'''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 __lowerCAmelCase				(							UpperCamelCase__       )			->					Tuple:
    __lowerCamelCase						=						'''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=20_48       ):
                f.write(UpperCamelCase__       )






def __lowerCAmelCase				(							)			->					VivitConfig:
    __lowerCamelCase						=						VivitConfig()

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

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






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






def __lowerCAmelCase				(							UpperCamelCase__       )			->					str:
    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 __lowerCAmelCase				(							UpperCamelCase__       )			->					int:
    return current.flatten()






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






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






def __lowerCAmelCase				(							UpperCamelCase__       ,						UpperCamelCase__       )			->					List[str]:
    __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 __lowerCAmelCase				(							UpperCamelCase__       )			->					Dict:
    return sum([1 if '''encoderblock_''' in k else 0 for k in state_dict['''optimizer''']['''target''']['''Transformer'''].keys()]       )






def __lowerCAmelCase				(							UpperCamelCase__       ,						UpperCamelCase__=False       )			->					Optional[int]:
    __lowerCamelCase						=						get_n_layers(UpperCamelCase__       )

    __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(UpperCamelCase__       ):
        new_state.update(transform_state_encoder_block(UpperCamelCase__       ,						UpperCamelCase__       )       )

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






def __lowerCAmelCase				(							)			->					VivitImageProcessor:
    __lowerCamelCase						=						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 __lowerCAmelCase				(							UpperCamelCase__       )			->					Dict:
    __lowerCamelCase						=						'''checkpoint'''

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

    __lowerCamelCase						=						restore_checkpoint(UpperCamelCase__       ,						UpperCamelCase__       )
    __lowerCamelCase						=						transform_state(UpperCamelCase__       ,						classification_head=UpperCamelCase__       )

    __lowerCamelCase						=						get_vivit_config()

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

    __lowerCamelCase						=						VivitForVideoClassification(UpperCamelCase__       )
    model.load_state_dict(UpperCamelCase__       )
    model.eval()

    __lowerCamelCase						=						get_processor()

    __lowerCamelCase						=						prepare_video()
    __lowerCamelCase						=						extractor(UpperCamelCase__       ,						return_tensors='''pt'''       )

    __lowerCamelCase						=						model(**UpperCamelCase__       )

    __lowerCamelCase						=						torch.Size([1, 4_00]       )
    __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]       ,						UpperCamelCase__       ,						atol=1E-4       ), outputs.logits[0, :5]

    model.save_pretrained(UpperCamelCase__       )
    extractor.save_pretrained(UpperCamelCase__       )


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)

'''simple docstring'''




def __lowerCAmelCase				(							UpperCamelCase__       )			->					int:
    __lowerCamelCase						=						len(UpperCamelCase__       )
    __lowerCamelCase						=						[0] * length
    __lowerCamelCase						=						[[False for i in range(UpperCamelCase__       )] for j in range(UpperCamelCase__       )]
    for i, c in enumerate(UpperCamelCase__       ):
        __lowerCamelCase						=						i
        for j in range(i + 1       ):
            if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
                __lowerCamelCase						=						True
                __lowerCamelCase						=						min(UpperCamelCase__       ,						0 if j == 0 else (cut[j - 1] + 1)       )
        __lowerCamelCase						=						mincut
    return cut[length - 1]


if __name__ == "__main__":
  __UpperCAmelCase  							=input("Enter the string: ").strip()
  __UpperCAmelCase  							=find_minimum_partitions(s)
  print(f'Minimum number of partitions required for the \'{s}\' is {ans}')
