
from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers.models.swin.modeling_tf_swin import (
       TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFSwinForImageClassification,
       TFSwinForMaskedImageModeling,
       TFSwinModel,
   )


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor





class 				UpperCamelCase__		:






       """simple docstring"""


       def __init__(							self ,     _A ,     _A=13 ,     _A=32 ,     _A=2 ,     _A=3 ,     _A=16 ,     _A=[1, 2, 1] ,     _A=[2, 2, 4] ,     _A=2 ,     _A=2.0 ,     _A=True ,     _A=0.0 ,     _A=0.0 ,     _A=0.1 ,     _A="gelu" ,     _A=False ,     _A=True ,     _A=0.02 ,     _A=1E-5 ,     _A=True ,     _A=None ,     _A=True ,     _A=10 ,     _A=8 ,     )				->				List[Any]:
          SCREAMING_SNAKE_CASE_	=      parent
          SCREAMING_SNAKE_CASE_	=      batch_size
          SCREAMING_SNAKE_CASE_	=      image_size
          SCREAMING_SNAKE_CASE_	=      patch_size
          SCREAMING_SNAKE_CASE_	=      num_channels
          SCREAMING_SNAKE_CASE_	=      embed_dim
          SCREAMING_SNAKE_CASE_	=      depths
          SCREAMING_SNAKE_CASE_	=      num_heads
          SCREAMING_SNAKE_CASE_	=      window_size
          SCREAMING_SNAKE_CASE_	=      mlp_ratio
          SCREAMING_SNAKE_CASE_	=      qkv_bias
          SCREAMING_SNAKE_CASE_	=      hidden_dropout_prob
          SCREAMING_SNAKE_CASE_	=      attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_	=      drop_path_rate
          SCREAMING_SNAKE_CASE_	=      hidden_act
          SCREAMING_SNAKE_CASE_	=      use_absolute_embeddings
          SCREAMING_SNAKE_CASE_	=      patch_norm
          SCREAMING_SNAKE_CASE_	=      layer_norm_eps
          SCREAMING_SNAKE_CASE_	=      initializer_range
          SCREAMING_SNAKE_CASE_	=      is_training
          SCREAMING_SNAKE_CASE_	=      scope
          SCREAMING_SNAKE_CASE_	=      use_labels
          SCREAMING_SNAKE_CASE_	=      type_sequence_label_size
          SCREAMING_SNAKE_CASE_	=      encoder_stride



       def     _UpperCamelCase	(							self	)				->				Optional[Any]:
          SCREAMING_SNAKE_CASE_	=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

          SCREAMING_SNAKE_CASE_	=      self.get_config()

          return config, pixel_values, labels



       def     _UpperCamelCase	(							self	)				->				Optional[int]:
          return SwinConfig(
              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 ,     encoder_stride=self.encoder_stride ,     )



       def     _UpperCamelCase	(							self ,     _A ,     _A ,     _A	)				->				Tuple:
          SCREAMING_SNAKE_CASE_	=      TFSwinModel(config=__A	)
          SCREAMING_SNAKE_CASE_	=      model(__A	)

          SCREAMING_SNAKE_CASE_	=      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths	) - 1))
          SCREAMING_SNAKE_CASE_	=      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     _UpperCamelCase	(							self ,     _A ,     _A ,     _A	)				->				Dict:
          SCREAMING_SNAKE_CASE_	=      TFSwinForMaskedImageModeling(config=__A	)
          SCREAMING_SNAKE_CASE_	=      model(__A	)
          self.parent.assertEqual(
              result.logits.shape ,     (self.batch_size, self.num_channels, self.image_size, self.image_size)	)

          # test greyscale images
          SCREAMING_SNAKE_CASE_	=      1
          SCREAMING_SNAKE_CASE_	=      TFSwinForMaskedImageModeling(__A	)

          SCREAMING_SNAKE_CASE_	=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
          SCREAMING_SNAKE_CASE_	=      model(__A	)
          self.parent.assertEqual(result.logits.shape ,     (self.batch_size, 1, self.image_size, self.image_size)	)



       def     _UpperCamelCase	(							self ,     _A ,     _A ,     _A	)				->				Any:
          SCREAMING_SNAKE_CASE_	=      self.type_sequence_label_size
          SCREAMING_SNAKE_CASE_	=      TFSwinForImageClassification(__A	)
          SCREAMING_SNAKE_CASE_	=      model(__A ,     labels=__A	)
          self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.type_sequence_label_size)	)

          # test greyscale images
          SCREAMING_SNAKE_CASE_	=      1
          SCREAMING_SNAKE_CASE_	=      TFSwinForImageClassification(__A	)
          SCREAMING_SNAKE_CASE_	=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
          SCREAMING_SNAKE_CASE_	=      model(__A	)
          self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.type_sequence_label_size)	)




       def     _UpperCamelCase	(							self	)				->				Optional[int]:
          SCREAMING_SNAKE_CASE_	=      self.prepare_config_and_inputs()
          SCREAMING_SNAKE_CASE_	=      config_and_inputs
          SCREAMING_SNAKE_CASE_	=      {'''pixel_values''': pixel_values}
          return config, inputs_dict







@require_tf
class 				UpperCamelCase__		(	lowerCamelCase__       ,    lowerCamelCase__       ,    unittest.TestCase	):






       """simple docstring"""


       UpperCAmelCase_				       =(
           (
               TFSwinModel,
               TFSwinForImageClassification,
               TFSwinForMaskedImageModeling,
           )
           if is_tf_available()
           else ()
       )
       UpperCAmelCase_				       =(
           {"feature-extraction": TFSwinModel, "image-classification": TFSwinForImageClassification}
           if is_tf_available()
           else {}
       )

       UpperCAmelCase_				       =False
       UpperCAmelCase_				       =False
       UpperCAmelCase_				       =False
       UpperCAmelCase_				       =False



       def     _UpperCamelCase	(							self	)				->				int:
          SCREAMING_SNAKE_CASE_	=      TFSwinModelTester(self	)
          SCREAMING_SNAKE_CASE_	=      ConfigTester(self ,     config_class=__A ,     embed_dim=37	)



       def     _UpperCamelCase	(							self	)				->				str:
          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     _UpperCamelCase	(							self	)				->				Optional[int]:
          return



       def     _UpperCamelCase	(							self	)				->				Any:
          SCREAMING_SNAKE_CASE_	=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__A	)



       def     _UpperCamelCase	(							self	)				->				Any:
          SCREAMING_SNAKE_CASE_	=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*__A	)



       def     _UpperCamelCase	(							self	)				->				str:
          SCREAMING_SNAKE_CASE_	=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*__A	)



       @unittest.skip(reason='''Swin does not use inputs_embeds'''	)
       def     _UpperCamelCase	(							self	)				->				List[str]:
          pass



       def     _UpperCamelCase	(							self	)				->				str:
          SCREAMING_SNAKE_CASE_	=      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE_	=      model_class(__A	)
             self.assertIsInstance(model.get_input_embeddings() ,     tf.keras.layers.Layer	)
             SCREAMING_SNAKE_CASE_	=      model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(__A ,     tf.keras.layers.Dense	)	)



       def     _UpperCamelCase	(							self	)				->				Union[str, Any]:
          SCREAMING_SNAKE_CASE_	=      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE_	=      model_class(__A	)
             SCREAMING_SNAKE_CASE_	=      inspect.signature(model.call	)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             SCREAMING_SNAKE_CASE_	=      [*signature.parameters.keys()]

             SCREAMING_SNAKE_CASE_	=      ['''pixel_values''']
             self.assertListEqual(arg_names[:1] ,     __A	)



       def     _UpperCamelCase	(							self	)				->				List[str]:
          SCREAMING_SNAKE_CASE_	=      self.model_tester.prepare_config_and_inputs_for_common()
          SCREAMING_SNAKE_CASE_	=      True

          for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE_	=      True
             SCREAMING_SNAKE_CASE_	=      False
             SCREAMING_SNAKE_CASE_	=      True
             SCREAMING_SNAKE_CASE_	=      model_class(__A	)
             SCREAMING_SNAKE_CASE_	=      model(**self._prepare_for_class(__A ,     __A	)	)
             SCREAMING_SNAKE_CASE_	=      outputs.attentions
             SCREAMING_SNAKE_CASE_	=      len(self.model_tester.depths	)
             self.assertEqual(len(__A	) ,     __A	)

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             SCREAMING_SNAKE_CASE_	=      True
             SCREAMING_SNAKE_CASE_	=      config.window_size**2
             SCREAMING_SNAKE_CASE_	=      model_class(__A	)
             SCREAMING_SNAKE_CASE_	=      model(**self._prepare_for_class(__A ,     __A	)	)
             SCREAMING_SNAKE_CASE_	=      outputs.attentions
             self.assertEqual(len(__A	) ,     __A	)

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

             # Check attention is always last and order is fine
             SCREAMING_SNAKE_CASE_	=      True
             SCREAMING_SNAKE_CASE_	=      True
             SCREAMING_SNAKE_CASE_	=      model_class(__A	)
             SCREAMING_SNAKE_CASE_	=      model(**self._prepare_for_class(__A ,     __A	)	)

             if hasattr(self.model_tester ,     '''num_hidden_states_types'''	):
                SCREAMING_SNAKE_CASE_	=      self.model_tester.num_hidden_states_types
             else:
                # also another +1 for reshaped_hidden_states
                SCREAMING_SNAKE_CASE_	=      2
             self.assertEqual(out_len + added_hidden_states ,     len(__A	)	)

             SCREAMING_SNAKE_CASE_	=      outputs.attentions

             self.assertEqual(len(__A	) ,     __A	)

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



       def     _UpperCamelCase	(							self ,     _A ,     _A ,     _A ,     _A	)				->				List[Any]:
          SCREAMING_SNAKE_CASE_	=      model_class(__A	)
          SCREAMING_SNAKE_CASE_	=      model(**self._prepare_for_class(__A ,     __A	)	)
          SCREAMING_SNAKE_CASE_	=      outputs.hidden_states

          SCREAMING_SNAKE_CASE_	=      getattr(
              self.model_tester ,     '''expected_num_hidden_layers''' ,     len(self.model_tester.depths	) + 1	)
          self.assertEqual(len(__A	) ,     __A	)

          # Swin has a different seq_length
          SCREAMING_SNAKE_CASE_	=      to_atuple(config.patch_size	)

          SCREAMING_SNAKE_CASE_	=      (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] ,     )

          SCREAMING_SNAKE_CASE_	=      outputs.reshaped_hidden_states
          self.assertEqual(len(__A	) ,     __A	)

          SCREAMING_SNAKE_CASE_	=      reshaped_hidden_states[0].shape

          SCREAMING_SNAKE_CASE_	=      tf.reshape(reshaped_hidden_states[0] ,     (batch_size, num_channels, height * width)	)
          SCREAMING_SNAKE_CASE_	=      tf.transpose(__A ,     (0, 2, 1)	)

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



       def     _UpperCamelCase	(							self	)				->				Optional[Any]:
          SCREAMING_SNAKE_CASE_	=      self.model_tester.prepare_config_and_inputs_for_common()

          SCREAMING_SNAKE_CASE_	=      to_atuple(self.model_tester.image_size	)

          for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE_	=      True
             self.check_hidden_states_output(__A ,     __A ,     __A ,     __A	)

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

             self.check_hidden_states_output(__A ,     __A ,     __A ,     __A	)



       def     _UpperCamelCase	(							self	)				->				str:
          SCREAMING_SNAKE_CASE_	=      self.model_tester.prepare_config_and_inputs_for_common()
          SCREAMING_SNAKE_CASE_	=      3

          SCREAMING_SNAKE_CASE_	=      to_atuple(self.model_tester.image_size	)
          SCREAMING_SNAKE_CASE_	=      to_atuple(config.patch_size	)

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

          for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE_	=      True
             self.check_hidden_states_output(__A ,     __A ,     __A ,     (padded_height, padded_width)	)

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




       @slow
       def     _UpperCamelCase	(							self	)				->				Dict:
          for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             SCREAMING_SNAKE_CASE_	=      TFSwinModel.from_pretrained(__A	)
             self.assertIsNotNone(__A	)







@require_vision
@require_tf
class 				UpperCamelCase__		(	unittest.TestCase	):






       """simple docstring"""


       @cached_property
       def     _UpperCamelCase	(							self	)				->				Optional[int]:
          return (
              AutoImageProcessor.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''	)
              if is_vision_available()
              else None
          )




       @slow
       def     _UpperCamelCase	(							self	)				->				Optional[int]:
          SCREAMING_SNAKE_CASE_	=      TFSwinForImageClassification.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''	)
          SCREAMING_SNAKE_CASE_	=      self.default_image_processor

          SCREAMING_SNAKE_CASE_	=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
          SCREAMING_SNAKE_CASE_	=      image_processor(images=__A ,     return_tensors='''tf'''	)

          # forward pass
          SCREAMING_SNAKE_CASE_	=      model(__A	)

          # verify the logits
          SCREAMING_SNAKE_CASE_	=      tf.TensorShape((1, 1000)	)
          self.assertEqual(outputs.logits.shape ,     __A	)
          SCREAMING_SNAKE_CASE_	=      tf.constant([-0.0948, -0.6454, -0.0921]	)
          self.assertTrue(np.allclose(outputs.logits[0, :3] ,     __A ,     atol=1E-4	)	)





import argparse
import json
import os.path
from collections import OrderedDict

import numpy as np
import requests
import torch
from flax.training.checkpoints import restore_checkpoint
from huggingface_hub import hf_hub_download

from transformers import VivitConfig, VivitForVideoClassification, VivitImageProcessor
from transformers.image_utils import PILImageResampling

def      a__	(						UpperCAmelCase				:     Dict     )    ->		Any:
  UpperCAmelCase			:				List[str]										=					'''https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'''

  with open(UpperCAmelCase							,					'''wb'''     ) as f:
    with requests.get(UpperCAmelCase							,					stream=UpperCAmelCase     ) as req:
      for chunk in req.iter_content(chunk_size=2_048     ):
        f.write(UpperCAmelCase     )

def      a__	(						)    ->		VivitConfig:
  UpperCAmelCase			:				Any										=					VivitConfig()

  UpperCAmelCase			:				Any										=					400
  UpperCAmelCase			:				int										=					'''huggingface/label-files'''
  UpperCAmelCase			:				int										=					'''kinetics400-id2label.json'''

  UpperCAmelCase			:				Tuple										=					json.load(open(hf_hub_download(UpperCAmelCase							,					UpperCAmelCase							,					repo_type='''dataset'''     )							,					'''r'''     )     )
  UpperCAmelCase			:				Dict										=					{int(UpperCAmelCase     ): v for k, v in idalabel.items()}
  UpperCAmelCase			:				Optional[Any]										=					idalabel
  UpperCAmelCase			:				Tuple										=					{v: k for k, v in idalabel.items()}
  return config

def      a__	(						)    ->		List[Any]:
  UpperCAmelCase			:				int										=					hf_hub_download(
      repo_id='''hf-internal-testing/spaghetti-video'''							,					filename='''eating_spaghetti_32_frames.npy'''							,					repo_type='''dataset'''     )
  UpperCAmelCase			:				Tuple										=					np.load(UpperCAmelCase     )
  return list(UpperCAmelCase     )

def      a__	(						UpperCAmelCase				:     np.ndarray     )    ->		Any:
  if np.ndim(UpperCAmelCase     ) == 2:
    return transform_attention_bias(UpperCAmelCase     )

  elif np.ndim(UpperCAmelCase     ) == 3:
    return transform_attention_kernel(UpperCAmelCase     )

  else:
    raise Exception(f'''Invalid number of dimesions: {np.ndim(UpperCAmelCase     )}'''     )

def      a__	(						UpperCAmelCase				:     np.ndarray     )    ->		List[Any]:
  return current.flatten()

def      a__	(						UpperCAmelCase				:     np.ndarray     )    ->		Optional[Any]:
  return np.reshape(UpperCAmelCase							,					(current.shape[0], current.shape[1] * current.shape[2])     ).T

def      a__	(						UpperCAmelCase				:     np.ndarray     )    ->		Any:
  return np.reshape(UpperCAmelCase							,					(current.shape[0] * current.shape[1], current.shape[2])     ).T

def      a__	(						UpperCAmelCase				:     Dict							,					UpperCAmelCase				:     Union[str, Any]     )    ->		Dict:
  UpperCAmelCase			:				Any										=					state_dict['''optimizer''']['''target''']['''Transformer'''][f'''encoderblock_{i}''']

  UpperCAmelCase			:				Dict										=					f'''encoder.layer.{i}.'''
  UpperCAmelCase			:				Optional[int]										=					{
      prefix + '''intermediate.dense.bias''': state['''MlpBlock_0''']['''Dense_0''']['''bias'''],
      prefix + '''intermediate.dense.weight''': np.transpose(state['''MlpBlock_0''']['''Dense_0''']['''kernel''']     ),
      prefix + '''output.dense.bias''': state['''MlpBlock_0''']['''Dense_1''']['''bias'''],
      prefix + '''output.dense.weight''': np.transpose(state['''MlpBlock_0''']['''Dense_1''']['''kernel''']     ),
      prefix + '''layernorm_before.bias''': state['''LayerNorm_0''']['''bias'''],
      prefix + '''layernorm_before.weight''': state['''LayerNorm_0''']['''scale'''],
      prefix + '''layernorm_after.bias''': state['''LayerNorm_1''']['''bias'''],
      prefix + '''layernorm_after.weight''': state['''LayerNorm_1''']['''scale'''],
      prefix
      + '''attention.attention.query.bias''': transform_attention(
          state['''MultiHeadDotProductAttention_0''']['''query''']['''bias''']     ),
      prefix
      + '''attention.attention.query.weight''': transform_attention(
          state['''MultiHeadDotProductAttention_0''']['''query''']['''kernel''']     ),
      prefix
      + '''attention.attention.key.bias''': transform_attention(state['''MultiHeadDotProductAttention_0''']['''key''']['''bias''']     ),
      prefix
      + '''attention.attention.key.weight''': transform_attention(
          state['''MultiHeadDotProductAttention_0''']['''key''']['''kernel''']     ),
      prefix
      + '''attention.attention.value.bias''': transform_attention(
          state['''MultiHeadDotProductAttention_0''']['''value''']['''bias''']     ),
      prefix
      + '''attention.attention.value.weight''': transform_attention(
          state['''MultiHeadDotProductAttention_0''']['''value''']['''kernel''']     ),
      prefix + '''attention.output.dense.bias''': state['''MultiHeadDotProductAttention_0''']['''out''']['''bias'''],
      prefix
      + '''attention.output.dense.weight''': transform_attention_output_weight(
          state['''MultiHeadDotProductAttention_0''']['''out''']['''kernel''']     ),
  }

  return new_state

def      a__	(						UpperCAmelCase				:     Any     )    ->		List[str]:
  return sum([1 if '''encoderblock_''' in k else 0 for k in state_dict['''optimizer''']['''target''']['''Transformer'''].keys()]     )

def      a__	(						UpperCAmelCase				:     Any							,					UpperCAmelCase				:     Dict=False     )    ->		Dict:
  UpperCAmelCase			:				int										=					get_n_layers(UpperCAmelCase     )

  UpperCAmelCase			:				List[str]										=					OrderedDict()

  UpperCAmelCase			:				int										=					state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''bias''']
  UpperCAmelCase			:				Tuple										=					state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''scale''']

  UpperCAmelCase			:				Union[str, Any]										=					np.transpose(
      state_dict['''optimizer''']['''target''']['''embedding''']['''kernel''']							,					(4, 3, 0, 1, 2)     )
  UpperCAmelCase			:				List[str]										=					state_dict['''optimizer''']['''target''']['''embedding''']['''bias''']

  UpperCAmelCase			:				Optional[int]										=					state_dict['''optimizer''']['''target''']['''cls''']
  UpperCAmelCase			:				str										=					state_dict['''optimizer''']['''target''']['''Transformer''']['''posembed_input'''][
      '''pos_embedding'''
  ]

  for i in range(UpperCAmelCase     ):
    new_state.update(transform_state_encoder_block(UpperCAmelCase							,					UpperCAmelCase     )     )

  if classification_head:
    UpperCAmelCase			:				List[Any]										=					{'''vivit.''' + k: v for k, v in new_state.items()}
    UpperCAmelCase			:				str										=					np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''kernel''']     )
    UpperCAmelCase			:				Dict										=					np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''bias''']     )

  return {k: torch.tensor(UpperCAmelCase     ) for k, v in new_state.items()}

def      a__	(						)    ->		VivitImageProcessor:
  UpperCAmelCase			:				Optional[int]										=					VivitImageProcessor()

  assert extractor.do_resize is True
  assert extractor.size == {"shortest_edge": 256}
  assert extractor.do_center_crop is True
  assert extractor.crop_size == {"width": 224, "height": 224}
  assert extractor.resample == PILImageResampling.BILINEAR

  # here: https://github.com/deepmind/dmvr/blob/master/dmvr/modalities.py
  # one can seen that add_image has default values for normalization_mean and normalization_std set to 0 and 1
  # which effectively means no normalization (and ViViT does not overwrite those when calling this func)
  assert extractor.do_normalize is False
  assert extractor.do_rescale is True
  assert extractor.rescale_factor == 1 / 255

  # zero-centering = True in original implementation
  assert extractor.do_zero_centering is True

  return extractor

def      a__	(						UpperCAmelCase				:     str     )    ->		Tuple:
  UpperCAmelCase			:				Union[str, Any]										=					'''checkpoint'''

  if not os.path.exists(UpperCAmelCase     ):
    download_checkpoint(UpperCAmelCase     )

  UpperCAmelCase			:				Tuple										=					restore_checkpoint(UpperCAmelCase							,					UpperCAmelCase     )
  UpperCAmelCase			:				Any										=					transform_state(UpperCAmelCase							,					classification_head=UpperCAmelCase     )

  UpperCAmelCase			:				Union[str, Any]										=					get_vivit_config()

  assert config.image_size == 224
  assert config.num_frames == 32

  UpperCAmelCase			:				Optional[int]										=					VivitForVideoClassification(UpperCAmelCase     )
  model.load_state_dict(UpperCAmelCase     )
  model.eval()

  UpperCAmelCase			:				List[Any]										=					get_processor()

  UpperCAmelCase			:				str										=					prepare_video()
  UpperCAmelCase			:				Union[str, Any]										=					extractor(UpperCAmelCase							,					return_tensors='''pt'''     )

  UpperCAmelCase			:				Optional[Any]										=					model(**UpperCAmelCase     )

  UpperCAmelCase			:				str										=					torch.Size([1, 400]     )
  UpperCAmelCase			:				int										=					torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658]     )

  assert outputs.logits.shape == expected_shape
  assert torch.allclose(outputs.logits[0, :5]							,					UpperCAmelCase							,					atol=1E-4     ), outputs.logits[0, :5]

  model.save_pretrained(UpperCAmelCase     )
  extractor.save_pretrained(UpperCAmelCase     )


if __name__ == "__main__":
     _lowerCamelCase							:  Optional[Any]     =				argparse.ArgumentParser()

     parser.add_argument("--output_model_name", "-o", type=str, help="Output path for the converted HuggingFace model")

     _lowerCamelCase							:  str     =				parser.parse_args()
     convert(args.output_model_name)





