
import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
   import jax
   import jax.numpy as jnp

   from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor






class     UpperCAmelCase						(		unittest.TestCase					):



   def __init__(self						:   Optional[int]       ,		snake_case__						:   List[Any]       ,		snake_case__						:   List[Any]=3       ,		snake_case__						:   List[str]=32       ,		snake_case__						:   int=3       ,		snake_case__						:   List[str]=10       ,		snake_case__						:   int=[10, 20, 30, 40]       ,		snake_case__						:   Optional[Any]=[1, 1, 2, 1]       ,		snake_case__						:   Union[str, Any]=True       ,		snake_case__						:   List[str]=True       ,		snake_case__						:   str="relu"       ,		snake_case__						:   List[Any]=3       ,		snake_case__						:   Union[str, Any]=None       ,		)						->      str:


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= parent
          snake_case			:  Optional[Any]     							= batch_size
          snake_case			:  Optional[int]     							= image_size
          snake_case			:  str     							= num_channels
          snake_case			:  Dict     							= embeddings_size
          snake_case			:  List[str]     							= hidden_sizes
          snake_case			:  int     							= depths
          snake_case			:  Union[str, Any]     							= is_training
          snake_case			:  Optional[Any]     							= use_labels
          snake_case			:  List[str]     							= hidden_act
          snake_case			:  Dict     							= num_labels
          snake_case			:  Tuple     							= scope
          snake_case			:  Optional[Any]     							= len(snake_case__					)



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


          '''simple docstring'''


          snake_case			:  int     							= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

          return config, pixel_values



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      Any:


          '''simple docstring'''


          return RegNetConfig(
              num_channels=self.num_channels       ,		embeddings_size=self.embeddings_size       ,		hidden_sizes=self.hidden_sizes       ,		depths=self.depths       ,		hidden_act=self.hidden_act       ,		num_labels=self.num_labels       ,		image_size=self.image_size       ,		)



   def 							_SCREAMING_SNAKE_CASE  (self						:   int       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   List[str]					)						->      Optional[Any]:


          '''simple docstring'''


          snake_case			:  Any     							= FlaxRegNetModel(config=snake_case__					)
          snake_case			:  Any     							= model(snake_case__					)

          # Output shape (b, c, h, w)
          self.parent.assertEqual(
              result.last_hidden_state.shape       ,		(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,		)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]       ,		snake_case__						:   Union[str, Any]       ,		snake_case__						:   Dict					)						->      List[str]:


          '''simple docstring'''


          snake_case			:  Tuple     							= self.num_labels
          snake_case			:  Optional[int]     							= FlaxRegNetForImageClassification(config=snake_case__					)
          snake_case			:  Optional[int]     							= model(snake_case__					)
          self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_labels)					)



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


          '''simple docstring'''


          snake_case			:  Dict     							= self.prepare_config_and_inputs()
          snake_case							,   snake_case			:  str     							= config_and_inputs
          snake_case			:  Optional[int]     							= {"pixel_values": pixel_values}
          return config, inputs_dict






@require_flax
class     UpperCAmelCase						(		A_			,unittest.TestCase					):
   A__   :						str			=	(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

   A__   :						str			=	False
   A__   :						str			=	False
   A__   :						List[str]			=	False



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]					)						->      None:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= FlaxRegNetModelTester(self					)
          snake_case			:  Dict     							= ConfigTester(self       ,		config_class=snake_case__       ,		has_text_modality=snake_case__					)



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


          '''simple docstring'''


          self.create_and_test_config_common_properties()
          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 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]					)						->      Union[str, Any]:


          '''simple docstring'''


          return



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]					)						->      Tuple:


          '''simple docstring'''


          snake_case			:  str     							= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case__					)



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


          '''simple docstring'''


          snake_case			:  str     							= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case__					)



   @unittest.skip(reason="RegNet does not use inputs_embeds"					)
   def 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      Union[str, Any]:


          '''simple docstring'''


          pass



   @unittest.skip(reason="RegNet does not support input and output embeddings"					)
   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]					)						->      Optional[Any]:


          '''simple docstring'''


          pass



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]					)						->      Tuple:


          '''simple docstring'''


          snake_case							,   snake_case			:  Union[str, Any]     							= self.model_tester.prepare_config_and_inputs_for_common()

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

                 snake_case			:  Tuple     							= ["pixel_values"]
                 self.assertListEqual(arg_names[:1]       ,		snake_case__					)



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


          '''simple docstring'''


          def check_hidden_states_output(snake_case__						:   Dict       ,		snake_case__						:   Optional[int]       ,		snake_case__						:   Dict					):
                 snake_case			:  List[str]     							= model_class(snake_case__					)

                 snake_case			:  Union[str, Any]     							= model(**self._prepare_for_class(snake_case__       ,		snake_case__					)					)

                 snake_case			:  Any     							= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 snake_case			:  Optional[int]     							= self.model_tester.num_stages
                 self.assertEqual(len(snake_case__					)       ,		expected_num_stages + 1					)

          snake_case							,   snake_case			:  Optional[Any]     							= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 snake_case			:  Tuple     							= True
                 check_hidden_states_output(snake_case__       ,		snake_case__       ,		snake_case__					)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 snake_case			:  Optional[int]     							= True

                 check_hidden_states_output(snake_case__       ,		snake_case__       ,		snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]					)						->      Tuple:


          '''simple docstring'''


          snake_case							,   snake_case			:  Optional[Any]     							= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__					):
                        snake_case			:  Optional[Any]     							= self._prepare_for_class(snake_case__       ,		snake_case__					)
                        snake_case			:  List[Any]     							= model_class(snake_case__					)

                        @jax.jit
                        def model_jitted(snake_case__						:   Tuple       ,		**snake_case__						:   List[Any]					):
                               return model(pixel_values=snake_case__       ,		**snake_case__					)

                        with self.subTest("JIT Enabled"					):
                               snake_case			:  Union[str, Any]     							= model_jitted(**snake_case__					).to_tuple()

                        with self.subTest("JIT Disabled"					):
                               with jax.disable_jit():
                                      snake_case			:  Optional[int]     							= model_jitted(**snake_case__					).to_tuple()

                        self.assertEqual(len(snake_case__					)       ,		len(snake_case__					)					)
                        for jitted_output, output in zip(snake_case__       ,		snake_case__					):
                               self.assertEqual(jitted_output.shape       ,		output.shape					)



def      UpperCamelCase   (       ):
       snake_case			:  List[str]     							= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
       return image






@require_flax
class     UpperCAmelCase						(		unittest.TestCase					):



   @cached_property
   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]					)						->      Optional[Any]:


          '''simple docstring'''


          return AutoImageProcessor.from_pretrained("facebook/regnet-y-040"					) if is_vision_available() else None



   @slow
   def 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      Optional[int]:


          '''simple docstring'''


          snake_case			:  List[str]     							= FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040"					)

          snake_case			:  Dict     							= self.default_image_processor
          snake_case			:  Union[str, Any]     							= prepare_img()
          snake_case			:  str     							= image_processor(images=snake_case__       ,		return_tensors="np"					)

          snake_case			:  Optional[int]     							= model(**snake_case__					)

          # verify the logits
          snake_case			:  Optional[Any]     							= (1, 10_00)
          self.assertEqual(outputs.logits.shape       ,		snake_case__					)

          snake_case			:  Optional[int]     							= jnp.array([-0.4180, -1.5051, -3.4836]					)

          self.assertTrue(jnp.allclose(outputs.logits[0, :3]       ,		snake_case__       ,		atol=1e-4					)					)







import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)


def      UpperCamelCase   (       __lowerCamelCase	:					Dict       ):
       snake_case			:  Any     							= VideoMAEConfig()

       set_architecture_configs(__lowerCamelCase							,   __lowerCamelCase       )

       if "finetuned" not in model_name:
              snake_case			:  int     							= False

       if "finetuned" in model_name:
              snake_case			:  Dict     							= "huggingface/label-files"
              if "kinetics" in model_name:
                     snake_case			:  List[str]     							= 400
                     snake_case			:  List[str]     							= "kinetics400-id2label.json"
              elif "ssv2" in model_name:
                     snake_case			:  List[str]     							= 174
                     snake_case			:  Optional[int]     							= "something-something-v2-id2label.json"
              else:
                     raise ValueError("Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned."       )
              snake_case			:  Tuple     							= json.load(open(hf_hub_download(__lowerCamelCase							,   __lowerCamelCase							,   repo_type="dataset"       )							,   "r"       )       )
              snake_case			:  Dict     							= {int(__lowerCamelCase       ): v for k, v in idalabel.items()}
              snake_case			:  Optional[Any]     							= idalabel
              snake_case			:  Tuple     							= {v: k for k, v in idalabel.items()}

       return config


def      UpperCamelCase   (       __lowerCamelCase	:					Optional[Any]							,   __lowerCamelCase	:					int       ):
       if "small" in model_name:
              snake_case			:  Dict     							= 384
              snake_case			:  Optional[Any]     							= 1536
              snake_case			:  Union[str, Any]     							= 12
              snake_case			:  Dict     							= 16
              snake_case			:  List[str]     							= 12
              snake_case			:  List[str]     							= 3
              snake_case			:  str     							= 192
              snake_case			:  Union[str, Any]     							= 768
       elif "large" in model_name:
              snake_case			:  Tuple     							= 1024
              snake_case			:  List[Any]     							= 4096
              snake_case			:  Any     							= 24
              snake_case			:  Optional[int]     							= 16
              snake_case			:  List[str]     							= 12
              snake_case			:  int     							= 8
              snake_case			:  Any     							= 512
              snake_case			:  List[Any]     							= 2048
       elif "huge" in model_name:
              snake_case			:  Any     							= 1280
              snake_case			:  List[str]     							= 5120
              snake_case			:  Dict     							= 32
              snake_case			:  int     							= 16
              snake_case			:  int     							= 12
              snake_case			:  Tuple     							= 8
              snake_case			:  str     							= 640
              snake_case			:  List[Any]     							= 2560
       elif "base" not in model_name:
              raise ValueError("Model name should include either \"small\", \"base\", \"large\", or \"huge\""       )


def      UpperCamelCase   (       __lowerCamelCase	:					Any       ):
       if "encoder." in name:
              snake_case			:  Dict     							= name.replace("encoder."							,   ""       )
       if "cls_token" in name:
              snake_case			:  str     							= name.replace("cls_token"							,   "videomae.embeddings.cls_token"       )
       if "decoder_pos_embed" in name:
              snake_case			:  Tuple     							= name.replace("decoder_pos_embed"							,   "decoder.decoder_pos_embed"       )
       if "pos_embed" in name and "decoder" not in name:
              snake_case			:  str     							= name.replace("pos_embed"							,   "videomae.embeddings.position_embeddings"       )
       if "patch_embed.proj" in name:
              snake_case			:  int     							= name.replace("patch_embed.proj"							,   "videomae.embeddings.patch_embeddings.projection"       )
       if "patch_embed.norm" in name:
              snake_case			:  List[Any]     							= name.replace("patch_embed.norm"							,   "videomae.embeddings.norm"       )
       if "decoder.blocks" in name:
              snake_case			:  Tuple     							= name.replace("decoder.blocks"							,   "decoder.decoder_layers"       )
       if "blocks" in name:
              snake_case			:  Any     							= name.replace("blocks"							,   "videomae.encoder.layer"       )
       if "attn.proj" in name:
              snake_case			:  int     							= name.replace("attn.proj"							,   "attention.output.dense"       )
       if "attn" in name and "bias" not in name:
              snake_case			:  Optional[Any]     							= name.replace("attn"							,   "attention.self"       )
       if "attn" in name:
              snake_case			:  List[str]     							= name.replace("attn"							,   "attention.attention"       )
       if "norm1" in name:
              snake_case			:  Optional[int]     							= name.replace("norm1"							,   "layernorm_before"       )
       if "norm2" in name:
              snake_case			:  Any     							= name.replace("norm2"							,   "layernorm_after"       )
       if "mlp.fc1" in name:
              snake_case			:  Optional[int]     							= name.replace("mlp.fc1"							,   "intermediate.dense"       )
       if "mlp.fc2" in name:
              snake_case			:  Union[str, Any]     							= name.replace("mlp.fc2"							,   "output.dense"       )
       if "decoder_embed" in name:
              snake_case			:  Optional[int]     							= name.replace("decoder_embed"							,   "decoder.decoder_embed"       )
       if "decoder_norm" in name:
              snake_case			:  Tuple     							= name.replace("decoder_norm"							,   "decoder.decoder_norm"       )
       if "decoder_pred" in name:
              snake_case			:  str     							= name.replace("decoder_pred"							,   "decoder.decoder_pred"       )
       if "norm.weight" in name and "decoder" not in name and "fc" not in name:
              snake_case			:  List[Any]     							= name.replace("norm.weight"							,   "videomae.layernorm.weight"       )
       if "norm.bias" in name and "decoder" not in name and "fc" not in name:
              snake_case			:  Dict     							= name.replace("norm.bias"							,   "videomae.layernorm.bias"       )
       if "head" in name and "decoder" not in name:
              snake_case			:  Union[str, Any]     							= name.replace("head"							,   "classifier"       )

       return name


def      UpperCamelCase   (       __lowerCamelCase	:					Optional[Any]							,   __lowerCamelCase	:					Any       ):
       for key in orig_state_dict.copy().keys():
              snake_case			:  Optional[Any]     							= orig_state_dict.pop(__lowerCamelCase       )

              if key.startswith("encoder."       ):
                     snake_case			:  Optional[Any]     							= key.replace("encoder."							,   ""       )

              if "qkv" in key:
                     snake_case			:  Tuple     							= key.split("."       )
                     if key.startswith("decoder.blocks"       ):
                            snake_case			:  str     							= config.decoder_hidden_size
                            snake_case			:  Optional[Any]     							= int(key_split[2]       )
                            snake_case			:  Tuple     							= "decoder.decoder_layers."
                            if "weight" in key:
                                   snake_case			:  Optional[Any]     							= val[:dim, :]
                                   snake_case			:  List[str]     							= val[dim : dim * 2, :]
                                   snake_case			:  str     							= val[-dim:, :]
                     else:
                            snake_case			:  str     							= config.hidden_size
                            snake_case			:  str     							= int(key_split[1]       )
                            snake_case			:  Union[str, Any]     							= "videomae.encoder.layer."
                            if "weight" in key:
                                   snake_case			:  int     							= val[:dim, :]
                                   snake_case			:  int     							= val[dim : dim * 2, :]
                                   snake_case			:  Union[str, Any]     							= val[-dim:, :]
              else:
                     snake_case			:  Optional[Any]     							= val

       return orig_state_dict


def      UpperCamelCase   (       ):
       snake_case			:  Union[str, Any]     							= hf_hub_download(
           repo_id="hf-internal-testing/spaghetti-video"							,   filename="eating_spaghetti.npy"							,   repo_type="dataset"       )
       snake_case			:  Optional[Any]     							= np.load(__lowerCamelCase       )
       return list(__lowerCamelCase       )


def      UpperCamelCase   (       __lowerCamelCase	:					Optional[Any]							,   __lowerCamelCase	:					Union[str, Any]							,   __lowerCamelCase	:					Optional[Any]							,   __lowerCamelCase	:					Any       ):
       snake_case			:  List[str]     							= get_videomae_config(__lowerCamelCase       )

       if "finetuned" in model_name:
              snake_case			:  List[Any]     							= VideoMAEForVideoClassification(__lowerCamelCase       )
       else:
              snake_case			:  List[str]     							= VideoMAEForPreTraining(__lowerCamelCase       )

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

       model.load_state_dict(__lowerCamelCase       )
       model.eval()

       # verify model on basic input
       snake_case			:  Tuple     							= VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]							,   image_std=[0.5, 0.5, 0.5]       )
       snake_case			:  Dict     							= prepare_video()
       snake_case			:  Dict     							= image_processor(__lowerCamelCase							,   return_tensors="pt"       )

       if "finetuned" not in model_name:
              snake_case			:  List[Any]     							= hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos"							,   filename="bool_masked_pos.pt"       )
              snake_case			:  Union[str, Any]     							= torch.load(__lowerCamelCase       )

       snake_case			:  Any     							= model(**__lowerCamelCase       )
       snake_case			:  List[Any]     							= outputs.logits

       snake_case			:  Any     							= [
           "videomae-small-finetuned-kinetics",
           "videomae-small-finetuned-ssv2",
           # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
           "videomae-base-short",
           "videomae-base-short-finetuned-kinetics",
           "videomae-base",
           "videomae-base-finetuned-kinetics",
           "videomae-large",
           "videomae-large-finetuned-kinetics",
           "videomae-huge-finetuned-kinetics",
           # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
           "videomae-base-short-ssv2",
           "videomae-base-short-finetuned-ssv2",
           "videomae-base-ssv2",
           "videomae-base-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 == "videomae-small-finetuned-kinetics":
              snake_case			:  List[str]     							= torch.Size([1, 400]       )
              snake_case			:  List[str]     							= torch.tensor([-0.9291, -0.4061, -0.9307]       )
       elif model_name == "videomae-small-finetuned-ssv2":
              snake_case			:  Dict     							= torch.Size([1, 174]       )
              snake_case			:  Tuple     							= torch.tensor([0.2671, -0.4689, -0.8235]       )
       elif model_name == "videomae-base":
              snake_case			:  Union[str, Any]     							= torch.Size([1, 1408, 1536]       )
              snake_case			:  Union[str, Any]     							= torch.tensor([[0.7739, 0.7968, 0.7089], [0.6701, 0.7487, 0.6209], [0.4287, 0.5158, 0.4773]]       )
       elif model_name == "videomae-base-short":
              snake_case			:  str     							= torch.Size([1, 1408, 1536]       )
              snake_case			:  List[Any]     							= torch.tensor([[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]       )
              # we verified the loss both for normalized and unnormalized targets for this one
              snake_case			:  int     							= torch.tensor([0.5142]       ) if config.norm_pix_loss else torch.tensor([0.6469]       )
       elif model_name == "videomae-large":
              snake_case			:  Optional[int]     							= torch.Size([1, 1408, 1536]       )
              snake_case			:  Tuple     							= torch.tensor([[0.7149, 0.7997, 0.6966], [0.6768, 0.7869, 0.6948], [0.5139, 0.6221, 0.5605]]       )
       elif model_name == "videomae-large-finetuned-kinetics":
              snake_case			:  Dict     							= torch.Size([1, 400]       )
              snake_case			:  Any     							= torch.tensor([0.0771, 0.0011, -0.3625]       )
       elif model_name == "videomae-huge-finetuned-kinetics":
              snake_case			:  str     							= torch.Size([1, 400]       )
              snake_case			:  List[str]     							= torch.tensor([0.2433, 0.1632, -0.4894]       )
       elif model_name == "videomae-base-short-finetuned-kinetics":
              snake_case			:  int     							= torch.Size([1, 400]       )
              snake_case			:  Tuple     							= torch.tensor([0.6588, 0.0990, -0.2493]       )
       elif model_name == "videomae-base-finetuned-kinetics":
              snake_case			:  Optional[Any]     							= torch.Size([1, 400]       )
              snake_case			:  Dict     							= torch.tensor([0.3669, -0.0688, -0.2421]       )
       elif model_name == "videomae-base-short-ssv2":
              snake_case			:  List[Any]     							= torch.Size([1, 1408, 1536]       )
              snake_case			:  Union[str, Any]     							= torch.tensor([[0.4712, 0.5296, 0.5786], [0.2278, 0.2729, 0.4026], [0.0352, 0.0730, 0.2506]]       )
       elif model_name == "videomae-base-short-finetuned-ssv2":
              snake_case			:  Any     							= torch.Size([1, 174]       )
              snake_case			:  str     							= torch.tensor([-0.0537, -0.1539, -0.3266]       )
       elif model_name == "videomae-base-ssv2":
              snake_case			:  Dict     							= torch.Size([1, 1408, 1536]       )
              snake_case			:  List[Any]     							= torch.tensor([[0.8131, 0.8727, 0.8546], [0.7366, 0.9377, 0.8870], [0.5935, 0.8874, 0.8564]]       )
       elif model_name == "videomae-base-finetuned-ssv2":
              snake_case			:  List[Any]     							= torch.Size([1, 174]       )
              snake_case			:  Optional[int]     							= torch.tensor([0.1961, -0.8337, -0.6389]       )
       else:
              raise ValueError(f"""Model name not supported. Should be one of {model_names}"""       )

       # verify logits
       assert logits.shape == expected_shape
       if "finetuned" in model_name:
              assert torch.allclose(logits[0, :3]							,   __lowerCamelCase							,   atol=1E-4       )
       else:
              print("Logits:"							,   logits[0, :3, :3]       )
              assert torch.allclose(logits[0, :3, :3]							,   __lowerCamelCase							,   atol=1E-4       )
       print("Logits ok!"       )

       # verify loss, if applicable
       if model_name == "videomae-base-short":
              snake_case			:  List[Any]     							= outputs.loss
              assert torch.allclose(__lowerCamelCase							,   __lowerCamelCase							,   atol=1E-4       )
              print("Loss 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(__lowerCamelCase							,   organization="nielsr"       )


if __name__ == "__main__":
   __lowerCamelCase				=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--checkpoint_url""",
       default="""https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4""",
       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="""/Users/nielsrogge/Documents/VideoMAE/Test""",
       type=str,
       help="""Path to the output PyTorch model directory.""",
   )
   parser.add_argument("""--model_name""", default="""videomae-base""", 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_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)





