



import argparse
import json

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

from transformers import TimesformerConfig, TimesformerForVideoClassification, VideoMAEImageProcessor


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Any       ):






 '''simple docstring'''

 a							=		TimesformerConfig()

 if "large" in model_name:
  a							=		96

 if "hr" in model_name:
  a							=		16
  a							=		4_48

 a							=		"huggingface/label-files"
 if "k400" in model_name:
  a							=		4_00
  a							=		"kinetics400-id2label.json"
 elif "k600" in model_name:
  a							=		6_00
  a							=		"kinetics600-id2label.json"
 elif "ssv2" in model_name:
  a							=		1_74
  a							=		"something-something-v2-id2label.json"
 else:
  raise ValueError("Model name should either contain 'k400', 'k600' or 'ssv2'."       )
 a							=		json.load(open(hf_hub_download(UpperCAmelCase__			,       UpperCAmelCase__			,       repo_type="dataset"       )			,       "r"       )       )
 a							=		{int(UpperCAmelCase__       ): v for k, v in idalabel.items()}
 a							=		idalabel
 a							=		{v: k for k, v in idalabel.items()}

 return config


def 						UpperCAmelCase__   (   UpperCAmelCase__			:int       ):






 '''simple docstring'''

 if "encoder." in name:
  a							=		name.replace("encoder."			,       ""       )
 if "cls_token" in name:
  a							=		name.replace("cls_token"			,       "timesformer.embeddings.cls_token"       )
 if "pos_embed" in name:
  a							=		name.replace("pos_embed"			,       "timesformer.embeddings.position_embeddings"       )
 if "time_embed" in name:
  a							=		name.replace("time_embed"			,       "timesformer.embeddings.time_embeddings"       )
 if "patch_embed.proj" in name:
  a							=		name.replace("patch_embed.proj"			,       "timesformer.embeddings.patch_embeddings.projection"       )
 if "patch_embed.norm" in name:
  a							=		name.replace("patch_embed.norm"			,       "timesformer.embeddings.norm"       )
 if "blocks" in name:
  a							=		name.replace("blocks"			,       "timesformer.encoder.layer"       )
 if "attn.proj" in name:
  a							=		name.replace("attn.proj"			,       "attention.output.dense"       )
 if "attn" in name and "bias" not in name and "temporal" not in name:
  a							=		name.replace("attn"			,       "attention.self"       )
 if "attn" in name and "temporal" not in name:
  a							=		name.replace("attn"			,       "attention.attention"       )
 if "temporal_norm1" in name:
  a							=		name.replace("temporal_norm1"			,       "temporal_layernorm"       )
 if "temporal_attn.proj" in name:
  a							=		name.replace("temporal_attn"			,       "temporal_attention.output.dense"       )
 if "temporal_fc" in name:
  a							=		name.replace("temporal_fc"			,       "temporal_dense"       )
 if "norm1" in name and "temporal" not in name:
  a							=		name.replace("norm1"			,       "layernorm_before"       )
 if "norm2" in name:
  a							=		name.replace("norm2"			,       "layernorm_after"       )
 if "mlp.fc1" in name:
  a							=		name.replace("mlp.fc1"			,       "intermediate.dense"       )
 if "mlp.fc2" in name:
  a							=		name.replace("mlp.fc2"			,       "output.dense"       )
 if "norm.weight" in name and "fc" not in name and "temporal" not in name:
  a							=		name.replace("norm.weight"			,       "timesformer.layernorm.weight"       )
 if "norm.bias" in name and "fc" not in name and "temporal" not in name:
  a							=		name.replace("norm.bias"			,       "timesformer.layernorm.bias"       )
 if "head" in name:
  a							=		name.replace("head"			,       "classifier"       )

 return name


def 						UpperCAmelCase__   (   UpperCAmelCase__			:List[str]			,       UpperCAmelCase__			:Optional[int]       ):






 '''simple docstring'''

 for key in orig_state_dict.copy().keys():
  a							=		orig_state_dict.pop(UpperCAmelCase__       )

  if key.startswith("model."       ):
   a							=		key.replace("model."			,       ""       )

  if "qkv" in key:
   a							=		key.split("."       )
   a							=		int(key_split[1]       )
   a							=		"timesformer.encoder.layer."
   if "temporal" in key:
    a							=		".temporal_attention.attention.qkv."
   else:
    a							=		".attention.attention.qkv."
   if "weight" in key:
    a							=		val
   else:
    a							=		val
  else:
   a							=		val

 return orig_state_dict


def 						UpperCAmelCase__   (   ):






 '''simple docstring'''

 a							=		hf_hub_download(
     repo_id="hf-internal-testing/spaghetti-video"			,       filename="eating_spaghetti.npy"			,       repo_type="dataset"       )
 a							=		np.load(UpperCAmelCase__       )
 return list(UpperCAmelCase__       )


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Optional[Any]			,       UpperCAmelCase__			:Tuple			,       UpperCAmelCase__			:Union[str, Any]			,       UpperCAmelCase__			:Tuple       ):






 '''simple docstring'''

 a							=		get_timesformer_config(UpperCAmelCase__       )

 a							=		TimesformerForVideoClassification(UpperCAmelCase__       )

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

 model.load_state_dict(UpperCAmelCase__       )
 model.eval()

 # verify model on basic input
 a							=		VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]			,       image_std=[0.5, 0.5, 0.5]       )
 a							=		prepare_video()
 a							=		image_processor(video[:8]			,       return_tensors="pt"       )

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

 a							=		[
     # Kinetics-400 checkpoints (hr = high resolution input of 448px instead of 224px)
     "timesformer-base-finetuned-k400",
     "timesformer-large-finetuned-k400",
     "timesformer-hr-finetuned-k400",
     # Kinetics-600 checkpoints (hr = high resolution input of 448px instead of 224px)
     "timesformer-base-finetuned-k600",
     "timesformer-large-finetuned-k600",
     "timesformer-hr-finetuned-k600",
     # Something-Something-v2 checkpoints (hr = high resolution input of 448px instead of 224px)
     "timesformer-base-finetuned-ssv2",
     "timesformer-large-finetuned-ssv2",
     "timesformer-hr-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 == "timesformer-base-finetuned-k400":
  a							=		torch.Size([1, 4_00]       )
  a							=		torch.tensor([-0.3016, -0.7713, -0.4205]       )
 elif model_name == "timesformer-base-finetuned-k600":
  a							=		torch.Size([1, 6_00]       )
  a							=		torch.tensor([-0.7267, -0.7466, 3.2404]       )
 elif model_name == "timesformer-base-finetuned-ssv2":
  a							=		torch.Size([1, 1_74]       )
  a							=		torch.tensor([-0.9059, 0.6433, -3.1457]       )
 elif model_name == "timesformer-large-finetuned-k400":
  a							=		torch.Size([1, 4_00]       )
  a							=		torch.tensor([0, 0, 0]       )
 elif model_name == "timesformer-large-finetuned-k600":
  a							=		torch.Size([1, 6_00]       )
  a							=		torch.tensor([0, 0, 0]       )
 elif model_name == "timesformer-large-finetuned-ssv2":
  a							=		torch.Size([1, 1_74]       )
  a							=		torch.tensor([0, 0, 0]       )
 elif model_name == "timesformer-hr-finetuned-k400":
  a							=		torch.Size([1, 4_00]       )
  a							=		torch.tensor([-0.9617, -3.7311, -3.7708]       )
 elif model_name == "timesformer-hr-finetuned-k600":
  a							=		torch.Size([1, 6_00]       )
  a							=		torch.tensor([2.5273, 0.7127, 1.8848]       )
 elif model_name == "timesformer-hr-finetuned-ssv2":
  a							=		torch.Size([1, 1_74]       )
  a							=		torch.tensor([-3.6756, -0.7513, 0.7180]       )
 else:
  raise ValueError(F"""Model name not supported. Should be one of {model_names}"""       )

 # verify logits
 assert logits.shape == expected_shape
 assert torch.allclose(logits[0, :3]			,       UpperCAmelCase__			,       atol=1E-4       )
 print("Logits 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(UpperCAmelCase__       )
  model.save_pretrained(UpperCAmelCase__       )

 if push_to_hub:
  print("Pushing to the hub..."       )
  model.push_to_hub(F"""fcakyon/{model_name}"""       )


if __name__ == "__main__":
     A_		:  List[Any]										=  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--checkpoint_url''',
         default='''https://drive.google.com/u/1/uc?id=17yvuYp9L4mn-HpIcK5Zo6K3UoOy1kA5l&export=download''',
         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='''''',
         type=str,
         help='''Path to the output PyTorch model directory.''',
     )
     parser.add_argument('''--model_name''', default='''timesformer-base-finetuned-k400''', 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.'''
     )

     A_		:  Any										=  parser.parse_args()
     convert_timesformer_checkpoint(
         args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
     )


'''simple docstring'''

from itertools import compress, repeat
from math import ceil, sqrt



def 				UpperCamelCase__      (      a__   ):


						'''simple docstring'''






						if num <= 2:
												return []
						if num == 3:
												return [2]

						# Odd sieve for numbers in range [3, num - 1]
						_lowerCAmelCase					    =bytearray(B'\x01'   ) * ((num >> 1) - 1)

						for i in range(3      ,   int(sqrt(a__   )   ) + 1      ,   2   ):
												if sieve[(i >> 1) - 1]:
																		_lowerCAmelCase					    =i**2
																		_lowerCAmelCase					    =repeat(
																		    0      ,   ceil((num - i_squared) / (i << 1)   )   )

						return [2] + list(compress(range(3      ,   a__      ,   2   )      ,   a__   )   )


if __name__ == "__main__":
							import doctest

							doctest.testmod()





