


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  a   (lowerCAmelCase__   ):
   __a					=  VideoMAEConfig()

   set_architecture_configs(lowerCAmelCase__		,				lowerCAmelCase__   )

   if "finetuned" not in model_name:
      __a					=  False

   if "finetuned" in model_name:
      __a					=  """huggingface/label-files"""
      if "kinetics" in model_name:
         __a					=  400
         __a					=  """kinetics400-id2label.json"""
      elif "ssv2" in model_name:
         __a					=  174
         __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(lowerCAmelCase__		,				lowerCAmelCase__		,				repo_type="""dataset"""   )		,				"""r"""   )   )
      __a					=  {int(lowerCAmelCase__   ): v for k, v in idalabel.items()}
      __a					=  idalabel
      __a					=  {v: k for k, v in idalabel.items()}

   return config




def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   if "small" in model_name:
      __a					=  384
      __a					=  1_536
      __a					=  12
      __a					=  16
      __a					=  12
      __a					=  3
      __a					=  192
      __a					=  768
   elif "large" in model_name:
      __a					=  1_024
      __a					=  4_096
      __a					=  24
      __a					=  16
      __a					=  12
      __a					=  8
      __a					=  512
      __a					=  2_048
   elif "huge" in model_name:
      __a					=  1_280
      __a					=  5_120
      __a					=  32
      __a					=  16
      __a					=  12
      __a					=  8
      __a					=  640
      __a					=  2_560
   elif "base" not in model_name:
      raise ValueError("""Model name should include either \"small\", \"base\", \"large\", or \"huge\""""   )




def  a   (lowerCAmelCase__   ):
   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  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   for key in orig_state_dict.copy().keys():
      __a					=  orig_state_dict.pop(lowerCAmelCase__   )

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




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  get_videomae_config(lowerCAmelCase__   )

   if "finetuned" in model_name:
      __a					=  VideoMAEForVideoClassification(lowerCAmelCase__   )
   else:
      __a					=  VideoMAEForPreTraining(lowerCAmelCase__   )

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

   model.load_state_dict(lowerCAmelCase__   )
   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(lowerCAmelCase__		,				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(lowerCAmelCase__   )

   __a					=  model(**lowerCAmelCase__   )
   __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, 400]   )
      __a					=  torch.tensor([-0.9_2_9_1, -0.4_0_6_1, -0.9_3_0_7]   )
   elif model_name == "videomae-small-finetuned-ssv2":
      __a					=  torch.Size([1, 174]   )
      __a					=  torch.tensor([0.2_6_7_1, -0.4_6_8_9, -0.8_2_3_5]   )
   elif model_name == "videomae-base":
      __a					=  torch.Size([1, 1_408, 1_536]   )
      __a					=  torch.tensor([[0.7_7_3_9, 0.7_9_6_8, 0.7_0_8_9], [0.6_7_0_1, 0.7_4_8_7, 0.6_2_0_9], [0.4_2_8_7, 0.5_1_5_8, 0.4_7_7_3]]   )
   elif model_name == "videomae-base-short":
      __a					=  torch.Size([1, 1_408, 1_536]   )
      __a					=  torch.tensor([[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]]   )
      # we verified the loss both for normalized and unnormalized targets for this one
      __a					=  torch.tensor([0.5_1_4_2]   ) if config.norm_pix_loss else torch.tensor([0.6_4_6_9]   )
   elif model_name == "videomae-large":
      __a					=  torch.Size([1, 1_408, 1_536]   )
      __a					=  torch.tensor([[0.7_1_4_9, 0.7_9_9_7, 0.6_9_6_6], [0.6_7_6_8, 0.7_8_6_9, 0.6_9_4_8], [0.5_1_3_9, 0.6_2_2_1, 0.5_6_0_5]]   )
   elif model_name == "videomae-large-finetuned-kinetics":
      __a					=  torch.Size([1, 400]   )
      __a					=  torch.tensor([0.0_7_7_1, 0.0_0_1_1, -0.3_6_2_5]   )
   elif model_name == "videomae-huge-finetuned-kinetics":
      __a					=  torch.Size([1, 400]   )
      __a					=  torch.tensor([0.2_4_3_3, 0.1_6_3_2, -0.4_8_9_4]   )
   elif model_name == "videomae-base-short-finetuned-kinetics":
      __a					=  torch.Size([1, 400]   )
      __a					=  torch.tensor([0.6_5_8_8, 0.0_9_9_0, -0.2_4_9_3]   )
   elif model_name == "videomae-base-finetuned-kinetics":
      __a					=  torch.Size([1, 400]   )
      __a					=  torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1]   )
   elif model_name == "videomae-base-short-ssv2":
      __a					=  torch.Size([1, 1_408, 1_536]   )
      __a					=  torch.tensor([[0.4_7_1_2, 0.5_2_9_6, 0.5_7_8_6], [0.2_2_7_8, 0.2_7_2_9, 0.4_0_2_6], [0.0_3_5_2, 0.0_7_3_0, 0.2_5_0_6]]   )
   elif model_name == "videomae-base-short-finetuned-ssv2":
      __a					=  torch.Size([1, 174]   )
      __a					=  torch.tensor([-0.0_5_3_7, -0.1_5_3_9, -0.3_2_6_6]   )
   elif model_name == "videomae-base-ssv2":
      __a					=  torch.Size([1, 1_408, 1_536]   )
      __a					=  torch.tensor([[0.8_1_3_1, 0.8_7_2_7, 0.8_5_4_6], [0.7_3_6_6, 0.9_3_7_7, 0.8_8_7_0], [0.5_9_3_5, 0.8_8_7_4, 0.8_5_6_4]]   )
   elif model_name == "videomae-base-finetuned-ssv2":
      __a					=  torch.Size([1, 174]   )
      __a					=  torch.tensor([0.1_9_6_1, -0.8_3_3_7, -0.6_3_8_9]   )
   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]		,				lowerCAmelCase__		,				atol=1E-4   )
   else:
      print("""Logits:"""		,				logits[0, :3, :3]   )
      assert torch.allclose(logits[0, :3, :3]		,				lowerCAmelCase__		,				atol=1E-4   )
   print("""Logits ok!"""   )

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

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


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

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










'''simple docstring'''

from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							["""vqvae"""]
 def __init__(							self :Dict		,			lowercase_ :AutoencoderKL		,			lowercase_ :UNetaDConditionModel		,			lowercase_ :Mel		,			lowercase_ :Union[DDIMScheduler, DDPMScheduler]		,			)->				Optional[Any]:
     super().__init__()
     self.register_modules(unet=lowercase_		,			scheduler=lowercase_		,			mel=lowercase_		,			vqvae=lowercase_				)
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				int:
     return 50 if isinstance(self.scheduler		,			lowercase_				) else 10_00
 @torch.no_grad()
 def __call__(							self :Optional[Any]		,			lowercase_ :int = 1		,			lowercase_ :str = None		,			lowercase_ :np.ndarray = None		,			lowercase_ :int = 0		,			lowercase_ :int = 0		,			lowercase_ :int = None		,			lowercase_ :torch.Generator = None		,			lowercase_ :float = 0		,			lowercase_ :float = 0		,			lowercase_ :torch.Generator = None		,			lowercase_ :float = 0		,			lowercase_ :torch.Tensor = None		,			lowercase_ :torch.Tensor = None		,			lowercase_ :Tuple=True		,			)->				Union[
     Union[AudioPipelineOutput, ImagePipelineOutput],
     Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
 ]:
     A__							=     steps or self.get_default_steps()
     self.scheduler.set_timesteps(lowercase_				)
     A__							=     step_generator or generator
     # For backwards compatibility
     if type(self.unet.config.sample_size				) == int:
         A__							=     (self.unet.config.sample_size, self.unet.config.sample_size)
     if noise is None:
         A__							=     randn_tensor(
             (
                 batch_size,
                 self.unet.config.in_channels,
                 self.unet.config.sample_size[0],
                 self.unet.config.sample_size[1],
             )		,			generator=lowercase_		,			device=self.device		,			)
     A__							=     noise
     A__							=     None

     if audio_file is not None or raw_audio is not None:
         self.mel.load_audio(lowercase_		,			lowercase_				)
         A__							=     self.mel.audio_slice_to_image(lowercase_				)
         A__							=     np.frombuffer(input_image.tobytes()		,			dtype="uint8"				).reshape(
             (input_image.height, input_image.width)				)
         A__							=     (input_image / 2_55) * 2 - 1
         A__							=     torch.tensor(input_image[np.newaxis, :, :]		,			dtype=torch.float				).to(self.device				)

         if self.vqvae is not None:
             A__							=     self.vqvae.encode(torch.unsqueeze(lowercase_		,			0				)				).latent_dist.sample(
                 generator=lowercase_				)[0]
             A__							=     self.vqvae.config.scaling_factor * input_images

         if start_step > 0:
             A__							=     self.scheduler.add_noise(lowercase_		,			lowercase_		,			self.scheduler.timesteps[start_step - 1]				)

         A__							=     (
             self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
         )
         A__							=     int(mask_start_secs * pixels_per_second				)
         A__							=     int(mask_end_secs * pixels_per_second				)
         A__							=     self.scheduler.add_noise(lowercase_		,			lowercase_		,			torch.tensor(self.scheduler.timesteps[start_step:]				)				)

     for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]				)				):
         if isinstance(self.unet		,			lowercase_				):
             A__							=     self.unet(lowercase_		,			lowercase_		,			lowercase_				)["sample"]
         else:
             A__							=     self.unet(lowercase_		,			lowercase_				)["sample"]

         if isinstance(self.scheduler		,			lowercase_				):
             A__							=     self.scheduler.step(
                 model_output=lowercase_		,			timestep=lowercase_		,			sample=lowercase_		,			eta=lowercase_		,			generator=lowercase_		,			)["prev_sample"]
         else:
             A__							=     self.scheduler.step(
                 model_output=lowercase_		,			timestep=lowercase_		,			sample=lowercase_		,			generator=lowercase_		,			)["prev_sample"]

         if mask is not None:
             if mask_start > 0:
                 A__							=     mask[:, step, :, :mask_start]
             if mask_end > 0:
                 A__							=     mask[:, step, :, -mask_end:]

     if self.vqvae is not None:
         # 0.18215 was scaling factor used in training to ensure unit variance
         A__							=     1 / self.vqvae.config.scaling_factor * images
         A__							=     self.vqvae.decode(lowercase_				)["sample"]

     A__							=     (images / 2 + 0.5).clamp(0		,			1				)
     A__							=     images.cpu().permute(0		,			2		,			3		,			1				).numpy()
     A__							=     (images * 2_55).round().astype("uint8"				)
     A__							=     list(
         (Image.fromarray(_[:, :, 0]				) for _ in images)
         if images.shape[3] == 1
         else (Image.fromarray(lowercase_		,			mode="RGB"				).convert("L"				) for _ in images)				)

     A__							=     [self.mel.image_to_audio(lowercase_				) for _ in images]
     if not return_dict:
         return images, (self.mel.get_sample_rate(), audios)

     return BaseOutput(**AudioPipelineOutput(np.array(lowercase_				)[:, np.newaxis, :]				)		,			**ImagePipelineOutput(lowercase_				)				)
 @torch.no_grad()
 def 	UpperCAmelCase_				(							self :Optional[Any]		,			lowercase_ :List[Image.Image]		,			lowercase_ :int = 50				)->				np.ndarray:
     assert isinstance(self.scheduler		,			lowercase_				)
     self.scheduler.set_timesteps(lowercase_				)
     A__							=     np.array(
         [np.frombuffer(image.tobytes()		,			dtype="uint8"				).reshape((1, image.height, image.width)				) for image in images]				)
     A__							=     (sample / 2_55) * 2 - 1
     A__							=     torch.Tensor(lowercase_				).to(self.device				)

     for t in self.progress_bar(torch.flip(self.scheduler.timesteps		,			(0,)				)				):
         A__							=     t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
         A__							=     self.scheduler.alphas_cumprod[t]
         A__							=     (
             self.scheduler.alphas_cumprod[prev_timestep]
             if prev_timestep >= 0
             else self.scheduler.final_alpha_cumprod
         )
         A__							=     1 - alpha_prod_t
         A__							=     self.unet(lowercase_		,			lowercase_				)["sample"]
         A__							=     (1 - alpha_prod_t_prev) ** 0.5 * model_output
         A__							=     (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
         A__							=     sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

     return sample



 @staticmethod
 def 	UpperCAmelCase_				(							lowercase_ :torch.Tensor		,			lowercase_ :torch.Tensor		,			lowercase_ :float				)->				torch.Tensor:
     A__							=     acos(torch.dot(torch.flatten(lowercase_				)		,			torch.flatten(lowercase_				)				) / torch.norm(lowercase_				) / torch.norm(lowercase_				)				)
     return sin((1 - alpha) * theta				) * xa / sin(lowercase_				) + sin(alpha * theta				) * xa / sin(lowercase_				)



