






"""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     A(  snake_case_		):
							"""simple docstring"""





							lowercase__:    Dict        =      "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     A(  ):
							"""simple docstring"""





							lowercase__:    Optional[Any]        =      VivitConfig()

							lowercase__:    Optional[int]        =      400
							lowercase__:    str        =      "huggingface/label-files"
							lowercase__:    Optional[Any]        =      "kinetics400-id2label.json"

							lowercase__:    Tuple        =      json.load(open(hf_hub_download(_UpperCamelCase						,       _UpperCamelCase						,       repo_type="dataset"		)						,       "r"		)		)
							lowercase__:    int        =      {int(_UpperCamelCase		): v for k, v in idalabel.items()}
							lowercase__:    int        =      idalabel
							lowercase__:    int        =      {v: k for k, v in idalabel.items()}
							return config






def     A(  ):
							"""simple docstring"""





							lowercase__:    Dict        =      hf_hub_download(
							    repo_id="hf-internal-testing/spaghetti-video"						,       filename="eating_spaghetti_32_frames.npy"						,       repo_type="dataset"		)
							lowercase__:    Tuple        =      np.load(_UpperCamelCase		)
							return list(_UpperCamelCase		)






def     A(  snake_case_		):
							"""simple docstring"""





							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     A(  snake_case_		):
							"""simple docstring"""





							return current.flatten()






def     A(  snake_case_		):
							"""simple docstring"""





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






def     A(  snake_case_		):
							"""simple docstring"""





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






def     A(  snake_case_						,       snake_case_		):
							"""simple docstring"""





							lowercase__:    int        =      state_dict["optimizer"]["target"]["Transformer"][F"""encoderblock_{i}"""]

							lowercase__:    int        =      F"""encoder.layer.{i}."""
							lowercase__:    int        =      {
							    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     A(  snake_case_		):
							"""simple docstring"""





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






def     A(  snake_case_						,       snake_case_=False		):
							"""simple docstring"""





							lowercase__:    Optional[int]        =      get_n_layers(_UpperCamelCase		)

							lowercase__:    int        =      OrderedDict()

							lowercase__:    Optional[int]        =      state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["bias"]
							lowercase__:    Optional[Any]        =      state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["scale"]

							lowercase__:    Union[str, Any]        =      np.transpose(
							    state_dict["optimizer"]["target"]["embedding"]["kernel"]						,       (4, 3, 0, 1, 2)		)
							lowercase__:    str        =      state_dict["optimizer"]["target"]["embedding"]["bias"]

							lowercase__:    Optional[int]        =      state_dict["optimizer"]["target"]["cls"]
							lowercase__:    Optional[int]        =      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:
														lowercase__:    List[str]        =      {"vivit." + k: v for k, v in new_state.items()}
														lowercase__:    Dict        =      np.transpose(state_dict["optimizer"]["target"]["output_projection"]["kernel"]		)
														lowercase__:    int        =      np.transpose(state_dict["optimizer"]["target"]["output_projection"]["bias"]		)

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






def     A(  ):
							"""simple docstring"""





							lowercase__:    List[Any]        =      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     A(  snake_case_		):
							"""simple docstring"""





							lowercase__:    Optional[Any]        =      "checkpoint"

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

							lowercase__:    Union[str, Any]        =      restore_checkpoint(_UpperCamelCase						,       _UpperCamelCase		)
							lowercase__:    List[str]        =      transform_state(_UpperCamelCase						,       classification_head=_UpperCamelCase		)

							lowercase__:    Tuple        =      get_vivit_config()

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

							lowercase__:    str        =      VivitForVideoClassification(_UpperCamelCase		)
							model.load_state_dict(_UpperCamelCase		)
							model.eval()

							lowercase__:    Optional[Any]        =      get_processor()

							lowercase__:    Optional[int]        =      prepare_video()
							lowercase__:    str        =      extractor(_UpperCamelCase						,       return_tensors="pt"		)

							lowercase__:    Dict        =      model(**_UpperCamelCase		)

							lowercase__:    Tuple        =      torch.Size([1, 400]		)
							lowercase__:    Dict        =      torch.tensor([-1.05_43, 2.07_64, -0.21_04, 0.44_39, -0.96_58]		)

							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"""





UpperCamelCase			=      {
    """0""": 1,
    """1""": 1,
    """2""": 2,
    """3""": 6,
    """4""": 24,
    """5""": 1_20,
    """6""": 7_20,
    """7""": 50_40,
    """8""": 4_03_20,
    """9""": 36_28_80,
}

UpperCamelCase			=      {1_45: 1_45}

UpperCamelCase			=      {
    1_45: 0,
    1_69: 3,
    3_63_01: 3,
    14_54: 3,
    8_71: 2,
    4_53_61: 2,
    8_72: 2,
}






def     A(  snake_case_		):
       """simple docstring"""





       if n in CACHE_SUM_DIGIT_FACTORIALS:
              return CACHE_SUM_DIGIT_FACTORIALS[n]
       lowercase__:    Any        =      sum(DIGIT_FACTORIALS[let] for let in str(snake_case_		)		)
       lowercase__:    Optional[Any]        =      ret
       return ret






def     A(  snake_case_						,       snake_case_ = None		):
       """simple docstring"""





       lowercase__:    Optional[int]        =      previous or set()
       if n in CHAIN_LENGTH_CACHE:
              return CHAIN_LENGTH_CACHE[n]
       lowercase__:    Tuple        =      sum_digit_factorials(snake_case_		)
       if next_number in previous:
              lowercase__:    List[Any]        =      0
              return 0
       else:
              previous.add(snake_case_		)
              lowercase__:    Optional[int]        =      1 + chain_length(snake_case_						,       snake_case_		)
              lowercase__:    Any        =      ret
              return ret






def     A(  snake_case_ = 60						,       snake_case_ = 1000000		):
       """simple docstring"""





       return sum(1 for i in range(1						,       snake_case_		) if chain_length(snake_case_		) == num_terms		)


if __name__ == "__main__":
  print(F"{solution() = }")






