

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 			__UpperCamelCase    (lowerCAmelCase					:  int     )     ->  Optional[Any]:
      A    =				'https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'

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


def 			__UpperCamelCase    ()     ->  VivitConfig:
      A    =				VivitConfig()

      A    =				400
      A    =				'huggingface/label-files'
      A    =				'kinetics400-id2label.json'

      A    =				json.load(open(hf_hub_download(lowerCAmelCase,    lowerCAmelCase,    repo_type='dataset'     ),    'r'     )     )
      A    =				{int(lowerCAmelCase     ): v for k, v in idalabel.items()}
      A    =				idalabel
      A    =				{v: k for k, v in idalabel.items()}
      return config


def 			__UpperCamelCase    ()     ->  int:
      A    =				hf_hub_download(
          repo_id='hf-internal-testing/spaghetti-video',    filename='eating_spaghetti_32_frames.npy',    repo_type='dataset'     )
      A    =				np.load(lowerCAmelCase     )
      return list(lowerCAmelCase     )


def 			__UpperCamelCase    (lowerCAmelCase					:  np.ndarray     )     ->  Union[str, Any]:
      if np.ndim(lowerCAmelCase     ) == 2:
            return transform_attention_bias(lowerCAmelCase     )

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

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


def 			__UpperCamelCase    (lowerCAmelCase					:  np.ndarray     )     ->  Optional[Any]:
      return current.flatten()


def 			__UpperCamelCase    (lowerCAmelCase					:  np.ndarray     )     ->  List[str]:
      return np.reshape(lowerCAmelCase,    (current.shape[0], current.shape[1] * current.shape[2])     ).T


def 			__UpperCamelCase    (lowerCAmelCase					:  np.ndarray     )     ->  Dict:
      return np.reshape(lowerCAmelCase,    (current.shape[0] * current.shape[1], current.shape[2])     ).T


def 			__UpperCamelCase    (lowerCAmelCase					:  int,    lowerCAmelCase					:  Any     )     ->  int:
      A    =				state_dict['optimizer']['target']['Transformer'][f'''encoderblock_{i}''']

      A    =				f'''encoder.layer.{i}.'''
      A    =				{
          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 			__UpperCamelCase    (lowerCAmelCase					:  Tuple     )     ->  Optional[int]:
      return sum([1 if 'encoderblock_' in k else 0 for k in state_dict['optimizer']['target']['Transformer'].keys()]     )


def 			__UpperCamelCase    (lowerCAmelCase					:  Optional[Any],    lowerCAmelCase					:  Optional[int]=False     )     ->  Optional[int]:
      A    =				get_n_layers(lowerCAmelCase     )

      A    =				OrderedDict()

      A    =				state_dict['optimizer']['target']['Transformer']['encoder_norm']['bias']
      A    =				state_dict['optimizer']['target']['Transformer']['encoder_norm']['scale']

      A    =				np.transpose(
          state_dict['optimizer']['target']['embedding']['kernel'],    (4, 3, 0, 1, 2)     )
      A    =				state_dict['optimizer']['target']['embedding']['bias']

      A    =				state_dict['optimizer']['target']['cls']
      A    =				state_dict['optimizer']['target']['Transformer']['posembed_input'][
          'pos_embedding'
      ]

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

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

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


def 			__UpperCamelCase    ()     ->  VivitImageProcessor:
      A    =				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 			__UpperCamelCase    (lowerCAmelCase					:  str     )     ->  Optional[int]:
      A    =				'checkpoint'

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

      A    =				restore_checkpoint(lowerCAmelCase,    lowerCAmelCase     )
      A    =				transform_state(lowerCAmelCase,    classification_head=lowerCAmelCase     )

      A    =				get_vivit_config()

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

      A    =				VivitForVideoClassification(lowerCAmelCase     )
      model.load_state_dict(lowerCAmelCase     )
      model.eval()

      A    =				get_processor()

      A    =				prepare_video()
      A    =				extractor(lowerCAmelCase,    return_tensors='pt'     )

      A    =				model(**lowerCAmelCase     )

      A    =				torch.Size([1, 400]     )
      A    =				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],    lowerCAmelCase,    atol=1E-4     ), outputs.logits[0, :5]

      model.save_pretrained(lowerCAmelCase     )
      extractor.save_pretrained(lowerCAmelCase     )


if __name__ == "__main__":
  snake_case_    	=			argparse.ArgumentParser()

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

  snake_case_    	=			parser.parse_args()
  convert(args.output_model_name)






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import EfficientNetImageProcessor



class        _UpperCAmelCase (   unittest.TestCase			):
  '''simple docstring'''







  def __init__( self	: Optional[Any]		,						UpperCamelCase__	: List[Any]		,						UpperCamelCase__	: Dict=13		,						UpperCamelCase__	: Dict=3		,						UpperCamelCase__	: Tuple=18		,						UpperCamelCase__	: Optional[int]=30		,						UpperCamelCase__	: List[str]=400		,						UpperCamelCase__	: Tuple=True		,						UpperCamelCase__	: List[str]=None		,						UpperCamelCase__	: Optional[int]=True		,						UpperCamelCase__	: int=[0.5, 0.5, 0.5]		,						UpperCamelCase__	: Any=[0.5, 0.5, 0.5]		,						):
        A    =				size if size is not None else {'height': 18, 'width': 18}
        A    =				parent
        A    =				batch_size
        A    =				num_channels
        A    =				image_size
        A    =				min_resolution
        A    =				max_resolution
        A    =				do_resize
        A    =				size
        A    =				do_normalize
        A    =				image_mean
        A    =				image_std







  def 	UpperCamelCase       ( self	: str ):
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
        }



@require_torch
@require_vision
class        _UpperCAmelCase (   __lowercase     ,				unittest.TestCase			):
  '''simple docstring'''







  SCREAMING_SNAKE_CASE							:				Dict					      =				EfficientNetImageProcessor if is_vision_available() else None







  def 	UpperCamelCase       ( self	: Tuple ):
        A    =				EfficientNetImageProcessorTester(self )







  @property
  def 	UpperCamelCase       ( self	: Optional[int] ):
        return self.image_processor_tester.prepare_image_processor_dict()







  def 	UpperCamelCase       ( self	: List[str] ):
        A    =				self.image_processing_class(**self.image_processor_dict )
        self.assertTrue(hasattr(UpperCamelCase__		,						'image_mean' ) )
        self.assertTrue(hasattr(UpperCamelCase__		,						'image_std' ) )
        self.assertTrue(hasattr(UpperCamelCase__		,						'do_normalize' ) )
        self.assertTrue(hasattr(UpperCamelCase__		,						'do_resize' ) )
        self.assertTrue(hasattr(UpperCamelCase__		,						'size' ) )







  def 	UpperCamelCase       ( self	: Union[str, Any] ):
        A    =				self.image_processing_class.from_dict(self.image_processor_dict )
        self.assertEqual(image_processor.size		,						{'height': 18, 'width': 18} )

        A    =				self.image_processing_class.from_dict(self.image_processor_dict		,						size=42 )
        self.assertEqual(image_processor.size		,						{'height': 42, 'width': 42} )







  def 	UpperCamelCase       ( self	: Optional[int] ):
        # Initialize image_processing
        A    =				self.image_processing_class(**self.image_processor_dict )
        # create random PIL images
        A    =				prepare_image_inputs(self.image_processor_tester		,						equal_resolution=UpperCamelCase__ )
        for image in image_inputs:
              self.assertIsInstance(UpperCamelCase__		,						Image.Image )

        # Test not batched input
        A    =				image_processing(image_inputs[0]		,						return_tensors='pt' ).pixel_values
        self.assertEqual(
            encoded_images.shape		,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )		,						)

        # Test batched
        A    =				image_processing(UpperCamelCase__		,						return_tensors='pt' ).pixel_values
        self.assertEqual(
            encoded_images.shape		,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )		,						)







  def 	UpperCamelCase       ( self	: Optional[int] ):
        # Initialize image_processing
        A    =				self.image_processing_class(**self.image_processor_dict )
        # create random numpy tensors
        A    =				prepare_image_inputs(self.image_processor_tester		,						equal_resolution=UpperCamelCase__		,						numpify=UpperCamelCase__ )
        for image in image_inputs:
              self.assertIsInstance(UpperCamelCase__		,						np.ndarray )

        # Test not batched input
        A    =				image_processing(image_inputs[0]		,						return_tensors='pt' ).pixel_values
        self.assertEqual(
            encoded_images.shape		,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )		,						)

        # Test batched
        A    =				image_processing(UpperCamelCase__		,						return_tensors='pt' ).pixel_values
        self.assertEqual(
            encoded_images.shape		,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )		,						)







  def 	UpperCamelCase       ( self	: Optional[Any] ):
        # Initialize image_processing
        A    =				self.image_processing_class(**self.image_processor_dict )
        # create random PyTorch tensors
        A    =				prepare_image_inputs(self.image_processor_tester		,						equal_resolution=UpperCamelCase__		,						torchify=UpperCamelCase__ )
        for image in image_inputs:
              self.assertIsInstance(UpperCamelCase__		,						torch.Tensor )

        # Test not batched input
        A    =				image_processing(image_inputs[0]		,						return_tensors='pt' ).pixel_values
        self.assertEqual(
            encoded_images.shape		,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )		,						)

        # Test batched
        A    =				image_processing(UpperCamelCase__		,						return_tensors='pt' ).pixel_values
        self.assertEqual(
            encoded_images.shape		,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )		,						)



