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()
_lowercase							:      Union[str, Any]      							=logging.get_logger(__name__)



def A__  (			lowercase:     List[Any],		lowercase:     Tuple=False					)				->		str:
       A						:     Union[str, Any]         =[]
       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"
              A						:     List[Any]         =[(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 A__  (			lowercase:     Optional[int],		lowercase:     Optional[int],		lowercase:     Union[str, Any]=False					)				->		Optional[int]:
       for i in range(config.num_hidden_layers					):
              if base_model:
                     A						:     Optional[Any]         =''
              else:
                     A						:     str         ='vit.'
              # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
              A						:     Dict         =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						:     Optional[Any]         =in_proj_weight[
                  : config.hidden_size, :
              ]
              A						:     str         =in_proj_bias[: config.hidden_size]
              A						:     List[str]         =in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              A						:     Dict         =in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              A						:     Dict         =in_proj_weight[
                  -config.hidden_size :, :
              ]
              A						:     Any         =in_proj_bias[-config.hidden_size :]



def A__  (			lowercase:     Tuple					)				->		Tuple:
       A						:     List[str]         =['head.weight', 'head.bias']
       for k in ignore_keys:
              state_dict.pop(lowercase,		lowercase					)



def A__  (			lowercase:     Optional[int],		lowercase:     Optional[Any],		lowercase:     str					)				->		Any:
       A						:     str         =dct.pop(lowercase					)
       A						:     Tuple         =val



def A__  (			)				->		Optional[Any]:
       A						:     int         ='http://images.cocodataset.org/val2017/000000039769.jpg'
       A						:     List[str]         =Image.open(requests.get(lowercase,		stream=lowercase					).raw					)
       return im



@torch.no_grad()
def A__  (			lowercase:     str,		lowercase:     Union[str, Any],		lowercase:     Optional[int]=True					)				->		Dict:
       A						:     Any         =ViTConfig()
       # patch_size
       if model_name[-1] == "8":
              A						:     List[str]         =8
       # set labels if required
       if not base_model:
              A						:     Optional[int]         =1_000
              A						:     Optional[Any]         ='huggingface/label-files'
              A						:     Dict         ='imagenet-1k-id2label.json'
              A						:     int         =json.load(open(hf_hub_download(lowercase,		lowercase,		repo_type='dataset'					),		'r'					)					)
              A						:     List[str]         ={int(lowercase					): v for k, v in idalabel.items()}
              A						:     Optional[int]         =idalabel
              A						:     Union[str, Any]         ={v: k for k, v in idalabel.items()}
       # size of the architecture
       if model_name in ["dino_vits8", "dino_vits16"]:
              A						:     str         =384
              A						:     List[Any]         =1_536
              A						:     Tuple         =12
              A						:     List[str]         =6

       # load original model from torch hub
       A						:     List[str]         =torch.hub.load('facebookresearch/dino:main',		lowercase					)
       original_model.eval()

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

       # load HuggingFace model
       if base_model:
              A						:     List[str]         =ViTModel(lowercase,		add_pooling_layer=lowercase					).eval()
       else:
              A						:     Dict         =ViTForImageClassification(lowercase					).eval()
       model.load_state_dict(lowercase					)

       # Check outputs on an image, prepared by ViTImageProcessor
       A						:     Optional[Any]         =ViTImageProcessor()
       A						:     str         =image_processor(images=prepare_img(),		return_tensors='pt'					)
       A						:     Tuple         =encoding['pixel_values']
       A						:     Optional[Any]         =model(lowercase					)

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

       Path(lowercase					).mkdir(exist_ok=lowercase					)
       print(F'Saving model {model_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__":
    _lowercase							:      Union[str, Any]      							=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)
    _lowercase							:      List[Any]      							=parser.parse_args()
    convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)







import heapq



def A__  (			lowercase:     dict					)				->		set[int]:
       A						:     list[list]         =[]

       # for each node and his adjacency list add them and the rank of the node to queue
       # using heapq module the queue will be filled like a Priority Queue
       # heapq works with a min priority queue, so I used -1*len(v) to build it
       for key, value in graph.items():
              # O(log(n))
              heapq.heappush(lowercase,		[-1 * len(lowercase					), (key, value)]					)

       # chosen_vertices = set of chosen vertices
       A						:     Dict         =set()

       # while queue isn't empty and there are still edges
       #   (queue[0][0] is the rank of the node with max rank)
       while queue and queue[0][0] != 0:
              # extract vertex with max rank from queue and add it to chosen_vertices
              A						:     List[str]         =heapq.heappop(lowercase					)[1][0]
              chosen_vertices.add(lowercase					)

              # Remove all arcs adjacent to argmax
              for elem in queue:
                     # if v haven't adjacent node, skip
                     if elem[0] == 0:
                            continue
                     # if argmax is reachable from elem
                     # remove argmax from elem's adjacent list and update his rank
                     if argmax in elem[1][1]:
                            A						:     str         =elem[1][1].index(lowercase					)
                            del elem[1][1][index]
                            elem[0] += 1
        # re-order the queue
              heapq.heapify(lowercase					)
       return chosen_vertices


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    _lowercase							:      List[Any]      							={0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
    print(f'''Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}''')






