





'''simple docstring'''







import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)

def    __UpperCamelCase	(	a    : Optional[Any]     ) ->Dict:
 snake_case				=			VideoMAEConfig()

 set_architecture_configs(a					,   a     )

 if "finetuned" not in model_name:
  snake_case				=			False

 if "finetuned" in model_name:
  snake_case				=			'''huggingface/label-files'''
  if "kinetics" in model_name:
   snake_case				=			400
   snake_case				=			'''kinetics400-id2label.json'''
  elif "ssv2" in model_name:
   snake_case				=			174
   snake_case				=			'''something-something-v2-id2label.json'''
  else:
   raise ValueError('''Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.'''     )
  snake_case				=			json.load(open(hf_hub_download(a					,   a					,   repo_type='''dataset'''     )					,   '''r'''     )     )
  snake_case				=			{int(a     ): v for k, v in idalabel.items()}
  snake_case				=			idalabel
  snake_case				=			{v: k for k, v in idalabel.items()}

 return config

def    __UpperCamelCase	(	a    : Any					,   a    : List[Any]     ) ->int:
 if "small" in model_name:
  snake_case				=			384
  snake_case				=			1536
  snake_case				=			12
  snake_case				=			16
  snake_case				=			12
  snake_case				=			3
  snake_case				=			192
  snake_case				=			768
 elif "large" in model_name:
  snake_case				=			1024
  snake_case				=			4096
  snake_case				=			24
  snake_case				=			16
  snake_case				=			12
  snake_case				=			8
  snake_case				=			512
  snake_case				=			2048
 elif "huge" in model_name:
  snake_case				=			1280
  snake_case				=			5120
  snake_case				=			32
  snake_case				=			16
  snake_case				=			12
  snake_case				=			8
  snake_case				=			640
  snake_case				=			2560
 elif "base" not in model_name:
  raise ValueError('''Model name should include either \"small\", \"base\", \"large\", or \"huge\"'''     )

def    __UpperCamelCase	(	a    : Union[str, Any]     ) ->Optional[Any]:
 if "encoder." in name:
  snake_case				=			name.replace('''encoder.'''					,   ''''''     )
 if "cls_token" in name:
  snake_case				=			name.replace('''cls_token'''					,   '''videomae.embeddings.cls_token'''     )
 if "decoder_pos_embed" in name:
  snake_case				=			name.replace('''decoder_pos_embed'''					,   '''decoder.decoder_pos_embed'''     )
 if "pos_embed" in name and "decoder" not in name:
  snake_case				=			name.replace('''pos_embed'''					,   '''videomae.embeddings.position_embeddings'''     )
 if "patch_embed.proj" in name:
  snake_case				=			name.replace('''patch_embed.proj'''					,   '''videomae.embeddings.patch_embeddings.projection'''     )
 if "patch_embed.norm" in name:
  snake_case				=			name.replace('''patch_embed.norm'''					,   '''videomae.embeddings.norm'''     )
 if "decoder.blocks" in name:
  snake_case				=			name.replace('''decoder.blocks'''					,   '''decoder.decoder_layers'''     )
 if "blocks" in name:
  snake_case				=			name.replace('''blocks'''					,   '''videomae.encoder.layer'''     )
 if "attn.proj" in name:
  snake_case				=			name.replace('''attn.proj'''					,   '''attention.output.dense'''     )
 if "attn" in name and "bias" not in name:
  snake_case				=			name.replace('''attn'''					,   '''attention.self'''     )
 if "attn" in name:
  snake_case				=			name.replace('''attn'''					,   '''attention.attention'''     )
 if "norm1" in name:
  snake_case				=			name.replace('''norm1'''					,   '''layernorm_before'''     )
 if "norm2" in name:
  snake_case				=			name.replace('''norm2'''					,   '''layernorm_after'''     )
 if "mlp.fc1" in name:
  snake_case				=			name.replace('''mlp.fc1'''					,   '''intermediate.dense'''     )
 if "mlp.fc2" in name:
  snake_case				=			name.replace('''mlp.fc2'''					,   '''output.dense'''     )
 if "decoder_embed" in name:
  snake_case				=			name.replace('''decoder_embed'''					,   '''decoder.decoder_embed'''     )
 if "decoder_norm" in name:
  snake_case				=			name.replace('''decoder_norm'''					,   '''decoder.decoder_norm'''     )
 if "decoder_pred" in name:
  snake_case				=			name.replace('''decoder_pred'''					,   '''decoder.decoder_pred'''     )
 if "norm.weight" in name and "decoder" not in name and "fc" not in name:
  snake_case				=			name.replace('''norm.weight'''					,   '''videomae.layernorm.weight'''     )
 if "norm.bias" in name and "decoder" not in name and "fc" not in name:
  snake_case				=			name.replace('''norm.bias'''					,   '''videomae.layernorm.bias'''     )
 if "head" in name and "decoder" not in name:
  snake_case				=			name.replace('''head'''					,   '''classifier'''     )

 return name

def    __UpperCamelCase	(	a    : Any					,   a    : List[str]     ) ->Tuple:
 for key in orig_state_dict.copy().keys():
  snake_case				=			orig_state_dict.pop(a     )

  if key.startswith('''encoder.'''     ):
   snake_case				=			key.replace('''encoder.'''					,   ''''''     )

  if "qkv" in key:
   snake_case				=			key.split('''.'''     )
   if key.startswith('''decoder.blocks'''     ):
    snake_case				=			config.decoder_hidden_size
    snake_case				=			int(key_split[2]     )
    snake_case				=			'''decoder.decoder_layers.'''
    if "weight" in key:
     snake_case				=			val[:dim, :]
     snake_case				=			val[dim : dim * 2, :]
     snake_case				=			val[-dim:, :]
   else:
    snake_case				=			config.hidden_size
    snake_case				=			int(key_split[1]     )
    snake_case				=			'''videomae.encoder.layer.'''
    if "weight" in key:
     snake_case				=			val[:dim, :]
     snake_case				=			val[dim : dim * 2, :]
     snake_case				=			val[-dim:, :]
  else:
   snake_case				=			val

 return orig_state_dict

def    __UpperCamelCase	(	) ->List[str]:
 snake_case				=			hf_hub_download(
     repo_id='''hf-internal-testing/spaghetti-video'''					,   filename='''eating_spaghetti.npy'''					,   repo_type='''dataset'''     )
 snake_case				=			np.load(a     )
 return list(a     )

def    __UpperCamelCase	(	a    : Union[str, Any]					,   a    : Dict					,   a    : List[Any]					,   a    : Any     ) ->List[Any]:
 snake_case				=			get_videomae_config(a     )

 if "finetuned" in model_name:
  snake_case				=			VideoMAEForVideoClassification(a     )
 else:
  snake_case				=			VideoMAEForPreTraining(a     )

 # download original checkpoint, hosted on Google Drive
 snake_case				=			'''pytorch_model.bin'''
 gdown.cached_download(a					,   a					,   quiet=a     )
 snake_case				=			torch.load(a					,   map_location='''cpu'''     )
 if "model" in files:
  snake_case				=			files['''model''']
 else:
  snake_case				=			files['''module''']
 snake_case				=			convert_state_dict(a					,   a     )

 model.load_state_dict(a     )
 model.eval()

 # verify model on basic input
 snake_case				=			VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]					,   image_std=[0.5, 0.5, 0.5]     )
 snake_case				=			prepare_video()
 snake_case				=			image_processor(a					,   return_tensors='''pt'''     )

 if "finetuned" not in model_name:
  snake_case				=			hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos'''					,   filename='''bool_masked_pos.pt'''     )
  snake_case				=			torch.load(a     )

 snake_case				=			model(**a     )
 snake_case				=			outputs.logits

 snake_case				=			[
     '''videomae-small-finetuned-kinetics''',
     '''videomae-small-finetuned-ssv2''',
     # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
     '''videomae-base-short''',
     '''videomae-base-short-finetuned-kinetics''',
     '''videomae-base''',
     '''videomae-base-finetuned-kinetics''',
     '''videomae-large''',
     '''videomae-large-finetuned-kinetics''',
     '''videomae-huge-finetuned-kinetics''',
     # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
     '''videomae-base-short-ssv2''',
     '''videomae-base-short-finetuned-ssv2''',
     '''videomae-base-ssv2''',
     '''videomae-base-finetuned-ssv2''',
 ]

 # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
 if model_name == "videomae-small-finetuned-kinetics":
  snake_case				=			torch.Size([1, 400]     )
  snake_case				=			torch.tensor([-0.9291, -0.4061, -0.9307]     )
 elif model_name == "videomae-small-finetuned-ssv2":
  snake_case				=			torch.Size([1, 174]     )
  snake_case				=			torch.tensor([0.2671, -0.4689, -0.8235]     )
 elif model_name == "videomae-base":
  snake_case				=			torch.Size([1, 1408, 1536]     )
  snake_case				=			torch.tensor([[0.7739, 0.7968, 0.7089], [0.6701, 0.7487, 0.6209], [0.4287, 0.5158, 0.4773]]     )
 elif model_name == "videomae-base-short":
  snake_case				=			torch.Size([1, 1408, 1536]     )
  snake_case				=			torch.tensor([[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]     )
  # we verified the loss both for normalized and unnormalized targets for this one
  snake_case				=			torch.tensor([0.5142]     ) if config.norm_pix_loss else torch.tensor([0.6469]     )
 elif model_name == "videomae-large":
  snake_case				=			torch.Size([1, 1408, 1536]     )
  snake_case				=			torch.tensor([[0.7149, 0.7997, 0.6966], [0.6768, 0.7869, 0.6948], [0.5139, 0.6221, 0.5605]]     )
 elif model_name == "videomae-large-finetuned-kinetics":
  snake_case				=			torch.Size([1, 400]     )
  snake_case				=			torch.tensor([0.0771, 0.0011, -0.3625]     )
 elif model_name == "videomae-huge-finetuned-kinetics":
  snake_case				=			torch.Size([1, 400]     )
  snake_case				=			torch.tensor([0.2433, 0.1632, -0.4894]     )
 elif model_name == "videomae-base-short-finetuned-kinetics":
  snake_case				=			torch.Size([1, 400]     )
  snake_case				=			torch.tensor([0.6588, 0.0990, -0.2493]     )
 elif model_name == "videomae-base-finetuned-kinetics":
  snake_case				=			torch.Size([1, 400]     )
  snake_case				=			torch.tensor([0.3669, -0.0688, -0.2421]     )
 elif model_name == "videomae-base-short-ssv2":
  snake_case				=			torch.Size([1, 1408, 1536]     )
  snake_case				=			torch.tensor([[0.4712, 0.5296, 0.5786], [0.2278, 0.2729, 0.4026], [0.0352, 0.0730, 0.2506]]     )
 elif model_name == "videomae-base-short-finetuned-ssv2":
  snake_case				=			torch.Size([1, 174]     )
  snake_case				=			torch.tensor([-0.0537, -0.1539, -0.3266]     )
 elif model_name == "videomae-base-ssv2":
  snake_case				=			torch.Size([1, 1408, 1536]     )
  snake_case				=			torch.tensor([[0.8131, 0.8727, 0.8546], [0.7366, 0.9377, 0.8870], [0.5935, 0.8874, 0.8564]]     )
 elif model_name == "videomae-base-finetuned-ssv2":
  snake_case				=			torch.Size([1, 174]     )
  snake_case				=			torch.tensor([0.1961, -0.8337, -0.6389]     )
 else:
  raise ValueError(f"""Model name not supported. Should be one of {model_names}"""     )

 # verify logits
 assert logits.shape == expected_shape
 if "finetuned" in model_name:
  assert torch.allclose(logits[0, :3]					,   a					,   atol=1e-4     )
 else:
  print('''Logits:'''					,   logits[0, :3, :3]     )
  assert torch.allclose(logits[0, :3, :3]					,   a					,   atol=1e-4     )
 print('''Logits ok!'''     )

 # verify loss, if applicable
 if model_name == "videomae-base-short":
  snake_case				=			outputs.loss
  assert torch.allclose(a					,   a					,   atol=1e-4     )
  print('''Loss ok!'''     )

 if pytorch_dump_folder_path is not None:
  print(f"""Saving model and image processor to {pytorch_dump_folder_path}"""     )
  image_processor.save_pretrained(a     )
  model.save_pretrained(a     )

 if push_to_hub:
  print('''Pushing to the hub...'''     )
  model.push_to_hub(a					,   organization='''nielsr'''     )


if __name__ == "__main__":
   _lowercase       						=  argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--checkpoint_url',
       default='https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4',
       type=str,
       help=(
           'URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'
           ' download link.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_folder_path',
       default='/Users/nielsrogge/Documents/VideoMAE/Test',
       type=str,
       help='Path to the output PyTorch model directory.',
   )
   parser.add_argument('--model_name', default='videomae-base', type=str, help='Name of the model.')
   parser.add_argument(
       '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
   )

   _lowercase       						=  parser.parse_args()
   convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)








'''simple docstring'''







import argparse
import copy

def    __UpperCamelCase	(	a    : Union[str, Any]     ) ->Tuple:
 snake_case				=			{}

 with open(a     ) as f:
  for line in f:
   if line.split()[0] not in dict_of_neighbours:
    snake_case				=			[]
    _list.append([line.split()[1], line.split()[2]]     )
    snake_case				=			_list
   else:
    dict_of_neighbours[line.split()[0]].append(
        [line.split()[1], line.split()[2]]     )
   if line.split()[1] not in dict_of_neighbours:
    snake_case				=			[]
    _list.append([line.split()[0], line.split()[2]]     )
    snake_case				=			_list
   else:
    dict_of_neighbours[line.split()[1]].append(
        [line.split()[0], line.split()[2]]     )

 return dict_of_neighbours

def    __UpperCamelCase	(	a    : Dict					,   a    : Tuple     ) ->int:

 with open(a     ) as f:
  snake_case				=			f.read(1     )
 snake_case				=			start_node

 snake_case				=			[]

 snake_case				=			start_node

 snake_case				=			0
 while visiting not in first_solution:
  snake_case				=			1_0000
  for k in dict_of_neighbours[visiting]:
   if int(k[1]     ) < int(a     ) and k[0] not in first_solution:
    snake_case				=			k[1]
    snake_case				=			k[0]

  first_solution.append(a     )
  snake_case				=			distance_of_first_solution + int(a     )
  snake_case				=			best_node

 first_solution.append(a     )

 snake_case				=			0
 for k in dict_of_neighbours[first_solution[-2]]:
  if k[0] == start_node:
   break
  position += 1

 snake_case				=			(
     distance_of_first_solution
     + int(dict_of_neighbours[first_solution[-2]][position][1]     )
     - 1_0000
 )
 return first_solution, distance_of_first_solution

def    __UpperCamelCase	(	a    : Optional[int]					,   a    : str     ) ->str:
 snake_case				=			[]

 for n in solution[1:-1]:
  snake_case				=			solution.index(a     )
  for kn in solution[1:-1]:
   snake_case				=			solution.index(a     )
   if n == kn:
    continue

   snake_case				=			copy.deepcopy(a     )
   snake_case				=			kn
   snake_case				=			n

   snake_case				=			0

   for k in _tmp[:-1]:
    snake_case				=			_tmp[_tmp.index(a     ) + 1]
    for i in dict_of_neighbours[k]:
     if i[0] == next_node:
      snake_case				=			distance + int(i[1]     )
   _tmp.append(a     )

   if _tmp not in neighborhood_of_solution:
    neighborhood_of_solution.append(_tmp     )

 snake_case				=			len(neighborhood_of_solution[0]     ) - 1

 neighborhood_of_solution.sort(key=lambda a     : x[index_of_last_item_in_the_list]     )
 return neighborhood_of_solution

def    __UpperCamelCase	(	a    : Any					,   a    : Optional[Any]					,   a    : int					,   a    : Optional[int]					,   a    : Union[str, Any]     ) ->List[Any]:
 snake_case				=			1
 snake_case				=			first_solution
 snake_case				=			[]
 snake_case				=			distance_of_first_solution
 snake_case				=			solution

 while count <= iters:
  snake_case				=			find_neighborhood(a					,   a     )
  snake_case				=			0
  snake_case				=			neighborhood[index_of_best_solution]
  snake_case				=			len(a     ) - 1

  snake_case				=			False
  while not found:
   snake_case				=			0
   while i < len(a     ):
    if best_solution[i] != solution[i]:
     snake_case				=			best_solution[i]
     snake_case				=			solution[i]
     break
    snake_case				=			i + 1

   if [first_exchange_node, second_exchange_node] not in tabu_list and [
       second_exchange_node,
       first_exchange_node,
   ] not in tabu_list:
    tabu_list.append([first_exchange_node, second_exchange_node]     )
    snake_case				=			True
    snake_case				=			best_solution[:-1]
    snake_case				=			neighborhood[index_of_best_solution][best_cost_index]
    if cost < best_cost:
     snake_case				=			cost
     snake_case				=			solution
   else:
    snake_case				=			index_of_best_solution + 1
    snake_case				=			neighborhood[index_of_best_solution]

  if len(a     ) >= size:
   tabu_list.pop(0     )

  snake_case				=			count + 1

 return best_solution_ever, best_cost

def    __UpperCamelCase	(	a    : Union[str, Any]=None     ) ->Optional[Any]:
 snake_case				=			generate_neighbours(args.File     )

 snake_case				,		snake_case				=			generate_first_solution(
     args.File					,   a     )

 snake_case				,		snake_case				=			tabu_search(
     a					,   a					,   a					,   args.Iterations					,   args.Size					,   )

 print(f"""Best solution: {best_sol}, with total distance: {best_cost}."""     )


if __name__ == "__main__":
   _lowercase       						=			argparse.ArgumentParser(description='Tabu Search')
   parser.add_argument(
       '-f',
       '--File',
       type=str,
       help='Path to the file containing the data',
       required=True,
   )
   parser.add_argument(
       '-i',
       '--Iterations',
       type=int,
       help='How many iterations the algorithm should perform',
       required=True,
   )
   parser.add_argument(
       '-s', '--Size', type=int, help='Size of the tabu list', required=True
   )

   # Pass the arguments to main method
   main(parser.parse_args())

