



"""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()
__SCREAMING_SNAKE_CASE  :						Tuple    							=      logging.get_logger(__name__)



def        lowerCAmelCase_(   lowercase_     :							Any						,   lowercase_     :							Optional[Any]=False							)	->       Tuple:
				_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_(   lowercase_     :							Tuple						,   lowercase_     :							Optional[Any]						,   lowercase_     :							Any=False							)	->       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_(   lowercase_     :							Union[str, Any]						,   lowercase_     :							Union[str, Any]						,   lowercase_     :							str							)	->       Tuple:
				_lowerCamelCase        =				dct.pop(lowercase_							)
				_lowerCamelCase        =				val



def        lowerCAmelCase_(   )	->       Tuple:
				_lowerCamelCase        =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				_lowerCamelCase        =				Image.open(requests.get(lowercase_						,   stream=lowercase_							).raw							)
				return im



@torch.no_grad()
def        lowerCAmelCase_(   lowercase_     :							Optional[int]						,   lowercase_     :							Optional[Any]							)	->       List[str]:
				_lowerCamelCase        =				DeiTConfig()
				# all deit models have fine-tuned heads
				_lowerCamelCase        =				False
				# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
				_lowerCamelCase        =				10_00
				_lowerCamelCase        =				'''huggingface/label-files'''
				_lowerCamelCase        =				'''imagenet-1k-id2label.json'''
				_lowerCamelCase        =				json.load(open(hf_hub_download(lowercase_						,   lowercase_						,   repo_type='''dataset'''							)						,   '''r'''							)							)
				_lowerCamelCase        =				{int(lowercase_							): 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_92
								_lowerCamelCase        =				7_68
								_lowerCamelCase        =				12
								_lowerCamelCase        =				3
				elif deit_name[9:].startswith('''small'''							):
								_lowerCamelCase        =				3_84
								_lowerCamelCase        =				15_36
								_lowerCamelCase        =				12
								_lowerCamelCase        =				6
				if deit_name[9:].startswith('''base'''							):
								pass
				elif deit_name[4:].startswith('''large'''							):
								_lowerCamelCase        =				10_24
								_lowerCamelCase        =				40_96
								_lowerCamelCase        =				24
								_lowerCamelCase        =				16

				# load original model from timm
				_lowerCamelCase        =				timm.create_model(lowercase_						,   pretrained=lowercase_							)
				timm_model.eval()

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

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

				_lowerCamelCase        =				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__":
			__SCREAMING_SNAKE_CASE  :						int    							=      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.'''
			)

			__SCREAMING_SNAKE_CASE  :						Tuple    							=      parser.parse_args()
			convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)





"""simple docstring"""
from typing import Any


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				data
									_lowerCamelCase        =				None


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(    self   ):
									_lowerCamelCase        =				None

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.head
									while temp is not None:
													print(temp.data				,       end=''' '''   )
													_lowerCamelCase        =				temp.next
									print()

					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				Node(lowerCamelCase__   )
									_lowerCamelCase        =				self.head
									_lowerCamelCase        =				new_node





					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__   ):
									if node_data_a == node_data_a:
													return
									else:
													_lowerCamelCase        =				self.head
													while node_a is not None and node_a.data != node_data_a:
																	_lowerCamelCase        =				node_a.next

													_lowerCamelCase        =				self.head
													while node_a is not None and node_a.data != node_data_a:
																	_lowerCamelCase        =				node_a.next

													if node_a is None or node_a is None:
																	return

													_lowerCamelCase  ,     _lowerCamelCase        =				node_a.data, node_a.data


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						List[Any]    							=      LinkedList()
			for i in range(5, 0, -1):
						ll.push(i)

			ll.print_list()

			ll.swap_nodes(1, 4)
			print('''After swapping''')
			ll.print_list()
