




import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig, TimesformerForVideoClassification, VideoMAEImageProcessor
def 					A_			(		snake_case_     :       List[Any]    ):







    '''simple docstring'''
    UpperCamelCase				:   int          =							TimesformerConfig()

    if "large" in model_name:
        UpperCamelCase				:   Optional[int]          =							9_6

    if "hr" in model_name:
        UpperCamelCase				:   Any          =							1_6
        UpperCamelCase				:   Optional[Any]          =							4_4_8

    UpperCamelCase				:   int          =							"huggingface/label-files"
    if "k400" in model_name:
        UpperCamelCase				:   List[Any]          =							4_0_0
        UpperCamelCase				:   int          =							"kinetics400-id2label.json"
    elif "k600" in model_name:
        UpperCamelCase				:   Dict          =							6_0_0
        UpperCamelCase				:   Optional[Any]          =							"kinetics600-id2label.json"
    elif "ssv2" in model_name:
        UpperCamelCase				:   Dict          =							1_7_4
        UpperCamelCase				:   Optional[Any]          =							"something-something-v2-id2label.json"
    else:
        raise ValueError("""Model name should either contain 'k400', 'k600' or 'ssv2'."""    )
    UpperCamelCase				:   str          =							json.load(open(hf_hub_download(snake_case_						,snake_case_						,repo_type="""dataset"""    )						,"""r"""    )    )
    UpperCamelCase				:   List[Any]          =							{int(snake_case_    ): v for k, v in idalabel.items()}
    UpperCamelCase				:   List[Any]          =							idalabel
    UpperCamelCase				:   Any          =							{v: k for k, v in idalabel.items()}

    return config
def 					A_			(		snake_case_     :       Dict    ):







    '''simple docstring'''



    if "encoder." in name:
        UpperCamelCase				:   List[Any]          =							name.replace("""encoder."""						,""""""    )
    if "cls_token" in name:
        UpperCamelCase				:   str          =							name.replace("""cls_token"""						,"""timesformer.embeddings.cls_token"""    )
    if "pos_embed" in name:
        UpperCamelCase				:   Optional[Any]          =							name.replace("""pos_embed"""						,"""timesformer.embeddings.position_embeddings"""    )
    if "time_embed" in name:
        UpperCamelCase				:   int          =							name.replace("""time_embed"""						,"""timesformer.embeddings.time_embeddings"""    )
    if "patch_embed.proj" in name:
        UpperCamelCase				:   int          =							name.replace("""patch_embed.proj"""						,"""timesformer.embeddings.patch_embeddings.projection"""    )
    if "patch_embed.norm" in name:
        UpperCamelCase				:   List[str]          =							name.replace("""patch_embed.norm"""						,"""timesformer.embeddings.norm"""    )
    if "blocks" in name:
        UpperCamelCase				:   str          =							name.replace("""blocks"""						,"""timesformer.encoder.layer"""    )
    if "attn.proj" in name:
        UpperCamelCase				:   Optional[Any]          =							name.replace("""attn.proj"""						,"""attention.output.dense"""    )
    if "attn" in name and "bias" not in name and "temporal" not in name:
        UpperCamelCase				:   str          =							name.replace("""attn"""						,"""attention.self"""    )
    if "attn" in name and "temporal" not in name:
        UpperCamelCase				:   Any          =							name.replace("""attn"""						,"""attention.attention"""    )
    if "temporal_norm1" in name:
        UpperCamelCase				:   Optional[Any]          =							name.replace("""temporal_norm1"""						,"""temporal_layernorm"""    )
    if "temporal_attn.proj" in name:
        UpperCamelCase				:   Dict          =							name.replace("""temporal_attn"""						,"""temporal_attention.output.dense"""    )
    if "temporal_fc" in name:
        UpperCamelCase				:   Union[str, Any]          =							name.replace("""temporal_fc"""						,"""temporal_dense"""    )
    if "norm1" in name and "temporal" not in name:
        UpperCamelCase				:   Any          =							name.replace("""norm1"""						,"""layernorm_before"""    )
    if "norm2" in name:
        UpperCamelCase				:   int          =							name.replace("""norm2"""						,"""layernorm_after"""    )
    if "mlp.fc1" in name:
        UpperCamelCase				:   int          =							name.replace("""mlp.fc1"""						,"""intermediate.dense"""    )
    if "mlp.fc2" in name:
        UpperCamelCase				:   Optional[int]          =							name.replace("""mlp.fc2"""						,"""output.dense"""    )
    if "norm.weight" in name and "fc" not in name and "temporal" not in name:
        UpperCamelCase				:   List[str]          =							name.replace("""norm.weight"""						,"""timesformer.layernorm.weight"""    )
    if "norm.bias" in name and "fc" not in name and "temporal" not in name:
        UpperCamelCase				:   List[Any]          =							name.replace("""norm.bias"""						,"""timesformer.layernorm.bias"""    )
    if "head" in name:
        UpperCamelCase				:   List[Any]          =							name.replace("""head"""						,"""classifier"""    )

    return name
def 					A_			(		snake_case_     :       List[Any]						,snake_case_     :       Any    ):







    '''simple docstring'''



    for key in orig_state_dict.copy().keys():
        UpperCamelCase				:   int          =							orig_state_dict.pop(snake_case_    )

        if key.startswith("""model."""    ):
            UpperCamelCase				:   Optional[int]          =							key.replace("""model."""						,""""""    )

        if "qkv" in key:
            UpperCamelCase				:   Tuple          =							key.split("""."""    )
            UpperCamelCase				:   Optional[Any]          =							int(key_split[1]    )
            UpperCamelCase				:   Tuple          =							"timesformer.encoder.layer."
            if "temporal" in key:
                UpperCamelCase				:   str          =							".temporal_attention.attention.qkv."
            else:
                UpperCamelCase				:   str          =							".attention.attention.qkv."
            if "weight" in key:
                UpperCamelCase				:   str          =							val
            else:
                UpperCamelCase				:   Dict          =							val
        else:
            UpperCamelCase				:   int          =							val

    return orig_state_dict
def 					A_			(		):







    '''simple docstring'''
    UpperCamelCase				:   Dict          =							hf_hub_download(
        repo_id="""hf-internal-testing/spaghetti-video"""						,filename="""eating_spaghetti.npy"""						,repo_type="""dataset"""    )
    UpperCamelCase				:   str          =							np.load(snake_case_    )
    return list(snake_case_    )
def 					A_			(		snake_case_     :       Optional[Any]						,snake_case_     :       Dict						,snake_case_     :       Tuple						,snake_case_     :       Union[str, Any]    ):







    '''simple docstring'''
    UpperCamelCase				:   int          =							get_timesformer_config(snake_case_    )

    UpperCamelCase				:   Dict          =							TimesformerForVideoClassification(snake_case_    )

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

    model.load_state_dict(snake_case_    )
    model.eval()

    # verify model on basic input
    UpperCamelCase				:   Any          =							VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]						,image_std=[0.5, 0.5, 0.5]    )
    UpperCamelCase				:   Optional[int]          =							prepare_video()
    UpperCamelCase				:   Tuple          =							image_processor(video[:8]						,return_tensors="""pt"""    )

    UpperCamelCase				:   int          =							model(**snake_case_    )
    UpperCamelCase				:   Any          =							outputs.logits

    UpperCamelCase				:   List[str]          =							[
        # Kinetics-400 checkpoints (hr = high resolution input of 448px instead of 224px)
        "timesformer-base-finetuned-k400",
        "timesformer-large-finetuned-k400",
        "timesformer-hr-finetuned-k400",
        # Kinetics-600 checkpoints (hr = high resolution input of 448px instead of 224px)
        "timesformer-base-finetuned-k600",
        "timesformer-large-finetuned-k600",
        "timesformer-hr-finetuned-k600",
        # Something-Something-v2 checkpoints (hr = high resolution input of 448px instead of 224px)
        "timesformer-base-finetuned-ssv2",
        "timesformer-large-finetuned-ssv2",
        "timesformer-hr-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 == "timesformer-base-finetuned-k400":
        UpperCamelCase				:   str          =							torch.Size([1, 4_0_0]    )
        UpperCamelCase				:   Optional[Any]          =							torch.tensor([-0.3016, -0.7713, -0.4205]    )
    elif model_name == "timesformer-base-finetuned-k600":
        UpperCamelCase				:   str          =							torch.Size([1, 6_0_0]    )
        UpperCamelCase				:   str          =							torch.tensor([-0.7267, -0.7466, 3.2404]    )
    elif model_name == "timesformer-base-finetuned-ssv2":
        UpperCamelCase				:   int          =							torch.Size([1, 1_7_4]    )
        UpperCamelCase				:   Optional[Any]          =							torch.tensor([-0.9059, 0.6433, -3.1457]    )
    elif model_name == "timesformer-large-finetuned-k400":
        UpperCamelCase				:   List[Any]          =							torch.Size([1, 4_0_0]    )
        UpperCamelCase				:   Optional[int]          =							torch.tensor([0, 0, 0]    )
    elif model_name == "timesformer-large-finetuned-k600":
        UpperCamelCase				:   List[str]          =							torch.Size([1, 6_0_0]    )
        UpperCamelCase				:   Optional[Any]          =							torch.tensor([0, 0, 0]    )
    elif model_name == "timesformer-large-finetuned-ssv2":
        UpperCamelCase				:   Union[str, Any]          =							torch.Size([1, 1_7_4]    )
        UpperCamelCase				:   int          =							torch.tensor([0, 0, 0]    )
    elif model_name == "timesformer-hr-finetuned-k400":
        UpperCamelCase				:   int          =							torch.Size([1, 4_0_0]    )
        UpperCamelCase				:   str          =							torch.tensor([-0.9617, -3.7311, -3.7708]    )
    elif model_name == "timesformer-hr-finetuned-k600":
        UpperCamelCase				:   Optional[Any]          =							torch.Size([1, 6_0_0]    )
        UpperCamelCase				:   Optional[Any]          =							torch.tensor([2.5273, 0.7127, 1.8848]    )
    elif model_name == "timesformer-hr-finetuned-ssv2":
        UpperCamelCase				:   Union[str, Any]          =							torch.Size([1, 1_7_4]    )
        UpperCamelCase				:   str          =							torch.tensor([-3.6756, -0.7513, 0.7180]    )
    else:
        raise ValueError(f'Model name not supported. Should be one of {model_names}'    )

    # verify logits
    assert logits.shape == expected_shape
    assert torch.allclose(logits[0, :3]						,snake_case_						,atol=1e-4    )
    print("""Logits 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(snake_case_    )
        model.save_pretrained(snake_case_    )

    if push_to_hub:
        print("""Pushing to the hub..."""    )
        model.push_to_hub(f'fcakyon/{model_name}'    )


if __name__ == "__main__":
  __A :		Optional[int]      							=    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--checkpoint_url''',
      default='''https://drive.google.com/u/1/uc?id=17yvuYp9L4mn-HpIcK5Zo6K3UoOy1kA5l&export=download''',
      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='''''',
      type=str,
      help='''Path to the output PyTorch model directory.''',
  )
  parser.add_argument('''--model_name''', default='''timesformer-base-finetuned-k400''', 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.'''
  )

  __A :		Union[str, Any]      							=    parser.parse_args()
  convert_timesformer_checkpoint(
      args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
  )










"""simple docstring"""




import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image
def 					A_			(		snake_case_     :       List[str]						,snake_case_     :       Union[str, Any]=2_5_6    ):







    '''simple docstring'''
    UpperCamelCase				:   Optional[int]          =							min(img.size    )

    if s < target_image_size:
        raise ValueError(f'min dim for image {s} < {target_image_size}'    )

    UpperCamelCase				:   Dict          =							target_image_size / s
    UpperCamelCase				:   Any          =							(round(r * img.size[1]    ), round(r * img.size[0]    ))
    UpperCamelCase				:   Union[str, Any]          =							TF.resize(snake_case_						,snake_case_						,interpolation=PIL.Image.LANCZOS    )
    UpperCamelCase				:   Optional[int]          =							TF.center_crop(snake_case_						,output_size=2 * [target_image_size]    )
    UpperCamelCase				:   Optional[Any]          =							torch.unsqueeze(T.ToTensor()(snake_case_    )						,0    )
    return img
def 					A_			(		snake_case_     :       List[Any]    ):







    '''simple docstring'''
    UpperCamelCase				:   Any          =							2.0 * x - 1.0
    return x
def 					A_			(		snake_case_     :       Tuple						,snake_case_     :       Optional[Any]=True						,snake_case_     :       Dict="RGB"    ):







    '''simple docstring'''
    UpperCamelCase				:   Dict          =							x.detach().cpu()
    if process:
        UpperCamelCase				:   str          =							post_process_tensor(snake_case_    )
    UpperCamelCase				:   List[Any]          =							x.numpy()
    if process:
        UpperCamelCase				:   Optional[int]          =							(2_5_5 * x).astype(np.uinta    )
    UpperCamelCase				:   List[Any]          =							Image.fromarray(snake_case_    )
    if not x.mode == mode:
        UpperCamelCase				:   Any          =							x.convert(snake_case_    )
    return x
def 					A_			(		snake_case_     :       int    ):







    '''simple docstring'''
    UpperCamelCase				:   int          =							torch.clamp(snake_case_						,-1.0						,1.0    )
    UpperCamelCase				:   Optional[Any]          =							(x + 1.0) / 2.0
    UpperCamelCase				:   Dict          =							x.permute(1						,2						,0    )
    return x


def 					A_			(		snake_case_     :       int    ):







    '''simple docstring'''
    UpperCamelCase				:   Optional[Any]          =							post_process_tensor(x.squeeze()    )
    return x.permute(2						,0						,1    ).unsqueeze(0    )




