

'''simple docstring'''







import collections
import inspect
import unittest

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

from ...test_backbone_common import BackboneTesterMixin
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 (
          FocalNetBackbone,
          FocalNetForImageClassification,
          FocalNetForMaskedImageModeling,
          FocalNetModel,
      )
      from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor


class        A__   :



    def __init__(       self		,					UpperCamelCase__		,					UpperCamelCase__=13		,					UpperCamelCase__=32		,					UpperCamelCase__=2		,					UpperCamelCase__=3		,					UpperCamelCase__=16		,					UpperCamelCase__=[32, 64, 128]		,					UpperCamelCase__=[1, 2, 1]		,					UpperCamelCase__=[2, 2, 4]		,					UpperCamelCase__=2		,					UpperCamelCase__=2.0		,					UpperCamelCase__=True		,					UpperCamelCase__=0.0		,					UpperCamelCase__=0.0		,					UpperCamelCase__=0.1		,					UpperCamelCase__="gelu"		,					UpperCamelCase__=False		,					UpperCamelCase__=True		,					UpperCamelCase__=0.02		,					UpperCamelCase__=1e-5		,					UpperCamelCase__=True		,					UpperCamelCase__=None		,					UpperCamelCase__=True		,					UpperCamelCase__=10		,					UpperCamelCase__=8		,					UpperCamelCase__=["stage1", "stage2"]		,					UpperCamelCase__=[1, 2]		,					)    ->   List[str]:





           '''simple docstring'''
           A_						       =   parent
           A_						       =   batch_size
           A_						       =   image_size
           A_						       =   patch_size
           A_						       =   num_channels
           A_						       =   embed_dim
           A_						       =   hidden_sizes
           A_						       =   depths
           A_						       =   num_heads
           A_						       =   window_size
           A_						       =   mlp_ratio
           A_						       =   qkv_bias
           A_						       =   hidden_dropout_prob
           A_						       =   attention_probs_dropout_prob
           A_						       =   drop_path_rate
           A_						       =   hidden_act
           A_						       =   use_absolute_embeddings
           A_						       =   patch_norm
           A_						       =   layer_norm_eps
           A_						       =   initializer_range
           A_						       =   is_training
           A_						       =   scope
           A_						       =   use_labels
           A_						       =   type_sequence_label_size
           A_						       =   encoder_stride
           A_						       =   out_features
           A_						       =   out_indices



    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           A_						       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

           A_						       =   None
           if self.use_labels:
                  A_						       =   ids_tensor([self.batch_size]		,					self.type_sequence_label_size							)

           A_						       =   self.get_config()

           return config, pixel_values, labels



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





           '''simple docstring'''
           return FocalNetConfig(
               image_size=self.image_size		,					patch_size=self.patch_size		,					num_channels=self.num_channels		,					embed_dim=self.embed_dim		,					hidden_sizes=self.hidden_sizes		,					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		,					encoder_stride=self.encoder_stride		,					out_features=self.out_features		,					out_indices=self.out_indices		,					)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   Optional[Any]:





           '''simple docstring'''
           A_						       =   FocalNetModel(config=UpperCamelCase__							)
           model.to(UpperCamelCase__							)
           model.eval()
           A_						       =   model(UpperCamelCase__							)

           A_						       =   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
           A_						       =   int(config.embed_dim * 2 ** (len(config.depths							) - 1)							)

           self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, expected_seq_len, expected_dim)							)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   int:





           '''simple docstring'''
           A_						       =   FocalNetBackbone(config=UpperCamelCase__							)
           model.to(UpperCamelCase__							)
           model.eval()
           A_						       =   model(UpperCamelCase__							)

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps							)		,					len(config.out_features							)							)
           self.parent.assertListEqual(list(result.feature_maps[0].shape							)		,					[self.batch_size, self.image_size, 8, 8]							)

           # verify channels
           self.parent.assertEqual(len(model.channels							)		,					len(config.out_features							)							)
           self.parent.assertListEqual(model.channels		,					config.hidden_sizes[:-1]							)

           # verify backbone works with out_features=None
           A_						       =   None
           A_						       =   FocalNetBackbone(config=UpperCamelCase__							)
           model.to(UpperCamelCase__							)
           model.eval()
           A_						       =   model(UpperCamelCase__							)

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps							)		,					1							)
           self.parent.assertListEqual(list(result.feature_maps[0].shape							)		,					[self.batch_size, self.image_size * 2, 4, 4]							)

           # verify channels
           self.parent.assertEqual(len(model.channels							)		,					1							)
           self.parent.assertListEqual(model.channels		,					[config.hidden_sizes[-1]]							)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   List[Any]:





           '''simple docstring'''
           A_						       =   FocalNetForMaskedImageModeling(config=UpperCamelCase__							)
           model.to(UpperCamelCase__							)
           model.eval()
           A_						       =   model(UpperCamelCase__							)
           self.parent.assertEqual(
               result.reconstruction.shape		,					(self.batch_size, self.num_channels, self.image_size, self.image_size)							)

           # test greyscale images
           A_						       =   1
           A_						       =   FocalNetForMaskedImageModeling(UpperCamelCase__							)
           model.to(UpperCamelCase__							)
           model.eval()

           A_						       =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
           A_						       =   model(UpperCamelCase__							)
           self.parent.assertEqual(result.reconstruction.shape		,					(self.batch_size, 1, self.image_size, self.image_size)							)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   str:





           '''simple docstring'''
           A_						       =   self.type_sequence_label_size
           A_						       =   FocalNetForImageClassification(UpperCamelCase__							)
           model.to(UpperCamelCase__							)
           model.eval()
           A_						       =   model(UpperCamelCase__		,					labels=UpperCamelCase__							)
           self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.type_sequence_label_size)							)

           # test greyscale images
           A_						       =   1
           A_						       =   FocalNetForImageClassification(UpperCamelCase__							)
           model.to(UpperCamelCase__							)
           model.eval()

           A_						       =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
           A_						       =   model(UpperCamelCase__							)
           self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.type_sequence_label_size)							)







    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           A_						       =   self.prepare_config_and_inputs()

           A_       ,      A_       ,      A_						       =   config_and_inputs
           A_						       =   {"""pixel_values""": pixel_values}
           return config, inputs_dict


@require_torch
class        A__   (   _snake_case    ,    _snake_case    ,    unittest.TestCase      ):
    lowercase						       =		(
        (
            FocalNetModel,
            FocalNetForImageClassification,
            FocalNetForMaskedImageModeling,
            FocalNetBackbone,
        )
        if is_torch_available()
        else ()
    )
    lowercase						       =		(
        {"feature-extraction": FocalNetModel, "image-classification": FocalNetForImageClassification}
        if is_torch_available()
        else {}
    )
    lowercase						       =		False

    lowercase						       =		False
    lowercase						       =		False
    lowercase						       =		False
    lowercase						       =		False



    def        snake_case_      (       self							)    ->   Optional[Any]:





           '''simple docstring'''
           A_						       =   FocalNetModelTester(self							)
           A_						       =   ConfigTester(self		,					config_class=UpperCamelCase__		,					embed_dim=37		,					has_text_modality=UpperCamelCase__							)



    def        snake_case_      (       self							)    ->   str:





           '''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        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           return



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





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase__							)



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





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*UpperCamelCase__							)



    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase__							)



    def        snake_case_      (       self							)    ->   Any:





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__							)



    @unittest.skip(reason="""FocalNet does not use inputs_embeds"""							)
    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           pass



    @unittest.skip(reason="""FocalNet does not use feedforward chunking"""							)
    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           pass



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





           '''simple docstring'''
           A_       ,      A_						       =   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes[:-1]:
                  A_						       =   model_class(UpperCamelCase__							)
                  self.assertIsInstance(model.get_input_embeddings()		,					(nn.Module)							)
                  A_						       =   model.get_output_embeddings()
                  self.assertTrue(x is None or isinstance(UpperCamelCase__		,					nn.Linear							)							)



    def        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           A_       ,      A_						       =   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes[:-1]:
                  A_						       =   model_class(UpperCamelCase__							)
                  A_						       =   inspect.signature(model.forward							)
                  # signature.parameters is an OrderedDict => so arg_names order is deterministic
                  A_						       =   [*signature.parameters.keys()]

                  A_						       =   ["""pixel_values"""]
                  self.assertListEqual(arg_names[:1]		,					UpperCamelCase__							)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   Optional[int]:





           '''simple docstring'''
           A_						       =   model_class(UpperCamelCase__							)
           model.to(UpperCamelCase__							)
           model.eval()

           with torch.no_grad():
                  A_						       =   model(**self._prepare_for_class(UpperCamelCase__		,					UpperCamelCase__							)							)

           A_						       =   outputs.hidden_states

           A_						       =   getattr(
               self.model_tester		,					"""expected_num_hidden_layers"""		,					len(self.model_tester.depths							) + 1							)
           self.assertEqual(len(UpperCamelCase__							)		,					UpperCamelCase__							)

           # FocalNet has a different seq_length
           A_						       =   (
               config.patch_size
               if isinstance(config.patch_size		,					collections.abc.Iterable							)
               else (config.patch_size, config.patch_size)
           )

           A_						       =   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

           self.assertListEqual(
               list(hidden_states[0].shape[-2:]							)		,					[num_patches, self.model_tester.embed_dim]		,					)

           A_						       =   outputs.reshaped_hidden_states
           self.assertEqual(len(UpperCamelCase__							)		,					UpperCamelCase__							)

           A_       ,      A_       ,      A_       ,      A_						       =   reshaped_hidden_states[0].shape
           A_						       =   (
               reshaped_hidden_states[0].view(UpperCamelCase__		,					UpperCamelCase__		,					height * width							).permute(0		,					2		,					1							)
           )
           self.assertListEqual(
               list(reshaped_hidden_states.shape[-2:]							)		,					[num_patches, self.model_tester.embed_dim]		,					)



    def        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           A_       ,      A_						       =   self.model_tester.prepare_config_and_inputs_for_common()

           A_						       =   (
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size		,					collections.abc.Iterable							)
               else (self.model_tester.image_size, self.model_tester.image_size)
           )

           for model_class in self.all_model_classes[:-1]:
                  A_						       =   True
                  self.check_hidden_states_output(UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)

                  # check that output_hidden_states also work using config
                  del inputs_dict["output_hidden_states"]
                  A_						       =   True

                  self.check_hidden_states_output(UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)



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





           '''simple docstring'''
           A_       ,      A_						       =   self.model_tester.prepare_config_and_inputs_for_common()
           A_						       =   3

           A_						       =   (
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size		,					collections.abc.Iterable							)
               else (self.model_tester.image_size, self.model_tester.image_size)
           )
           A_						       =   (
               config.patch_size
               if isinstance(config.patch_size		,					collections.abc.Iterable							)
               else (config.patch_size, config.patch_size)
           )

           A_						       =   image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
           A_						       =   image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

           for model_class in self.all_model_classes[:-1]:
                  A_						       =   True
                  self.check_hidden_states_output(UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					(padded_height, padded_width)							)

                  # check that output_hidden_states also work using config
                  del inputs_dict["output_hidden_states"]
                  A_						       =   True
                  self.check_hidden_states_output(UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					(padded_height, padded_width)							)



    @slow
    def        snake_case_      (       self							)    ->   List[str]:





           '''simple docstring'''
           for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A_						       =   FocalNetModel.from_pretrained(UpperCamelCase__							)
                  self.assertIsNotNone(UpperCamelCase__							)







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





           '''simple docstring'''
           A_       ,      A_						       =   self.model_tester.prepare_config_and_inputs_for_common()

           A_						       =   _config_zero_init(UpperCamelCase__							)
           for model_class in self.all_model_classes:
                  A_						       =   model_class(config=UpperCamelCase__							)
                  for name, param in model.named_parameters():
                         if "embeddings" not in name and 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'''		,					)


@require_vision
@require_torch
class        A__   (   unittest.TestCase      ):



    @cached_property
    def        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           # TODO update organization
           return AutoImageProcessor.from_pretrained("""microsoft/focalnet-tiny"""							) if is_vision_available() else None







    @slow
    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           A_						       =   FocalNetForImageClassification.from_pretrained("""microsoft/focalnet-tiny"""							).to(UpperCamelCase__							)
           A_						       =   self.default_image_processor

           A_						       =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
           A_						       =   image_processor(images=UpperCamelCase__		,					return_tensors="""pt"""							).to(UpperCamelCase__							)

           # forward pass
           with torch.no_grad():
                  A_						       =   model(**UpperCamelCase__							)

           # verify the logits
           A_						       =   torch.Size((1, 1000)							)
           self.assertEqual(outputs.logits.shape		,					UpperCamelCase__							)
           A_						       =   torch.tensor([0.2166, -0.4368, 0.2191]							).to(UpperCamelCase__							)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]		,					UpperCamelCase__		,					atol=1e-4							)							)
           self.assertTrue(outputs.logits.argmax(dim=-1							).item()		,					281							)


@require_torch
class        A__   (   _snake_case    ,    unittest.TestCase      ):
    lowercase						       =		(FocalNetBackbone,) if is_torch_available() else ()
    lowercase						       =		FocalNetConfig

    lowercase						       =		False



    def        snake_case_      (       self							)    ->   Any:





           '''simple docstring'''
           A_						       =   FocalNetModelTester(self							)



'''simple docstring'''







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__     (   UpperCAmelCase__					)      ->      Union[str, Any]:
       A_						       =   VideoMAEConfig()

       set_architecture_configs(UpperCAmelCase__,							UpperCAmelCase__					)

       if "finetuned" not in model_name:
              A_						       =   False

       if "finetuned" in model_name:
              A_						       =   """huggingface/label-files"""
              if "kinetics" in model_name:
                     A_						       =   4_00
                     A_						       =   """kinetics400-id2label.json"""
              elif "ssv2" in model_name:
                     A_						       =   1_74
                     A_						       =   """something-something-v2-id2label.json"""
              else:
                     raise ValueError("""Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned."""					)
              A_						       =   json.load(open(hf_hub_download(UpperCAmelCase__,							UpperCAmelCase__,							repo_type="""dataset"""					),							"""r"""					)					)
              A_						       =   {int(UpperCAmelCase__					): v for k, v in idalabel.items()}
              A_						       =   idalabel
              A_						       =   {v: k for k, v in idalabel.items()}

       return config
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      Dict:
       if "small" in model_name:
              A_						       =   3_84
              A_						       =   15_36
              A_						       =   12
              A_						       =   16
              A_						       =   12
              A_						       =   3
              A_						       =   1_92
              A_						       =   7_68
       elif "large" in model_name:
              A_						       =   10_24
              A_						       =   40_96
              A_						       =   24
              A_						       =   16
              A_						       =   12
              A_						       =   8
              A_						       =   5_12
              A_						       =   20_48
       elif "huge" in model_name:
              A_						       =   12_80
              A_						       =   51_20
              A_						       =   32
              A_						       =   16
              A_						       =   12
              A_						       =   8
              A_						       =   6_40
              A_						       =   25_60
       elif "base" not in model_name:
              raise ValueError("""Model name should include either \"small\", \"base\", \"large\", or \"huge\""""					)
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      List[Any]:
       if "encoder." in name:
              A_						       =   name.replace("""encoder.""",							""""""					)
       if "cls_token" in name:
              A_						       =   name.replace("""cls_token""",							"""videomae.embeddings.cls_token"""					)
       if "decoder_pos_embed" in name:
              A_						       =   name.replace("""decoder_pos_embed""",							"""decoder.decoder_pos_embed"""					)
       if "pos_embed" in name and "decoder" not in name:
              A_						       =   name.replace("""pos_embed""",							"""videomae.embeddings.position_embeddings"""					)
       if "patch_embed.proj" in name:
              A_						       =   name.replace("""patch_embed.proj""",							"""videomae.embeddings.patch_embeddings.projection"""					)
       if "patch_embed.norm" in name:
              A_						       =   name.replace("""patch_embed.norm""",							"""videomae.embeddings.norm"""					)
       if "decoder.blocks" in name:
              A_						       =   name.replace("""decoder.blocks""",							"""decoder.decoder_layers"""					)
       if "blocks" in name:
              A_						       =   name.replace("""blocks""",							"""videomae.encoder.layer"""					)
       if "attn.proj" in name:
              A_						       =   name.replace("""attn.proj""",							"""attention.output.dense"""					)
       if "attn" in name and "bias" not in name:
              A_						       =   name.replace("""attn""",							"""attention.self"""					)
       if "attn" in name:
              A_						       =   name.replace("""attn""",							"""attention.attention"""					)
       if "norm1" in name:
              A_						       =   name.replace("""norm1""",							"""layernorm_before"""					)
       if "norm2" in name:
              A_						       =   name.replace("""norm2""",							"""layernorm_after"""					)
       if "mlp.fc1" in name:
              A_						       =   name.replace("""mlp.fc1""",							"""intermediate.dense"""					)
       if "mlp.fc2" in name:
              A_						       =   name.replace("""mlp.fc2""",							"""output.dense"""					)
       if "decoder_embed" in name:
              A_						       =   name.replace("""decoder_embed""",							"""decoder.decoder_embed"""					)
       if "decoder_norm" in name:
              A_						       =   name.replace("""decoder_norm""",							"""decoder.decoder_norm"""					)
       if "decoder_pred" in name:
              A_						       =   name.replace("""decoder_pred""",							"""decoder.decoder_pred"""					)
       if "norm.weight" in name and "decoder" not in name and "fc" not in name:
              A_						       =   name.replace("""norm.weight""",							"""videomae.layernorm.weight"""					)
       if "norm.bias" in name and "decoder" not in name and "fc" not in name:
              A_						       =   name.replace("""norm.bias""",							"""videomae.layernorm.bias"""					)
       if "head" in name and "decoder" not in name:
              A_						       =   name.replace("""head""",							"""classifier"""					)

       return name
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      Optional[int]:
       for key in orig_state_dict.copy().keys():
              A_						       =   orig_state_dict.pop(UpperCAmelCase__					)

              if key.startswith("""encoder."""					):
                     A_						       =   key.replace("""encoder.""",							""""""					)

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

       return orig_state_dict
def    UpperCAmelCase__     (   )      ->      Any:
       A_						       =   hf_hub_download(
           repo_id="""hf-internal-testing/spaghetti-video""",							filename="""eating_spaghetti.npy""",							repo_type="""dataset"""					)
       A_						       =   np.load(UpperCAmelCase__					)
       return list(UpperCAmelCase__					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      int:
       A_						       =   get_videomae_config(UpperCAmelCase__					)

       if "finetuned" in model_name:
              A_						       =   VideoMAEForVideoClassification(UpperCAmelCase__					)
       else:
              A_						       =   VideoMAEForPreTraining(UpperCAmelCase__					)

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

       model.load_state_dict(UpperCAmelCase__					)
       model.eval()

       # verify model on basic input
       A_						       =   VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5],							image_std=[0.5, 0.5, 0.5]					)
       A_						       =   prepare_video()
       A_						       =   image_processor(UpperCAmelCase__,							return_tensors="""pt"""					)

       if "finetuned" not in model_name:
              A_						       =   hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos""",							filename="""bool_masked_pos.pt"""					)
              A_						       =   torch.load(UpperCAmelCase__					)

       A_						       =   model(**UpperCAmelCase__					)
       A_						       =   outputs.logits

       A_						       =   [
           """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":
              A_						       =   torch.Size([1, 4_00]					)
              A_						       =   torch.tensor([-0.9_291, -0.4_061, -0.9_307]					)
       elif model_name == "videomae-small-finetuned-ssv2":
              A_						       =   torch.Size([1, 1_74]					)
              A_						       =   torch.tensor([0.2_671, -0.4_689, -0.8_235]					)
       elif model_name == "videomae-base":
              A_						       =   torch.Size([1, 14_08, 15_36]					)
              A_						       =   torch.tensor([[0.7_739, 0.7_968, 0.7_089], [0.6_701, 0.7_487, 0.6_209], [0.4_287, 0.5_158, 0.4_773]]					)
       elif model_name == "videomae-base-short":
              A_						       =   torch.Size([1, 14_08, 15_36]					)
              A_						       =   torch.tensor([[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]]					)
              # we verified the loss both for normalized and unnormalized targets for this one
              A_						       =   torch.tensor([0.5_142]					) if config.norm_pix_loss else torch.tensor([0.6_469]					)
       elif model_name == "videomae-large":
              A_						       =   torch.Size([1, 14_08, 15_36]					)
              A_						       =   torch.tensor([[0.7_149, 0.7_997, 0.6_966], [0.6_768, 0.7_869, 0.6_948], [0.5_139, 0.6_221, 0.5_605]]					)
       elif model_name == "videomae-large-finetuned-kinetics":
              A_						       =   torch.Size([1, 4_00]					)
              A_						       =   torch.tensor([0.0_771, 0.0_011, -0.3_625]					)
       elif model_name == "videomae-huge-finetuned-kinetics":
              A_						       =   torch.Size([1, 4_00]					)
              A_						       =   torch.tensor([0.2_433, 0.1_632, -0.4_894]					)
       elif model_name == "videomae-base-short-finetuned-kinetics":
              A_						       =   torch.Size([1, 4_00]					)
              A_						       =   torch.tensor([0.6_588, 0.0_990, -0.2_493]					)
       elif model_name == "videomae-base-finetuned-kinetics":
              A_						       =   torch.Size([1, 4_00]					)
              A_						       =   torch.tensor([0.3_669, -0.0_688, -0.2_421]					)
       elif model_name == "videomae-base-short-ssv2":
              A_						       =   torch.Size([1, 14_08, 15_36]					)
              A_						       =   torch.tensor([[0.4_712, 0.5_296, 0.5_786], [0.2_278, 0.2_729, 0.4_026], [0.0_352, 0.0_730, 0.2_506]]					)
       elif model_name == "videomae-base-short-finetuned-ssv2":
              A_						       =   torch.Size([1, 1_74]					)
              A_						       =   torch.tensor([-0.0_537, -0.1_539, -0.3_266]					)
       elif model_name == "videomae-base-ssv2":
              A_						       =   torch.Size([1, 14_08, 15_36]					)
              A_						       =   torch.tensor([[0.8_131, 0.8_727, 0.8_546], [0.7_366, 0.9_377, 0.8_870], [0.5_935, 0.8_874, 0.8_564]]					)
       elif model_name == "videomae-base-finetuned-ssv2":
              A_						       =   torch.Size([1, 1_74]					)
              A_						       =   torch.tensor([0.1_961, -0.8_337, -0.6_389]					)
       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],							UpperCAmelCase__,							atol=1e-4					)
       else:
              print("""Logits:""",							logits[0, :3, :3]					)
              assert torch.allclose(logits[0, :3, :3],							UpperCAmelCase__,							atol=1e-4					)
       print("""Logits ok!"""					)

       # verify loss, if applicable
       if model_name == "videomae-base-short":
              A_						       =   outputs.loss
              assert torch.allclose(UpperCAmelCase__,							UpperCAmelCase__,							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(UpperCAmelCase__					)
              model.save_pretrained(UpperCAmelCase__					)

       if push_to_hub:
              print("""Pushing to the hub..."""					)
              model.push_to_hub(UpperCAmelCase__,							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)
