'''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 				__UpperCamelCase (					__UpperCAmelCase  :					str			)							->	Optional[Any]:
  lowercase				:							List[str]									=   '''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=2_048			):
        f.write(__UpperCAmelCase			)







def 				__UpperCamelCase (					)							->	VivitConfig:
  lowercase				:							int									=   VivitConfig()

  lowercase				:							str									=   400
  lowercase				:							Optional[Any]									=   '''huggingface/label-files'''
  lowercase				:							Optional[Any]									=   '''kinetics400-id2label.json'''

  lowercase				:							Tuple									=   json.load(open(hf_hub_download(__UpperCAmelCase ,   __UpperCAmelCase ,   repo_type='''dataset'''			) ,   '''r'''			)			)
  lowercase				:							List[Any]									=   {int(__UpperCAmelCase			): v for k, v in idalabel.items()}
  lowercase				:							Union[str, Any]									=   idalabel
  lowercase				:							Optional[Any]									=   {v: k for k, v in idalabel.items()}
  return config







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







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.ndarray			)							->	Dict:
  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 				__UpperCamelCase (					__UpperCAmelCase  :					np.ndarray			)							->	Optional[int]:
  return current.flatten()







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







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







def 				__UpperCamelCase (					__UpperCAmelCase  :					Union[str, Any] ,   __UpperCAmelCase  :					int			)							->	int:
  lowercase				:							str									=   state_dict['''optimizer''']['''target''']['''Transformer'''][f"""encoderblock_{i}"""]

  lowercase				:							Optional[int]									=   f"""encoder.layer.{i}."""
  lowercase				:							Tuple									=   {
      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 				__UpperCamelCase (					__UpperCAmelCase  :					Union[str, Any]			)							->	int:
  return sum([1 if '''encoderblock_''' in k else 0 for k in state_dict['''optimizer''']['''target''']['''Transformer'''].keys()]			)







def 				__UpperCamelCase (					__UpperCAmelCase  :					Optional[Any] ,   __UpperCAmelCase  :					Tuple=False			)							->	Optional[Any]:
  lowercase				:							str									=   get_n_layers(__UpperCAmelCase			)

  lowercase				:							Union[str, Any]									=   OrderedDict()

  lowercase				:							str									=   state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''bias''']
  lowercase				:							Tuple									=   state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''scale''']

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

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

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







def 				__UpperCamelCase (					)							->	VivitImageProcessor:
  lowercase				:							List[str]									=   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 				__UpperCamelCase (					__UpperCAmelCase  :					str			)							->	int:
  lowercase				:							Any									=   '''checkpoint'''

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

  lowercase				:							List[Any]									=   restore_checkpoint(__UpperCAmelCase ,   __UpperCAmelCase			)
  lowercase				:							int									=   transform_state(__UpperCAmelCase ,   classification_head=__UpperCAmelCase			)

  lowercase				:							str									=   get_vivit_config()

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

  lowercase				:							List[str]									=   VivitForVideoClassification(__UpperCAmelCase			)
  model.load_state_dict(__UpperCAmelCase			)
  model.eval()

  lowercase				:							Dict									=   get_processor()

  lowercase				:							List[str]									=   prepare_video()
  lowercase				:							int									=   extractor(__UpperCAmelCase ,   return_tensors='''pt'''			)

  lowercase				:							Optional[Any]									=   model(**__UpperCAmelCase			)

  lowercase				:							List[Any]									=   torch.Size([1, 400]			)
  lowercase				:							Tuple									=   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		:      Dict						   =argparse.ArgumentParser()

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

 _A		:      Tuple						   =parser.parse_args()
 convert(args.output_model_name)
'''simple docstring'''
def 				__UpperCamelCase (					__UpperCAmelCase  :					float ,   __UpperCAmelCase  :					float ,   __UpperCAmelCase  :					float			)							->	list[int]:
  if hue < 0 or hue > 360:
    raise Exception('''hue should be between 0 and 360'''			)

  if saturation < 0 or saturation > 1:
    raise Exception('''saturation should be between 0 and 1'''			)

  if value < 0 or value > 1:
    raise Exception('''value should be between 0 and 1'''			)

  lowercase				:							List[str]									=   value * saturation
  lowercase				:							Optional[Any]									=   hue / 60
  lowercase				:							Dict									=   chroma * (1 - abs(hue_section % 2 - 1			))
  lowercase				:							List[Any]									=   value - chroma

  if hue_section >= 0 and hue_section <= 1:
    lowercase				:							Optional[int]									=   round(255 * (chroma + match_value)			)
    lowercase				:							List[Any]									=   round(255 * (second_largest_component + match_value)			)
    lowercase				:							int									=   round(255 * (match_value)			)
  elif hue_section > 1 and hue_section <= 2:
    lowercase				:							Optional[Any]									=   round(255 * (second_largest_component + match_value)			)
    lowercase				:							Any									=   round(255 * (chroma + match_value)			)
    lowercase				:							Any									=   round(255 * (match_value)			)
  elif hue_section > 2 and hue_section <= 3:
    lowercase				:							Dict									=   round(255 * (match_value)			)
    lowercase				:							Optional[int]									=   round(255 * (chroma + match_value)			)
    lowercase				:							List[str]									=   round(255 * (second_largest_component + match_value)			)
  elif hue_section > 3 and hue_section <= 4:
    lowercase				:							List[str]									=   round(255 * (match_value)			)
    lowercase				:							List[str]									=   round(255 * (second_largest_component + match_value)			)
    lowercase				:							Tuple									=   round(255 * (chroma + match_value)			)
  elif hue_section > 4 and hue_section <= 5:
    lowercase				:							Optional[int]									=   round(255 * (second_largest_component + match_value)			)
    lowercase				:							Tuple									=   round(255 * (match_value)			)
    lowercase				:							Optional[int]									=   round(255 * (chroma + match_value)			)
  else:
    lowercase				:							int									=   round(255 * (chroma + match_value)			)
    lowercase				:							Optional[Any]									=   round(255 * (match_value)			)
    lowercase				:							Optional[int]									=   round(255 * (second_largest_component + match_value)			)

  return [red, green, blue]







def 				__UpperCamelCase (					__UpperCAmelCase  :					int ,   __UpperCAmelCase  :					int ,   __UpperCAmelCase  :					int			)							->	list[float]:
  if red < 0 or red > 255:
    raise Exception('''red should be between 0 and 255'''			)

  if green < 0 or green > 255:
    raise Exception('''green should be between 0 and 255'''			)

  if blue < 0 or blue > 255:
    raise Exception('''blue should be between 0 and 255'''			)

  lowercase				:							str									=   red / 255
  lowercase				:							Union[str, Any]									=   green / 255
  lowercase				:							Union[str, Any]									=   blue / 255
  lowercase				:							str									=   max(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)
  lowercase				:							int									=   value - min(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)
  lowercase				:							Optional[int]									=   0 if value == 0 else chroma / value

  if chroma == 0:
    lowercase				:							Dict									=   0.0
  elif value == float_red:
    lowercase				:							Tuple									=   60 * (0 + (float_green - float_blue) / chroma)
  elif value == float_green:
    lowercase				:							Optional[int]									=   60 * (2 + (float_blue - float_red) / chroma)
  else:
    lowercase				:							Dict									=   60 * (4 + (float_red - float_green) / chroma)

  lowercase				:							List[str]									=   (hue + 360) % 360

  return [hue, saturation, value]







def 				__UpperCamelCase (					__UpperCAmelCase  :					list[float] ,   __UpperCAmelCase  :					list[float]			)							->	bool:
  lowercase				:							int									=   abs(hsv_a[0] - hsv_a[0]			) < 0.2
  lowercase				:							Optional[int]									=   abs(hsv_a[1] - hsv_a[1]			) < 0.002
  lowercase				:							Dict									=   abs(hsv_a[2] - hsv_a[2]			) < 0.002

  return check_hue and check_saturation and check_value