






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 : Tuple						   =       logging.get_logger(__name__)
def       snake_case_						(     lowerCAmelCase_       :						Dict       ,       lowerCAmelCase_       :						Union[str, Any]=False      ):
							__lowercase      :							Optional[Any]				=     []
							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"
														__lowercase      :							Optional[int]				=     [(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_						(     lowerCAmelCase_       :						List[str]       ,       lowerCAmelCase_       :						Optional[Any]       ,       lowerCAmelCase_       :						str=False      ):
							for i in range(config.num_hidden_layers      ):
														if base_model:
																					__lowercase      :							List[str]				=     """"""
														else:
																					__lowercase      :							Optional[Any]				=     """deit."""
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														__lowercase      :							Dict				=     state_dict.pop(F"blocks.{i}.attn.qkv.weight"      )
														__lowercase      :							str				=     state_dict.pop(F"blocks.{i}.attn.qkv.bias"      )
														# next, add query, keys and values (in that order) to the state dict
														__lowercase      :							Optional[int]				=     in_proj_weight[
														    : config.hidden_size, :
														]
														__lowercase      :							Optional[int]				=     in_proj_bias[: config.hidden_size]
														__lowercase      :							str				=     in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														__lowercase      :							Tuple				=     in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														__lowercase      :							Dict				=     in_proj_weight[
														    -config.hidden_size :, :
														]
														__lowercase      :							Tuple				=     in_proj_bias[-config.hidden_size :]
def       snake_case_						(     lowerCAmelCase_       :						Dict       ,       lowerCAmelCase_       :						Dict       ,       lowerCAmelCase_       :						int      ):
							__lowercase      :							Union[str, Any]				=     dct.pop(lowerCAmelCase_      )
							__lowercase      :							Union[str, Any]				=     val
def       snake_case_						(     ):
							__lowercase      :							Optional[Any]				=     """http://images.cocodataset.org/val2017/000000039769.jpg"""
							__lowercase      :							List[Any]				=     Image.open(requests.get(lowerCAmelCase_       ,       stream=lowerCAmelCase_      ).raw      )
							return im
@torch.no_grad()
def       snake_case_						(     lowerCAmelCase_       :						List[str]       ,       lowerCAmelCase_       :						str      ):
							__lowercase      :							Tuple				=     DeiTConfig()
							# all deit models have fine-tuned heads
							__lowercase      :							Dict				=     False
							# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
							__lowercase      :							Any				=     1000
							__lowercase      :							str				=     """huggingface/label-files"""
							__lowercase      :							Union[str, Any]				=     """imagenet-1k-id2label.json"""
							__lowercase      :							Any				=     json.load(open(hf_hub_download(lowerCAmelCase_       ,       lowerCAmelCase_       ,       repo_type="""dataset"""      )       ,       """r"""      )      )
							__lowercase      :							Dict				=     {int(lowerCAmelCase_      ): v for k, v in idalabel.items()}
							__lowercase      :							int				=     idalabel
							__lowercase      :							Optional[Any]				=     {v: k for k, v in idalabel.items()}
							__lowercase      :							Union[str, Any]				=     int(deit_name[-6:-4]      )
							__lowercase      :							Any				=     int(deit_name[-3:]      )
							# size of the architecture
							if deit_name[9:].startswith("""tiny"""      ):
														__lowercase      :							Tuple				=     192
														__lowercase      :							Optional[int]				=     768
														__lowercase      :							str				=     12
														__lowercase      :							Tuple				=     3
							elif deit_name[9:].startswith("""small"""      ):
														__lowercase      :							List[str]				=     384
														__lowercase      :							Tuple				=     1536
														__lowercase      :							List[str]				=     12
														__lowercase      :							Optional[Any]				=     6
							if deit_name[9:].startswith("""base"""      ):
														pass
							elif deit_name[4:].startswith("""large"""      ):
														__lowercase      :							List[Any]				=     1024
														__lowercase      :							Optional[Any]				=     4096
														__lowercase      :							Optional[Any]				=     24
														__lowercase      :							Optional[Any]				=     16

							# load original model from timm
							__lowercase      :							int				=     timm.create_model(lowerCAmelCase_       ,       pretrained=lowerCAmelCase_      )
							timm_model.eval()

							# load state_dict of original model, remove and rename some keys
							__lowercase      :							Dict				=     timm_model.state_dict()
							__lowercase      :							List[str]				=     create_rename_keys(lowerCAmelCase_       ,       lowerCAmelCase_      )
							for src, dest in rename_keys:
														rename_key(lowerCAmelCase_       ,       lowerCAmelCase_       ,       lowerCAmelCase_      )
							read_in_q_k_v(lowerCAmelCase_       ,       lowerCAmelCase_       ,       lowerCAmelCase_      )

							# load HuggingFace model
							__lowercase      :							int				=     DeiTForImageClassificationWithTeacher(lowerCAmelCase_      ).eval()
							model.load_state_dict(lowerCAmelCase_      )

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

							__lowercase      :							Optional[int]				=     timm_model(lowerCAmelCase_      )
							assert timm_logits.shape == outputs.logits.shape
							assert torch.allclose(lowerCAmelCase_       ,       outputs.logits       ,       atol=1e-3      )

							Path(lowerCAmelCase_      ).mkdir(exist_ok=lowerCAmelCase_      )
							print(F"Saving model {deit_name} to {pytorch_dump_folder_path}"      )
							model.save_pretrained(lowerCAmelCase_      )
							print(F"Saving image processor to {pytorch_dump_folder_path}"      )
							image_processor.save_pretrained(lowerCAmelCase_      )


if __name__ == "__main__":
	lowerCamelCase : Dict						   =       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 : List[Any]						   =       parser.parse_args()
	convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)







def       snake_case_		(     lowerCAmelCase_       :						int       ,       lowerCAmelCase_       :						int      ):
							return int((input_a, input_a).count(0      ) == 0      )
def       snake_case_		(     ):
							assert and_gate(0       ,       0      ) == 0
							assert and_gate(0       ,       1      ) == 0
							assert and_gate(1       ,       0      ) == 0
							assert and_gate(1       ,       1      ) == 1


if __name__ == "__main__":
	test_and_gate()
	print(and_gate(1, 0))
	print(and_gate(0, 0))
	print(and_gate(0, 1))
	print(and_gate(1, 1))