


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_						(			__A :			int				)	->		Any:

       """simple docstring"""

       a_					:		Optional[int]							=  VideoMAEConfig()

       set_architecture_configs(_lowerCAmelCase  ,    _lowerCAmelCase				)

       if "finetuned" not in model_name:
              a_					:		List[str]							=  False

       if "finetuned" in model_name:
              a_					:		Union[str, Any]							=  """huggingface/label-files"""
              if "kinetics" in model_name:
                     a_					:		Tuple							=  4_00
                     a_					:		List[Any]							=  """kinetics400-id2label.json"""
              elif "ssv2" in model_name:
                     a_					:		Dict							=  1_74
                     a_					:		Optional[Any]							=  """something-something-v2-id2label.json"""
              else:
                     raise ValueError('Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.'				)
              a_					:		Union[str, Any]							=  json.load(open(hf_hub_download(_lowerCAmelCase  ,    _lowerCAmelCase  ,    repo_type='dataset'				)  ,    'r'				)				)
              a_					:		Any							=  {int(_lowerCAmelCase				): v for k, v in idalabel.items()}
              a_					:		Union[str, Any]							=  idalabel
              a_					:		Tuple							=  {v: k for k, v in idalabel.items()}

       return config







def        SCREAMING_SNAKE_CASE_						(			__A :			Tuple  ,    __A :			str				)	->		Any:

       """simple docstring"""

       if "small" in model_name:
              a_					:		Optional[int]							=  3_84
              a_					:		int							=  15_36
              a_					:		int							=  12
              a_					:		List[str]							=  16
              a_					:		Tuple							=  12
              a_					:		Optional[Any]							=  3
              a_					:		List[str]							=  1_92
              a_					:		List[str]							=  7_68
       elif "large" in model_name:
              a_					:		Dict							=  10_24
              a_					:		List[str]							=  40_96
              a_					:		List[str]							=  24
              a_					:		Tuple							=  16
              a_					:		List[Any]							=  12
              a_					:		Optional[int]							=  8
              a_					:		Dict							=  5_12
              a_					:		Tuple							=  20_48
       elif "huge" in model_name:
              a_					:		Tuple							=  12_80
              a_					:		Tuple							=  51_20
              a_					:		int							=  32
              a_					:		List[Any]							=  16
              a_					:		Any							=  12
              a_					:		int							=  8
              a_					:		List[str]							=  6_40
              a_					:		List[str]							=  25_60
       elif "base" not in model_name:
              raise ValueError('Model name should include either \"small\", \"base\", \"large\", or \"huge\"'				)







def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[int]				)	->		Optional[Any]:

       """simple docstring"""

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

       return name







def        SCREAMING_SNAKE_CASE_						(			__A :			List[str]  ,    __A :			Optional[int]				)	->		List[Any]:

       """simple docstring"""

       for key in orig_state_dict.copy().keys():
              a_					:		List[Any]							=  orig_state_dict.pop(_lowerCAmelCase				)

              if key.startswith('encoder.'				):
                     a_					:		int							=  key.replace('encoder.'  ,    ''				)

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

       return orig_state_dict







def        SCREAMING_SNAKE_CASE_						(			)	->		Optional[Any]:

       """simple docstring"""

       a_					:		Any							=  hf_hub_download(
           repo_id='hf-internal-testing/spaghetti-video'  ,    filename='eating_spaghetti.npy'  ,    repo_type='dataset'				)
       a_					:		Optional[Any]							=  np.load(_lowerCAmelCase				)
       return list(_lowerCAmelCase				)







def        SCREAMING_SNAKE_CASE_						(			__A :			int  ,    __A :			Optional[int]  ,    __A :			Any  ,    __A :			Dict				)	->		Dict:

       """simple docstring"""

       a_					:		str							=  get_videomae_config(_lowerCAmelCase				)

       if "finetuned" in model_name:
              a_					:		Any							=  VideoMAEForVideoClassification(_lowerCAmelCase				)
       else:
              a_					:		Optional[Any]							=  VideoMAEForPreTraining(_lowerCAmelCase				)

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

       model.load_state_dict(_lowerCAmelCase				)
       model.eval()

       # verify model on basic input
       a_					:		str							=  VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]  ,    image_std=[0.5, 0.5, 0.5]				)
       a_					:		List[Any]							=  prepare_video()
       a_					:		Optional[int]							=  image_processor(_lowerCAmelCase  ,    return_tensors='pt'				)

       if "finetuned" not in model_name:
              a_					:		Optional[int]							=  hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos'  ,    filename='bool_masked_pos.pt'				)
              a_					:		Optional[Any]							=  torch.load(_lowerCAmelCase				)

       a_					:		int							=  model(**_lowerCAmelCase				)
       a_					:		Tuple							=  outputs.logits

       a_					:		Union[str, 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":
              a_					:		List[Any]							=  torch.Size([1, 4_00]				)
              a_					:		Optional[Any]							=  torch.tensor([-0.9291, -0.4061, -0.9307]				)
       elif model_name == "videomae-small-finetuned-ssv2":
              a_					:		Union[str, Any]							=  torch.Size([1, 1_74]				)
              a_					:		List[str]							=  torch.tensor([0.2671, -0.4689, -0.8235]				)
       elif model_name == "videomae-base":
              a_					:		List[Any]							=  torch.Size([1, 14_08, 15_36]				)
              a_					:		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":
              a_					:		List[str]							=  torch.Size([1, 14_08, 15_36]				)
              a_					:		List[str]							=  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
              a_					:		Optional[Any]							=  torch.tensor([0.5142]				) if config.norm_pix_loss else torch.tensor([0.6469]				)
       elif model_name == "videomae-large":
              a_					:		Tuple							=  torch.Size([1, 14_08, 15_36]				)
              a_					:		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":
              a_					:		Any							=  torch.Size([1, 4_00]				)
              a_					:		List[Any]							=  torch.tensor([0.0771, 0.0011, -0.3625]				)
       elif model_name == "videomae-huge-finetuned-kinetics":
              a_					:		List[str]							=  torch.Size([1, 4_00]				)
              a_					:		Union[str, Any]							=  torch.tensor([0.2433, 0.1632, -0.4894]				)
       elif model_name == "videomae-base-short-finetuned-kinetics":
              a_					:		Dict							=  torch.Size([1, 4_00]				)
              a_					:		Optional[int]							=  torch.tensor([0.6588, 0.0990, -0.2493]				)
       elif model_name == "videomae-base-finetuned-kinetics":
              a_					:		int							=  torch.Size([1, 4_00]				)
              a_					:		Union[str, Any]							=  torch.tensor([0.3669, -0.0688, -0.2421]				)
       elif model_name == "videomae-base-short-ssv2":
              a_					:		List[str]							=  torch.Size([1, 14_08, 15_36]				)
              a_					:		Dict							=  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":
              a_					:		str							=  torch.Size([1, 1_74]				)
              a_					:		Any							=  torch.tensor([-0.0537, -0.1539, -0.3266]				)
       elif model_name == "videomae-base-ssv2":
              a_					:		Union[str, Any]							=  torch.Size([1, 14_08, 15_36]				)
              a_					:		List[str]							=  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":
              a_					:		Optional[Any]							=  torch.Size([1, 1_74]				)
              a_					:		List[Any]							=  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":
              a_					:		Tuple							=  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__":
   UpperCAmelCase_      : int  					=       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.'
   )

   UpperCAmelCase_      : int  					=       parser.parse_args()
   convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


SCREAMING_SNAKE_CASE_:Dict             =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:int             =						{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE_:Optional[Any]             =						[
    """small""",
    """small-base""",
    """medium""",
    """medium-base""",
    """intermediate""",
    """intermediate-base""",
    """large""",
    """large-base""",
    """xlarge""",
    """xlarge-base""",
]

SCREAMING_SNAKE_CASE_:int             =						{
    """vocab_file""": {
        """funnel-transformer/small""": """https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt""",
        """funnel-transformer/small-base""": """https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt""",
        """funnel-transformer/medium""": """https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt""",
        """funnel-transformer/medium-base""": (
            """https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt"""
        ),
        """funnel-transformer/intermediate""": (
            """https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt"""
        ),
        """funnel-transformer/intermediate-base""": (
            """https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt"""
        ),
        """funnel-transformer/large""": """https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt""",
        """funnel-transformer/large-base""": """https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt""",
        """funnel-transformer/xlarge""": """https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt""",
        """funnel-transformer/xlarge-base""": (
            """https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """funnel-transformer/small""": """https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json""",
        """funnel-transformer/small-base""": (
            """https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json"""
        ),
        """funnel-transformer/medium""": """https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json""",
        """funnel-transformer/medium-base""": (
            """https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json"""
        ),
        """funnel-transformer/intermediate""": (
            """https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json"""
        ),
        """funnel-transformer/intermediate-base""": (
            """https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json"""
        ),
        """funnel-transformer/large""": """https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json""",
        """funnel-transformer/large-base""": (
            """https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json"""
        ),
        """funnel-transformer/xlarge""": """https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json""",
        """funnel-transformer/xlarge-base""": (
            """https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json"""
        ),
    },
}
SCREAMING_SNAKE_CASE_:int             =						{F"""funnel-transformer/{name}""": 512 for name in _model_names}
SCREAMING_SNAKE_CASE_:Dict             =						{F"""funnel-transformer/{name}""": {"""do_lower_case""": True} for name in _model_names}






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: List[str]					=      VOCAB_FILES_NAMES
    __lowerCamelCase			: Union[str, Any]					=      PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase			: str					=      PRETRAINED_INIT_CONFIGURATION
    __lowerCamelCase			: Tuple					=      FunnelTokenizer
    __lowerCamelCase			: str					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase			: int					=      2






    def __init__(       self,		lowerCamelCase__=None,		lowerCamelCase__=None,		lowerCamelCase__=True,		lowerCamelCase__="<unk>",		lowerCamelCase__="<sep>",		lowerCamelCase__="<pad>",		lowerCamelCase__="<cls>",		lowerCamelCase__="<mask>",		lowerCamelCase__="<s>",		lowerCamelCase__="</s>",		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=None,		lowerCamelCase__="##",		**lowerCamelCase__,		):
        super().__init__(
            lowerCamelCase__,		tokenizer_file=lowerCamelCase__,		do_lower_case=lowerCamelCase__,		unk_token=lowerCamelCase__,		sep_token=lowerCamelCase__,		pad_token=lowerCamelCase__,		cls_token=lowerCamelCase__,		mask_token=lowerCamelCase__,		bos_token=lowerCamelCase__,		eos_token=lowerCamelCase__,		clean_text=lowerCamelCase__,		tokenize_chinese_chars=lowerCamelCase__,		strip_accents=lowerCamelCase__,		wordpieces_prefix=lowerCamelCase__,		**lowerCamelCase__,		)

        A :       List[str]          =   json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
        if (
            normalizer_state.get("""lowercase""",		lowerCamelCase__		) != do_lower_case
            or normalizer_state.get("""strip_accents""",		lowerCamelCase__		) != strip_accents
            or normalizer_state.get("""handle_chinese_chars""",		lowerCamelCase__		) != tokenize_chinese_chars
        ):
            A :       Optional[Any]          =   getattr(lowerCamelCase__,		normalizer_state.pop("""type"""		)		)
            A :       Union[str, Any]          =   do_lower_case
            A :       List[Any]          =   strip_accents
            A :       Optional[int]          =   tokenize_chinese_chars
            A :       Dict          =   normalizer_class(**lowerCamelCase__		)

        A :       List[Any]          =   do_lower_case






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__=None		):
        A :       Union[str, Any]          =   [self.cls_token_id] + token_ids_a + [self.sep_token_id]

        if token_ids_a:
            output += token_ids_a + [self.sep_token_id]

        return output






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = None		):
        A :       Any          =   [self.sep_token_id]
        A :       Any          =   [self.cls_token_id]
        if token_ids_a is None:
            return len(cls		) * [self.cls_token_type_id] + len(token_ids_a + sep		) * [0]
        return len(cls		) * [self.cls_token_type_id] + len(token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = None		):
        A :       int          =   self._tokenizer.model.save(lowerCamelCase__,		name=lowerCamelCase__		)
        return tuple(lowerCamelCase__		)


