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    lowerCAmelCase_  (		lowercase:				Any		)     ->					List[Any]:







 '''simple docstring'''
 _UpperCamelCase:  List[Any]    						=			VideoMAEConfig()

 set_architecture_configs(lowercase	,       lowercase		)

 if "finetuned" not in model_name:
  _UpperCamelCase:  str    						=			False

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

 return config




def    lowerCAmelCase_  (		lowercase:				Optional[int]	,       lowercase:				str		)     ->					Optional[int]:







 '''simple docstring'''
 if "small" in model_name:
  _UpperCamelCase:  List[Any]    						=			384
  _UpperCamelCase:  List[str]    						=			1_536
  _UpperCamelCase:  Tuple    						=			12
  _UpperCamelCase:  List[Any]    						=			16
  _UpperCamelCase:  Dict    						=			12
  _UpperCamelCase:  Any    						=			3
  _UpperCamelCase:  str    						=			192
  _UpperCamelCase:  Any    						=			768
 elif "large" in model_name:
  _UpperCamelCase:  int    						=			1_024
  _UpperCamelCase:  Optional[Any]    						=			4_096
  _UpperCamelCase:  List[Any]    						=			24
  _UpperCamelCase:  Union[str, Any]    						=			16
  _UpperCamelCase:  Tuple    						=			12
  _UpperCamelCase:  List[Any]    						=			8
  _UpperCamelCase:  Tuple    						=			512
  _UpperCamelCase:  Optional[Any]    						=			2_048
 elif "huge" in model_name:
  _UpperCamelCase:  int    						=			1_280
  _UpperCamelCase:  List[str]    						=			5_120
  _UpperCamelCase:  Dict    						=			32
  _UpperCamelCase:  List[str]    						=			16
  _UpperCamelCase:  Optional[int]    						=			12
  _UpperCamelCase:  str    						=			8
  _UpperCamelCase:  Any    						=			640
  _UpperCamelCase:  Optional[Any]    						=			2_560
 elif "base" not in model_name:
  raise ValueError('''Model name should include either "small", "base", "large", or "huge"'''		)




def    lowerCAmelCase_  (		lowercase:				List[Any]		)     ->					Union[str, Any]:







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

 return name




def    lowerCAmelCase_  (		lowercase:				List[str]	,       lowercase:				Any		)     ->					Optional[Any]:







 '''simple docstring'''
 for key in orig_state_dict.copy().keys():
  _UpperCamelCase:  List[Any]    						=			orig_state_dict.pop(lowercase		)

  if key.startswith('''encoder.'''		):
   _UpperCamelCase:  Optional[Any]    						=			key.replace('''encoder.'''	,       ''''''		)

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

 return orig_state_dict




def    lowerCAmelCase_  (		)     ->					List[str]:







 '''simple docstring'''
 _UpperCamelCase:  Tuple    						=			hf_hub_download(
     repo_id='''hf-internal-testing/spaghetti-video'''	,       filename='''eating_spaghetti.npy'''	,       repo_type='''dataset'''		)
 _UpperCamelCase:  List[Any]    						=			np.load(lowercase		)
 return list(lowercase		)




def    lowerCAmelCase_  (		lowercase:				Tuple	,       lowercase:				List[str]	,       lowercase:				Dict	,       lowercase:				str		)     ->					int:







 '''simple docstring'''
 _UpperCamelCase:  Optional[Any]    						=			get_videomae_config(lowercase		)

 if "finetuned" in model_name:
  _UpperCamelCase:  Dict    						=			VideoMAEForVideoClassification(lowercase		)
 else:
  _UpperCamelCase:  int    						=			VideoMAEForPreTraining(lowercase		)

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

 model.load_state_dict(lowercase		)
 model.eval()

 # verify model on basic input
 _UpperCamelCase:  Optional[Any]    						=			VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]	,       image_std=[0.5, 0.5, 0.5]		)
 _UpperCamelCase:  Optional[int]    						=			prepare_video()
 _UpperCamelCase:  int    						=			image_processor(lowercase	,       return_tensors='''pt'''		)

 if "finetuned" not in model_name:
  _UpperCamelCase:  str    						=			hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos'''	,       filename='''bool_masked_pos.pt'''		)
  _UpperCamelCase:  Optional[int]    						=			torch.load(lowercase		)

 _UpperCamelCase:  List[Any]    						=			model(**lowercase		)
 _UpperCamelCase:  Any    						=			outputs.logits

 _UpperCamelCase:  Optional[Any]    						=			[
     '''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":
  _UpperCamelCase:  List[str]    						=			torch.Size([1, 400]		)
  _UpperCamelCase:  Dict    						=			torch.tensor([-0.9291, -0.4061, -0.9307]		)
 elif model_name == "videomae-small-finetuned-ssv2":
  _UpperCamelCase:  str    						=			torch.Size([1, 174]		)
  _UpperCamelCase:  str    						=			torch.tensor([0.2671, -0.4689, -0.8235]		)
 elif model_name == "videomae-base":
  _UpperCamelCase:  Any    						=			torch.Size([1, 1_408, 1_536]		)
  _UpperCamelCase:  str    						=			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":
  _UpperCamelCase:  Dict    						=			torch.Size([1, 1_408, 1_536]		)
  _UpperCamelCase:  Optional[int]    						=			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
  _UpperCamelCase:  str    						=			torch.tensor([0.5142]		) if config.norm_pix_loss else torch.tensor([0.6469]		)
 elif model_name == "videomae-large":
  _UpperCamelCase:  str    						=			torch.Size([1, 1_408, 1_536]		)
  _UpperCamelCase:  int    						=			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":
  _UpperCamelCase:  Dict    						=			torch.Size([1, 400]		)
  _UpperCamelCase:  Dict    						=			torch.tensor([0.0771, 0.0011, -0.3625]		)
 elif model_name == "videomae-huge-finetuned-kinetics":
  _UpperCamelCase:  Tuple    						=			torch.Size([1, 400]		)
  _UpperCamelCase:  Optional[int]    						=			torch.tensor([0.2433, 0.1632, -0.4894]		)
 elif model_name == "videomae-base-short-finetuned-kinetics":
  _UpperCamelCase:  Dict    						=			torch.Size([1, 400]		)
  _UpperCamelCase:  str    						=			torch.tensor([0.6588, 0.0990, -0.2493]		)
 elif model_name == "videomae-base-finetuned-kinetics":
  _UpperCamelCase:  int    						=			torch.Size([1, 400]		)
  _UpperCamelCase:  List[str]    						=			torch.tensor([0.3669, -0.0688, -0.2421]		)
 elif model_name == "videomae-base-short-ssv2":
  _UpperCamelCase:  str    						=			torch.Size([1, 1_408, 1_536]		)
  _UpperCamelCase:  List[str]    						=			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":
  _UpperCamelCase:  str    						=			torch.Size([1, 174]		)
  _UpperCamelCase:  List[Any]    						=			torch.tensor([-0.0537, -0.1539, -0.3266]		)
 elif model_name == "videomae-base-ssv2":
  _UpperCamelCase:  str    						=			torch.Size([1, 1_408, 1_536]		)
  _UpperCamelCase:  int    						=			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":
  _UpperCamelCase:  Tuple    						=			torch.Size([1, 174]		)
  _UpperCamelCase:  Optional[Any]    						=			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]	,       lowercase	,       atol=1E-4		)
 else:
  print('''Logits:'''	,       logits[0, :3, :3]		)
  assert torch.allclose(logits[0, :3, :3]	,       lowercase	,       atol=1E-4		)
 print('''Logits ok!'''		)

 # verify loss, if applicable
 if model_name == "videomae-base-short":
  _UpperCamelCase:  List[Any]    						=			outputs.loss
  assert torch.allclose(lowercase	,       lowercase	,       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(lowercase		)
  model.save_pretrained(lowercase		)

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


if __name__ == "__main__":
  UpperCAmelCase_           =				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.'''
  )

  UpperCAmelCase_           =				parser.parse_args()
  convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup




def    lowerCAmelCase_  (		lowercase:				str = ""		)     ->					dict[str, float]:







 '''simple docstring'''
 _UpperCamelCase:  Tuple    						=			url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
 _UpperCamelCase:  Union[str, Any]    						=			BeautifulSoup(requests.get(lowercase		).text	,       '''html.parser'''		)
 _UpperCamelCase:  List[Any]    						=			soup.find_all('''td'''	,       attrs='''titleColumn'''		)
 _UpperCamelCase:  str    						=			soup.find_all('''td'''	,       class_='''ratingColumn imdbRating'''		)
 return {
     title.a.text: float(rating.strong.text		)
     for title, rating in zip(lowercase	,       lowercase		)
 }




def    lowerCAmelCase_  (		lowercase:				str = "IMDb_Top_250_Movies.csv"		)     ->					None:







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			get_imdb_top_aaa_movies()
 with open(lowercase	,       '''w'''	,       newline=''''''		) as out_file:
  _UpperCamelCase:  Optional[Any]    						=			csv.writer(lowercase		)
  writer.writerow(['''Movie title''', '''IMDb rating''']		)
  for title, rating in movies.items():
   writer.writerow([title, rating]		)


if __name__ == "__main__":
  write_movies()