




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 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       int:
   snake_case				:    Any           =	VideoMAEConfig()

   set_architecture_configs(lowercase						,lowercase					)

   if "finetuned" not in model_name:
      snake_case				:    str           =	False

   if "finetuned" in model_name:
      snake_case				:    Any           =	"""huggingface/label-files"""
      if "kinetics" in model_name:
         snake_case				:    List[str]           =	400
         snake_case				:    Tuple           =	"""kinetics400-id2label.json"""
      elif "ssv2" in model_name:
         snake_case				:    Dict           =	174
         snake_case				:    List[Any]           =	"""something-something-v2-id2label.json"""
      else:
         raise ValueError("""Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned."""					)
      snake_case				:    List[str]           =	json.load(open(hf_hub_download(lowercase						,lowercase						,repo_type="""dataset"""					)						,"""r"""					)					)
      snake_case				:    List[str]           =	{int(lowercase					): v for k, v in idalabel.items()}
      snake_case				:    List[Any]           =	idalabel
      snake_case				:    List[Any]           =	{v: k for k, v in idalabel.items()}

   return config




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Any:
   if "small" in model_name:
      snake_case				:    str           =	384
      snake_case				:    str           =	1536
      snake_case				:    Optional[Any]           =	12
      snake_case				:    Optional[int]           =	16
      snake_case				:    Any           =	12
      snake_case				:    Any           =	3
      snake_case				:    List[Any]           =	192
      snake_case				:    List[Any]           =	768
   elif "large" in model_name:
      snake_case				:    Optional[int]           =	1024
      snake_case				:    Optional[Any]           =	4096
      snake_case				:    Optional[Any]           =	24
      snake_case				:    List[Any]           =	16
      snake_case				:    Dict           =	12
      snake_case				:    Tuple           =	8
      snake_case				:    Tuple           =	512
      snake_case				:    Tuple           =	2048
   elif "huge" in model_name:
      snake_case				:    Optional[Any]           =	1280
      snake_case				:    Union[str, Any]           =	5120
      snake_case				:    Union[str, Any]           =	32
      snake_case				:    Any           =	16
      snake_case				:    int           =	12
      snake_case				:    Tuple           =	8
      snake_case				:    Dict           =	640
      snake_case				:    Optional[Any]           =	2560
   elif "base" not in model_name:
      raise ValueError("""Model name should include either \"small\", \"base\", \"large\", or \"huge\""""					)




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

   return name




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Optional[int]:
   for key in orig_state_dict.copy().keys():
      snake_case				:    List[Any]           =	orig_state_dict.pop(lowercase					)

      if key.startswith("""encoder."""					):
         snake_case				:    Optional[int]           =	key.replace("""encoder."""						,""""""					)

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

   return orig_state_dict




def 						SCREAMING_SNAKE_CASE__ (   )  ->       int:
   snake_case				:    Optional[int]           =	hf_hub_download(
       repo_id="""hf-internal-testing/spaghetti-video"""						,filename="""eating_spaghetti.npy"""						,repo_type="""dataset"""					)
   snake_case				:    Union[str, Any]           =	np.load(lowercase					)
   return list(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase						,lowercase					)  ->       Optional[Any]:
   snake_case				:    Tuple           =	get_videomae_config(lowercase					)

   if "finetuned" in model_name:
      snake_case				:    Dict           =	VideoMAEForVideoClassification(lowercase					)
   else:
      snake_case				:    Optional[Any]           =	VideoMAEForPreTraining(lowercase					)

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

   model.load_state_dict(lowercase					)
   model.eval()

   # verify model on basic input
   snake_case				:    List[Any]           =	VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]						,image_std=[0.5, 0.5, 0.5]					)
   snake_case				:    Optional[Any]           =	prepare_video()
   snake_case				:    List[str]           =	image_processor(lowercase						,return_tensors="""pt"""					)

   if "finetuned" not in model_name:
      snake_case				:    Dict           =	hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos"""						,filename="""bool_masked_pos.pt"""					)
      snake_case				:    List[str]           =	torch.load(lowercase					)

   snake_case				:    Any           =	model(**lowercase					)
   snake_case				:    List[Any]           =	outputs.logits

   snake_case				:    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":
      snake_case				:    Tuple           =	torch.Size([1, 400]					)
      snake_case				:    Dict           =	torch.tensor([-0.9291, -0.4061, -0.9307]					)
   elif model_name == "videomae-small-finetuned-ssv2":
      snake_case				:    List[str]           =	torch.Size([1, 174]					)
      snake_case				:    Optional[Any]           =	torch.tensor([0.2671, -0.4689, -0.8235]					)
   elif model_name == "videomae-base":
      snake_case				:    List[str]           =	torch.Size([1, 1408, 1536]					)
      snake_case				:    List[str]           =	torch.tensor([[0.7739, 0.7968, 0.7089], [0.6701, 0.7487, 0.6209], [0.4287, 0.5158, 0.4773]]					)
   elif model_name == "videomae-base-short":
      snake_case				:    Tuple           =	torch.Size([1, 1408, 1536]					)
      snake_case				:    List[Any]           =	torch.tensor([[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]					)
      # we verified the loss both for normalized and unnormalized targets for this one
      snake_case				:    List[str]           =	torch.tensor([0.5142]					) if config.norm_pix_loss else torch.tensor([0.6469]					)
   elif model_name == "videomae-large":
      snake_case				:    List[str]           =	torch.Size([1, 1408, 1536]					)
      snake_case				:    Union[str, Any]           =	torch.tensor([[0.7149, 0.7997, 0.6966], [0.6768, 0.7869, 0.6948], [0.5139, 0.6221, 0.5605]]					)
   elif model_name == "videomae-large-finetuned-kinetics":
      snake_case				:    str           =	torch.Size([1, 400]					)
      snake_case				:    Dict           =	torch.tensor([0.0771, 0.0011, -0.3625]					)
   elif model_name == "videomae-huge-finetuned-kinetics":
      snake_case				:    Tuple           =	torch.Size([1, 400]					)
      snake_case				:    Any           =	torch.tensor([0.2433, 0.1632, -0.4894]					)
   elif model_name == "videomae-base-short-finetuned-kinetics":
      snake_case				:    int           =	torch.Size([1, 400]					)
      snake_case				:    Tuple           =	torch.tensor([0.6588, 0.0990, -0.2493]					)
   elif model_name == "videomae-base-finetuned-kinetics":
      snake_case				:    Optional[Any]           =	torch.Size([1, 400]					)
      snake_case				:    Dict           =	torch.tensor([0.3669, -0.0688, -0.2421]					)
   elif model_name == "videomae-base-short-ssv2":
      snake_case				:    Tuple           =	torch.Size([1, 1408, 1536]					)
      snake_case				:    Optional[Any]           =	torch.tensor([[0.4712, 0.5296, 0.5786], [0.2278, 0.2729, 0.4026], [0.0352, 0.0730, 0.2506]]					)
   elif model_name == "videomae-base-short-finetuned-ssv2":
      snake_case				:    Optional[int]           =	torch.Size([1, 174]					)
      snake_case				:    str           =	torch.tensor([-0.0537, -0.1539, -0.3266]					)
   elif model_name == "videomae-base-ssv2":
      snake_case				:    Any           =	torch.Size([1, 1408, 1536]					)
      snake_case				:    Tuple           =	torch.tensor([[0.8131, 0.8727, 0.8546], [0.7366, 0.9377, 0.8870], [0.5935, 0.8874, 0.8564]]					)
   elif model_name == "videomae-base-finetuned-ssv2":
      snake_case				:    Union[str, Any]           =	torch.Size([1, 174]					)
      snake_case				:    int           =	torch.tensor([0.1961, -0.8337, -0.6389]					)
   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]						,lowercase						,atol=1E-4					)
   else:
      print("""Logits:"""						,logits[0, :3, :3]					)
      assert torch.allclose(logits[0, :3, :3]						,lowercase						,atol=1E-4					)
   print("""Logits ok!"""					)

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

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


if __name__ == "__main__":
       lowerCamelCase	:	Optional[Any]				=   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	:	Optional[Any]				=   parser.parse_args()
       convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)











from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments





