



'''simple docstring'''



import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator


def 							__UpperCAmelCase				(				UpperCamelCase__				:Optional[int]			)		->						str:
				snake_case__      :   Optional[int]      =    fname.split(os.path.sep			)[-1]
				return re.search(R'''^(.*)_\d+\.jpg$'''   ,				UpperCamelCase__			).groups()[0]






class 			_SCREAMING_SNAKE_CASE				(lowercase__      ):



			def __init__(     self		:						Tuple    ,			__UpperCamelCase		:						Tuple    ,			__UpperCamelCase		:						Any=None    ,			__UpperCamelCase		:						int=None			)      ->      List[str]:
							"""simple docstring"""





							snake_case__      :   Dict      =    file_names
							snake_case__      :   Tuple      =    image_transform
							snake_case__      :   Optional[Any]      =    label_to_id



			def __len__(     self		:						str			)      ->      Dict:
							"""simple docstring"""





							return len(self.file_names			)







			def __getitem__(     self		:						Any    ,			__UpperCamelCase		:						str			)      ->      Union[str, Any]:
							"""simple docstring"""





							snake_case__      :   str      =    self.file_names[idx]
							snake_case__      :   Tuple      =    PIL.Image.open(__UpperCamelCase			)
							snake_case__      :   Any      =    raw_image.convert('''RGB'''			)
							if self.image_transform is not None:
											snake_case__      :   int      =    self.image_transform(__UpperCamelCase			)
							snake_case__      :   Optional[int]      =    extract_label(__UpperCamelCase			)
							if self.label_to_id is not None:
											snake_case__      :   Optional[int]      =    self.label_to_id[label]
							return {"image": image, "label": label}



def 							__UpperCAmelCase				(				UpperCamelCase__				:List[Any]   ,				UpperCamelCase__				:Optional[Any]			)		->						int:
				# Initialize accelerator
				snake_case__      :   Union[str, Any]      =    Accelerator(cpu=args.cpu   ,				mixed_precision=args.mixed_precision			)

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				snake_case__      :   Any      =    config['''lr''']
				snake_case__      :   Dict      =    int(config['''num_epochs''']			)
				snake_case__      :   str      =    int(config['''seed''']			)
				snake_case__      :   Union[str, Any]      =    int(config['''batch_size''']			)
				snake_case__      :   int      =    config['''image_size''']
				if not isinstance(UpperCamelCase__   ,				(list, tuple)			):
								snake_case__      :   List[str]      =    (image_size, image_size)

				# Grab all the image filenames
				snake_case__      :   str      =    [os.path.join(args.data_dir   ,				UpperCamelCase__			) for fname in os.listdir(args.data_dir			) if fname.endswith('''.jpg'''			)]

				# Build the label correspondences
				snake_case__      :   int      =    [extract_label(UpperCamelCase__			) for fname in file_names]
				snake_case__      :   Dict      =    list(set(UpperCamelCase__			)			)
				id_to_label.sort()
				snake_case__      :   Optional[int]      =    {lbl: i for i, lbl in enumerate(UpperCamelCase__			)}

				# Set the seed before splitting the data.
				np.random.seed(UpperCamelCase__			)
				torch.manual_seed(UpperCamelCase__			)
				torch.cuda.manual_seed_all(UpperCamelCase__			)

				# Split our filenames between train and validation
				snake_case__      :   Optional[int]      =    np.random.permutation(len(UpperCamelCase__			)			)
				snake_case__      :   Tuple      =    int(0.8 * len(UpperCamelCase__			)			)
				snake_case__      :   Tuple      =    random_perm[:cut]
				snake_case__      :   int      =    random_perm[cut:]

				# For training we use a simple RandomResizedCrop
				snake_case__      :   Dict      =    Compose([RandomResizedCrop(UpperCamelCase__   ,				scale=(0.5, 1.0)			), ToTensor()]			)
				snake_case__      :   Union[str, Any]      =    PetsDataset(
				    [file_names[i] for i in train_split]   ,				image_transform=UpperCamelCase__   ,				label_to_id=UpperCamelCase__			)

				# For evaluation, we use a deterministic Resize
				snake_case__      :   Optional[Any]      =    Compose([Resize(UpperCamelCase__			), ToTensor()]			)
				snake_case__      :   Optional[int]      =    PetsDataset([file_names[i] for i in eval_split]   ,				image_transform=UpperCamelCase__   ,				label_to_id=UpperCamelCase__			)

				# Instantiate dataloaders.
				snake_case__      :   int      =    DataLoader(UpperCamelCase__   ,				shuffle=UpperCamelCase__   ,				batch_size=UpperCamelCase__   ,				num_workers=4			)
				snake_case__      :   Dict      =    DataLoader(UpperCamelCase__   ,				shuffle=UpperCamelCase__   ,				batch_size=UpperCamelCase__   ,				num_workers=4			)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				snake_case__      :   Dict      =    create_model('''resnet50d'''   ,				pretrained=UpperCamelCase__   ,				num_classes=len(UpperCamelCase__			)			)

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				snake_case__      :   Optional[Any]      =    model.to(accelerator.device			)

				# Freezing the base model
				for param in model.parameters():
								snake_case__      :   List[Any]      =    False
				for param in model.get_classifier().parameters():
								snake_case__      :   str      =    True

				# We normalize the batches of images to be a bit faster.
				snake_case__      :   List[Any]      =    torch.tensor(model.default_cfg['''mean''']			)[None, :, None, None].to(accelerator.device			)
				snake_case__      :   Tuple      =    torch.tensor(model.default_cfg['''std''']			)[None, :, None, None].to(accelerator.device			)

				# Instantiate optimizer
				snake_case__      :   Dict      =    torch.optim.Adam(params=model.parameters()   ,				lr=lr / 25			)

				# Instantiate learning rate scheduler
				snake_case__      :   List[str]      =    OneCycleLR(optimizer=UpperCamelCase__   ,				max_lr=UpperCamelCase__   ,				epochs=UpperCamelCase__   ,				steps_per_epoch=len(UpperCamelCase__			)			)

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				snake_case__  ,      snake_case__  ,      snake_case__  ,      snake_case__  ,      snake_case__      :   int      =    accelerator.prepare(
				    UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__			)

				# Now we train the model
				for epoch in range(UpperCamelCase__			):
								model.train()
								for step, batch in enumerate(UpperCamelCase__			):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												snake_case__      :   Optional[int]      =    {k: v.to(accelerator.device			) for k, v in batch.items()}
												snake_case__      :   Optional[Any]      =    (batch['''image'''] - mean) / std
												snake_case__      :   Optional[Any]      =    model(UpperCamelCase__			)
												snake_case__      :   str      =    torch.nn.functional.cross_entropy(UpperCamelCase__   ,				batch['''label''']			)
												accelerator.backward(UpperCamelCase__			)
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

								model.eval()
								snake_case__      :   List[Any]      =    0
								snake_case__      :   List[Any]      =    0
								for _, batch in enumerate(UpperCamelCase__			):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												snake_case__      :   int      =    {k: v.to(accelerator.device			) for k, v in batch.items()}
												snake_case__      :   int      =    (batch['''image'''] - mean) / std
												with torch.no_grad():
																snake_case__      :   Optional[int]      =    model(UpperCamelCase__			)
												snake_case__      :   Dict      =    outputs.argmax(dim=-1			)
												snake_case__  ,      snake_case__      :   Tuple      =    accelerator.gather_for_metrics((predictions, batch['''label'''])			)
												snake_case__      :   Tuple      =    predictions == references
												num_elems += accurate_preds.shape[0]
												accurate += accurate_preds.long().sum()

								snake_case__      :   str      =    accurate.item() / num_elems
								# Use accelerator.print to print only on the main process.
								accelerator.print(F'''epoch {epoch}: {100 * eval_metric:.2f}'''			)


def 							__UpperCAmelCase				(				)		->						Optional[int]:
				snake_case__      :   List[str]      =    argparse.ArgumentParser(description='''Simple example of training script.'''			)
				parser.add_argument('''--data_dir'''   ,				required=UpperCamelCase__   ,				help='''The data folder on disk.'''			)
				parser.add_argument(
				    '''--mixed_precision'''   ,				type=UpperCamelCase__   ,				default=UpperCamelCase__   ,				choices=['''no''', '''fp16''', '''bf16''', '''fp8''']   ,				help='''Whether to use mixed precision. Choose'''
				    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
				    '''and an Nvidia Ampere GPU.'''   ,				)
				parser.add_argument(
				    '''--checkpointing_steps'''   ,				type=UpperCamelCase__   ,				default=UpperCamelCase__   ,				help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''   ,				)
				parser.add_argument('''--cpu'''   ,				action='''store_true'''   ,				help='''If passed, will train on the CPU.'''			)
				snake_case__      :   Union[str, Any]      =    parser.parse_args()
				snake_case__      :   str      =    {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
				training_function(UpperCamelCase__   ,				UpperCamelCase__			)


if __name__ == "__main__":
				main()






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 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Any				      = TimesformerConfig()

    if "large" in model_name:
        SCREAMING_SNAKE_CASE				:				Dict				      = 96

    if "hr" in model_name:
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = 16
        SCREAMING_SNAKE_CASE				:				str				      = 448

    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = '''huggingface/label-files'''
    if "k400" in model_name:
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = 400
        SCREAMING_SNAKE_CASE				:				Tuple				      = '''kinetics400-id2label.json'''
    elif "k600" in model_name:
        SCREAMING_SNAKE_CASE				:				Any				      = 600
        SCREAMING_SNAKE_CASE				:				int				      = '''kinetics600-id2label.json'''
    elif "ssv2" in model_name:
        SCREAMING_SNAKE_CASE				:				Tuple				      = 174
        SCREAMING_SNAKE_CASE				:				int				      = '''something-something-v2-id2label.json'''
    else:
        raise ValueError('''Model name should either contain \'k400\', \'k600\' or \'ssv2\'.'''   )
    SCREAMING_SNAKE_CASE				:				List[str]				      = json.load(open(hf_hub_download(a__ ,						a__ ,						repo_type='''dataset'''   ) ,						'''r'''   )   )
    SCREAMING_SNAKE_CASE				:				List[Any]				      = {int(a__   ): v for k, v in idalabel.items()}
    SCREAMING_SNAKE_CASE				:				str				      = idalabel
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = {v: k for k, v in idalabel.items()}

    return config







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



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

    return name







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    for key in orig_state_dict.copy().keys():
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = orig_state_dict.pop(a__   )

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

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

    return orig_state_dict







def 				UpperCAmelCase_(    ):



    """simple docstring"""



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







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Dict				      = get_timesformer_config(a__   )

    SCREAMING_SNAKE_CASE				:				Tuple				      = TimesformerForVideoClassification(a__   )

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

    model.load_state_dict(a__   )
    model.eval()

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

    SCREAMING_SNAKE_CASE				:				Any				      = model(**a__   )
    SCREAMING_SNAKE_CASE				:				List[str]				      = outputs.logits

    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = [
        # 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":
        SCREAMING_SNAKE_CASE				:				Tuple				      = torch.Size([1, 400]   )
        SCREAMING_SNAKE_CASE				:				int				      = torch.tensor([-0.3_016, -0.7_713, -0.4_205]   )
    elif model_name == "timesformer-base-finetuned-k600":
        SCREAMING_SNAKE_CASE				:				Tuple				      = torch.Size([1, 600]   )
        SCREAMING_SNAKE_CASE				:				str				      = torch.tensor([-0.7_267, -0.7_466, 3.2_404]   )
    elif model_name == "timesformer-base-finetuned-ssv2":
        SCREAMING_SNAKE_CASE				:				Dict				      = torch.Size([1, 174]   )
        SCREAMING_SNAKE_CASE				:				int				      = torch.tensor([-0.9_059, 0.6_433, -3.1_457]   )
    elif model_name == "timesformer-large-finetuned-k400":
        SCREAMING_SNAKE_CASE				:				Any				      = torch.Size([1, 400]   )
        SCREAMING_SNAKE_CASE				:				int				      = torch.tensor([0, 0, 0]   )
    elif model_name == "timesformer-large-finetuned-k600":
        SCREAMING_SNAKE_CASE				:				str				      = torch.Size([1, 600]   )
        SCREAMING_SNAKE_CASE				:				str				      = torch.tensor([0, 0, 0]   )
    elif model_name == "timesformer-large-finetuned-ssv2":
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.Size([1, 174]   )
        SCREAMING_SNAKE_CASE				:				str				      = torch.tensor([0, 0, 0]   )
    elif model_name == "timesformer-hr-finetuned-k400":
        SCREAMING_SNAKE_CASE				:				List[str]				      = torch.Size([1, 400]   )
        SCREAMING_SNAKE_CASE				:				int				      = torch.tensor([-0.9_617, -3.7_311, -3.7_708]   )
    elif model_name == "timesformer-hr-finetuned-k600":
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.Size([1, 600]   )
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.tensor([2.5_273, 0.7_127, 1.8_848]   )
    elif model_name == "timesformer-hr-finetuned-ssv2":
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.Size([1, 174]   )
        SCREAMING_SNAKE_CASE				:				str				      = torch.tensor([-3.6_756, -0.7_513, 0.7_180]   )
    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] ,						a__ ,						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(a__   )
        model.save_pretrained(a__   )

    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__				:      str    		=     parser.parse_args()
      convert_timesformer_checkpoint(
          args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
      )
