




import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
 import torch

a_ 		=  logging.get_logger(__name__)




def        __SCREAMING_SNAKE_CASE (	)							->						str:

    """simple docstring"""


    return importlib.util.find_spec('''deepspeed'''   ) is not None


if is_accelerate_available() and is_deepspeed_available():
 from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
 # Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
 # Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
 from builtins import object as DeepSpeedConfig

class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      def __init__(  self       :			Optional[int]			,							snake_case       :			Optional[Any]					):
          # set global weakref object
          set_hf_deepspeed_config(self					)
          dep_version_check('''accelerate'''					)
          dep_version_check('''deepspeed'''					)
          super().__init__(snake_case					)







class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      def __init__(  self       :			List[Any]			,							snake_case       :			Dict					):
          super().__init__(snake_case					)
          __UpperCamelCase			=    None
          __UpperCamelCase			=    []







      def    snake_case						(  self       :			Dict					):
          if self._dtype is None:
              raise ValueError('''trainer_config_process() wasn\'t called yet to tell dtype'''					)
          return self._dtype







      def    snake_case						(  self       :			str			,							snake_case       :			int					):
          __UpperCamelCase			=    self.get_value(snake_case					)
          if val is None:
              return False
          else:
              return val == "auto"







      def    snake_case						(  self       :			str			,							snake_case       :			str			,							snake_case       :			Optional[int]			,							snake_case       :			Optional[int]=None			,							snake_case       :			Any=True					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.find_config_node(snake_case					)
          if config is None:
              return

          if config.get(snake_case					) == "auto":
              __UpperCamelCase			=    hf_val
              return

          if not must_match:
              return

          __UpperCamelCase			=    config.get(snake_case					)
          if ds_val is not None and ds_val != hf_val:
              self.mismatches.append(F"- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}"					)

      lowerCAmelCase__      :			List[str]     =       partialmethod(_SCREAMING_SNAKE_CASE				,      must_match=_SCREAMING_SNAKE_CASE )







      def    snake_case						(  self       :			List[Any]			,							snake_case       :			Optional[Any]					):
          __UpperCamelCase			=    args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
          self.fill_match(
              '''train_micro_batch_size_per_gpu'''			,							args.per_device_train_batch_size			,							'''per_device_train_batch_size'''					)
          self.fill_match('''gradient_accumulation_steps'''			,							args.gradient_accumulation_steps			,							'''gradient_accumulation_steps'''					)
          self.fill_match('''train_batch_size'''			,							snake_case			,							'''train_batch_size (calculated)'''					)
          self.fill_match('''gradient_clipping'''			,							args.max_grad_norm			,							'''max_grad_norm'''					)

          self.fill_match('''optimizer.params.lr'''			,							args.learning_rate			,							'''learning_rate'''					)
          self.fill_match('''optimizer.params.betas'''			,							[args.adam_betaa, args.adam_betaa]			,							'''adam_beta1+adam_beta2'''					)
          self.fill_match('''optimizer.params.eps'''			,							args.adam_epsilon			,							'''adam_epsilon'''					)
          self.fill_match('''optimizer.params.weight_decay'''			,							args.weight_decay			,							'''weight_decay'''					)

          self.fill_only('''scheduler.params.warmup_min_lr'''			,							0					)  # not a trainer arg
          self.fill_match('''scheduler.params.warmup_max_lr'''			,							args.learning_rate			,							'''learning_rate'''					)
          # total_num_steps - will get set in trainer_config_finalize

          # fp16
          if args.fpaa or args.fpaa_full_eval:
              __UpperCamelCase			=    '''apex''' if args.fpaa_backend == '''apex''' else '''amp'''
          else:
              __UpperCamelCase			=    None

          if args.save_on_each_node:
              # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
              __UpperCamelCase			=    self.config.get('''checkpoint'''			,							{}					)
              __UpperCamelCase			=    args.save_on_each_node

          # amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
          # any here unless the user did the work
          self.fill_match(
              '''fp16.enabled'''			,							((args.fpaa or args.fpaa_full_eval) and fpaa_backend == '''amp''')			,							'''fp16|fp16_full_eval+fp16_backend(amp)'''			,							)

          # apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
          # ZeRO features
          self.fill_match('''amp.enabled'''			,							fpaa_backend == '''apex'''			,							'''fp16+fp16_backend(apex)'''					)
          self.fill_match('''amp.opt_level'''			,							args.fpaa_opt_level			,							'''fp16_opt_level'''					)

          self.fill_match('''bf16.enabled'''			,							(args.bfaa or args.bfaa_full_eval)			,							'''bf16|bf16_full_eval'''					)

          # deepspeed's default mode is fp16 unless there is a config that says differently
          if self.is_true('''bf16.enabled'''					):
              __UpperCamelCase			=    torch.bfloataa
          elif self.is_false('''fp16.enabled'''					):
              __UpperCamelCase			=    torch.floataa
          else:
              __UpperCamelCase			=    torch.floataa







      def    snake_case						(  self       :			Dict			,							snake_case       :			Optional[Any]			,							snake_case       :			Optional[int]			,							snake_case       :			Optional[int]					):
          __UpperCamelCase			=    [
              '''zero_optimization.reduce_bucket_size''',
              '''zero_optimization.stage3_prefetch_bucket_size''',
              '''zero_optimization.stage3_param_persistence_threshold''',
          ]
          __UpperCamelCase			=    [x for x in hidden_size_based_keys if self.is_auto(snake_case					)]

          if len(snake_case					) > 0:
              if hasattr(model.config			,							'''hidden_size'''					):
                  __UpperCamelCase			=    model.config.hidden_size
              elif hasattr(model.config			,							'''hidden_sizes'''					):
                  # if there are many hidden sizes pick the largest one
                  __UpperCamelCase			=    max(model.config.hidden_sizes					)
              else:
                  raise ValueError(
                      '''The model\'s config file has neither `hidden_size` nor `hidden_sizes` entry, '''
                      '''therefore it\'s not possible to automatically fill out the following `auto` entries '''
                      F"in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing "
                      '''`auto` values for these keys with an integer value of your choice.'''					)

              self.fill_only('''zero_optimization.reduce_bucket_size'''			,							hidden_size * hidden_size					)
              if self.is_zeroa():
                  # automatically assign the optimal config values based on model config
                  self.fill_only('''zero_optimization.stage3_prefetch_bucket_size'''			,							0.9 * hidden_size * hidden_size					)
                  self.fill_only('''zero_optimization.stage3_param_persistence_threshold'''			,							10 * hidden_size					)

        # scheduler
          self.fill_match('''scheduler.params.total_num_steps'''			,							snake_case			,							'''num_training_steps (calculated)'''					)
          self.fill_match('''scheduler.params.warmup_num_steps'''			,							args.get_warmup_steps(snake_case					)			,							'''warmup_steps'''					)

          if len(self.mismatches					) > 0:
              __UpperCamelCase			=    '''\n'''.join(self.mismatches					)
              raise ValueError(
                  '''Please correct the following DeepSpeed config values that mismatch TrainingArguments'''
                  F" values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to 'auto'."					)







a_ 		=  None




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						str:

    """simple docstring"""


    global _hf_deepspeed_config_weak_ref
    # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
    __UpperCamelCase			=    weakref.ref(lowercase_   )




def        __SCREAMING_SNAKE_CASE (	)							->						Optional[int]:

    """simple docstring"""


    global _hf_deepspeed_config_weak_ref
    __UpperCamelCase			=    None




def        __SCREAMING_SNAKE_CASE (	)							->						Optional[Any]:

    """simple docstring"""


    if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
        return _hf_deepspeed_config_weak_ref().is_zeroa()
    else:
        return False




def        __SCREAMING_SNAKE_CASE (	)							->						Optional[Any]:

    """simple docstring"""


    if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
        return _hf_deepspeed_config_weak_ref().config
    else:
        return None




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

    """simple docstring"""


    from accelerate.utils import DummyOptim, DummyScheduler

    __UpperCamelCase			=    hf_deepspeed_config.config

    # Optimizer + Scheduler
    # Currently supported combos:
    # 1. DS scheduler + DS optimizer: Yes
    # 2. HF scheduler + HF optimizer: Yes
    # 3. DS scheduler + HF optimizer: Yes
    # 4. HF scheduler + DS optimizer: No
    #
    # Unless Offload is enabled in which case it's:
    # 1. DS scheduler + DS optimizer: Yes
    # 2. HF scheduler + HF optimizer: Mostly*
    # 3. DS scheduler + HF optimizer: Mostly*
    # 4. HF scheduler + DS optimizer: No
    #
    # Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

    __UpperCamelCase			=    None
    if "optimizer" in config:
        if args.adafactor:
            raise ValueError(
                '''--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. '''
                '''Only one optimizer can be configured.'''   )
        __UpperCamelCase			=    DummyOptim(params=lowercase_   )
    else:
        if hf_deepspeed_config.is_offload():
            logger.info(
                '''Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the'''
                ''' custom optimizer has both CPU and GPU implementation (except LAMB)'''   )

        # ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
        # But trainer uses AdamW by default.
        __UpperCamelCase			=    trainer.create_optimizer()
        # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
        __UpperCamelCase			=    True

    __UpperCamelCase			=    None
    if "scheduler" in config:
        __UpperCamelCase			=    DummyScheduler(lowercase_   )
    else:
        if isinstance(lowercase_       ,  lowercase_   ):
            raise ValueError(
                '''Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '''
                '''Please configure a scheduler in the DeepSpeed config.'''   )
        __UpperCamelCase			=    trainer.create_scheduler(num_training_steps=lowercase_       ,  optimizer=lowercase_   )

    return optimizer, lr_scheduler




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_=False   )							->						Tuple:

    """simple docstring"""


    from deepspeed.utils import logger as ds_logger

    __UpperCamelCase			=    trainer.model
    __UpperCamelCase			=    trainer.args

    __UpperCamelCase			=    trainer.accelerator.state.deepspeed_plugin.hf_ds_config

    # resume config update - some bits like `model` and `num_training_steps` only become available during train
    hf_deepspeed_config.trainer_config_finalize(lowercase_       ,  lowercase_       ,  lowercase_   )

    # set the Deepspeed log level consistent with the Trainer
    ds_logger.setLevel(args.get_process_log_level()   )

    if inference:
        # only Z3 makes sense for the inference
        if not hf_deepspeed_config.is_zeroa():
            raise ValueError('''ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config'''   )

        # in case the training config is re-used for inference
        hf_deepspeed_config.del_config_sub_tree('''optimizer'''   )
        hf_deepspeed_config.del_config_sub_tree('''lr_scheduler'''   )
        __UpperCamelCase		,   __UpperCamelCase			=    None, None
        __UpperCamelCase			=    None
    else:
        __UpperCamelCase			=    None  # important for when deepspeed_init is used as re-init
        __UpperCamelCase			=    list(filter(lambda lowercase_   : p.requires_grad       ,  model.parameters()   )   )
        __UpperCamelCase		,   __UpperCamelCase			=    deepspeed_optim_sched(
            lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )

    # keep for quick debug:
    # from pprint import pprint; pprint(config)

    return optimizer, lr_scheduler




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						int:

    """simple docstring"""


    import glob

    __UpperCamelCase			=    sorted(glob.glob(F"{checkpoint_path}/global_step*"   )   )

    if len(lowercase_   ) > 0:
        logger.info(F"Attempting to resume from {checkpoint_path}"   )
        # this magically updates self.optimizer and self.lr_scheduler
        __UpperCamelCase		,   __UpperCamelCase			=    deepspeed_engine.load_checkpoint(
            lowercase_       ,  load_optimizer_states=lowercase_       ,  load_lr_scheduler_states=lowercase_   )
        if load_path is None:
            raise ValueError(F"[deepspeed] failed to resume from checkpoint {checkpoint_path}"   )
    else:
        raise ValueError(F"Can't find a valid checkpoint at {checkpoint_path}"   )











'''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       (				lowerCamelCase :    str       ):






	'''simple docstring'''




	__lowerCAmelCase       				=	TimesformerConfig()

	if "large" in model_name:
		__lowerCAmelCase       				=	96

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

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

	return config



def 	__lowerCAmelCase       (				lowerCamelCase :    int       ):






	'''simple docstring'''




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

	return name



def 	__lowerCAmelCase       (				lowerCamelCase :    int					,		lowerCamelCase :    List[str]       ):






	'''simple docstring'''




	for key in orig_state_dict.copy().keys():
		__lowerCAmelCase       				=	orig_state_dict.pop(lowerCamelCase       )

		if key.startswith("model."       ):
			__lowerCAmelCase       				=	key.replace("model."					,		""       )

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

	return orig_state_dict



def 	__lowerCAmelCase       (				):






	'''simple docstring'''




	__lowerCAmelCase       				=	hf_hub_download(
	    repo_id="hf-internal-testing/spaghetti-video"					,		filename="eating_spaghetti.npy"					,		repo_type="dataset"       )
	__lowerCAmelCase       				=	np.load(lowerCamelCase       )
	return list(lowerCamelCase       )



def 	__lowerCAmelCase       (				lowerCamelCase :    Union[str, Any]					,		lowerCamelCase :    Dict					,		lowerCamelCase :    List[Any]					,		lowerCamelCase :    Optional[Any]       ):






	'''simple docstring'''




	__lowerCAmelCase       				=	get_timesformer_config(lowerCamelCase       )

	__lowerCAmelCase       				=	TimesformerForVideoClassification(lowerCamelCase       )

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

	model.load_state_dict(lowerCamelCase       )
	model.eval()

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

	__lowerCAmelCase       				=	model(**lowerCamelCase       )
	__lowerCAmelCase       				=	outputs.logits

	__lowerCAmelCase       				=	[
	    # 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":
		__lowerCAmelCase       				=	torch.Size([1, 4_00]       )
		__lowerCAmelCase       				=	torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]       )
	elif model_name == "timesformer-base-finetuned-k600":
		__lowerCAmelCase       				=	torch.Size([1, 6_00]       )
		__lowerCAmelCase       				=	torch.tensor([-0.7_2_6_7, -0.7_4_6_6, 3.2_4_0_4]       )
	elif model_name == "timesformer-base-finetuned-ssv2":
		__lowerCAmelCase       				=	torch.Size([1, 1_74]       )
		__lowerCAmelCase       				=	torch.tensor([-0.9_0_5_9, 0.6_4_3_3, -3.1_4_5_7]       )
	elif model_name == "timesformer-large-finetuned-k400":
		__lowerCAmelCase       				=	torch.Size([1, 4_00]       )
		__lowerCAmelCase       				=	torch.tensor([0, 0, 0]       )
	elif model_name == "timesformer-large-finetuned-k600":
		__lowerCAmelCase       				=	torch.Size([1, 6_00]       )
		__lowerCAmelCase       				=	torch.tensor([0, 0, 0]       )
	elif model_name == "timesformer-large-finetuned-ssv2":
		__lowerCAmelCase       				=	torch.Size([1, 1_74]       )
		__lowerCAmelCase       				=	torch.tensor([0, 0, 0]       )
	elif model_name == "timesformer-hr-finetuned-k400":
		__lowerCAmelCase       				=	torch.Size([1, 4_00]       )
		__lowerCAmelCase       				=	torch.tensor([-0.9_6_1_7, -3.7_3_1_1, -3.7_7_0_8]       )
	elif model_name == "timesformer-hr-finetuned-k600":
		__lowerCAmelCase       				=	torch.Size([1, 6_00]       )
		__lowerCAmelCase       				=	torch.tensor([2.5_2_7_3, 0.7_1_2_7, 1.8_8_4_8]       )
	elif model_name == "timesformer-hr-finetuned-ssv2":
		__lowerCAmelCase       				=	torch.Size([1, 1_74]       )
		__lowerCAmelCase       				=	torch.tensor([-3.6_7_5_6, -0.7_5_1_3, 0.7_1_8_0]       )
	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__":
	lowerCAmelCase						:		Dict						      = 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						:		Optional[int]						      = parser.parse_args()
	convert_timesformer_checkpoint(
	    args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
	)