

'''simple docstring'''







import math

from numpy import inf
from scipy.integrate import quad
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      float:
       if num <= 0:
              raise ValueError("""math domain error"""					)

       return quad(UpperCAmelCase__,							0,							UpperCAmelCase__,							args=(UpperCAmelCase__)					)[0]
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      float:
       return math.pow(UpperCAmelCase__,							z - 1					) * math.exp(-x					)


if __name__ == "__main__":
      from doctest import testmod

      testmod()



'''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__     (   UpperCAmelCase__					)      ->      Union[str, Any]:
       A_						       =   VideoMAEConfig()

       set_architecture_configs(UpperCAmelCase__,							UpperCAmelCase__					)

       if "finetuned" not in model_name:
              A_						       =   False

       if "finetuned" in model_name:
              A_						       =   """huggingface/label-files"""
              if "kinetics" in model_name:
                     A_						       =   4_00
                     A_						       =   """kinetics400-id2label.json"""
              elif "ssv2" in model_name:
                     A_						       =   1_74
                     A_						       =   """something-something-v2-id2label.json"""
              else:
                     raise ValueError("""Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned."""					)
              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__,							UpperCAmelCase__					)      ->      Dict:
       if "small" in model_name:
              A_						       =   3_84
              A_						       =   15_36
              A_						       =   12
              A_						       =   16
              A_						       =   12
              A_						       =   3
              A_						       =   1_92
              A_						       =   7_68
       elif "large" in model_name:
              A_						       =   10_24
              A_						       =   40_96
              A_						       =   24
              A_						       =   16
              A_						       =   12
              A_						       =   8
              A_						       =   5_12
              A_						       =   20_48
       elif "huge" in model_name:
              A_						       =   12_80
              A_						       =   51_20
              A_						       =   32
              A_						       =   16
              A_						       =   12
              A_						       =   8
              A_						       =   6_40
              A_						       =   25_60
       elif "base" not in model_name:
              raise ValueError("""Model name should include either \"small\", \"base\", \"large\", or \"huge\""""					)
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      List[Any]:
       if "encoder." in name:
              A_						       =   name.replace("""encoder.""",							""""""					)
       if "cls_token" in name:
              A_						       =   name.replace("""cls_token""",							"""videomae.embeddings.cls_token"""					)
       if "decoder_pos_embed" in name:
              A_						       =   name.replace("""decoder_pos_embed""",							"""decoder.decoder_pos_embed"""					)
       if "pos_embed" in name and "decoder" not in name:
              A_						       =   name.replace("""pos_embed""",							"""videomae.embeddings.position_embeddings"""					)
       if "patch_embed.proj" in name:
              A_						       =   name.replace("""patch_embed.proj""",							"""videomae.embeddings.patch_embeddings.projection"""					)
       if "patch_embed.norm" in name:
              A_						       =   name.replace("""patch_embed.norm""",							"""videomae.embeddings.norm"""					)
       if "decoder.blocks" in name:
              A_						       =   name.replace("""decoder.blocks""",							"""decoder.decoder_layers"""					)
       if "blocks" in name:
              A_						       =   name.replace("""blocks""",							"""videomae.encoder.layer"""					)
       if "attn.proj" in name:
              A_						       =   name.replace("""attn.proj""",							"""attention.output.dense"""					)
       if "attn" in name and "bias" not in name:
              A_						       =   name.replace("""attn""",							"""attention.self"""					)
       if "attn" in name:
              A_						       =   name.replace("""attn""",							"""attention.attention"""					)
       if "norm1" 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 "decoder_embed" in name:
              A_						       =   name.replace("""decoder_embed""",							"""decoder.decoder_embed"""					)
       if "decoder_norm" in name:
              A_						       =   name.replace("""decoder_norm""",							"""decoder.decoder_norm"""					)
       if "decoder_pred" in name:
              A_						       =   name.replace("""decoder_pred""",							"""decoder.decoder_pred"""					)
       if "norm.weight" in name and "decoder" not in name and "fc" not in name:
              A_						       =   name.replace("""norm.weight""",							"""videomae.layernorm.weight"""					)
       if "norm.bias" in name and "decoder" not in name and "fc" not in name:
              A_						       =   name.replace("""norm.bias""",							"""videomae.layernorm.bias"""					)
       if "head" in name and "decoder" not in name:
              A_						       =   name.replace("""head""",							"""classifier"""					)

       return name
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      Optional[int]:
       for key in orig_state_dict.copy().keys():
              A_						       =   orig_state_dict.pop(UpperCAmelCase__					)

              if key.startswith("""encoder."""					):
                     A_						       =   key.replace("""encoder.""",							""""""					)

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

       return orig_state_dict
def    UpperCAmelCase__     (   )      ->      Any:
       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__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      int:
       A_						       =   get_videomae_config(UpperCAmelCase__					)

       if "finetuned" in model_name:
              A_						       =   VideoMAEForVideoClassification(UpperCAmelCase__					)
       else:
              A_						       =   VideoMAEForPreTraining(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"""]
       else:
              A_						       =   files["""module"""]
       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(UpperCAmelCase__,							return_tensors="""pt"""					)

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

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

       A_						       =   [
           """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":
              A_						       =   torch.Size([1, 4_00]					)
              A_						       =   torch.tensor([-0.9_291, -0.4_061, -0.9_307]					)
       elif model_name == "videomae-small-finetuned-ssv2":
              A_						       =   torch.Size([1, 1_74]					)
              A_						       =   torch.tensor([0.2_671, -0.4_689, -0.8_235]					)
       elif model_name == "videomae-base":
              A_						       =   torch.Size([1, 14_08, 15_36]					)
              A_						       =   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":
              A_						       =   torch.Size([1, 14_08, 15_36]					)
              A_						       =   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
              A_						       =   torch.tensor([0.5_142]					) if config.norm_pix_loss else torch.tensor([0.6_469]					)
       elif model_name == "videomae-large":
              A_						       =   torch.Size([1, 14_08, 15_36]					)
              A_						       =   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":
              A_						       =   torch.Size([1, 4_00]					)
              A_						       =   torch.tensor([0.0_771, 0.0_011, -0.3_625]					)
       elif model_name == "videomae-huge-finetuned-kinetics":
              A_						       =   torch.Size([1, 4_00]					)
              A_						       =   torch.tensor([0.2_433, 0.1_632, -0.4_894]					)
       elif model_name == "videomae-base-short-finetuned-kinetics":
              A_						       =   torch.Size([1, 4_00]					)
              A_						       =   torch.tensor([0.6_588, 0.0_990, -0.2_493]					)
       elif model_name == "videomae-base-finetuned-kinetics":
              A_						       =   torch.Size([1, 4_00]					)
              A_						       =   torch.tensor([0.3_669, -0.0_688, -0.2_421]					)
       elif model_name == "videomae-base-short-ssv2":
              A_						       =   torch.Size([1, 14_08, 15_36]					)
              A_						       =   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":
              A_						       =   torch.Size([1, 1_74]					)
              A_						       =   torch.tensor([-0.0_537, -0.1_539, -0.3_266]					)
       elif model_name == "videomae-base-ssv2":
              A_						       =   torch.Size([1, 14_08, 15_36]					)
              A_						       =   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":
              A_						       =   torch.Size([1, 1_74]					)
              A_						       =   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],							UpperCAmelCase__,							atol=1e-4					)
       else:
              print("""Logits:""",							logits[0, :3, :3]					)
              assert torch.allclose(logits[0, :3, :3],							UpperCAmelCase__,							atol=1e-4					)
       print("""Logits ok!"""					)

       # verify loss, if applicable
       if model_name == "videomae-base-short":
              A_						       =   outputs.loss
              assert torch.allclose(UpperCAmelCase__,							UpperCAmelCase__,							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(UpperCAmelCase__					)
              model.save_pretrained(UpperCAmelCase__					)

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


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

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