





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()
UpperCAmelCase_									=       logging.get_logger(__name__)





def   lowerCamelCase__       (       UpperCamelCase__     :    List[Any]    ,      UpperCamelCase__     :    Optional[Any]=False  )     ->	Optional[int]:



					'''simple docstring'''




					_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"
										_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   lowerCamelCase__       (       UpperCamelCase__     :    Optional[Any]    ,      UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Tuple=False  )     ->	str:



					'''simple docstring'''




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





def   lowerCamelCase__       (       UpperCamelCase__     :    Optional[int]    ,      UpperCamelCase__     :    int    ,      UpperCamelCase__     :    Union[str, Any]  )     ->	Union[str, Any]:



					'''simple docstring'''




					_snake_case													=			dct.pop(A__  )
					_snake_case													=			val





def   lowerCamelCase__       (       )     ->	Any:



					'''simple docstring'''




					_snake_case													=			'http://images.cocodataset.org/val2017/000000039769.jpg'
					_snake_case													=			Image.open(requests.get(A__    ,      stream=A__  ).raw  )
					return im





@torch.no_grad()
def   lowerCamelCase__       (       UpperCamelCase__     :    List[Any]    ,      UpperCamelCase__     :    Optional[Any]  )     ->	Union[str, Any]:



					'''simple docstring'''




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

					# load original model from timm
					_snake_case													=			timm.create_model(A__    ,      pretrained=A__  )
					timm_model.eval()

					# load state_dict of original model, remove and rename some keys
					_snake_case													=			timm_model.state_dict()
					_snake_case													=			create_rename_keys(A__    ,      A__  )
					for src, dest in rename_keys:
										rename_key(A__    ,      A__    ,      A__  )
					read_in_q_k_v(A__    ,      A__    ,      A__  )

					# load HuggingFace model
					_snake_case													=			DeiTForImageClassificationWithTeacher(A__  ).eval()
					model.load_state_dict(A__  )

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

					_snake_case													=			timm_model(A__  )
					assert timm_logits.shape == outputs.logits.shape
					assert torch.allclose(A__    ,      outputs.logits    ,      atol=1e-3  )

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


if __name__ == "__main__":
					UpperCAmelCase_									=       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."""
					)

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










from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst





def   lowerCamelCase__       (       )     ->	List[str]:



					'''simple docstring'''




					_snake_case							,       _snake_case													=			9, 14  # noqa: F841
					_snake_case													=			[
					    [0, 1, 4],
					    [0, 7, 8],
					    [1, 2, 8],
					    [7, 8, 7],
					    [7, 6, 1],
					    [2, 8, 2],
					    [8, 6, 6],
					    [2, 3, 7],
					    [2, 5, 4],
					    [6, 5, 2],
					    [3, 5, 14],
					    [3, 4, 9],
					    [5, 4, 10],
					    [1, 7, 11],
					]

					_snake_case													=			defaultdict(UpperCamelCase__  )
					for nodea, nodea, cost in edges:
										adjancency[nodea].append([nodea, cost]  )
										adjancency[nodea].append([nodea, cost]  )

					_snake_case													=			mst(UpperCamelCase__  )

					_snake_case													=			[
					    [7, 6, 1],
					    [2, 8, 2],
					    [6, 5, 2],
					    [0, 1, 4],
					    [2, 5, 4],
					    [2, 3, 7],
					    [0, 7, 8],
					    [3, 4, 9],
					]

					for answer in expected:
										_snake_case													=			tuple(answer[:2]  )
										_snake_case													=			tuple(edge[::-1]  )
										assert edge in result or reverse in result



