



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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
snake_case    :						Any  =						logging.get_logger(__name__)
def 	snake_case__       (					__lowercase							,     __lowercase=False       )		->		List[Any]:







						"""simple docstring"""

						A__	:							str						    = []
						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'vit.encoder.layer.{i}.layernorm_before.weight')       )
												rename_keys.append((F'blocks.{i}.norm1.bias', F'vit.encoder.layer.{i}.layernorm_before.bias')       )
												rename_keys.append((F'blocks.{i}.attn.proj.weight', F'vit.encoder.layer.{i}.attention.output.dense.weight')       )
												rename_keys.append((F'blocks.{i}.attn.proj.bias', F'vit.encoder.layer.{i}.attention.output.dense.bias')       )
												rename_keys.append((F'blocks.{i}.norm2.weight', F'vit.encoder.layer.{i}.layernorm_after.weight')       )
												rename_keys.append((F'blocks.{i}.norm2.bias', F'vit.encoder.layer.{i}.layernorm_after.bias')       )
												rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'vit.encoder.layer.{i}.intermediate.dense.weight')       )
												rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'vit.encoder.layer.{i}.intermediate.dense.bias')       )
												rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'vit.encoder.layer.{i}.output.dense.weight')       )
												rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'vit.encoder.layer.{i}.output.dense.bias')       )

						# projection layer + position embeddings
						rename_keys.extend(
						    [
						        ("cls_token", "vit.embeddings.cls_token"),
						        ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
						        ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
						        ("pos_embed", "vit.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 "vit" from all keys that start with "vit"
												A__	:							List[str]						    = [(pair[0], pair[1][4:]) if pair[1].startswith("vit"       ) else pair for pair in rename_keys]
						else:
												# layernorm + classification head
												rename_keys.extend(
												    [
												        ("norm.weight", "vit.layernorm.weight"),
												        ("norm.bias", "vit.layernorm.bias"),
												        ("head.weight", "classifier.weight"),
												        ("head.bias", "classifier.bias"),
												    ]       )

						return rename_keys
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase=False       )		->		Union[str, Any]:







						"""simple docstring"""

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







						"""simple docstring"""

						A__	:							str						    = ["head.weight", "head.bias"]
						for k in ignore_keys:
												state_dict.pop(__lowercase							,     __lowercase       )
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		Optional[Any]:







						"""simple docstring"""

						A__	:							int						    = dct.pop(__lowercase       )
						A__	:							Union[str, Any]						    = val
def 	snake_case__       (					)		->		Optional[int]:







						"""simple docstring"""

						A__	:							Union[str, Any]						    = "http://images.cocodataset.org/val2017/000000039769.jpg"
						A__	:							Dict						    = Image.open(requests.get(__lowercase							,     stream=__lowercase       ).raw       )
						return im
@torch.no_grad()
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		Union[str, Any]:







						"""simple docstring"""

						A__	:							Dict						    = ViTConfig()
						A__	:							Union[str, Any]						    = False
						# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
						if vit_name[-5:] == "in21k":
												A__	:							Optional[Any]						    = True
												A__	:							int						    = int(vit_name[-1_2:-1_0]       )
												A__	:							int						    = int(vit_name[-9:-6]       )
						else:
												A__	:							Dict						    = 1_0_0_0
												A__	:							List[Any]						    = "huggingface/label-files"
												A__	:							Any						    = "imagenet-1k-id2label.json"
												A__	:							int						    = json.load(open(hf_hub_download(__lowercase							,     __lowercase							,     repo_type="dataset"       )							,     "r"       )       )
												A__	:							Optional[int]						    = {int(__lowercase       ): v for k, v in idalabel.items()}
												A__	:							List[Any]						    = idalabel
												A__	:							Dict						    = {v: k for k, v in idalabel.items()}
												A__	:							Union[str, Any]						    = int(vit_name[-6:-4]       )
												A__	:							Optional[int]						    = int(vit_name[-3:]       )
						# size of the architecture
						if "deit" in vit_name:
												if vit_name[9:].startswith("tiny"       ):
																		A__	:							Dict						    = 1_9_2
																		A__	:							Optional[Any]						    = 7_6_8
																		A__	:							Any						    = 1_2
																		A__	:							Dict						    = 3
												elif vit_name[9:].startswith("small"       ):
																		A__	:							List[Any]						    = 3_8_4
																		A__	:							Dict						    = 1_5_3_6
																		A__	:							int						    = 1_2
																		A__	:							Optional[Any]						    = 6
												else:
																		pass
						else:
												if vit_name[4:].startswith("small"       ):
																		A__	:							Union[str, Any]						    = 7_6_8
																		A__	:							Union[str, Any]						    = 2_3_0_4
																		A__	:							Optional[int]						    = 8
																		A__	:							Union[str, Any]						    = 8
												elif vit_name[4:].startswith("base"       ):
																		pass
												elif vit_name[4:].startswith("large"       ):
																		A__	:							int						    = 1_0_2_4
																		A__	:							Optional[Any]						    = 4_0_9_6
																		A__	:							Tuple						    = 2_4
																		A__	:							List[Any]						    = 1_6
												elif vit_name[4:].startswith("huge"       ):
																		A__	:							Union[str, Any]						    = 1_2_8_0
																		A__	:							str						    = 5_1_2_0
																		A__	:							str						    = 3_2
																		A__	:							List[str]						    = 1_6

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

						# load state_dict of original model, remove and rename some keys
						A__	:							Dict						    = timm_model.state_dict()
						if base_model:
												remove_classification_head_(__lowercase       )
						A__	:							Dict						    = 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
						if vit_name[-5:] == "in21k":
												A__	:							Tuple						    = ViTModel(__lowercase       ).eval()
						else:
												A__	:							Dict						    = ViTForImageClassification(__lowercase       ).eval()
						model.load_state_dict(__lowercase       )

						# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
						if "deit" in vit_name:
												A__	:							Dict						    = DeiTImageProcessor(size=config.image_size       )
						else:
												A__	:							Union[str, Any]						    = ViTImageProcessor(size=config.image_size       )
						A__	:							str						    = image_processor(images=prepare_img()							,     return_tensors="pt"       )
						A__	:							int						    = encoding["pixel_values"]
						A__	:							Optional[int]						    = model(__lowercase       )

						if base_model:
												A__	:							List[Any]						    = timm_model.forward_features(__lowercase       )
												assert timm_pooled_output.shape == outputs.pooler_output.shape
												assert torch.allclose(__lowercase							,     outputs.pooler_output							,     atol=1E-3       )
						else:
												A__	:							str						    = 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 {vit_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__":
		snake_case    :						int  =						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--vit_name',
		    default='vit_base_patch16_224',
		    type=str,
		    help='Name of the ViT 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.'
		)

		snake_case    :						Union[str, Any]  =						parser.parse_args()
		convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)




from __future__ import annotations

snake_case    :						Optional[Any]  =						{
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}




class 							lowerCAmelCase__  :







					def __init__(       self      :     Optional[Any]			,		_A      :     dict[str, list[str]]			,		_A      :     str):
											A__	:							Optional[Any]						    = graph
											# mapping node to its parent in resulting breadth first tree
											A__	:							dict[str, str | None]						    = {}
											A__	:							List[str]						    = source_vertex







					def  _lowercase			(       self      :     List[Any]):
											A__	:							str						    = {self.source_vertex}
											A__	:							List[str]						    = None
											A__	:							List[str]						    = [self.source_vertex]  # first in first out queue

											while queue:
																	A__	:							int						    = queue.pop(0)
																	for adjacent_vertex in self.graph[vertex]:
																							if adjacent_vertex not in visited:
																													visited.add(_A)
																													A__	:							Any						    = vertex
																													queue.append(_A)







					def  _lowercase			(       self      :     int			,		_A      :     str):
											if target_vertex == self.source_vertex:
																	return self.source_vertex

											A__	:							List[Any]						    = self.parent.get(_A)
											if target_vertex_parent is None:
																	A__	:							Union[str, Any]						    = (
																	    F'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
																	)
																	raise ValueError(_A)

											return self.shortest_path(_A) + F'->{target_vertex}'


if __name__ == "__main__":
		snake_case    :						Any  =						Graph(graph, 'G')
		g.breath_first_search()
		print(g.shortest_path('D'))
		print(g.shortest_path('G'))
		print(g.shortest_path('Foo'))