





"""simple docstring"""
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


lowerCamelCase_          =	logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

lowerCamelCase_          =	{
    "base": AutoModel,
    "sequence-classification": AutoModelForSequenceClassification,
    "question-answering": AutoModelForQuestionAnswering,
    "pretraining": AutoModelForPreTraining,
    "token-classification": AutoModelForTokenClassification,
    "language-modeling": AutoModelWithLMHead,
    "summarization": AutoModelForSeqaSeqLM,
    "translation": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
lowerCamelCase_          =	{
    "linear": get_linear_schedule_with_warmup,
    "cosine": get_cosine_schedule_with_warmup,
    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
    "polynomial": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
lowerCamelCase_          =	sorted(arg_to_scheduler.keys())
lowerCamelCase_          =	"{" + ", ".join(arg_to_scheduler_choices) + "}"






class       _SCREAMING_SNAKE_CASE(     pl.LightningModule						):




       def __init__(		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=None	,SCREAMING_SNAKE_CASE__="base"	,SCREAMING_SNAKE_CASE__=None	,SCREAMING_SNAKE_CASE__=None	,SCREAMING_SNAKE_CASE__=None	,**SCREAMING_SNAKE_CASE__	,)    ->   List[Any]:





             """simple docstring"""






             super().__init__()
             # TODO: move to self.save_hyperparameters()
             # self.save_hyperparameters()
             # can also expand arguments into trainer signature for easier reading

             self.save_hyperparameters(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :List[str]	       =						0
             __SCREAMING_SNAKE_CASE     :Tuple	       =						Path(self.hparams.output_dir							)
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						self.hparams.cache_dir if self.hparams.cache_dir else None
             if config is None:
                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						AutoConfig.from_pretrained(
                       self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path	,**({'''num_labels''': num_labels} if num_labels is not None else {})	,cache_dir=SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__	,)
             else:
                   __SCREAMING_SNAKE_CASE     :PretrainedConfig	       =						config

             __SCREAMING_SNAKE_CASE     :List[str]	       =						('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
             for p in extra_model_params:
                   if getattr(self.hparams	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							):
                         assert hasattr(self.config	,SCREAMING_SNAKE_CASE__							), f'''model config doesn\'t have a `{p}` attribute'''
                         setattr(self.config	,SCREAMING_SNAKE_CASE__	,getattr(self.hparams	,SCREAMING_SNAKE_CASE__							)							)

             if tokenizer is None:
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						AutoTokenizer.from_pretrained(
                       self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path	,cache_dir=SCREAMING_SNAKE_CASE__	,)
             else:
                   __SCREAMING_SNAKE_CASE     :PreTrainedTokenizer	       =						tokenizer
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						MODEL_MODES[mode]
             if model is None:
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						self.model_type.from_pretrained(
                       self.hparams.model_name_or_path	,from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path							)	,config=self.config	,cache_dir=SCREAMING_SNAKE_CASE__	,)
             else:
                   __SCREAMING_SNAKE_CASE     :Dict	       =						model




       def 					_UpperCamelCase  (		self	,*SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__							)    ->   Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :List[str]	       =						self.model_type.from_pretrained(*SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self							)    ->   List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						arg_to_scheduler[self.hparams.lr_scheduler]
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						get_schedule_func(
                 self.opt	,num_warmup_steps=self.hparams.warmup_steps	,num_training_steps=self.total_steps()							)
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						{'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
             return scheduler




       def 					_UpperCamelCase  (		self							)    ->   List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						self.model
             __SCREAMING_SNAKE_CASE     :Any	       =						['''bias''', '''LayerNorm.weight''']
             __SCREAMING_SNAKE_CASE     :Tuple	       =						[
                 {
                     '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay							)],
                     '''weight_decay''': self.hparams.weight_decay,
                 },
                 {
                     '''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay							)],
                     '''weight_decay''': 0.0,
                 },
             ]
             if self.hparams.adafactor:
                   __SCREAMING_SNAKE_CASE     :str	       =						Adafactor(
                       SCREAMING_SNAKE_CASE__	,lr=self.hparams.learning_rate	,scale_parameter=SCREAMING_SNAKE_CASE__	,relative_step=SCREAMING_SNAKE_CASE__							)

             else:
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						AdamW(
                       SCREAMING_SNAKE_CASE__	,lr=self.hparams.learning_rate	,eps=self.hparams.adam_epsilon							)
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						optimizer

             __SCREAMING_SNAKE_CASE     :List[str]	       =						self.get_lr_scheduler()

             return [optimizer], [scheduler]




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)    ->   Any:





             """simple docstring"""






             return self.validation_step(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Union[str, Any]:





             """simple docstring"""






             return self.validation_end(SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self							)    ->   int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						max(1	,self.hparams.gpus							)  # TODO: consider num_tpu_cores
             __SCREAMING_SNAKE_CASE     :int	       =						self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
             return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Union[str, Any]:





             """simple docstring"""






             if mode == "test":
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						len(self.test_dataloader().dataset							)
             else:
                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						self.get_dataloader('''train'''	,self.hparams.train_batch_size	,shuffle=SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE     :str	       =						len(self.train_dataloader().dataset							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = False							)    ->   Dict:





             """simple docstring"""






             raise NotImplementedError('''You must implement this for your task'''							)




       def 					_UpperCamelCase  (		self							)    ->   Union[str, Any]:





             """simple docstring"""






             return self.train_loader




       def 					_UpperCamelCase  (		self							)    ->   Union[str, Any]:





             """simple docstring"""






             return self.get_dataloader('''dev'''	,self.hparams.eval_batch_size	,shuffle=SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self							)    ->   Union[str, Any]:





             """simple docstring"""






             return self.get_dataloader('''test'''	,self.hparams.eval_batch_size	,shuffle=SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Optional[Any]:





             """simple docstring"""






             return os.path.join(
                 self.hparams.data_dir	,'''cached_{}_{}_{}'''.format(
                     SCREAMING_SNAKE_CASE__	,list(filter(SCREAMING_SNAKE_CASE__	,self.hparams.model_name_or_path.split('''/'''							)							)							).pop()	,str(self.hparams.max_seq_length							)	,)	,)




       @pl.utilities.rank_zero_only
       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   None:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Dict	       =						self.output_dir.joinpath('''best_tfmr'''							)
             __SCREAMING_SNAKE_CASE     :int	       =						self.step_count
             self.model.save_pretrained(SCREAMING_SNAKE_CASE__							)
             self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__							)




       @staticmethod
       def 					_UpperCamelCase  (		SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)    ->   List[str]:





             """simple docstring"""






             parser.add_argument(
                 '''--model_name_or_path'''	,default=SCREAMING_SNAKE_CASE__	,type=SCREAMING_SNAKE_CASE__	,required=SCREAMING_SNAKE_CASE__	,help='''Path to pretrained model or model identifier from huggingface.co/models'''	,)
             parser.add_argument(
                 '''--config_name'''	,default=''''''	,type=SCREAMING_SNAKE_CASE__	,help='''Pretrained config name or path if not the same as model_name'''							)
             parser.add_argument(
                 '''--tokenizer_name'''	,default=SCREAMING_SNAKE_CASE__	,type=SCREAMING_SNAKE_CASE__	,help='''Pretrained tokenizer name or path if not the same as model_name'''	,)
             parser.add_argument(
                 '''--cache_dir'''	,default=''''''	,type=SCREAMING_SNAKE_CASE__	,help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''	,)
             parser.add_argument(
                 '''--encoder_layerdrop'''	,type=SCREAMING_SNAKE_CASE__	,help='''Encoder layer dropout probability (Optional). Goes into model.config'''	,)
             parser.add_argument(
                 '''--decoder_layerdrop'''	,type=SCREAMING_SNAKE_CASE__	,help='''Decoder layer dropout probability (Optional). Goes into model.config'''	,)
             parser.add_argument(
                 '''--dropout'''	,type=SCREAMING_SNAKE_CASE__	,help='''Dropout probability (Optional). Goes into model.config'''	,)
             parser.add_argument(
                 '''--attention_dropout'''	,type=SCREAMING_SNAKE_CASE__	,help='''Attention dropout probability (Optional). Goes into model.config'''	,)
             parser.add_argument('''--learning_rate'''	,default=5E-5	,type=SCREAMING_SNAKE_CASE__	,help='''The initial learning rate for Adam.'''							)
             parser.add_argument(
                 '''--lr_scheduler'''	,default='''linear'''	,choices=SCREAMING_SNAKE_CASE__	,metavar=SCREAMING_SNAKE_CASE__	,type=SCREAMING_SNAKE_CASE__	,help='''Learning rate scheduler'''	,)
             parser.add_argument('''--weight_decay'''	,default=0.0	,type=SCREAMING_SNAKE_CASE__	,help='''Weight decay if we apply some.'''							)
             parser.add_argument('''--adam_epsilon'''	,default=1E-8	,type=SCREAMING_SNAKE_CASE__	,help='''Epsilon for Adam optimizer.'''							)
             parser.add_argument('''--warmup_steps'''	,default=0	,type=SCREAMING_SNAKE_CASE__	,help='''Linear warmup over warmup_steps.'''							)
             parser.add_argument('''--num_workers'''	,default=4	,type=SCREAMING_SNAKE_CASE__	,help='''kwarg passed to DataLoader'''							)
             parser.add_argument('''--num_train_epochs'''	,dest='''max_epochs'''	,default=3	,type=SCREAMING_SNAKE_CASE__							)
             parser.add_argument('''--train_batch_size'''	,default=32	,type=SCREAMING_SNAKE_CASE__							)
             parser.add_argument('''--eval_batch_size'''	,default=32	,type=SCREAMING_SNAKE_CASE__							)
             parser.add_argument('''--adafactor'''	,action='''store_true'''							)






class       _SCREAMING_SNAKE_CASE(     pl.Callback						):




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)    ->   Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :List[str]	       =						trainer.lr_schedulers[0]['''scheduler''']
             __SCREAMING_SNAKE_CASE     :str	       =						{f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()							)}
             pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)    ->   Dict:





             """simple docstring"""






             rank_zero_info('''***** Validation results *****'''							)
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						trainer.callback_metrics
             # Log results
             for key in sorted(SCREAMING_SNAKE_CASE__							):
                   if key not in ["log", "progress_bar"]:
                         rank_zero_info('''{} = {}\n'''.format(SCREAMING_SNAKE_CASE__	,str(metrics[key]							)							)							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)    ->   int:





             """simple docstring"""






             rank_zero_info('''***** Test results *****'''							)
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						trainer.callback_metrics
             # Log and save results to file
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						os.path.join(pl_module.hparams.output_dir	,'''test_results.txt'''							)
             with open(SCREAMING_SNAKE_CASE__	,'''w'''							) as writer:
                   for key in sorted(SCREAMING_SNAKE_CASE__							):
                         if key not in ["log", "progress_bar"]:
                               rank_zero_info('''{} = {}\n'''.format(SCREAMING_SNAKE_CASE__	,str(metrics[key]							)							)							)
                               writer.write('''{} = {}\n'''.format(SCREAMING_SNAKE_CASE__	,str(metrics[key]							)							)							)




def   __lowerCamelCase     (							a_  :	List[Any]				,     a_  :	str							)    ->   None:
      #  To allow all pl args uncomment the following line
      #  parser = pl.Trainer.add_argparse_args(parser)
      parser.add_argument(
          '''--output_dir'''				,     default=a_				,     type=a_				,     required=a_				,     help='''The output directory where the model predictions and checkpoints will be written.'''				,     )
      parser.add_argument(
          '''--fp16'''				,     action='''store_true'''				,     help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''				,     )

      parser.add_argument(
          '''--fp16_opt_level'''				,     type=a_				,     default='''O2'''				,     help=(
              '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
              '''See details at https://nvidia.github.io/apex/amp.html'''
          )				,     )
      parser.add_argument('''--n_tpu_cores'''				,     dest='''tpu_cores'''				,     type=a_							)
      parser.add_argument('''--max_grad_norm'''				,     dest='''gradient_clip_val'''				,     default=1.0				,     type=a_				,     help='''Max gradient norm'''							)
      parser.add_argument('''--do_train'''				,     action='''store_true'''				,     help='''Whether to run training.'''							)
      parser.add_argument('''--do_predict'''				,     action='''store_true'''				,     help='''Whether to run predictions on the test set.'''							)
      parser.add_argument(
          '''--gradient_accumulation_steps'''				,     dest='''accumulate_grad_batches'''				,     type=a_				,     default=1				,     help='''Number of updates steps to accumulate before performing a backward/update pass.'''				,     )
      parser.add_argument('''--seed'''				,     type=a_				,     default=42				,     help='''random seed for initialization'''							)
      parser.add_argument(
          '''--data_dir'''				,     default=a_				,     type=a_				,     required=a_				,     help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''				,     )







def   __lowerCamelCase     (							a_  :	BaseTransformer				,     a_  :	argparse.Namespace				,     a_  :	Optional[Any]=None				,     a_  :	Tuple=True				,     a_  :	Dict=[]				,     a_  :	Tuple=None				,     a_  :	int=None				,     **a_  :	Tuple				,     )    ->   Dict:
      pl.seed_everything(args.seed							)

      # init model
      __SCREAMING_SNAKE_CASE     :str	       =						Path(model.hparams.output_dir							)
      odir.mkdir(exist_ok=a_							)

      # add custom checkpoints
      if checkpoint_callback is None:
            __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						pl.callbacks.ModelCheckpoint(
                filepath=args.output_dir				,     prefix='''checkpoint'''				,     monitor='''val_loss'''				,     mode='''min'''				,     save_top_k=1							)
      if early_stopping_callback:
            extra_callbacks.append(a_							)
      if logging_callback is None:
            __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						LoggingCallback()

      __SCREAMING_SNAKE_CASE     :Tuple	       =						{}

      # TODO: remove with PyTorch 1.6 since pl uses native amp
      if args.fpaa:
            __SCREAMING_SNAKE_CASE     :Dict	       =						16
            __SCREAMING_SNAKE_CASE     :Any	       =						args.fpaa_opt_level

      if args.gpus > 1:
            __SCREAMING_SNAKE_CASE     :str	       =						'''ddp'''

      __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						args.accumulate_grad_batches
      __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						extra_train_kwargs.get('''accelerator'''				,     a_							)
      __SCREAMING_SNAKE_CASE     :Dict	       =						extra_train_kwargs.get('''profiler'''				,     a_							)

      __SCREAMING_SNAKE_CASE     :Dict	       =						pl.Trainer.from_argparse_args(
          a_				,     weights_summary=a_				,     callbacks=[logging_callback] + extra_callbacks				,     logger=a_				,     checkpoint_callback=a_				,     **a_				,     )

      if args.do_train:
            trainer.fit(a_							)

      return trainer






"""simple docstring"""
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   __lowerCamelCase     (							a_  :	Union[str, Any]							)    ->   Tuple:
      __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						TimesformerConfig()

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

      if "hr" in model_name:
            __SCREAMING_SNAKE_CASE     :List[Any]	       =						16
            __SCREAMING_SNAKE_CASE     :int	       =						4_48

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

      return config



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

      return name



def   __lowerCamelCase     (							a_  :	Optional[Any]				,     a_  :	Tuple							)    ->   Optional[int]:
      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     :Union[str, Any]	       =						key.replace('''model.'''				,     ''''''							)

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

      return orig_state_dict



def   __lowerCamelCase     (							)    ->   Optional[int]:
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						hf_hub_download(
          repo_id='''hf-internal-testing/spaghetti-video'''				,     filename='''eating_spaghetti.npy'''				,     repo_type='''dataset'''							)
      __SCREAMING_SNAKE_CASE     :Tuple	       =						np.load(a_							)
      return list(a_							)



def   __lowerCamelCase     (							a_  :	List[Any]				,     a_  :	int				,     a_  :	Optional[int]				,     a_  :	Optional[Any]							)    ->   Union[str, Any]:
      __SCREAMING_SNAKE_CASE     :List[Any]	       =						get_timesformer_config(a_							)

      __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						TimesformerForVideoClassification(a_							)

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

      model.load_state_dict(a_							)
      model.eval()

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

      __SCREAMING_SNAKE_CASE     :Tuple	       =						model(**a_							)
      __SCREAMING_SNAKE_CASE     :List[str]	       =						outputs.logits

      __SCREAMING_SNAKE_CASE     :Dict	       =						[
          # 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     :Union[str, Any]	       =						torch.Size([1, 4_00]							)
            __SCREAMING_SNAKE_CASE     :int	       =						torch.tensor([-0.3016, -0.7713, -0.4205]							)
      elif model_name == "timesformer-base-finetuned-k600":
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						torch.Size([1, 6_00]							)
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						torch.tensor([-0.7267, -0.7466, 3.2404]							)
      elif model_name == "timesformer-base-finetuned-ssv2":
            __SCREAMING_SNAKE_CASE     :List[str]	       =						torch.Size([1, 1_74]							)
            __SCREAMING_SNAKE_CASE     :Tuple	       =						torch.tensor([-0.9059, 0.6433, -3.1457]							)
      elif model_name == "timesformer-large-finetuned-k400":
            __SCREAMING_SNAKE_CASE     :Tuple	       =						torch.Size([1, 4_00]							)
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						torch.tensor([0, 0, 0]							)
      elif model_name == "timesformer-large-finetuned-k600":
            __SCREAMING_SNAKE_CASE     :List[Any]	       =						torch.Size([1, 6_00]							)
            __SCREAMING_SNAKE_CASE     :str	       =						torch.tensor([0, 0, 0]							)
      elif model_name == "timesformer-large-finetuned-ssv2":
            __SCREAMING_SNAKE_CASE     :int	       =						torch.Size([1, 1_74]							)
            __SCREAMING_SNAKE_CASE     :Any	       =						torch.tensor([0, 0, 0]							)
      elif model_name == "timesformer-hr-finetuned-k400":
            __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						torch.Size([1, 4_00]							)
            __SCREAMING_SNAKE_CASE     :Dict	       =						torch.tensor([-0.9617, -3.7311, -3.7708]							)
      elif model_name == "timesformer-hr-finetuned-k600":
            __SCREAMING_SNAKE_CASE     :int	       =						torch.Size([1, 6_00]							)
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						torch.tensor([2.5273, 0.7127, 1.8848]							)
      elif model_name == "timesformer-hr-finetuned-ssv2":
            __SCREAMING_SNAKE_CASE     :List[Any]	       =						torch.Size([1, 1_74]							)
            __SCREAMING_SNAKE_CASE     :List[Any]	       =						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]				,     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__":
  lowerCamelCase_          =	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."
  )

  lowerCamelCase_          =	parser.parse_args()
  convert_timesformer_checkpoint(
      args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
  )