





"""simple docstring"""
import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import SwinaSRForImageSuperResolution, SwinaSRModel
   from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
   from PIL import Image

   from transformers import SwinaSRImageProcessor
class lowerCamelCase__  :



   def __init__(       self      ,   snake_case      ,   snake_case=1_3      ,   snake_case=3_2      ,   snake_case=1      ,   snake_case=3      ,   snake_case=1_6      ,   snake_case=[1, 2, 1]      ,   snake_case=[2, 2, 4]      ,   snake_case=2      ,   snake_case=2.0      ,   snake_case=True      ,   snake_case=0.0      ,   snake_case=0.0      ,   snake_case=0.1      ,   snake_case="gelu"      ,   snake_case=False      ,   snake_case=True      ,   snake_case=0.02      ,   snake_case=1E-5      ,   snake_case=True      ,   snake_case=None      ,   snake_case=False      ,   snake_case=2      ,   ) ->    Dict:







      """simple docstring"""
      lowercase    :			Any  =       parent
      lowercase    :			List[str]  =       batch_size
      lowercase    :			List[str]  =       image_size
      lowercase    :			Union[str, Any]  =       patch_size
      lowercase    :			Optional[Any]  =       num_channels
      lowercase    :			Union[str, Any]  =       embed_dim
      lowercase    :			Optional[int]  =       depths
      lowercase    :			List[Any]  =       num_heads
      lowercase    :			Optional[int]  =       window_size
      lowercase    :			Tuple  =       mlp_ratio
      lowercase    :			Any  =       qkv_bias
      lowercase    :			Optional[int]  =       hidden_dropout_prob
      lowercase    :			List[Any]  =       attention_probs_dropout_prob
      lowercase    :			Tuple  =       drop_path_rate
      lowercase    :			int  =       hidden_act
      lowercase    :			List[Any]  =       use_absolute_embeddings
      lowercase    :			Optional[int]  =       patch_norm
      lowercase    :			Optional[Any]  =       layer_norm_eps
      lowercase    :			str  =       initializer_range
      lowercase    :			int  =       is_training
      lowercase    :			List[str]  =       scope
      lowercase    :			Any  =       use_labels
      lowercase    :			Dict  =       upscale

      # here we set some attributes to make tests pass
      lowercase    :			Tuple  =       len(snake_case	)
      lowercase    :			Any  =       embed_dim
      lowercase    :			Union[str, Any]  =       (image_size // patch_size) ** 2



   def    _UpperCAmelCase	(       self	) ->    Any:







      """simple docstring"""
      lowercase    :			List[Any]  =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

      lowercase    :			str  =       None
      if self.use_labels:
         lowercase    :			Union[str, Any]  =       ids_tensor([self.batch_size]      ,   self.type_sequence_label_size	)

      lowercase    :			List[str]  =       self.get_config()

      return config, pixel_values, labels



   def    _UpperCAmelCase	(       self	) ->    int:







      """simple docstring"""
      return SwinaSRConfig(
          image_size=self.image_size      ,   patch_size=self.patch_size      ,   num_channels=self.num_channels      ,   embed_dim=self.embed_dim      ,   depths=self.depths      ,   num_heads=self.num_heads      ,   window_size=self.window_size      ,   mlp_ratio=self.mlp_ratio      ,   qkv_bias=self.qkv_bias      ,   hidden_dropout_prob=self.hidden_dropout_prob      ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,   drop_path_rate=self.drop_path_rate      ,   hidden_act=self.hidden_act      ,   use_absolute_embeddings=self.use_absolute_embeddings      ,   path_norm=self.patch_norm      ,   layer_norm_eps=self.layer_norm_eps      ,   initializer_range=self.initializer_range      ,   upscale=self.upscale      ,   )



   def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case      ,   snake_case	) ->    Tuple:







      """simple docstring"""
      lowercase    :			Dict  =       SwinaSRModel(config=snake_case	)
      model.to(snake_case	)
      model.eval()
      lowercase    :			Optional[int]  =       model(snake_case	)

      self.parent.assertEqual(
          result.last_hidden_state.shape      ,   (self.batch_size, self.embed_dim, self.image_size, self.image_size)	)



   def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case      ,   snake_case	) ->    List[Any]:







      """simple docstring"""
      lowercase    :			str  =       SwinaSRForImageSuperResolution(snake_case	)
      model.to(snake_case	)
      model.eval()
      lowercase    :			str  =       model(snake_case	)

      lowercase    :			List[Any]  =       self.image_size * self.upscale
      self.parent.assertEqual(
          result.reconstruction.shape      ,   (self.batch_size, self.num_channels, expected_image_size, expected_image_size)	)





   def    _UpperCAmelCase	(       self	) ->    Tuple:







      """simple docstring"""
      lowercase    :			List[str]  =       self.prepare_config_and_inputs()
      lowercase  ,					lowercase  ,					lowercase    :			Any  =       config_and_inputs
      lowercase    :			List[Any]  =       {"""pixel_values""": pixel_values}
      return config, inputs_dict




@require_torch
class lowerCamelCase__  (    __UpperCamelCase  ,				__UpperCamelCase  ,				unittest.TestCase				):
   __UpperCAmelCase        =					(SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
   __UpperCAmelCase        =					{"""feature-extraction""": SwinaSRModel} if is_torch_available() else {}

   __UpperCAmelCase        =					False
   __UpperCAmelCase        =					False
   __UpperCAmelCase        =					False
   __UpperCAmelCase        =					False
   __UpperCAmelCase        =					False



   def    _UpperCAmelCase	(       self	) ->    Dict:







      """simple docstring"""
      lowercase    :			Optional[int]  =       SwinaSRModelTester(self	)
      lowercase    :			Union[str, Any]  =       ConfigTester(self      ,   config_class=snake_case      ,   embed_dim=3_7	)



   def    _UpperCAmelCase	(       self	) ->    str:







      """simple docstring"""
      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()



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







      """simple docstring"""
      lowercase    :			Tuple  =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*snake_case	)



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







      """simple docstring"""
      lowercase    :			Tuple  =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_super_resolution(*snake_case	)



   @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""	)
   def    _UpperCAmelCase	(       self	) ->    int:







      """simple docstring"""
      pass



   @unittest.skip(reason="""Swin2SR does not use inputs_embeds"""	)
   def    _UpperCAmelCase	(       self	) ->    Optional[Any]:







      """simple docstring"""
      pass



   @unittest.skip(reason="""Swin2SR does not support training yet"""	)
   def    _UpperCAmelCase	(       self	) ->    Tuple:







      """simple docstring"""
      pass



   @unittest.skip(reason="""Swin2SR does not support training yet"""	)
   def    _UpperCAmelCase	(       self	) ->    Any:







      """simple docstring"""
      pass



   def    _UpperCAmelCase	(       self	) ->    Any:







      """simple docstring"""
      lowercase  ,					lowercase    :			List[Any]  =       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         lowercase    :			Dict  =       model_class(snake_case	)
         self.assertIsInstance(model.get_input_embeddings()      ,   (nn.Module)	)
         lowercase    :			Optional[Any]  =       model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(snake_case      ,   nn.Linear	)	)



   def    _UpperCAmelCase	(       self	) ->    Optional[int]:







      """simple docstring"""
      lowercase  ,					lowercase    :			Dict  =       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         lowercase    :			Tuple  =       model_class(snake_case	)
         lowercase    :			List[Any]  =       inspect.signature(model.forward	)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         lowercase    :			str  =       [*signature.parameters.keys()]

         lowercase    :			List[Any]  =       ["""pixel_values"""]
         self.assertListEqual(arg_names[:1]      ,   snake_case	)



   @slow
   def    _UpperCAmelCase	(       self	) ->    Optional[Any]:







      """simple docstring"""
      for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowercase    :			Optional[Any]  =       SwinaSRModel.from_pretrained(snake_case	)
         self.assertIsNotNone(snake_case	)



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







      """simple docstring"""
      lowercase  ,					lowercase    :			List[str]  =       self.model_tester.prepare_config_and_inputs_for_common()

      lowercase    :			Union[str, Any]  =       _config_zero_init(snake_case	)
      for model_class in self.all_model_classes:
         lowercase    :			Any  =       model_class(config=snake_case	)
         for name, param in model.named_parameters():
            if "logit_scale" in name:
               continue
            if param.requires_grad:
               self.assertIn(
                   ((param.data.mean() * 1E9).round() / 1E9).item()      ,   [0.0, 1.0]      ,   msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''      ,   )





   def    _UpperCAmelCase	(       self	) ->    str:







      """simple docstring"""
      lowercase  ,					lowercase    :			Optional[Any]  =       self.model_tester.prepare_config_and_inputs_for_common()
      lowercase    :			Tuple  =       True

      for model_class in self.all_model_classes:
         lowercase    :			Tuple  =       True
         lowercase    :			int  =       False
         lowercase    :			str  =       True
         lowercase    :			Union[str, Any]  =       model_class(snake_case	)
         model.to(snake_case	)
         model.eval()
         with torch.no_grad():
            lowercase    :			List[Any]  =       model(**self._prepare_for_class(snake_case      ,   snake_case	)	)
         lowercase    :			Optional[Any]  =       outputs.attentions
         lowercase    :			int  =       len(self.model_tester.depths	)
         self.assertEqual(len(snake_case	)      ,   snake_case	)

         # check that output_attentions also work using config
         del inputs_dict["output_attentions"]
         lowercase    :			Optional[int]  =       True
         lowercase    :			List[str]  =       config.window_size**2
         lowercase    :			List[Any]  =       model_class(snake_case	)
         model.to(snake_case	)
         model.eval()
         with torch.no_grad():
            lowercase    :			int  =       model(**self._prepare_for_class(snake_case      ,   snake_case	)	)
         lowercase    :			Optional[int]  =       outputs.attentions
         self.assertEqual(len(snake_case	)      ,   snake_case	)

         self.assertListEqual(
             list(attentions[0].shape[-3:]	)      ,   [self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,   )
         lowercase    :			int  =       len(snake_case	)

         # Check attention is always last and order is fine
         lowercase    :			Dict  =       True
         lowercase    :			Optional[int]  =       True
         lowercase    :			Any  =       model_class(snake_case	)
         model.to(snake_case	)
         model.eval()
         with torch.no_grad():
            lowercase    :			Optional[Any]  =       model(**self._prepare_for_class(snake_case      ,   snake_case	)	)

         self.assertEqual(out_len + 1      ,   len(snake_case	)	)

         lowercase    :			Optional[Any]  =       outputs.attentions

         self.assertEqual(len(snake_case	)      ,   snake_case	)

         self.assertListEqual(
             list(self_attentions[0].shape[-3:]	)      ,   [self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,   )




@require_vision
@require_torch
@slow
class lowerCamelCase__  (    unittest.TestCase				):



   def    _UpperCAmelCase	(       self	) ->    Optional[int]:







      """simple docstring"""
      lowercase    :			Optional[Any]  =       SwinaSRImageProcessor()
      lowercase    :			Dict  =       SwinaSRForImageSuperResolution.from_pretrained("""caidas/swin2SR-classical-sr-x2-64"""	).to(snake_case	)

      lowercase    :			int  =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
      lowercase    :			Tuple  =       processor(images=snake_case      ,   return_tensors="""pt"""	).to(snake_case	)

      # forward pass
      with torch.no_grad():
         lowercase    :			Dict  =       model(**snake_case	)

      # verify the logits
      lowercase    :			str  =       torch.Size([1, 3, 9_7_6, 1_2_9_6]	)
      self.assertEqual(outputs.reconstruction.shape      ,   snake_case	)
      lowercase    :			Tuple  =       torch.tensor(
          [[0.54_58, 0.55_46, 0.56_38], [0.55_26, 0.55_65, 0.56_51], [0.53_96, 0.54_26, 0.56_21]]	).to(snake_case	)
      self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]      ,   snake_case      ,   atol=1E-4	)	)









"""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 			__snake_case						(      __A    )		->				Optional[int]:
   lowercase    :			int  =       TimesformerConfig()

   if "large" in model_name:
      lowercase    :			str  =       96

   if "hr" in model_name:
      lowercase    :			Tuple  =       16
      lowercase    :			Any  =       448

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

   return config




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

   return name




def 			__snake_case						(      __A ,__A    )		->				List[str]:
   for key in orig_state_dict.copy().keys():
      lowercase    :			str  =       orig_state_dict.pop(__A    )

      if key.startswith("""model."""    ):
         lowercase    :			Union[str, Any]  =       key.replace("""model.""" ,""""""    )

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

   return orig_state_dict




def 			__snake_case						(      )		->				Dict:
   lowercase    :			str  =       hf_hub_download(
       repo_id="""hf-internal-testing/spaghetti-video""" ,filename="""eating_spaghetti.npy""" ,repo_type="""dataset"""    )
   lowercase    :			List[Any]  =       np.load(__A    )
   return list(__A    )




def 			__snake_case						(      __A ,__A ,__A ,__A    )		->				Union[str, Any]:
   lowercase    :			Union[str, Any]  =       get_timesformer_config(__A    )

   lowercase    :			Any  =       TimesformerForVideoClassification(__A    )

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

   model.load_state_dict(__A    )
   model.eval()

   # verify model on basic input
   lowercase    :			Union[str, Any]  =       VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] ,image_std=[0.5, 0.5, 0.5]    )
   lowercase    :			Dict  =       prepare_video()
   lowercase    :			Optional[int]  =       image_processor(video[:8] ,return_tensors="""pt"""    )

   lowercase    :			Dict  =       model(**__A    )
   lowercase    :			int  =       outputs.logits

   lowercase    :			List[str]  =       [
       # 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":
      lowercase    :			Optional[int]  =       torch.Size([1, 400]    )
      lowercase    :			Dict  =       torch.tensor([-0.30_16, -0.77_13, -0.42_05]    )
   elif model_name == "timesformer-base-finetuned-k600":
      lowercase    :			str  =       torch.Size([1, 600]    )
      lowercase    :			List[Any]  =       torch.tensor([-0.72_67, -0.74_66, 3.24_04]    )
   elif model_name == "timesformer-base-finetuned-ssv2":
      lowercase    :			Tuple  =       torch.Size([1, 174]    )
      lowercase    :			Optional[Any]  =       torch.tensor([-0.90_59, 0.64_33, -3.14_57]    )
   elif model_name == "timesformer-large-finetuned-k400":
      lowercase    :			str  =       torch.Size([1, 400]    )
      lowercase    :			List[str]  =       torch.tensor([0, 0, 0]    )
   elif model_name == "timesformer-large-finetuned-k600":
      lowercase    :			List[Any]  =       torch.Size([1, 600]    )
      lowercase    :			str  =       torch.tensor([0, 0, 0]    )
   elif model_name == "timesformer-large-finetuned-ssv2":
      lowercase    :			Dict  =       torch.Size([1, 174]    )
      lowercase    :			List[Any]  =       torch.tensor([0, 0, 0]    )
   elif model_name == "timesformer-hr-finetuned-k400":
      lowercase    :			Tuple  =       torch.Size([1, 400]    )
      lowercase    :			Union[str, Any]  =       torch.tensor([-0.96_17, -3.73_11, -3.77_08]    )
   elif model_name == "timesformer-hr-finetuned-k600":
      lowercase    :			Union[str, Any]  =       torch.Size([1, 600]    )
      lowercase    :			Any  =       torch.tensor([2.52_73, 0.71_27, 1.88_48]    )
   elif model_name == "timesformer-hr-finetuned-ssv2":
      lowercase    :			int  =       torch.Size([1, 174]    )
      lowercase    :			Optional[Any]  =       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] ,__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:	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."
   )

   lowerCAmelCase:	int							      =parser.parse_args()
   convert_timesformer_checkpoint(
       args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
   )


