




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	(       __UpperCamelCase			:						Dict      )  ->							Union[str, Any]:
    UpperCAmelCase_							 =     '''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=2048      ):
                f.write(__UpperCamelCase      )




def 					SCREAMING_SNAKE_CASE	(       )  ->							VivitConfig:
    UpperCAmelCase_							 =     VivitConfig()

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

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




def 					SCREAMING_SNAKE_CASE	(       )  ->							Union[str, Any]:
    UpperCAmelCase_							 =     hf_hub_download(
        repo_id='''hf-internal-testing/spaghetti-video'''       ,     filename='''eating_spaghetti_32_frames.npy'''       ,     repo_type='''dataset'''      )
    UpperCAmelCase_							 =     np.load(__UpperCamelCase      )
    return list(__UpperCamelCase      )




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						np.ndarray      )  ->							Optional[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 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						np.ndarray      )  ->							Tuple:
    return current.flatten()




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




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




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						str       ,     __UpperCamelCase			:						List[str]      )  ->							Optional[Any]:
    UpperCAmelCase_							 =     state_dict['''optimizer''']['''target''']['''Transformer'''][f'encoderblock_{i}']

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




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						Tuple       ,     __UpperCamelCase			:						Any=False      )  ->							Dict:
    UpperCAmelCase_							 =     get_n_layers(__UpperCamelCase      )

    UpperCAmelCase_							 =     OrderedDict()

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

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

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

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




def 					SCREAMING_SNAKE_CASE	(       )  ->							VivitImageProcessor:
    UpperCAmelCase_							 =     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	(       __UpperCamelCase			:						str      )  ->							List[str]:
    UpperCAmelCase_							 =     '''checkpoint'''

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

    UpperCAmelCase_							 =     restore_checkpoint(__UpperCamelCase       ,     __UpperCamelCase      )
    UpperCAmelCase_							 =     transform_state(__UpperCamelCase       ,     classification_head=__UpperCamelCase      )

    UpperCAmelCase_							 =     get_vivit_config()

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

    UpperCAmelCase_							 =     VivitForVideoClassification(__UpperCamelCase      )
    model.load_state_dict(__UpperCamelCase      )
    model.eval()

    UpperCAmelCase_							 =     get_processor()

    UpperCAmelCase_							 =     prepare_video()
    UpperCAmelCase_							 =     extractor(__UpperCamelCase       ,     return_tensors='''pt'''      )

    UpperCAmelCase_							 =     model(**__UpperCamelCase      )

    UpperCAmelCase_							 =     torch.Size([1, 400]      )
    UpperCAmelCase_							 =     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]       ,     __UpperCamelCase       ,     atol=1e-4      ), outputs.logits[0, :5]

    model.save_pretrained(__UpperCamelCase      )
    extractor.save_pretrained(__UpperCamelCase      )


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

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

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






from dataclasses import dataclass
from typing import Callable, Optional

import datasets


@dataclass
class 						a__							(   datasets.BuilderConfig		):
			A__				:   Optional[Callable]								=			None
			A__				:   Optional[dict]								=			None
			A__				:   Optional[datasets.Features]								=			None



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Any:
								assert self.generator is not None, "generator must be specified"

								if self.gen_kwargs is None:
													__a							=   {}






class 						a__							(   datasets.GeneratorBasedBuilder		):
			A__				:   List[Any]								=			GeneratorConfig



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Tuple:
								return datasets.DatasetInfo(features=self.config.features    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    Optional[int]:
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,						gen_kwargs=self.config.gen_kwargs    )]







			def __SCREAMING_SNAKE_CASE							(						self      ,						**UpperCAmelCase    ) ->    Tuple:
								for idx, ex in enumerate(self.config.generator(**UpperCAmelCase    )    ):
													yield idx, ex




