

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

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


logging.set_verbosity_info()
_a:    int 							=			logging.get_logger(__name__)



def       __lowerCAmelCase      (						A       ,     A=False    ):
	UpperCAmelCase_       				=			[]
	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"),
		    ]    )

		# if just the base model, we should remove "vit" from all keys that start with "vit"
		UpperCAmelCase_       				=			[(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       __lowerCAmelCase      (						A       ,     A       ,     A=False    ):
	for i in range(config.num_hidden_layers    ):
		if base_model:
			UpperCAmelCase_       				=			""
		else:
			UpperCAmelCase_       				=			"vit."
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		UpperCAmelCase_       				=			state_dict.pop(F"blocks.{i}.attn.qkv.weight"    )
		UpperCAmelCase_       				=			state_dict.pop(F"blocks.{i}.attn.qkv.bias"    )
		# next, add query, keys and values (in that order) to the state dict
		UpperCAmelCase_       				=			in_proj_weight[
		    : config.hidden_size, :
		]
		UpperCAmelCase_       				=			in_proj_bias[: config.hidden_size]
		UpperCAmelCase_       				=			in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		UpperCAmelCase_       				=			in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		UpperCAmelCase_       				=			in_proj_weight[
		    -config.hidden_size :, :
		]
		UpperCAmelCase_       				=			in_proj_bias[-config.hidden_size :]



def       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			["head.weight", "head.bias"]
	for k in ignore_keys:
		state_dict.pop(A       ,     A    )



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			dct.pop(A    )
	UpperCAmelCase_       				=			val



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



@torch.no_grad()
def       __lowerCAmelCase      (						A       ,     A       ,     A=True    ):
	UpperCAmelCase_       				=			ViTConfig()
	# patch_size
	if model_name[-1] == "8":
		UpperCAmelCase_       				=			8
	# set labels if required
	if not base_model:
		UpperCAmelCase_       				=			1000
		UpperCAmelCase_       				=			"huggingface/label-files"
		UpperCAmelCase_       				=			"imagenet-1k-id2label.json"
		UpperCAmelCase_       				=			json.load(open(hf_hub_download(A       ,     A       ,     repo_type="dataset"    )       ,     "r"    )    )
		UpperCAmelCase_       				=			{int(A    ): v for k, v in idalabel.items()}
		UpperCAmelCase_       				=			idalabel
		UpperCAmelCase_       				=			{v: k for k, v in idalabel.items()}
	# size of the architecture
	if model_name in ["dino_vits8", "dino_vits16"]:
		UpperCAmelCase_       				=			384
		UpperCAmelCase_       				=			1536
		UpperCAmelCase_       				=			12
		UpperCAmelCase_       				=			6

	# load original model from torch hub
	UpperCAmelCase_       				=			torch.hub.load("facebookresearch/dino:main"       ,     A    )
	original_model.eval()

	# load state_dict of original model, remove and rename some keys
	UpperCAmelCase_       				=			original_model.state_dict()
	if base_model:
		remove_classification_head_(A    )
	UpperCAmelCase_       				=			create_rename_keys(A       ,     base_model=A    )
	for src, dest in rename_keys:
		rename_key(A       ,     A       ,     A    )
	read_in_q_k_v(A       ,     A       ,     A    )

	# load HuggingFace model
	if base_model:
		UpperCAmelCase_       				=			ViTModel(A       ,     add_pooling_layer=A    ).eval()
	else:
		UpperCAmelCase_       				=			ViTForImageClassification(A    ).eval()
	model.load_state_dict(A    )

	# Check outputs on an image, prepared by ViTImageProcessor
	UpperCAmelCase_       				=			ViTImageProcessor()
	UpperCAmelCase_       				=			image_processor(images=prepare_img()       ,     return_tensors="pt"    )
	UpperCAmelCase_       				=			encoding["pixel_values"]
	UpperCAmelCase_       				=			model(A    )

	if base_model:
		UpperCAmelCase_       				=			original_model(A    )
		assert torch.allclose(A       ,     outputs.last_hidden_state[:, 0, :]       ,     atol=1E-1    )
	else:
		UpperCAmelCase_       				=			original_model(A    )
		assert logits.shape == outputs.logits.shape
		assert torch.allclose(A       ,     outputs.logits       ,     atol=1E-3    )

	Path(A    ).mkdir(exist_ok=A    )
	print(F"Saving model {model_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__":
				_a:    int 							=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--model_name""",
				    default="""dino_vitb16""",
				    type=str,
				    help="""Name of the model trained with DINO you'd like to convert.""",
				)
				parser.add_argument(
				    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
				)
				parser.add_argument(
				    """--base_model""",
				    action="""store_true""",
				    help="""Whether to only convert the base model (no projection head weights).""",
				)

				parser.set_defaults(base_model=True)
				_a:    Union[str, Any] 							=			parser.parse_args()
				convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)


from __future__ import annotations



def       __lowerCAmelCase      (						A       ,     A    ):
	print(F"Vertex\tShortest Distance from vertex {src}"    )
	for i, d in enumerate(A    ):
		print(F"{i}\t\t{d}"    )



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	for j in range(A    ):
		UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			(graph[j][k] for k in ["src", "dst", "weight"])
		if distance[u] != float("inf"    ) and distance[u] + w < distance[v]:
			return True
	return False



def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			[float("inf"    )] * vertex_count
	UpperCAmelCase_       				=			0.0

	for _ in range(vertex_count - 1    ):
		for j in range(A    ):
			UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			(graph[j][k] for k in ["src", "dst", "weight"])

			if distance[u] != float("inf"    ) and distance[u] + w < distance[v]:
				UpperCAmelCase_       				=			distance[u] + w

	UpperCAmelCase_       				=			check_negative_cycle(A       ,     A       ,     A    )
	if negative_cycle_exists:
		raise Exception("Negative cycle found"    )

	return distance


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_a:    List[str] 							=			int(input("""Enter number of vertices: """).strip())
				_a:    str 							=			int(input("""Enter number of edges: """).strip())

				_a:    list[dict[str, int]] 							=			[{} for _ in range(E)]

				for i in range(E):
								print("""Edge """, i + 1)
								_a							,			_a							,			_a:    Any 							=			(
								    int(x)
								    for x in input("""Enter source, destination, weight: """).strip().split(""" """)
								)
								_a:    str 							=			{"""src""": src, """dst""": dest, """weight""": weight}

				_a:    List[Any] 							=			int(input("""\nEnter shortest path source:""").strip())
				_a:    Optional[int] 							=			bellman_ford(graph, V, E, source)
				print_distance(shortest_distance, 0)