




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



def      UpperCamelCase__   (						lowercase__     :   Union[str, Any]				,   lowercase__     :   List[str]=False      ):
		snake_case						:  Tuple    	= []
		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"
				snake_case						:  List[Any]    	= [(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      UpperCamelCase__   (						lowercase__     :   Tuple				,   lowercase__     :   Union[str, Any]				,   lowercase__     :   Union[str, Any]=False      ):
		for i in range(config.num_hidden_layers      ):
				if base_model:
						snake_case						:  Optional[int]    	= ""
				else:
						snake_case						:  Tuple    	= "deit."
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				snake_case						:  List[Any]    	= state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''      )
				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
				snake_case						:  Optional[int]    	= in_proj_weight[
				    : config.hidden_size, :
				]
				snake_case						:  List[str]    	= in_proj_bias[: config.hidden_size]
				snake_case						:  Optional[Any]    	= in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				snake_case						:  Dict    	= in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				snake_case						:  List[Any]    	= in_proj_weight[
				    -config.hidden_size :, :
				]
				snake_case						:  List[str]    	= in_proj_bias[-config.hidden_size :]



def      UpperCamelCase__   (						lowercase__     :   Dict				,   lowercase__     :   List[Any]				,   lowercase__     :   str      ):
		snake_case						:  List[str]    	= dct.pop(lowercase__      )
		snake_case						:  List[Any]    	= val



def      UpperCamelCase__   (						):
		snake_case						:  List[str]    	= "http://images.cocodataset.org/val2017/000000039769.jpg"
		snake_case						:  int    	= Image.open(requests.get(lowercase__				,   stream=lowercase__      ).raw      )
		return im



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

		# load original model from timm
		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
		snake_case						:  Tuple    	= timm_model.state_dict()
		snake_case						:  Dict    	= 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
		snake_case						:  int    	= DeiTForImageClassificationWithTeacher(lowercase__      ).eval()
		model.load_state_dict(lowercase__      )

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

		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__":
							__A       						=	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."
							)

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





'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


__snake_case    						=logging.get_logger(__name__)

class 		UpperCAmelCase_      (  __lowercase		):





     def __init__( self       :      Dict    ,      *UpperCAmelCase__       :      Dict    ,      **UpperCAmelCase__       :      List[str]    )				->  None:
        warnings.warn(
            'The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
            ' use FlavaImageProcessor instead.'    ,      UpperCAmelCase__    ,      )
        super().__init__(*UpperCAmelCase__    ,      **UpperCAmelCase__    )





