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_ )		->    Any:




       '''simple docstring'''






       _A=						'https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'

       with open(_lowercase							,       'wb' ) as f:
              with requests.get(_lowercase							,       stream=_lowercase ) as req:
                     for chunk in req.iter_content(chunk_size=20_48 ):
                            f.write(_lowercase )

def 				UpperCamelCase					(     )		->    List[Any]:




       '''simple docstring'''






       _A=						VivitConfig()

       _A=						4_00
       _A=						'huggingface/label-files'
       _A=						'kinetics400-id2label.json'

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

def 				UpperCamelCase					(     )		->    List[str]:




       '''simple docstring'''






       _A=						hf_hub_download(
           repo_id='hf-internal-testing/spaghetti-video'							,       filename='eating_spaghetti_32_frames.npy'							,       repo_type='dataset' )
       _A=						np.load(_lowercase )
       return list(_lowercase )

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    Optional[Any]:




       '''simple docstring'''






       if np.ndim(_lowercase ) == 2:
              return transform_attention_bias(_lowercase )

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

       else:
              raise Exception(F"Invalid number of dimesions: {np.ndim(_lowercase )}" )

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    int:




       '''simple docstring'''






       return current.flatten()

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    Tuple:




       '''simple docstring'''






       return np.reshape(_lowercase							,       (current.shape[0], current.shape[1] * current.shape[2]) ).T

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    int:




       '''simple docstring'''






       return np.reshape(_lowercase							,       (current.shape[0] * current.shape[1], current.shape[2]) ).T

def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_ )		->    Any:




       '''simple docstring'''






       _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_ )		->    List[str]:




       '''simple docstring'''






       return sum([1 if 'encoderblock_' in k else 0 for k in state_dict['optimizer']['target']['Transformer'].keys()] )

def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_=False )		->    str:




       '''simple docstring'''






       _A=						get_n_layers(_lowercase )

       _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(_lowercase ):
              new_state.update(transform_state_encoder_block(_lowercase							,       _lowercase ) )

       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(_lowercase ) for k, v in new_state.items()}

def 				UpperCamelCase					(     )		->    int:




       '''simple docstring'''






       _A=						VivitImageProcessor()

       assert extractor.do_resize is True
       assert extractor.size == {"shortest_edge": 2_56}
       assert extractor.do_center_crop is True
       assert extractor.crop_size == {"width": 2_24, "height": 2_24}
       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 / 2_55

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

       return extractor

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    int:




       '''simple docstring'''






       _A=						'checkpoint'

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

       _A=						restore_checkpoint(_lowercase							,       _lowercase )
       _A=						transform_state(_lowercase							,       classification_head=_lowercase )

       _A=						get_vivit_config()

       assert config.image_size == 2_24
       assert config.num_frames == 32

       _A=						VivitForVideoClassification(_lowercase )
       model.load_state_dict(_lowercase )
       model.eval()

       _A=						get_processor()

       _A=						prepare_video()
       _A=						extractor(_lowercase							,       return_tensors='pt' )

       _A=						model(**_lowercase )

       _A=						torch.Size([1, 4_00] )
       _A=						torch.tensor([-1.0_543, 2.0_764, -0.2_104, 0.4_439, -0.9_658] )

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

       model.save_pretrained(_lowercase )
       extractor.save_pretrained(_lowercase )


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

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

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







import json

import requests

from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info
def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    class        snake_case_				:







       """simple docstring"""



       def __init__(     self:						Tuple ,							snake_case__:						Optional[int]						)	->		None:


           """simple docstring"""



           assert isinstance(snake_case__ ,							(bytes, str)						)
           _lowercase     			=						content

       def       SCREAMING_SNAKE_CASE__					(     self:						Tuple						)	->		str:


           """simple docstring"""



           return json.loads(self.content						)

    def mock_response(*_lowercase,   **_lowercase   ):
        assert args[0] == AUTHENTICATED_USER_ENDPOINT
        assert "Authorization" in kwargs["headers"]
        assert kwargs["headers"]["Authorization"].startswith('token '   )
        assert "Accept" in kwargs["headers"]
        return FakeResponse(b'{"login":"test","id":1}'   )

    monkeypatch.setattr(_lowercase,   'get',   _lowercase   )
    _lowercase     			=						fetch_github_info('token'   )
    assert result["login"] == "test"
    assert result["id"] == 1
