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    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase											=  TimesformerConfig()

    if "large" in model_name:
        __UpperCamelCase											=  96

    if "hr" in model_name:
        __UpperCamelCase											=  16
        __UpperCamelCase											=  4_48

    __UpperCamelCase											=  '''huggingface/label-files'''
    if "k400" in model_name:
        __UpperCamelCase											=  4_00
        __UpperCamelCase											=  '''kinetics400-id2label.json'''
    elif "k600" in model_name:
        __UpperCamelCase											=  6_00
        __UpperCamelCase											=  '''kinetics600-id2label.json'''
    elif "ssv2" in model_name:
        __UpperCamelCase											=  1_74
        __UpperCamelCase											=  '''something-something-v2-id2label.json'''
    else:
        raise ValueError('''Model name should either contain \'k400\', \'k600\' or \'ssv2\'.''' )
    __UpperCamelCase											=  json.load(open(hf_hub_download(lowercase_   ,       lowercase_   ,       repo_type='''dataset''' )   ,       '''r''' ) )
    __UpperCamelCase											=  {int(lowercase_ ): v for k, v in idalabel.items()}
    __UpperCamelCase											=  idalabel
    __UpperCamelCase											=  {v: k for k, v in idalabel.items()}

    return config




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		List[Any]:

    """simple docstring"""


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

    return name




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		str:

    """simple docstring"""


    for key in orig_state_dict.copy().keys():
        __UpperCamelCase											=  orig_state_dict.pop(lowercase_ )

        if key.startswith('''model.''' ):
            __UpperCamelCase											=  key.replace('''model.'''   ,       '''''' )

        if "qkv" in key:
            __UpperCamelCase											=  key.split('''.''' )
            __UpperCamelCase											=  int(key_split[1] )
            __UpperCamelCase											=  '''timesformer.encoder.layer.'''
            if "temporal" in key:
                __UpperCamelCase											=  '''.temporal_attention.attention.qkv.'''
            else:
                __UpperCamelCase											=  '''.attention.attention.qkv.'''
            if "weight" in key:
                __UpperCamelCase											=  val
            else:
                __UpperCamelCase											=  val
        else:
            __UpperCamelCase											=  val

    return orig_state_dict




def    __SCREAMING_SNAKE_CASE				(					)    ->		Optional[Any]:

    """simple docstring"""


    __UpperCamelCase											=  hf_hub_download(
        repo_id='''hf-internal-testing/spaghetti-video'''   ,       filename='''eating_spaghetti.npy'''   ,       repo_type='''dataset''' )
    __UpperCamelCase											=  np.load(lowercase_ )
    return list(lowercase_ )




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ )    ->		Any:

    """simple docstring"""


    __UpperCamelCase											=  get_timesformer_config(lowercase_ )

    __UpperCamelCase											=  TimesformerForVideoClassification(lowercase_ )

    # download original checkpoint, hosted on Google Drive
    __UpperCamelCase											=  '''pytorch_model.bin'''
    gdown.cached_download(lowercase_   ,       lowercase_   ,       quiet=lowercase_ )
    __UpperCamelCase											=  torch.load(lowercase_   ,       map_location='''cpu''' )
    if "model" in files:
        __UpperCamelCase											=  files['''model''']
    elif "module" in files:
        __UpperCamelCase											=  files['''module''']
    else:
        __UpperCamelCase											=  files['''model_state''']
    __UpperCamelCase											=  convert_state_dict(lowercase_   ,       lowercase_ )

    model.load_state_dict(lowercase_ )
    model.eval()

    # verify model on basic input
    __UpperCamelCase											=  VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]   ,       image_std=[0.5, 0.5, 0.5] )
    __UpperCamelCase											=  prepare_video()
    __UpperCamelCase											=  image_processor(video[:8]   ,       return_tensors='''pt''' )

    __UpperCamelCase											=  model(**lowercase_ )
    __UpperCamelCase											=  outputs.logits

    __UpperCamelCase											=  [
        # 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											=  torch.Size([1, 4_00] )
        __UpperCamelCase											=  torch.tensor([-0.3016, -0.7713, -0.4205] )
    elif model_name == "timesformer-base-finetuned-k600":
        __UpperCamelCase											=  torch.Size([1, 6_00] )
        __UpperCamelCase											=  torch.tensor([-0.7267, -0.7466, 3.2404] )
    elif model_name == "timesformer-base-finetuned-ssv2":
        __UpperCamelCase											=  torch.Size([1, 1_74] )
        __UpperCamelCase											=  torch.tensor([-0.9059, 0.6433, -3.1457] )
    elif model_name == "timesformer-large-finetuned-k400":
        __UpperCamelCase											=  torch.Size([1, 4_00] )
        __UpperCamelCase											=  torch.tensor([0, 0, 0] )
    elif model_name == "timesformer-large-finetuned-k600":
        __UpperCamelCase											=  torch.Size([1, 6_00] )
        __UpperCamelCase											=  torch.tensor([0, 0, 0] )
    elif model_name == "timesformer-large-finetuned-ssv2":
        __UpperCamelCase											=  torch.Size([1, 1_74] )
        __UpperCamelCase											=  torch.tensor([0, 0, 0] )
    elif model_name == "timesformer-hr-finetuned-k400":
        __UpperCamelCase											=  torch.Size([1, 4_00] )
        __UpperCamelCase											=  torch.tensor([-0.9617, -3.7311, -3.7708] )
    elif model_name == "timesformer-hr-finetuned-k600":
        __UpperCamelCase											=  torch.Size([1, 6_00] )
        __UpperCamelCase											=  torch.tensor([2.5273, 0.7127, 1.8848] )
    elif model_name == "timesformer-hr-finetuned-ssv2":
        __UpperCamelCase											=  torch.Size([1, 1_74] )
        __UpperCamelCase											=  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]   ,       lowercase_   ,       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(lowercase_ )
        model.save_pretrained(lowercase_ )

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


if __name__ == "__main__":
 a_										=       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_										=       parser.parse_args()
 convert_timesformer_checkpoint(
     args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
 )












def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		list:

    """simple docstring"""


    __UpperCamelCase											=  len(lowercase_ )

    # Set the gap value to be decreased by a factor of 1.3
    # after each iteration
    __UpperCamelCase											=  1.3

    # Continue sorting until the gap is 1
    while gap > 1:
        # Decrease the gap value
        __UpperCamelCase											=  int(gap / shrink )

        # Sort the elements using insertion sort
        for i in range(lowercase_   ,       len(lowercase_ ) ):
            __UpperCamelCase											=  collection[i]
            __UpperCamelCase											=  i
            while j >= gap and collection[j - gap] > temp:
                __UpperCamelCase											=  collection[j - gap]
                j -= gap
            __UpperCamelCase											=  temp

    return collection


if __name__ == "__main__":
 import doctest

 doctest.testmod()






