
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   (       __lowerCamelCase	:					Dict       ):
       snake_case			:  Any     							= VideoMAEConfig()

       set_architecture_configs(__lowerCamelCase							,   __lowerCamelCase       )

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

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

       return config


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


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

       return name


def      UpperCamelCase   (       __lowerCamelCase	:					Optional[Any]							,   __lowerCamelCase	:					Any       ):
       for key in orig_state_dict.copy().keys():
              snake_case			:  Optional[Any]     							= orig_state_dict.pop(__lowerCamelCase       )

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

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

       return orig_state_dict


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


def      UpperCamelCase   (       __lowerCamelCase	:					Optional[Any]							,   __lowerCamelCase	:					Union[str, Any]							,   __lowerCamelCase	:					Optional[Any]							,   __lowerCamelCase	:					Any       ):
       snake_case			:  List[str]     							= get_videomae_config(__lowerCamelCase       )

       if "finetuned" in model_name:
              snake_case			:  List[Any]     							= VideoMAEForVideoClassification(__lowerCamelCase       )
       else:
              snake_case			:  List[str]     							= VideoMAEForPreTraining(__lowerCamelCase       )

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

       model.load_state_dict(__lowerCamelCase       )
       model.eval()

       # verify model on basic input
       snake_case			:  Tuple     							= VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]							,   image_std=[0.5, 0.5, 0.5]       )
       snake_case			:  Dict     							= prepare_video()
       snake_case			:  Dict     							= image_processor(__lowerCamelCase							,   return_tensors="pt"       )

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

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

       snake_case			:  Any     							= [
           "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			:  List[str]     							= torch.Size([1, 400]       )
              snake_case			:  List[str]     							= torch.tensor([-0.9291, -0.4061, -0.9307]       )
       elif model_name == "videomae-small-finetuned-ssv2":
              snake_case			:  Dict     							= torch.Size([1, 174]       )
              snake_case			:  Tuple     							= torch.tensor([0.2671, -0.4689, -0.8235]       )
       elif model_name == "videomae-base":
              snake_case			:  Union[str, Any]     							= torch.Size([1, 1408, 1536]       )
              snake_case			:  Union[str, Any]     							= 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			:  str     							= 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			:  int     							= torch.tensor([0.5142]       ) if config.norm_pix_loss else torch.tensor([0.6469]       )
       elif model_name == "videomae-large":
              snake_case			:  Optional[int]     							= torch.Size([1, 1408, 1536]       )
              snake_case			:  Tuple     							= 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			:  Dict     							= torch.Size([1, 400]       )
              snake_case			:  Any     							= torch.tensor([0.0771, 0.0011, -0.3625]       )
       elif model_name == "videomae-huge-finetuned-kinetics":
              snake_case			:  str     							= torch.Size([1, 400]       )
              snake_case			:  List[str]     							= 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			:  List[Any]     							= torch.Size([1, 1408, 1536]       )
              snake_case			:  Union[str, 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			:  Any     							= torch.Size([1, 174]       )
              snake_case			:  str     							= torch.tensor([-0.0537, -0.1539, -0.3266]       )
       elif model_name == "videomae-base-ssv2":
              snake_case			:  Dict     							= torch.Size([1, 1408, 1536]       )
              snake_case			:  List[Any]     							= 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			:  List[Any]     							= torch.Size([1, 174]       )
              snake_case			:  Optional[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]							,   __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":
              snake_case			:  List[Any]     							= 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__":
   __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)







import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


__lowerCamelCase				=			"""\
@article{hendrycksmath2021,
  title={Measuring Mathematical Problem Solving With the MATH Dataset},
  author={Dan Hendrycks
    and Collin Burns
    and Saurav Kadavath
    and Akul Arora
    and Steven Basart
    and Eric Tang
    and Dawn Song
    and Jacob Steinhardt},
  journal={arXiv preprint arXiv:2103.03874},
  year={2021}
}
"""


__lowerCamelCase				=			"""\
This metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.
It first canonicalizes the inputs (e.g., converting \"1/2\" to \"\\frac{1}{2}\") and then computes accuracy.
"""


__lowerCamelCase				=			R"""
Calculates accuracy after canonicalizing inputs.

Args:
    predictions: list of predictions to score. Each prediction
        is a string that contains natural language and LaTex.
    references: list of reference for each prediction. Each
        reference is a string that contains natural language
        and LaTex.
Returns:
    accuracy: accuracy after canonicalizing inputs
        (e.g., converting \"1/2\" to \"\\frac{1}{2}\")

Examples:
    >>> metric = datasets.load_metric(\"competition_math\")
    >>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])
    >>> print(results)
    {'accuracy': 1.0}
"""






@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION			,_KWARGS_DESCRIPTION					)
class     UpperCAmelCase						(		datasets.Metric					):



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]					)						->      Optional[Any]:


          '''simple docstring'''


          return datasets.MetricInfo(
              description=_DESCRIPTION       ,		citation=_CITATION       ,		inputs_description=_KWARGS_DESCRIPTION       ,		features=datasets.Features(
                  {
                      "predictions": datasets.Value("string"					),
                      "references": datasets.Value("string"					),
                  }					)       ,		homepage="https://github.com/hendrycks/math"       ,		codebase_urls=["https://github.com/hendrycks/math"]       ,		)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]       ,		snake_case__						:   str       ,		snake_case__						:   List[Any]					)						->      Optional[Any]:


          '''simple docstring'''


          snake_case			:  Optional[int]     							= 0.0
          for i, j in zip(snake_case__       ,		snake_case__					):
                 n_correct += 1.0 if math_equivalence.is_equiv(snake_case__       ,		snake_case__					) else 0.0
          snake_case			:  Optional[int]     							= n_correct / len(snake_case__					)
          return {
              "accuracy": accuracy,
          }





