





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






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str       , UpperCamelCase__:				List[str]       , UpperCamelCase__:				List[str]     ):
		SCREAMING_SNAKE_CASE__								=   dct.pop(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   val






def 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
		SCREAMING_SNAKE_CASE__								=   Image.open(requests.get(UpperCamelCase__       , stream=UpperCamelCase__     ).raw     )
		return im






@torch.no_grad()
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Any       , UpperCamelCase__:				Tuple     ):
		SCREAMING_SNAKE_CASE__								=   DeiTConfig()
		# all deit models have fine-tuned heads
		SCREAMING_SNAKE_CASE__								=   False
		# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
		SCREAMING_SNAKE_CASE__								=   1_000
		SCREAMING_SNAKE_CASE__								=   """huggingface/label-files"""
		SCREAMING_SNAKE_CASE__								=   """imagenet-1k-id2label.json"""
		SCREAMING_SNAKE_CASE__								=   json.load(open(hf_hub_download(UpperCamelCase__       , UpperCamelCase__       , repo_type="""dataset"""     )       , """r"""     )     )
		SCREAMING_SNAKE_CASE__								=   {int(UpperCamelCase__     ): v for k, v in idalabel.items()}
		SCREAMING_SNAKE_CASE__								=   idalabel
		SCREAMING_SNAKE_CASE__								=   {v: k for k, v in idalabel.items()}
		SCREAMING_SNAKE_CASE__								=   int(deit_name[-6:-4]     )
		SCREAMING_SNAKE_CASE__								=   int(deit_name[-3:]     )
		# size of the architecture
		if deit_name[9:].startswith("""tiny"""     ):
				SCREAMING_SNAKE_CASE__								=   192
				SCREAMING_SNAKE_CASE__								=   768
				SCREAMING_SNAKE_CASE__								=   12
				SCREAMING_SNAKE_CASE__								=   3
		elif deit_name[9:].startswith("""small"""     ):
				SCREAMING_SNAKE_CASE__								=   384
				SCREAMING_SNAKE_CASE__								=   1_536
				SCREAMING_SNAKE_CASE__								=   12
				SCREAMING_SNAKE_CASE__								=   6
		if deit_name[9:].startswith("""base"""     ):
				pass
		elif deit_name[4:].startswith("""large"""     ):
				SCREAMING_SNAKE_CASE__								=   1_024
				SCREAMING_SNAKE_CASE__								=   4_096
				SCREAMING_SNAKE_CASE__								=   24
				SCREAMING_SNAKE_CASE__								=   16

		# load original model from timm
		SCREAMING_SNAKE_CASE__								=   timm.create_model(UpperCamelCase__       , pretrained=UpperCamelCase__     )
		timm_model.eval()

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

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

		SCREAMING_SNAKE_CASE__								=   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)






from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       ["image_processor", "tokenizer"]
							lowerCamelCase_							     =       "AutoImageProcessor"
							lowerCamelCase_							     =       "AutoTokenizer"


							def __init__(				self     :Optional[int]				,					__A     :Optional[Any]				,					__A     :Dict		) -> Dict:



									"""simple docstring"""



									super().__init__(__A				,					__A		)
									SCREAMING_SNAKE_CASE__								=   self.image_processor


							def __call__(				self     :int				,					__A     :str=None				,					__A     :int=None				,					__A     :Union[str, Any]=None				,					**__A     :str		) -> Optional[Any]:



									"""simple docstring"""



									if text is None and images is None:
											raise ValueError("""You have to specify either text or images. Both cannot be none."""		)

									if text is not None:
											SCREAMING_SNAKE_CASE__								=   self.tokenizer(__A				,					return_tensors=__A				,					**__A		)

									if images is not None:
											SCREAMING_SNAKE_CASE__								=   self.image_processor(__A				,					return_tensors=__A				,					**__A		)

									if text is not None and images is not None:
											SCREAMING_SNAKE_CASE__								=   image_features.pixel_values
											return encoding
									elif text is not None:
											return encoding
									else:
											return BatchEncoding(data=dict(**__A		)				,					tensor_type=__A		)


							def   _snake_case	(				self     :str				,					*__A     :List[str]				,					**__A     :List[str]		) -> List[Any]:



									"""simple docstring"""



									return self.tokenizer.batch_decode(*__A				,					**__A		)


							def   _snake_case	(				self     :List[str]				,					*__A     :Any				,					**__A     :Any		) -> Tuple:



									"""simple docstring"""



									return self.tokenizer.decode(*__A				,					**__A		)


							@property
							def   _snake_case	(				self     :Dict		) -> List[Any]:



									"""simple docstring"""



									return ["input_ids", "attention_mask", "pixel_values"]