

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    (				SCREAMING_SNAKE_CASE ) ->       Optional[int]:






    '''simple docstring'''
    __UpperCAmelCase			   =     TimesformerConfig()

    if "large" in model_name:
        __UpperCAmelCase			   =     9_6

    if "hr" in model_name:
        __UpperCAmelCase			   =     1_6
        __UpperCAmelCase			   =     4_4_8

    __UpperCAmelCase			   =     '''huggingface/label-files'''
    if "k400" in model_name:
        __UpperCAmelCase			   =     4_0_0
        __UpperCAmelCase			   =     '''kinetics400-id2label.json'''
    elif "k600" in model_name:
        __UpperCAmelCase			   =     6_0_0
        __UpperCAmelCase			   =     '''kinetics600-id2label.json'''
    elif "ssv2" in model_name:
        __UpperCAmelCase			   =     1_7_4
        __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(_lowerCAmelCase    ,				_lowerCAmelCase    ,				repo_type='''dataset''' )    ,				'''r''' ) )
    __UpperCAmelCase			   =     {int(_lowerCAmelCase ): v for k, v in idalabel.items()}
    __UpperCAmelCase			   =     idalabel
    __UpperCAmelCase			   =     {v: k for k, v in idalabel.items()}

    return config


def  __a    (				SCREAMING_SNAKE_CASE ) ->       int:






    '''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  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       List[str]:






    '''simple docstring'''
    for key in orig_state_dict.copy().keys():
        __UpperCAmelCase			   =     orig_state_dict.pop(_lowerCAmelCase )

        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  __a    (				) ->       str:






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


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       List[str]:






    '''simple docstring'''
    __UpperCAmelCase			   =     get_timesformer_config(_lowerCAmelCase )

    __UpperCAmelCase			   =     TimesformerForVideoClassification(_lowerCAmelCase )

    # download original checkpoint, hosted on Google Drive
    __UpperCAmelCase			   =     '''pytorch_model.bin'''
    gdown.cached_download(_lowerCAmelCase    ,				_lowerCAmelCase    ,				quiet=_lowerCAmelCase )
    __UpperCAmelCase			   =     torch.load(_lowerCAmelCase    ,				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(_lowerCAmelCase    ,				_lowerCAmelCase )

    model.load_state_dict(_lowerCAmelCase )
    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(**_lowerCAmelCase )
    __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_0_0] )
        __UpperCAmelCase			   =     torch.tensor([-0.3016, -0.7713, -0.4205] )
    elif model_name == "timesformer-base-finetuned-k600":
        __UpperCAmelCase			   =     torch.Size([1, 6_0_0] )
        __UpperCAmelCase			   =     torch.tensor([-0.7267, -0.7466, 3.2404] )
    elif model_name == "timesformer-base-finetuned-ssv2":
        __UpperCAmelCase			   =     torch.Size([1, 1_7_4] )
        __UpperCAmelCase			   =     torch.tensor([-0.9059, 0.6433, -3.1457] )
    elif model_name == "timesformer-large-finetuned-k400":
        __UpperCAmelCase			   =     torch.Size([1, 4_0_0] )
        __UpperCAmelCase			   =     torch.tensor([0, 0, 0] )
    elif model_name == "timesformer-large-finetuned-k600":
        __UpperCAmelCase			   =     torch.Size([1, 6_0_0] )
        __UpperCAmelCase			   =     torch.tensor([0, 0, 0] )
    elif model_name == "timesformer-large-finetuned-ssv2":
        __UpperCAmelCase			   =     torch.Size([1, 1_7_4] )
        __UpperCAmelCase			   =     torch.tensor([0, 0, 0] )
    elif model_name == "timesformer-hr-finetuned-k400":
        __UpperCAmelCase			   =     torch.Size([1, 4_0_0] )
        __UpperCAmelCase			   =     torch.tensor([-0.9617, -3.7311, -3.7708] )
    elif model_name == "timesformer-hr-finetuned-k600":
        __UpperCAmelCase			   =     torch.Size([1, 6_0_0] )
        __UpperCAmelCase			   =     torch.tensor([2.5273, 0.7127, 1.8848] )
    elif model_name == "timesformer-hr-finetuned-ssv2":
        __UpperCAmelCase			   =     torch.Size([1, 1_7_4] )
        __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]    ,				_lowerCAmelCase    ,				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(_lowerCAmelCase )
        model.save_pretrained(_lowerCAmelCase )

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


if __name__ == "__main__":
   A_						:     Tuple			  =				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
   )







import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


A_						:     int			  =				logging.get_logger(__name__)
class    A_				( _a  ):


    '''simple docstring'''






    def __init__(self       , *lowercase__       , **lowercase__					)       -> None:
        warnings.warn(
            '''The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
            ''' Please use ConvNextImageProcessor instead.'''       , lowercase__       , )
        super().__init__(*lowercase__       , **lowercase__					)




