





"""simple docstring"""


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 SCREAMING_SNAKE_CASE_   (       snake_case   :					Dict		)->		List[Any]:
       _lowerCamelCase    	=					'https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'

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





def SCREAMING_SNAKE_CASE_   (       )->		VivitConfig:
       _lowerCamelCase    	=					VivitConfig()

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

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





def SCREAMING_SNAKE_CASE_   (       )->		Tuple:
       _lowerCamelCase    	=					hf_hub_download(
           repo_id='hf-internal-testing/spaghetti-video'						,  filename='eating_spaghetti_32_frames.npy'						,  repo_type='dataset'		)
       _lowerCamelCase    	=					np.load(snake_case		)
       return list(snake_case		)





def SCREAMING_SNAKE_CASE_   (       snake_case   :					np.ndarray		)->		List[Any]:
       if np.ndim(snake_case		) == 2:
              return transform_attention_bias(snake_case		)

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

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





def SCREAMING_SNAKE_CASE_   (       snake_case   :					np.ndarray		)->		Optional[int]:
       return current.flatten()





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





def SCREAMING_SNAKE_CASE_   (       snake_case   :					np.ndarray		)->		Union[str, Any]:
       return np.reshape(snake_case						,  (current.shape[0] * current.shape[1], current.shape[2])		).T





def SCREAMING_SNAKE_CASE_   (       snake_case   :					int						,  snake_case   :					Optional[Any]		)->		Optional[Any]:
       _lowerCamelCase    	=					state_dict['optimizer']['target']['Transformer'][f'encoderblock_{i}']

       _lowerCamelCase    	=					f'encoder.layer.{i}.'
       _lowerCamelCase    	=					{
           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 SCREAMING_SNAKE_CASE_   (       snake_case   :					List[Any]		)->		Dict:
       return sum([1 if 'encoderblock_' in k else 0 for k in state_dict['optimizer']['target']['Transformer'].keys()]		)





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Dict						,  snake_case   :					Union[str, Any]=False		)->		int:
       _lowerCamelCase    	=					get_n_layers(snake_case		)

       _lowerCamelCase    	=					OrderedDict()

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

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

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

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

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

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





def SCREAMING_SNAKE_CASE_   (       )->		VivitImageProcessor:
       _lowerCamelCase    	=					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 SCREAMING_SNAKE_CASE_   (       snake_case   :					str		)->		Any:
       _lowerCamelCase    	=					'checkpoint'

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

       _lowerCamelCase    	=					restore_checkpoint(snake_case						,  snake_case		)
       _lowerCamelCase    	=					transform_state(snake_case						,  classification_head=snake_case		)

       _lowerCamelCase    	=					get_vivit_config()

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

       _lowerCamelCase    	=					VivitForVideoClassification(snake_case		)
       model.load_state_dict(snake_case		)
       model.eval()

       _lowerCamelCase    	=					get_processor()

       _lowerCamelCase    	=					prepare_video()
       _lowerCamelCase    	=					extractor(snake_case						,  return_tensors='pt'		)

       _lowerCamelCase    	=					model(**snake_case		)

       _lowerCamelCase    	=					torch.Size([1, 400]		)
       _lowerCamelCase    	=					torch.tensor([-1.0_5_4_3, 2.0_7_6_4, -0.2_1_0_4, 0.4_4_3_9, -0.9_6_5_8]		)

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

       model.save_pretrained(snake_case		)
       extractor.save_pretrained(snake_case		)


if __name__ == "__main__":
      A_     :      Optional[int]								=argparse.ArgumentParser()

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

      A_     :      Any								=parser.parse_args()
      convert(args.output_model_name)













"""simple docstring"""


def SCREAMING_SNAKE_CASE_   (       snake_case   :					str		)->		list:
       _lowerCamelCase    	=					[]
       _lowerCamelCase    	=					1

       for i in range(len(snake_case		)		):
              if i + 1 < len(snake_case		) and text[i] == text[i + 1]:
                     count += 1
              else:
                     encoded.append((text[i], count)		)
                     _lowerCamelCase    	=					1

       return encoded





def SCREAMING_SNAKE_CASE_   (       snake_case   :					list		)->		str:
       return "".join(char * length for char, length in encoded		)


if __name__ == "__main__":
      from doctest import testmod

      testmod(name="""run_length_encode""", verbose=True)
      testmod(name="""run_length_decode""", verbose=True)






