


'''simple docstring'''





import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case=False  ,		__snake_case=False  ,		__snake_case="data2vec."		)  ->    str:







 """simple docstring"""


 _lowercase							   ='''backbone.''' if is_semantic else ''''''

 _lowercase							   =[]
 for i in range(config.num_hidden_layers		):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (F"{prefix}blocks.{i}.norm1.weight", F"{hf_prefix}encoder.layer.{i}.layernorm_before.weight")		)
  rename_keys.append((F"{prefix}blocks.{i}.norm1.bias", F"{hf_prefix}encoder.layer.{i}.layernorm_before.bias")		)
  rename_keys.append(
      (F"{prefix}blocks.{i}.attn.proj.weight", F"{hf_prefix}encoder.layer.{i}.attention.output.dense.weight")		)
  rename_keys.append(
      (F"{prefix}blocks.{i}.attn.proj.bias", F"{hf_prefix}encoder.layer.{i}.attention.output.dense.bias")		)
  rename_keys.append(
      (F"{prefix}blocks.{i}.norm2.weight", F"{hf_prefix}encoder.layer.{i}.layernorm_after.weight")		)
  rename_keys.append((F"{prefix}blocks.{i}.norm2.bias", F"{hf_prefix}encoder.layer.{i}.layernorm_after.bias")		)
  rename_keys.append(
      (F"{prefix}blocks.{i}.mlp.fc1.weight", F"{hf_prefix}encoder.layer.{i}.intermediate.dense.weight")		)
  rename_keys.append(
      (F"{prefix}blocks.{i}.mlp.fc1.bias", F"{hf_prefix}encoder.layer.{i}.intermediate.dense.bias")		)
  rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.weight", F"{hf_prefix}encoder.layer.{i}.output.dense.weight")		)
  rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.bias", F"{hf_prefix}encoder.layer.{i}.output.dense.bias")		)

 # projection layer + position embeddings
 rename_keys.extend(
     [
         (F"{prefix}cls_token", F"{hf_prefix}embeddings.cls_token"),
         (F"{prefix}patch_embed.proj.weight", F"{hf_prefix}embeddings.patch_embeddings.projection.weight"),
         (F"{prefix}patch_embed.proj.bias", F"{hf_prefix}embeddings.patch_embeddings.projection.bias"),
     ]		)

 if has_lm_head:
  # mask token + shared relative position bias + layernorm
  rename_keys.extend(
      [
          ('''mask_token''', F"{hf_prefix}embeddings.mask_token"),
          (
              '''rel_pos_bias.relative_position_bias_table''',
              F"{hf_prefix}encoder.relative_position_bias.relative_position_bias_table",
          ),
          (
              '''rel_pos_bias.relative_position_index''',
              F"{hf_prefix}encoder.relative_position_bias.relative_position_index",
          ),
          ('''norm.weight''', '''layernorm.weight'''),
          ('''norm.bias''', '''layernorm.bias'''),
      ]		)
 elif is_semantic:
  # semantic segmentation classification heads
  rename_keys.extend(
      [
          ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
          ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
          ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
          ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
      ]		)
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ('''fc_norm.weight''', F"{hf_prefix}pooler.layernorm.weight"),
          ('''fc_norm.bias''', F"{hf_prefix}pooler.layernorm.bias"),
          ('''head.weight''', '''classifier.weight'''),
          ('''head.bias''', '''classifier.bias'''),
      ]		)

 return rename_keys
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case  ,		__snake_case=False  ,		__snake_case=False  ,		__snake_case="data2vec_vision."		)  ->    List[str]:







 """simple docstring"""


 for i in range(config.num_hidden_layers		):
  _lowercase							   ='''backbone.''' if is_semantic else ''''''
  # queries, keys and values
  _lowercase							   =state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight"		)
  _lowercase							   =state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias"		)
  _lowercase							   =state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias"		)

  _lowercase							   =in_proj_weight[
      : config.hidden_size, :
  ]
  _lowercase							   =q_bias
  _lowercase							   =in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  _lowercase							   =in_proj_weight[
      -config.hidden_size :, :
  ]
  _lowercase							   =v_bias

  # gamma_1 and gamma_2
  # we call them lambda because otherwise they are renamed when using .from_pretrained
  _lowercase							   =state_dict.pop(F"{prefix}blocks.{i}.gamma_1"		)
  _lowercase							   =state_dict.pop(F"{prefix}blocks.{i}.gamma_2"		)

  _lowercase							   =gamma_a
  _lowercase							   =gamma_a

  # relative_position bias table + index
  if not has_lm_head:
   # each layer has its own relative position bias
   _lowercase							   =state_dict.pop(F"{prefix}blocks.{i}.attn.relative_position_bias_table"		)
   _lowercase							   =state_dict.pop(F"{prefix}blocks.{i}.attn.relative_position_index"		)

   _lowercase							   =table
   _lowercase							   =index
def        UpperCAmelCase_      (							)  ->    List[str]:







 """simple docstring"""


 _lowercase							   =argparse.ArgumentParser(
     '''Convert Data2VecVision to HF for image classification and pretraining'''  ,		add_help=lowercase__		)
 parser.add_argument('''--hf_checkpoint_name'''  ,		type=lowercase__		)
 parser.add_argument('''--input_size'''  ,		default=224  ,		type=lowercase__  ,		help='''images input size'''		)
 parser.add_argument('''--beit_checkpoint'''  ,		default=''''''  ,		help='''beit checkpoint'''		)

 return parser.parse_args()
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case  ,		__snake_case		)  ->    Optional[int]:







 """simple docstring"""


 def load_state_dict(__snake_case  ,		__snake_case  ,		__snake_case=""  ,		__snake_case="relative_position_index"		):
  _lowercase							   =[]
  _lowercase							   =[]
  _lowercase							   =[]
  # copy state_dict so _load_from_state_dict can modify it
  _lowercase							   =getattr(lowercase__  ,		'''_metadata'''  ,		lowercase__		)
  _lowercase							   =state_dict.copy()
  if metadata is not None:
   _lowercase							   =metadata

  def load(__snake_case  ,		__snake_case=""		):
   _lowercase							   ={} if metadata is None else metadata.get(prefix[:-1]  ,		{}		)
   module._load_from_state_dict(
       lowercase__  ,		lowercase__  ,		lowercase__  ,		lowercase__  ,		lowercase__  ,		lowercase__  ,		lowercase__		)
   for name, child in module._modules.items():
    if child is not None:
     load(lowercase__  ,		prefix + name + '''.'''		)

  load(lowercase__  ,		prefix=lowercase__		)

  _lowercase							   =[]
  _lowercase							   =[]
  for key in missing_keys:
   _lowercase							   =True
   for ignore_key in ignore_missing.split('''|'''		):
    if ignore_key in key:
     _lowercase							   =False
     break
   if keep_flag:
    warn_missing_keys.append(lowercase__		)
   else:
    ignore_missing_keys.append(lowercase__		)

  _lowercase							   =warn_missing_keys

  if len(lowercase__		) > 0:
   print(
       '''Weights of {} not initialized from pretrained model: {}'''.format(
           model.__class__.__name__  ,		lowercase__		)		)
  if len(lowercase__		) > 0:
   print('''Weights from pretrained model not used in {}: {}'''.format(model.__class__.__name__  ,		lowercase__		)		)
  if len(lowercase__		) > 0:
   print(
       '''Ignored weights of {} not initialized from pretrained model: {}'''.format(
           model.__class__.__name__  ,		lowercase__		)		)
  if len(lowercase__		) > 0:
   print('''\n'''.join(lowercase__		)		)

 _lowercase							   ={
     '''pretrained''': False,
     '''use_shared_rel_pos_bias''': True,
     '''use_abs_pos_emb''': False,
     '''init_values''': 0.1,
 }

 if is_finetuned:
  model_kwargs.update(
      {
          '''num_classes''': 1000,
          '''use_mean_pooling''': True,
          '''init_scale''': 0.0_01,
          '''use_rel_pos_bias''': True,
      }		)

 _lowercase							   =create_model(
     '''beit_large_patch16_224''' if is_large else '''beit_base_patch16_224'''  ,		**lowercase__  ,		)
 _lowercase							   =model.patch_embed.patch_size
 _lowercase							   =(args.input_size // patch_size[0], args.input_size // patch_size[1])
 _lowercase							   =torch.load(args.beit_checkpoint  ,		map_location='''cpu'''		)

 print(F"Load ckpt from {args.beit_checkpoint}"		)
 _lowercase							   =None
 for model_key in ("model", "module"):
  if model_key in checkpoint:
   _lowercase							   =checkpoint[model_key]
   print(F"Load state_dict by model_key = {model_key}"		)
   break

 _lowercase							   =list(checkpoint_model.keys()		)
 for key in all_keys:
  if "relative_position_index" in key:
   checkpoint_model.pop(lowercase__		)

  if "relative_position_bias_table" in key:
   _lowercase							   =checkpoint_model[key]
   _lowercase   ,					_lowercase							   =rel_pos_bias.size()
   _lowercase   ,					_lowercase							   =model.state_dict()[key].size()
   _lowercase							   =model.patch_embed.patch_shape
   if dst_patch_shape[0] != dst_patch_shape[1]:
    raise NotImplementedError()

 load_state_dict(lowercase__  ,		lowercase__  ,		prefix=''''''		)

 return model
def        UpperCAmelCase_      (							)  ->    int:







 """simple docstring"""


 _lowercase							   =get_args()

 _lowercase							   ='''ft1k''' in args.hf_checkpoint_name
 _lowercase							   ='''large''' in args.hf_checkpoint_name

 if is_finetuned:
  # To convert Beit's data2vec_vision to HF you need to copy
  # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
  # into this folder.
  import modeling_finetune  # noqa: F401
 else:
  # To convert Beit's data2vec_vision to HF you need to copy
  # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
  # into this folder
  # IMPORTANT: Note that for now we've only converted the down-stream
  # model and not the full pretrained model. This means for the integration
  # test you need to add a `return x` after the following line:
  # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
  # to make the integration test pass.
  import modeling_cyclical  # noqa: F401

 # 1. Create model config
 _lowercase							   =DataaVecVisionConfig()
 if is_finetuned:
  _lowercase							   =True
  _lowercase							   =False
  _lowercase							   =True
  _lowercase							   =1000

  _lowercase							   ='''huggingface/label-files'''
  _lowercase							   ='''imagenet-1k-id2label.json'''
  _lowercase							   =json.load(open(hf_hub_download(lowercase__  ,		lowercase__  ,		repo_type='''dataset'''		)  ,		'''r'''		)		)
  _lowercase							   ={int(lowercase__		): v for k, v in idalabel.items()}
  _lowercase							   =idalabel
  _lowercase							   ={v: k for k, v in idalabel.items()}
 else:
  _lowercase							   =False
  _lowercase							   =True
  _lowercase							   =False

 if is_large:
  _lowercase							   =1024
  _lowercase							   =4096
  _lowercase							   =24
  _lowercase							   =16

 # 2. Load Beit model
 _lowercase							   =load_beit_model(lowercase__  ,		lowercase__  ,		lowercase__		)
 orig_model.eval()

 # 3. Forward Beit model
 _lowercase							   =BeitImageProcessor(size=config.image_size  ,		do_center_crop=lowercase__		)
 _lowercase							   =Image.open('''../../../../tests/fixtures/tests_samples/COCO/000000039769.png'''		)
 _lowercase							   =image_processor(images=lowercase__  ,		return_tensors='''pt'''		)
 _lowercase							   =encoding['''pixel_values''']

 _lowercase							   =(pixel_values,) if is_finetuned else (pixel_values, None)
 with torch.no_grad():
  _lowercase							   =orig_model(*lowercase__		)

 # 4. Load HF Data2VecVision model
 if is_finetuned:
  _lowercase							   =DataaVecVisionForImageClassification(lowercase__		)
  hf_model.eval()
  _lowercase							   =False
  _lowercase							   ='''data2vec_vision.'''
 else:
  _lowercase							   =DataaVecVisionModel(lowercase__		)
  hf_model.eval()
  _lowercase							   =True
  _lowercase							   =''''''

 _lowercase							   =create_rename_keys(lowercase__  ,		hf_prefix=lowercase__  ,		has_lm_head=lowercase__		)
 _lowercase							   =orig_model.state_dict()
 for src, dest in rename_keys:
  _lowercase							   =state_dict.pop(lowercase__		)
  _lowercase							   =val

 read_in_q_k_v(lowercase__  ,		lowercase__  ,		hf_prefix=lowercase__  ,		has_lm_head=lowercase__		)
 _lowercase   ,					_lowercase							   =hf_model.load_state_dict(lowercase__  ,		strict=lowercase__		)
 print('''HF missing'''  ,		lowercase__		)
 print('''HF unexpected_keys'''  ,		lowercase__		)

 # 5. Forward HF Data2VecVision model
 with torch.no_grad():
  _lowercase							   =hf_model(lowercase__		)

 _lowercase							   =hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

 # 6. Compare
 _lowercase							   =torch.max(torch.abs(hf_output - orig_model_output		)		).item()

 print(F"max_absolute_diff = {max_absolute_diff}"		)
 _lowercase							   =torch.allclose(lowercase__  ,		lowercase__  ,		atol=1e-3		)
 print('''Do both models output the same tensors?'''  ,		'''🔥''' if success else '''💩'''		)
 if not success:
  raise Exception('''Something went wRoNg'''		)

 # 7. Save
 print(F"Saving to {args.hf_checkpoint_name}"		)
 hf_model.save_pretrained(args.hf_checkpoint_name		)
 image_processor.save_pretrained(args.hf_checkpoint_name		)


if __name__ == "__main__":
  main()
  # Run the following to convert checkpoints
  #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
  #          --beit_checkpoint ./pretrained_base.pt \
  #          --hf_checkpoint_name "./data2vec-vision-base"
  #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
  #          --beit_checkpoint ./finetuned_base.pt \
  #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
  #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
  #          --beit_checkpoint ./pretrained_large.pt \
  #          --hf_checkpoint_name "./data2vec-vision-large"
  #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
  #          --beit_checkpoint ./finetuned_large.pt \
  #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"





from collections import deque
def        UpperCAmelCase_      (							__snake_case		)  ->    Any:







 """simple docstring"""


 return input(__snake_case		).strip().split(''' '''		)
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    dict[int, list[int]]:







 """simple docstring"""


 _lowercase							   ={}
 for i in range(__snake_case		):
  _lowercase							   =[]

 for e in range(__snake_case		):
  _lowercase   ,					_lowercase							   =(int(__snake_case		) for i in _input(F"Edge {e + 1}: <node1> <node2> "		))
  graph[x].append(__snake_case		)
 return graph
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    dict[int, list[int]]:







 """simple docstring"""


 _lowercase							   ={}
 for i in range(__snake_case		):
  _lowercase							   =[]

 for e in range(__snake_case		):
  _lowercase   ,					_lowercase							   =(int(__snake_case		) for i in _input(F"Edge {e + 1}: <node1> <node2> "		))
  graph[x].append(__snake_case		)
  graph[y].append(__snake_case		)
 return graph
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    dict[int, list[tuple[int, int]]]:







 """simple docstring"""


 _lowercase							   ={}
 for i in range(__snake_case		):
  _lowercase							   =[]

 for e in range(__snake_case		):
  _lowercase   ,					_lowercase   ,					_lowercase							   =(int(__snake_case		) for i in _input(F"Edge {e + 1}: <node1> <node2> <weight> "		))
  graph[x].append((y, w)		)
  graph[y].append((x, w)		)
 return graph


if __name__ == "__main__":
  UpperCAmelCase__			,UpperCAmelCase__     			=							(int(i) for i in _input('''Number of nodes and edges: '''))

  UpperCAmelCase__     			=							int(
      _input(
          '''Press 1 or 2 or 3 \n'''
          '''1. Unweighted directed \n'''
          '''2. Unweighted undirected \n'''
          '''3. Weighted undirected \n'''
      )[0]
  )

  UpperCAmelCase__     			=							{
      1: initialize_unweighted_directed_graph,
      2: initialize_unweighted_undirected_graph,
      3: initialize_weighted_undirected_graph,
  }[graph_choice](n, m)
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    Tuple:







 """simple docstring"""


 _lowercase   ,					_lowercase							   ={s}, [s]
 print(__snake_case		)
 while _s:
  _lowercase							   =0
  for i in g[_s[-1]]:
   if i not in vis:
    _s.append(__snake_case		)
    vis.add(__snake_case		)
    _lowercase							   =1
    print(__snake_case		)
    break
  if not flag:
   _s.pop()
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    int:







 """simple docstring"""


 _lowercase   ,					_lowercase							   ={s}, deque([s]		)
 print(__snake_case		)
 while q:
  _lowercase							   =q.popleft()
  for v in g[u]:
   if v not in vis:
    vis.add(__snake_case		)
    q.append(__snake_case		)
    print(__snake_case		)
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    Optional[int]:







 """simple docstring"""


 _lowercase   ,					_lowercase   ,					_lowercase							   ={s: 0}, set(), {s: 0}
 while True:
  if len(__snake_case		) == len(__snake_case		) - 1:
   break
  _lowercase							   =100000
  for i in dist:
   if i not in known and dist[i] < mini:
    _lowercase							   =dist[i]
    _lowercase							   =i
  known.add(__snake_case		)
  for v in g[u]:
   if v[0] not in known and dist[u] + v[1] < dist.get(v[0]  ,		100000		):
    _lowercase							   =dist[u] + v[1]
    _lowercase							   =u
 for i in dist:
  if i != s:
   print(dist[i]		)
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case=None  ,		__snake_case=None		)  ->    Tuple:







 """simple docstring"""


 if q is None:
  _lowercase							   =[1]
 if ind is None:
  _lowercase							   =[0] * (len(__snake_case		) + 1)  # SInce oth Index is ignored
  for u in g:
   for v in g[u]:
    ind[v] += 1
  _lowercase							   =deque()
  for i in g:
   if ind[i] == 0:
    q.append(__snake_case		)
 if len(__snake_case		) == 0:
  return
 _lowercase							   =q.popleft()
 print(__snake_case		)
 for w in g[v]:
  ind[w] -= 1
  if ind[w] == 0:
   q.append(__snake_case		)
 topo(__snake_case  ,		__snake_case  ,		__snake_case		)
def        UpperCAmelCase_      (							)  ->    Tuple:







 """simple docstring"""


 _lowercase							   =input().strip()
 _lowercase							   =[]
 for _ in range(__snake_case		):
  a.append(map(__snake_case  ,		input().strip().split()		)		)
 return a, n
def        UpperCAmelCase_      (							__snake_case		)  ->    Any:







 """simple docstring"""


 ((_lowercase)   ,					(_lowercase))							   =a_and_n
 _lowercase							   =list(__snake_case		)
 _lowercase							   =[[0] * n for i in range(__snake_case		)]
 for k in range(__snake_case		):
  for i in range(__snake_case		):
   for j in range(__snake_case		):
    if dist[i][j] > dist[i][k] + dist[k][j]:
     _lowercase							   =dist[i][k] + dist[k][j]
     _lowercase							   =k
 print(__snake_case		)
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    Optional[Any]:







 """simple docstring"""


 _lowercase   ,					_lowercase   ,					_lowercase							   ={s: 0}, set(), {s: 0}
 while True:
  if len(__snake_case		) == len(__snake_case		) - 1:
   break
  _lowercase							   =100000
  for i in dist:
   if i not in known and dist[i] < mini:
    _lowercase							   =dist[i]
    _lowercase							   =i
  known.add(__snake_case		)
  for v in g[u]:
   if v[0] not in known and v[1] < dist.get(v[0]  ,		100000		):
    _lowercase							   =v[1]
    _lowercase							   =u
 return dist
def        UpperCAmelCase_      (							)  ->    Dict:







 """simple docstring"""


 _lowercase   ,					_lowercase							   =map(__snake_case  ,		input().split(''' '''		)		)
 _lowercase							   =[]
 for _ in range(__snake_case		):
  edges.append(map(__snake_case  ,		input().split(''' '''		)		)		)
 return edges, n
def        UpperCAmelCase_      (							__snake_case		)  ->    str:







 """simple docstring"""


 ((_lowercase)   ,					(_lowercase))							   =e_and_n
 e.sort(reverse=__snake_case  ,		key=lambda __snake_case		: x[2]		)
 _lowercase							   =[{i} for i in range(1  ,		n + 1		)]
 while True:
  if len(__snake_case		) == 1:
   break
  print(__snake_case		)
  _lowercase							   =e.pop()
  for i in range(len(__snake_case		)		):
   if x[0] in s[i]:
    break
  for j in range(len(__snake_case		)		):
   if x[1] in s[j]:
    if i == j:
     break
    s[j].update(s[i]		)
    s.pop(__snake_case		)
    break



def        UpperCAmelCase_      (							__snake_case		)  ->    Tuple:







 """simple docstring"""


 _lowercase							   =[]
 for node in graph:
  if not graph[node]:
   isolated.append(__snake_case		)
 return isolated

