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()
_lowerCAmelCase       =		logging.get_logger(__name__)
def 					_snake_case	(  __snake_case  ,     __snake_case=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 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case=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 					_snake_case	(  __snake_case						):
					_UpperCamelCase			     =     ['''head.weight''', '''head.bias''']
					for k in ignore_keys:
										state_dict.pop(__snake_case  ,     __snake_case						)
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case						):
					_UpperCamelCase			     =     dct.pop(__snake_case						)
					_UpperCamelCase			     =     val
def 					_snake_case	(  ):
					_UpperCamelCase			     =     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_UpperCamelCase			     =     Image.open(requests.get(__snake_case  ,     stream=__snake_case						).raw						)
					return im
@torch.no_grad()
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case=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(__snake_case  ,     __snake_case  ,     repo_type='''dataset'''						)  ,     '''r'''						)						)
										_UpperCamelCase			     =     {int(__snake_case						): 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'''  ,     __snake_case						)
					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_(__snake_case						)
					_UpperCamelCase			     =     create_rename_keys(__snake_case  ,     base_model=__snake_case						)
					for src, dest in rename_keys:
										rename_key(__snake_case  ,     __snake_case  ,     __snake_case						)
					read_in_q_k_v(__snake_case  ,     __snake_case  ,     __snake_case						)

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

					# 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(__snake_case						)

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

					Path(__snake_case						).mkdir(exist_ok=__snake_case						)
					print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""						)
					model.save_pretrained(__snake_case						)
					print(f"""Saving image processor to {pytorch_dump_folder_path}"""						)
					image_processor.save_pretrained(__snake_case						)


if __name__ == "__main__":
					_lowerCAmelCase       =		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)
					_lowerCAmelCase       =		parser.parse_args()
					convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)







from __future__ import annotations


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[float]   ,    _lowercase     : Tuple			)   ->							int:




		'''simple docstring'''
		print(f"""Vertex\tShortest Distance from vertex {src}"""			)
		for i, d in enumerate(_lowercase			):
				print(f"""{i}\t\t{d}"""			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[dict[str, int]]   ,    _lowercase     : list[float]   ,    _lowercase     : int			)   ->							Any:




		'''simple docstring'''
		for j in range(_lowercase			):
				lowercase__   ,				lowercase__   ,				lowercase__					:      Dict      =		(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       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[dict[str, int]]   ,    _lowercase     : int   ,    _lowercase     : int   ,    _lowercase     : int			)   ->							list[float]:




		'''simple docstring'''
		lowercase__					:      Dict      =		[float('inf'			)] * vertex_count
		lowercase__					:      Dict      =		0.0

		for _ in range(vertex_count - 1			):
				for j in range(_lowercase			):
						lowercase__   ,				lowercase__   ,				lowercase__					:      int      =		(graph[j][k] for k in ['src', 'dst', 'weight'])

						if distance[u] != float('inf'			) and distance[u] + w < distance[v]:
								lowercase__					:      str      =		distance[u] + w

		lowercase__					:      str      =		check_negative_cycle(_lowercase   ,    _lowercase   ,    _lowercase			)
		if negative_cycle_exists:
				raise Exception('Negative cycle found'			)

		return distance


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			__UpperCamelCase:	Optional[int] = int(input("""Enter number of vertices: """).strip())
			__UpperCamelCase:	Union[str, Any] = int(input("""Enter number of edges: """).strip())

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

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

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


