





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 (  _a   :					Tuple			)	->			Tuple:
     lowerCAmelCase_					:       Optional[Any]    =		VideoMAEConfig()

     set_architecture_configs(A__					,   A__			)

     if "finetuned" not in model_name:
          lowerCAmelCase_					:       Tuple    =		False

     if "finetuned" in model_name:
          lowerCAmelCase_					:       str    =		"""huggingface/label-files"""
          if "kinetics" in model_name:
               lowerCAmelCase_					:       Dict    =		4_00
               lowerCAmelCase_					:       Optional[Any]    =		"""kinetics400-id2label.json"""
          elif "ssv2" in model_name:
               lowerCAmelCase_					:       List[str]    =		1_74
               lowerCAmelCase_					:       Tuple    =		"""something-something-v2-id2label.json"""
          else:
               raise ValueError("""Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned."""			)
          lowerCAmelCase_					:       int    =		json.load(open(hf_hub_download(A__					,   A__					,   repo_type="""dataset"""			)					,   """r"""			)			)
          lowerCAmelCase_					:       str    =		{int(A__			): v for k, v in idalabel.items()}
          lowerCAmelCase_					:       Optional[Any]    =		idalabel
          lowerCAmelCase_					:       Optional[Any]    =		{v: k for k, v in idalabel.items()}

     return config






def 							_lowerCAmelCase (  _a   :					Optional[int]					,   _a   :					Optional[Any]			)	->			List[str]:



     if "small" in model_name:
          lowerCAmelCase_					:       Tuple    =		3_84
          lowerCAmelCase_					:       str    =		15_36
          lowerCAmelCase_					:       List[str]    =		12
          lowerCAmelCase_					:       Tuple    =		16
          lowerCAmelCase_					:       List[Any]    =		12
          lowerCAmelCase_					:       int    =		3
          lowerCAmelCase_					:       Any    =		1_92
          lowerCAmelCase_					:       Optional[int]    =		7_68
     elif "large" in model_name:
          lowerCAmelCase_					:       List[Any]    =		10_24
          lowerCAmelCase_					:       str    =		40_96
          lowerCAmelCase_					:       Optional[int]    =		24
          lowerCAmelCase_					:       int    =		16
          lowerCAmelCase_					:       Dict    =		12
          lowerCAmelCase_					:       Union[str, Any]    =		8
          lowerCAmelCase_					:       Union[str, Any]    =		5_12
          lowerCAmelCase_					:       Any    =		20_48
     elif "huge" in model_name:
          lowerCAmelCase_					:       List[str]    =		12_80
          lowerCAmelCase_					:       str    =		51_20
          lowerCAmelCase_					:       Optional[int]    =		32
          lowerCAmelCase_					:       Dict    =		16
          lowerCAmelCase_					:       Any    =		12
          lowerCAmelCase_					:       Tuple    =		8
          lowerCAmelCase_					:       Dict    =		6_40
          lowerCAmelCase_					:       int    =		25_60
     elif "base" not in model_name:
          raise ValueError("""Model name should include either \"small\", \"base\", \"large\", or \"huge\""""			)






def 							_lowerCAmelCase (  _a   :					str			)	->			List[str]:



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

     return name






def 							_lowerCAmelCase (  _a   :					Optional[Any]					,   _a   :					Optional[Any]			)	->			List[str]:



     for key in orig_state_dict.copy().keys():
          lowerCAmelCase_					:       List[str]    =		orig_state_dict.pop(A__			)

          if key.startswith("""encoder."""			):
               lowerCAmelCase_					:       List[Any]    =		key.replace("""encoder."""					,   """"""			)

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

     return orig_state_dict






def 							_lowerCAmelCase (  )	->			Dict:
     lowerCAmelCase_					:       Optional[Any]    =		hf_hub_download(
         repo_id="""hf-internal-testing/spaghetti-video"""					,   filename="""eating_spaghetti.npy"""					,   repo_type="""dataset"""			)
     lowerCAmelCase_					:       Any    =		np.load(A__			)
     return list(A__			)






def 							_lowerCAmelCase (  _a   :					Optional[Any]					,   _a   :					Tuple					,   _a   :					Union[str, Any]					,   _a   :					int			)	->			Tuple:
     lowerCAmelCase_					:       int    =		get_videomae_config(A__			)

     if "finetuned" in model_name:
          lowerCAmelCase_					:       Any    =		VideoMAEForVideoClassification(A__			)
     else:
          lowerCAmelCase_					:       Optional[int]    =		VideoMAEForPreTraining(A__			)

     # download original checkpoint, hosted on Google Drive
     lowerCAmelCase_					:       Union[str, Any]    =		"""pytorch_model.bin"""
     gdown.cached_download(A__					,   A__					,   quiet=A__			)
     lowerCAmelCase_					:       Optional[int]    =		torch.load(A__					,   map_location="""cpu"""			)
     if "model" in files:
          lowerCAmelCase_					:       Optional[int]    =		files["""model"""]
     else:
          lowerCAmelCase_					:       Any    =		files["""module"""]
     lowerCAmelCase_					:       Dict    =		convert_state_dict(A__					,   A__			)

     model.load_state_dict(A__			)
     model.eval()

     # verify model on basic input
     lowerCAmelCase_					:       Optional[int]    =		VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]					,   image_std=[0.5, 0.5, 0.5]			)
     lowerCAmelCase_					:       int    =		prepare_video()
     lowerCAmelCase_					:       Optional[int]    =		image_processor(A__					,   return_tensors="""pt"""			)

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

     lowerCAmelCase_					:       int    =		model(**A__			)
     lowerCAmelCase_					:       Dict    =		outputs.logits

     lowerCAmelCase_					:       Optional[int]    =		[
         """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":
          lowerCAmelCase_					:       Union[str, Any]    =		torch.Size([1, 4_00]			)
          lowerCAmelCase_					:       List[Any]    =		torch.tensor([-0.9_291, -0.4_061, -0.9_307]			)
     elif model_name == "videomae-small-finetuned-ssv2":
          lowerCAmelCase_					:       int    =		torch.Size([1, 1_74]			)
          lowerCAmelCase_					:       int    =		torch.tensor([0.2_671, -0.4_689, -0.8_235]			)
     elif model_name == "videomae-base":
          lowerCAmelCase_					:       Any    =		torch.Size([1, 14_08, 15_36]			)
          lowerCAmelCase_					:       Dict    =		torch.tensor([[0.7_739, 0.7_968, 0.7_089], [0.6_701, 0.7_487, 0.6_209], [0.4_287, 0.5_158, 0.4_773]]			)
     elif model_name == "videomae-base-short":
          lowerCAmelCase_					:       Optional[int]    =		torch.Size([1, 14_08, 15_36]			)
          lowerCAmelCase_					:       str    =		torch.tensor([[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]]			)
          # we verified the loss both for normalized and unnormalized targets for this one
          lowerCAmelCase_					:       List[Any]    =		torch.tensor([0.5_142]			) if config.norm_pix_loss else torch.tensor([0.6_469]			)
     elif model_name == "videomae-large":
          lowerCAmelCase_					:       Optional[int]    =		torch.Size([1, 14_08, 15_36]			)
          lowerCAmelCase_					:       Any    =		torch.tensor([[0.7_149, 0.7_997, 0.6_966], [0.6_768, 0.7_869, 0.6_948], [0.5_139, 0.6_221, 0.5_605]]			)
     elif model_name == "videomae-large-finetuned-kinetics":
          lowerCAmelCase_					:       int    =		torch.Size([1, 4_00]			)
          lowerCAmelCase_					:       Optional[int]    =		torch.tensor([0.0_771, 0.0_011, -0.3_625]			)
     elif model_name == "videomae-huge-finetuned-kinetics":
          lowerCAmelCase_					:       Union[str, Any]    =		torch.Size([1, 4_00]			)
          lowerCAmelCase_					:       str    =		torch.tensor([0.2_433, 0.1_632, -0.4_894]			)
     elif model_name == "videomae-base-short-finetuned-kinetics":
          lowerCAmelCase_					:       Dict    =		torch.Size([1, 4_00]			)
          lowerCAmelCase_					:       int    =		torch.tensor([0.6_588, 0.0_990, -0.2_493]			)
     elif model_name == "videomae-base-finetuned-kinetics":
          lowerCAmelCase_					:       Tuple    =		torch.Size([1, 4_00]			)
          lowerCAmelCase_					:       str    =		torch.tensor([0.3_669, -0.0_688, -0.2_421]			)
     elif model_name == "videomae-base-short-ssv2":
          lowerCAmelCase_					:       Dict    =		torch.Size([1, 14_08, 15_36]			)
          lowerCAmelCase_					:       Optional[Any]    =		torch.tensor([[0.4_712, 0.5_296, 0.5_786], [0.2_278, 0.2_729, 0.4_026], [0.0_352, 0.0_730, 0.2_506]]			)
     elif model_name == "videomae-base-short-finetuned-ssv2":
          lowerCAmelCase_					:       Dict    =		torch.Size([1, 1_74]			)
          lowerCAmelCase_					:       Union[str, Any]    =		torch.tensor([-0.0_537, -0.1_539, -0.3_266]			)
     elif model_name == "videomae-base-ssv2":
          lowerCAmelCase_					:       Tuple    =		torch.Size([1, 14_08, 15_36]			)
          lowerCAmelCase_					:       Optional[int]    =		torch.tensor([[0.8_131, 0.8_727, 0.8_546], [0.7_366, 0.9_377, 0.8_870], [0.5_935, 0.8_874, 0.8_564]]			)
     elif model_name == "videomae-base-finetuned-ssv2":
          lowerCAmelCase_					:       List[Any]    =		torch.Size([1, 1_74]			)
          lowerCAmelCase_					:       Optional[int]    =		torch.tensor([0.1_961, -0.8_337, -0.6_389]			)
     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":
          lowerCAmelCase_					:       Optional[Any]    =		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__":
      UpperCAmelCase_					:   Dict 							=						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_					:   List[Any] 							=						parser.parse_args()
      convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)







UpperCAmelCase_					:   str 							=						"""Alexander Joslin"""

import operator as op

from .stack import Stack






def 							_lowerCAmelCase (  _a   :					str			)	->			int:
     lowerCAmelCase_					:       Any    =		{"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}

     lowerCAmelCase_					:       Stack[int]    =		Stack()
     lowerCAmelCase_					:       Stack[str]    =		Stack()

     for i in equation:
          if i.isdigit():
               # RULE 1
               operand_stack.push(int(_a			)			)
          elif i in operators:
               # RULE 2
               operator_stack.push(_a			)
          elif i == ")":
               # RULE 4
               lowerCAmelCase_					:       Optional[int]    =		operator_stack.peek()
               operator_stack.pop()
               lowerCAmelCase_					:       Union[str, Any]    =		operand_stack.peek()
               operand_stack.pop()
               lowerCAmelCase_					:       List[str]    =		operand_stack.peek()
               operand_stack.pop()

               lowerCAmelCase_					:       Dict    =		operators[opr](_a					,   _a			)
               operand_stack.push(_a			)

    # RULE 5
     return operand_stack.peek()


if __name__ == "__main__":
      UpperCAmelCase_					:   Dict 							=						"""(5 + ((4 * 2) * (2 + 3)))"""
      # answer = 45
      print(F"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")
