
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()
lowercase        =  logging.get_logger(__name__)



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



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Any			,				UpperCAmelCase__				:      Union[str, Any]			,				UpperCAmelCase__				:      List[str]       )  ->    Union[str, Any]:
							lowerCamelCase_							  =					dct.pop(UpperCAmelCase__       )
							lowerCamelCase_							  =					val



def      __lowerCAmelCase						(							)  ->    str:
							lowerCamelCase_							  =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowerCamelCase_							  =					Image.open(requests.get(UpperCAmelCase__			,				stream=UpperCAmelCase__       ).raw       )
							return im



@torch.no_grad()
def      __lowerCAmelCase						(							UpperCAmelCase__				:      List[str]			,				UpperCAmelCase__				:      List[Any]       )  ->    Optional[Any]:
							lowerCamelCase_							  =					DeiTConfig()
							# all deit models have fine-tuned heads
							lowerCamelCase_							  =					False
							# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
							lowerCamelCase_							  =					1_0_0_0
							lowerCamelCase_							  =					"""huggingface/label-files"""
							lowerCamelCase_							  =					"""imagenet-1k-id2label.json"""
							lowerCamelCase_							  =					json.load(open(hf_hub_download(UpperCAmelCase__			,				UpperCAmelCase__			,				repo_type="""dataset"""       )			,				"""r"""       )       )
							lowerCamelCase_							  =					{int(UpperCAmelCase__       ): v for k, v in idalabel.items()}
							lowerCamelCase_							  =					idalabel
							lowerCamelCase_							  =					{v: k for k, v in idalabel.items()}
							lowerCamelCase_							  =					int(deit_name[-6:-4]       )
							lowerCamelCase_							  =					int(deit_name[-3:]       )
							# size of the architecture
							if deit_name[9:].startswith("""tiny"""       ):
														lowerCamelCase_							  =					1_9_2
														lowerCamelCase_							  =					7_6_8
														lowerCamelCase_							  =					1_2
														lowerCamelCase_							  =					3
							elif deit_name[9:].startswith("""small"""       ):
														lowerCamelCase_							  =					3_8_4
														lowerCamelCase_							  =					1_5_3_6
														lowerCamelCase_							  =					1_2
														lowerCamelCase_							  =					6
							if deit_name[9:].startswith("""base"""       ):
														pass
							elif deit_name[4:].startswith("""large"""       ):
														lowerCamelCase_							  =					1_0_2_4
														lowerCamelCase_							  =					4_0_9_6
														lowerCamelCase_							  =					2_4
														lowerCamelCase_							  =					1_6

							# load original model from timm
							lowerCamelCase_							  =					timm.create_model(UpperCAmelCase__			,				pretrained=UpperCAmelCase__       )
							timm_model.eval()

							# load state_dict of original model, remove and rename some keys
							lowerCamelCase_							  =					timm_model.state_dict()
							lowerCamelCase_							  =					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
							lowerCamelCase_							  =					DeiTForImageClassificationWithTeacher(UpperCAmelCase__       ).eval()
							model.load_state_dict(UpperCAmelCase__       )

							# Check outputs on an image, prepared by DeiTImageProcessor
							lowerCamelCase_							  =					int(
							    (2_5_6 / 2_2_4) * config.image_size       )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
							lowerCamelCase_							  =					DeiTImageProcessor(size=UpperCAmelCase__			,				crop_size=config.image_size       )
							lowerCamelCase_							  =					image_processor(images=prepare_img()			,				return_tensors="""pt"""       )
							lowerCamelCase_							  =					encoding["""pixel_values"""]
							lowerCamelCase_							  =					model(UpperCAmelCase__       )

							lowerCamelCase_							  =					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__":
		lowercase        =  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.'''
		)

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







from abc import ABC, abstractmethod
from argparse import ArgumentParser





class       __A(				UpperCAmelCase       ):

					@staticmethod
					@abstractmethod
					def    lowercase__      (	__UpperCamelCase					:     ArgumentParser   ):
												raise NotImplementedError()



					@abstractmethod
					def    lowercase__      (	self					:     Any   ):
												raise NotImplementedError()





