
"""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 						__magic_name__		(				UpperCamelCase    :		Tuple    )    ->	List[Any]:
    a__    = '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 						__magic_name__		(				)    ->	VivitConfig:
    a__    = VivitConfig()

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

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



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



def 						__magic_name__		(				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 						__magic_name__		(				UpperCamelCase    :		np.ndarray    )    ->	Dict:
    return current.flatten()



def 						__magic_name__		(				UpperCamelCase    :		np.ndarray    )    ->	int:
    return np.reshape(UpperCamelCase       ,		(current.shape[0], current.shape[1] * current.shape[2])    ).T



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



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



def 						__magic_name__		(				UpperCamelCase    :		Dict       ,		UpperCamelCase    :		List[Any]=False    )    ->	List[Any]:
    a__    = get_n_layers(UpperCamelCase    )

    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(UpperCamelCase    ):
        new_state.update(transform_state_encoder_block(UpperCamelCase       ,		UpperCamelCase    )    )

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



def 						__magic_name__		(				)    ->	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 						__magic_name__		(				UpperCamelCase    :		str    )    ->	Tuple:
    a__    = 'checkpoint'

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

    a__    = restore_checkpoint(UpperCamelCase       ,		UpperCamelCase    )
    a__    = transform_state(UpperCamelCase       ,		classification_head=UpperCamelCase    )

    a__    = get_vivit_config()

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

    a__    = VivitForVideoClassification(UpperCamelCase    )
    model.load_state_dict(UpperCamelCase    )
    model.eval()

    a__    = get_processor()

    a__    = prepare_video()
    a__    = extractor(UpperCamelCase       ,		return_tensors='pt'    )

    a__    = model(**UpperCamelCase    )

    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]       ,		UpperCamelCase       ,		atol=1E-4    ), outputs.logits[0, :5]

    model.save_pretrained(UpperCamelCase    )
    extractor.save_pretrained(UpperCamelCase    )


if __name__ == "__main__":
    a		:					List[str]    	=					argparse.ArgumentParser()

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

    a		:					int    	=					parser.parse_args()
    convert(args.output_model_name)




"""simple docstring"""




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


a		:					str    	=					False







@nightly
@require_torch_gpu
class 						lowercase(unittest.TestCase					):



     def  lowercase__     (       self      )		->    Any:







         """simple docstring"""



         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()



     def  lowercase__     (       self      )		->    Optional[int]:







         """simple docstring"""



         a__    = VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'      )
         # remove text_unet
         pipe.remove_unused_weights()
         pipe.to(__SCREAMING_SNAKE_CASE      )
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

         a__    = load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'      )

         a__    = torch.manual_seed(0      )
         a__    = pipe(
             prompt='first prompt'    ,							image=__SCREAMING_SNAKE_CASE    ,							text_to_image_strength=0.75    ,							generator=__SCREAMING_SNAKE_CASE    ,							guidance_scale=7.5    ,							num_inference_steps=2    ,							output_type='numpy'    ,							).images

         with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(__SCREAMING_SNAKE_CASE      )
             a__    = VersatileDiffusionDualGuidedPipeline.from_pretrained(__SCREAMING_SNAKE_CASE      )

         pipe.to(__SCREAMING_SNAKE_CASE      )
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

         a__    = generator.manual_seed(0      )
         a__    = pipe(
             prompt='first prompt'    ,							image=__SCREAMING_SNAKE_CASE    ,							text_to_image_strength=0.75    ,							generator=__SCREAMING_SNAKE_CASE    ,							guidance_scale=7.5    ,							num_inference_steps=2    ,							output_type='numpy'    ,							).images

         assert np.abs(image - new_image      ).sum() < 1e-5, "Models don't have the same forward pass"




     def  lowercase__     (       self      )		->    Union[str, Any]:







         """simple docstring"""



         a__    = VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'      )
         pipe.remove_unused_weights()
         pipe.to(__SCREAMING_SNAKE_CASE      )
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

         a__    = 'cyberpunk 2077'
         a__    = load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'      )
         a__    = torch.manual_seed(0      )
         a__    = pipe(
             prompt=__SCREAMING_SNAKE_CASE    ,							image=__SCREAMING_SNAKE_CASE    ,							text_to_image_strength=0.75    ,							generator=__SCREAMING_SNAKE_CASE    ,							guidance_scale=7.5    ,							num_inference_steps=5_0    ,							output_type='numpy'    ,							).images

         a__    = image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         a__    = np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2


