


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'''

import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 						a_		(     snake_case    ):
							UpperCAmelCase			:		str        =				(CMStochasticIterativeScheduler,)
							UpperCAmelCase			:		int        =				10


							def    UpperCamelCase     (				self   :						Dict      ,						**a_   :						List[str]			)			->					Any:
										snake_case:    Any      							={
										    'num_train_timesteps': 2_0_1,
										    'sigma_min': 0.0_0_2,
										    'sigma_max': 8_0.0,
										}

										config.update(**a_			)
										return config


							def    UpperCamelCase     (				self   :						List[Any]			)			->					List[Any]:
										snake_case:    Any      							=1_0

										snake_case:    List[str]      							=self.get_scheduler_config()
										snake_case:    List[Any]      							=self.scheduler_classes[0](**a_			)

										scheduler.set_timesteps(a_			)

										snake_case:    Dict      							=scheduler.timesteps[0]
										snake_case:    Union[str, Any]      							=scheduler.timesteps[1]

										snake_case:    List[str]      							=self.dummy_sample
										snake_case:    List[str]      							=0.1 * sample

										snake_case:    int      							=scheduler.step(a_      ,						a_      ,						a_			).prev_sample
										snake_case:    Optional[Any]      							=scheduler.step(a_      ,						a_      ,						a_			).prev_sample

										self.assertEqual(output_a.shape      ,						sample.shape			)
										self.assertEqual(output_a.shape      ,						output_a.shape			)


							def    UpperCamelCase     (				self   :						int			)			->					int:
										for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=a_			)


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Dict:
										for clip_denoised in [True, False]:
													self.check_over_configs(clip_denoised=a_			)


							def    UpperCamelCase     (				self   :						Tuple			)			->					List[str]:
										snake_case:    List[Any]      							=self.scheduler_classes[0]
										snake_case:    List[Any]      							=self.get_scheduler_config()
										snake_case:    Any      							=scheduler_class(**a_			)

										snake_case:    Dict      							=1
										scheduler.set_timesteps(a_			)
										snake_case:    List[Any]      							=scheduler.timesteps

										snake_case:    Optional[Any]      							=torch.manual_seed(0			)

										snake_case:    Optional[Any]      							=self.dummy_model()
										snake_case:    List[Any]      							=self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(a_			):
													# 1. scale model input
													snake_case:    Any      							=scheduler.scale_model_input(a_      ,						a_			)

													# 2. predict noise residual
													snake_case:    List[str]      							=model(a_      ,						a_			)

													# 3. predict previous sample x_t-1
													snake_case:    Dict      							=scheduler.step(a_      ,						a_      ,						a_      ,						generator=a_			).prev_sample

													snake_case:    List[Any]      							=pred_prev_sample

										snake_case:    Optional[Any]      							=torch.sum(torch.abs(a_			)			)
										snake_case:    Optional[Any]      							=torch.mean(torch.abs(a_			)			)

										assert abs(result_sum.item() - 1_9_2.7_6_1_4			) < 1E-2
										assert abs(result_mean.item() - 0.2_5_1_0			) < 1E-3


							def    UpperCamelCase     (				self   :						Dict			)			->					Union[str, Any]:
										snake_case:    Dict      							=self.scheduler_classes[0]
										snake_case:    Tuple      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    List[Any]      							=[1_0_6, 0]
										scheduler.set_timesteps(timesteps=a_			)
										snake_case:    Optional[Any]      							=scheduler.timesteps

										snake_case:    Dict      							=torch.manual_seed(0			)

										snake_case:    Optional[int]      							=self.dummy_model()
										snake_case:    Any      							=self.dummy_sample_deter * scheduler.init_noise_sigma

										for t in timesteps:
													# 1. scale model input
													snake_case:    List[Any]      							=scheduler.scale_model_input(a_      ,						a_			)

													# 2. predict noise residual
													snake_case:    Any      							=model(a_      ,						a_			)

													# 3. predict previous sample x_t-1
													snake_case:    List[str]      							=scheduler.step(a_      ,						a_      ,						a_      ,						generator=a_			).prev_sample

													snake_case:    Optional[Any]      							=pred_prev_sample

										snake_case:    Union[str, Any]      							=torch.sum(torch.abs(a_			)			)
										snake_case:    Tuple      							=torch.mean(torch.abs(a_			)			)

										assert abs(result_sum.item() - 3_4_7.6_3_5_7			) < 1E-2
										assert abs(result_mean.item() - 0.4_5_2_7			) < 1E-3


							def    UpperCamelCase     (				self   :						int			)			->					Tuple:
										snake_case:    List[Any]      							=self.scheduler_classes[0]
										snake_case:    Union[str, Any]      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    str      							=[3_9, 3_0, 1_2, 1_5, 0]

										with self.assertRaises(a_      ,						msg='`timesteps` must be in descending order.'			):
													scheduler.set_timesteps(timesteps=a_			)


							def    UpperCamelCase     (				self   :						Dict			)			->					Optional[int]:
										snake_case:    Optional[Any]      							=self.scheduler_classes[0]
										snake_case:    Dict      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    Any      							=[3_9, 3_0, 1_2, 1, 0]
										snake_case:    List[Any]      							=len(a_			)

										with self.assertRaises(a_      ,						msg='Can only pass one of `num_inference_steps` or `timesteps`.'			):
													scheduler.set_timesteps(num_inference_steps=a_      ,						timesteps=a_			)




							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Tuple:
										snake_case:    Any      							=self.scheduler_classes[0]
										snake_case:    int      							=self.get_scheduler_config()
										snake_case:    Optional[Any]      							=scheduler_class(**a_			)

										snake_case:    List[Any]      							=[scheduler.config.num_train_timesteps]

										with self.assertRaises(
										    a_      ,						msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'      ,						):
													scheduler.set_timesteps(timesteps=a_			)




