase		).eval()
			model.load_state_dict(_UpperCamelCase		)

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

			UpperCAmelCase_  :				str      =      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__":
			lowerCamelCase_       			=   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.'''
			)

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











"""simple docstring"""
from ..utils import DummyObject, requires_backends







class     UpperCamelCase_ (metaclass=__A  ):
	__magic_name__			   =       ['''onnx''']





	def __init__(   self      :				List[Any]			,			*lowerCAmelCase_      :				Dict			,			**lowerCAmelCase_      :				Dict		)		->    Dict:
				requires_backends(self			,			["onnx"]		)





	@classmethod
	def   _SCREAMING_SNAKE_CASE   (   cls      :				Optional[Any]			,			*lowerCAmelCase_      :				Dict			,			**lowerCAmelCase_      :				Union[str, Any]		)		->    int:
				requires_backends(cls			,			["onnx"]		)





	@classmethod
	def   _SCREAMING_SNAKE_CASE   (   cls      :				str			,			*lowerCAmelCase_      :				Optional[Any]			,			**lowerCAmelCase_      :				str		)		->    Optional[Any]:
				requires_backends(cls			,			["onnx"]		)



