





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        __snake_case       (						lowerCAmelCase_  )							->	Tuple:
    SCREAMING_SNAKE_CASE__       							= TimesformerConfig()

    if "large" in model_name:
        SCREAMING_SNAKE_CASE__       							= 9_6

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

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

    return config


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

    return name


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Optional[Any]:
    for key in orig_state_dict.copy().keys():
        SCREAMING_SNAKE_CASE__       							= orig_state_dict.pop(lowerCAmelCase_  )

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

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

    return orig_state_dict


def        __snake_case       (						)							->	Tuple:
    SCREAMING_SNAKE_CASE__       							= hf_hub_download(
        repo_id='''hf-internal-testing/spaghetti-video'''   ,       filename='''eating_spaghetti.npy'''   ,       repo_type='''dataset'''  )
    SCREAMING_SNAKE_CASE__       							= np.load(lowerCAmelCase_  )
    return list(lowerCAmelCase_  )


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Tuple:
    SCREAMING_SNAKE_CASE__       							= get_timesformer_config(lowerCAmelCase_  )

    SCREAMING_SNAKE_CASE__       							= TimesformerForVideoClassification(lowerCAmelCase_  )

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

    model.load_state_dict(lowerCAmelCase_  )
    model.eval()

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

    SCREAMING_SNAKE_CASE__       							= model(**lowerCAmelCase_  )
    SCREAMING_SNAKE_CASE__       							= outputs.logits

    SCREAMING_SNAKE_CASE__       							= [
        # 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__       							= torch.Size([1, 4_0_0]  )
        SCREAMING_SNAKE_CASE__       							= torch.tensor([-0.30_16, -0.77_13, -0.42_05]  )
    elif model_name == "timesformer-base-finetuned-k600":
        SCREAMING_SNAKE_CASE__       							= torch.Size([1, 6_0_0]  )
        SCREAMING_SNAKE_CASE__       							= torch.tensor([-0.72_67, -0.74_66, 3.24_04]  )
    elif model_name == "timesformer-base-finetuned-ssv2":
        SCREAMING_SNAKE_CASE__       							= torch.Size([1, 1_7_4]  )
        SCREAMING_SNAKE_CASE__       							= torch.tensor([-0.90_59, 0.64_33, -3.14_57]  )
    elif model_name == "timesformer-large-finetuned-k400":
        SCREAMING_SNAKE_CASE__       							= torch.Size([1, 4_0_0]  )
        SCREAMING_SNAKE_CASE__       							= torch.tensor([0, 0, 0]  )
    elif model_name == "timesformer-large-finetuned-k600":
        SCREAMING_SNAKE_CASE__       							= torch.Size([1, 6_0_0]  )
        SCREAMING_SNAKE_CASE__       							= torch.tensor([0, 0, 0]  )
    elif model_name == "timesformer-large-finetuned-ssv2":
        SCREAMING_SNAKE_CASE__       							= torch.Size([1, 1_7_4]  )
        SCREAMING_SNAKE_CASE__       							= torch.tensor([0, 0, 0]  )
    elif model_name == "timesformer-hr-finetuned-k400":
        SCREAMING_SNAKE_CASE__       							= torch.Size([1, 4_0_0]  )
        SCREAMING_SNAKE_CASE__       							= torch.tensor([-0.96_17, -3.73_11, -3.77_08]  )
    elif model_name == "timesformer-hr-finetuned-k600":
        SCREAMING_SNAKE_CASE__       							= torch.Size([1, 6_0_0]  )
        SCREAMING_SNAKE_CASE__       							= torch.tensor([2.52_73, 0.71_27, 1.88_48]  )
    elif model_name == "timesformer-hr-finetuned-ssv2":
        SCREAMING_SNAKE_CASE__       							= torch.Size([1, 1_7_4]  )
        SCREAMING_SNAKE_CASE__       							= torch.tensor([-3.67_56, -0.75_13, 0.71_80]  )
    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				:							Union[str, Any]							= 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				:							Optional[int]							= parser.parse_args()
  convert_timesformer_checkpoint(
      args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
  )







import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
  print("""#################### AVAILABLE DEVICES ####################""")
  print(jax.devices())
  print("""###########################################################""")

  # setup for wandb sweep
  _A				:							Any							= Args()
  _A				:							Union[str, Any]							= wandb.init(project="""bigbird-natural-questions""", config=args.__dict__)
  _A				:							Optional[Any]							= dict(logger.config)
  del wandb_args["batch_size"]
  _A				:							Any							= replace(args, **wandb_args)
  _A				:							Dict							= args.base_dir + """-""" + wandb.run.id
  _A				:							Dict							= replace(args, base_dir=base_dir)
  print(args)

  _A				:							Tuple							= load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
  _A				:							Optional[Any]							= load_dataset("""json""", data_files=args.val_data_path)["""train"""]

  # drop extra batch for now
  _A				:							Dict							= range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
  _A				:							int							= tr_dataset.shuffle().select(indices)
  _A				:							Optional[int]							= range(len(val_dataset) - len(val_dataset) % args.batch_size)
  _A				:							Dict							= val_dataset.shuffle().select(indices)

  if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
    _A				:							Optional[Any]							= tr_dataset.shuffle().select(range(8_00_00))
    _A				:							Tuple							= val_dataset.shuffle().select(range(80_00))

  print(tr_dataset)
  print(val_dataset)

  _A				:							int							= FlaxBigBirdForNaturalQuestions.from_pretrained(
      args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
  )
  _A				:							Any							= BigBirdTokenizerFast.from_pretrained(args.model_id)
  _A				:							Optional[Any]							= DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

  _A				:							int							= {
      """lr""": args.lr,
      """init_lr""": args.init_lr,
      """warmup_steps""": args.warmup_steps,
      """num_train_steps""": args.max_epochs * (len(tr_dataset) // args.batch_size),
      """weight_decay""": args.weight_decay,
  }
  _A,					_A				:							Union[str, Any]							= build_tx(**tx_args)

  _A				:							str							= Trainer(
      args=args,
      data_collator=data_collator,
      model_save_fn=model.save_pretrained,
      train_step_fn=train_step,
      val_step_fn=val_step,
      logger=logger,
      scheduler_fn=lr,
  )

  _A				:							Tuple							= None
  _A				:							Dict							= trainer.create_state(model, tx, num_train_steps=tx_args["""num_train_steps"""], ckpt_dir=ckpt_dir)
  try:
    trainer.train(state, tr_dataset, val_dataset)
  except KeyboardInterrupt:
    print("""Oooops; TRAINING STOPPED UNFORTUNATELY""")

  print("""SAVING WEIGHTS IN `final-weights`""")
  _A				:							Tuple							= jax_utils.unreplicate(state.params)
  model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)
