




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__  (							_A			):






  """simple docstring"""


  a_		    =  '''https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'''

  with open(_A      ,      '''wb'''			) as f:
    with requests.get(_A      ,      stream=_A			) as req:
      for chunk in req.iter_content(chunk_size=2_048			):
        f.write(_A			)






def 				UpperCAmelCase__  (							):






  """simple docstring"""


  a_		    =  VivitConfig()

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

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






def 				UpperCAmelCase__  (							):






  """simple docstring"""


  a_		    =  hf_hub_download(
      repo_id='''hf-internal-testing/spaghetti-video'''      ,      filename='''eating_spaghetti_32_frames.npy'''      ,      repo_type='''dataset'''			)
  a_		    =  np.load(_A			)
  return list(_A			)






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  if np.ndim(_A			) == 2:
    return transform_attention_bias(_A			)

  elif np.ndim(_A			) == 3:
    return transform_attention_kernel(_A			)

  else:
    raise Exception(f"Invalid number of dimesions: {np.ndim(_A			)}"			)






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  return current.flatten()






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


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






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


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






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  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 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


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






def 				UpperCAmelCase__  (							_A      ,      _A=False			):






  """simple docstring"""


  a_		    =  get_n_layers(_A			)

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

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






def 				UpperCAmelCase__  (							):






  """simple docstring"""


  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 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  '''checkpoint'''

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

  a_		    =  restore_checkpoint(_A      ,      _A			)
  a_		    =  transform_state(_A      ,      classification_head=_A			)

  a_		    =  get_vivit_config()

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

  a_		    =  VivitForVideoClassification(_A			)
  model.load_state_dict(_A			)
  model.eval()

  a_		    =  get_processor()

  a_		    =  prepare_video()
  a_		    =  extractor(_A      ,      return_tensors='''pt'''			)

  a_		    =  model(**_A			)

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

  model.save_pretrained(_A			)
  extractor.save_pretrained(_A			)


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)






from __future__ import annotations

from typing import Any






def 				UpperCAmelCase__  (							*_A			):






  """simple docstring"""


  if all(_check_not_integer(_A			) for m in matrix_s			):
    for i in matrix_s[1:]:
      _verify_matrix_sizes(matrix_s[0]      ,      _A			)
    return [[sum(_A			) for t in zip(*_A			)] for m in zip(*_A			)]
  raise TypeError('''Expected a matrix, got int/list instead'''			)






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  if (
      _check_not_integer(_A			)
      and _check_not_integer(_A			)
      and _verify_matrix_sizes(_A      ,      _A			)
  ):
    return [[i - j for i, j in zip(*_A			)] for m in zip(_A      ,      _A			)]
  raise TypeError('''Expected a matrix, got int/list instead'''			)






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  return [[x * n for x in row] for row in matrix]






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  if _check_not_integer(_A			) and _check_not_integer(_A			):
    a_    , a_		    =  _verify_matrix_sizes(_A      ,      _A			)

  if cols[0] != rows[1]:
    a_		    =  (
        '''Cannot multiply matrix of dimensions '''
        f"({rows[0]},{cols[0]}) and ({rows[1]},{cols[1]})"
    )
    raise ValueError(_A			)
  return [
      [sum(m * n for m, n in zip(_A      ,      _A			)			) for j in zip(*_A			)] for i in matrix_a
  ]






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  int(_A			)
  return [[int(row == column			) for column in range(_A			)] for row in range(_A			)]






def 				UpperCAmelCase__  (							_A      ,      _A = True			):






  """simple docstring"""


  if _check_not_integer(_A			):
    if return_map:
      return map(_A      ,      zip(*_A			)			)
    else:
      return list(map(_A      ,      zip(*_A			)			)			)
  raise TypeError('''Expected a matrix, got int/list instead'''			)






def 				UpperCAmelCase__  (							_A      ,      _A      ,      _A			):






  """simple docstring"""


  a_		    =  matrix[:row] + matrix[row + 1 :]
  return [row[:column] + row[column + 1 :] for row in minor]






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  if len(_A			) == 1:
    return matrix[0][0]

  return sum(
      x * determinant(minor(_A      ,      0      ,      _A			)			) * (-1) ** i
      for i, x in enumerate(matrix[0]			)			)






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  determinant(_A			)
  if det == 0:
    return None

  a_		    =  [
      [determinant(minor(_A      ,      _A      ,      _A			)			) for j in range(len(_A			)			)]
      for i in range(len(_A			)			)
  ]

  a_		    =  [
      [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row]			)]
      for row in range(len(_A			)			)
  ]
  a_		    =  list(transpose(_A			)			)
  return scalar_multiply(_A      ,      1 / det			)






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  return not isinstance(_A      ,      _A			) and not isinstance(matrix[0]      ,      _A			)






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  return len(_A			), len(matrix[0]			)






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  a_		    =  _shape(_A			) + _shape(_A			)
  if shape[0] != shape[3] or shape[1] != shape[2]:
    a_		    =  (
        '''operands could not be broadcast together with shape '''
        f"({shape[0], shape[1]}), ({shape[2], shape[3]})"
    )
    raise ValueError(_A			)
  return (shape[0], shape[2]), (shape[1], shape[3])






def 				UpperCAmelCase__  (							):






  """simple docstring"""


  a_		    =  [[12, 10], [3, 9]]
  a_		    =  [[3, 4], [7, 4]]
  a_		    =  [[11, 12, 13, 14], [21, 22, 23, 24], [31, 32, 33, 34], [41, 42, 43, 44]]
  a_		    =  [[3, 0, 2], [2, 0, -2], [0, 1, 1]]
  print(f"Add Operation, {add(_A      ,      _A			) = } \n"			)
  print(
      f"Multiply Operation, {multiply(_A      ,      _A			) = } \n"      ,      )
  print(f"Identity: {identity(5			)}\n"			)
  print(f"Minor of {matrix_c} = {minor(_A      ,      1      ,      2			)} \n"			)
  print(f"Determinant of {matrix_b} = {determinant(_A			)} \n"			)
  print(f"Inverse of {matrix_d} = {inverse(_A			)}\n"			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()
