



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     (_lowerCAmelCase):
					UpperCamelCase_			= "https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint"

					with open(_lowerCAmelCase ,						"wb") as f:
										with requests.get(_lowerCAmelCase ,						stream=_lowerCAmelCase) as req:
															for chunk in req.iter_content(chunk_size=20_48):
																				f.write(_lowerCAmelCase)



def  _lowerCAmelCase     ():
					UpperCamelCase_			= VivitConfig()

					UpperCamelCase_			= 4_00
					UpperCamelCase_			= "huggingface/label-files"
					UpperCamelCase_			= "kinetics400-id2label.json"

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



def  _lowerCAmelCase     ():
					UpperCamelCase_			= hf_hub_download(
					    repo_id="hf-internal-testing/spaghetti-video" ,						filename="eating_spaghetti_32_frames.npy" ,						repo_type="dataset")
					UpperCamelCase_			= np.load(_lowerCAmelCase)
					return list(_lowerCAmelCase)



def  _lowerCAmelCase     (_lowerCAmelCase):
					if np.ndim(_lowerCAmelCase) == 2:
										return transform_attention_bias(_lowerCAmelCase)

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

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



def  _lowerCAmelCase     (_lowerCAmelCase):
					return current.flatten()



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



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



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase):
					UpperCamelCase_			= state_dict["optimizer"]["target"]["Transformer"][f"""encoderblock_{i}"""]

					UpperCamelCase_			= f"""encoder.layer.{i}."""
					UpperCamelCase_			= {
					    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     (_lowerCAmelCase):
					return sum([1 if "encoderblock_" in k else 0 for k in state_dict["optimizer"]["target"]["Transformer"].keys()])



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase=False):
					UpperCamelCase_			= get_n_layers(_lowerCAmelCase)

					UpperCamelCase_			= OrderedDict()

					UpperCamelCase_			= state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["bias"]
					UpperCamelCase_			= state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["scale"]

					UpperCamelCase_			= np.transpose(
					    state_dict["optimizer"]["target"]["embedding"]["kernel"] ,						(4, 3, 0, 1, 2))
					UpperCamelCase_			= state_dict["optimizer"]["target"]["embedding"]["bias"]

					UpperCamelCase_			= state_dict["optimizer"]["target"]["cls"]
					UpperCamelCase_			= state_dict["optimizer"]["target"]["Transformer"]["posembed_input"][
					    "pos_embedding"
					]

					for i in range(_lowerCAmelCase):
										new_state.update(transform_state_encoder_block(_lowerCAmelCase ,						_lowerCAmelCase))

					if classification_head:
										UpperCamelCase_			= {"vivit." + k: v for k, v in new_state.items()}
										UpperCamelCase_			= np.transpose(state_dict["optimizer"]["target"]["output_projection"]["kernel"])
										UpperCamelCase_			= np.transpose(state_dict["optimizer"]["target"]["output_projection"]["bias"])

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



def  _lowerCAmelCase     ():
					UpperCamelCase_			= 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     (_lowerCAmelCase):
					UpperCamelCase_			= "checkpoint"

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

					UpperCamelCase_			= restore_checkpoint(_lowerCAmelCase ,						_lowerCAmelCase)
					UpperCamelCase_			= transform_state(_lowerCAmelCase ,						classification_head=_lowerCAmelCase)

					UpperCamelCase_			= get_vivit_config()

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

					UpperCamelCase_			= VivitForVideoClassification(_lowerCAmelCase)
					model.load_state_dict(_lowerCAmelCase)
					model.eval()

					UpperCamelCase_			= get_processor()

					UpperCamelCase_			= prepare_video()
					UpperCamelCase_			= extractor(_lowerCAmelCase ,						return_tensors="pt")

					UpperCamelCase_			= model(**_lowerCAmelCase)

					UpperCamelCase_			= torch.Size([1, 4_00])
					UpperCamelCase_			= 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] ,						_lowerCAmelCase ,						atol=1e-4), outputs.logits[0, :5]

					model.save_pretrained(_lowerCAmelCase)
					extractor.save_pretrained(_lowerCAmelCase)


if __name__ == "__main__":
	UpperCAmelCase :				Optional[int]							=argparse.ArgumentParser()

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

	UpperCAmelCase :				Dict							=parser.parse_args()
	convert(args.output_model_name)



import functools
from math import sqrt
from time import time



def  snake_case_(_UpperCamelCase					,			*_UpperCamelCase					,			**_UpperCamelCase	)				->      str:



  """simple docstring"""






  _snake_case		=				time()
  _snake_case		=				func(*_UpperCamelCase					,			**_UpperCamelCase	)
  _snake_case		=				time()
  if int(end - start	) > 0:
    print(F"""{func.__name__} runtime: {(end - start):0.4f} s"""	)
  else:
    print(F"""{func.__name__} runtime: {(end - start) * 1_000:0.4f} ms"""	)
  return output



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  if n < 0:
    raise Exception('''n is negative'''	)
  if n == 0:
    return [0]
  _snake_case		=				[0, 1]
  for _ in range(n - 1	):
    fib.append(fib[-1] + fib[-2]	)
  return fib



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  def fib_recursive_term(_UpperCamelCase	) -> int:
    if i < 0:
      raise Exception('''n is negative'''	)
    if i < 2:
      return i
    return fib_recursive_term(i - 1	) + fib_recursive_term(i - 2	)

  if n < 0:
    raise Exception('''n is negative'''	)
  return [fib_recursive_term(_UpperCamelCase	) for i in range(n + 1	)]



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  @functools.cache
  def fib_recursive_term(_UpperCamelCase	) -> int:
    if i < 0:
      raise Exception('''n is negative'''	)
    if i < 2:
      return i
    return fib_recursive_term(i - 1	) + fib_recursive_term(i - 2	)

  if n < 0:
    raise Exception('''n is negative'''	)
  return [fib_recursive_term(_UpperCamelCase	) for i in range(n + 1	)]



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  if n < 0:
    raise Exception('''n is negative'''	)
  # Cache must be outside recursuive function
  # other it will reset every time it calls itself.
  _snake_case		=				{0: 0, 1: 1, 2: 1}  # Prefilled cache

  def rec_fn_memoized(_UpperCamelCase	) -> int:
    if num in cache:
      return cache[num]

    _snake_case		=				rec_fn_memoized(num - 1	) + rec_fn_memoized(num - 2	)
    _snake_case		=				value
    return value

  return [rec_fn_memoized(_UpperCamelCase	) for i in range(n + 1	)]



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  if n < 0:
    raise Exception('''n is negative'''	)
  if n >= 1_475:
    raise Exception('''n is too large'''	)
  _snake_case		=				sqrt(5	)
  _snake_case		=				(1 + sqrt_a) / 2
  return [round(phi**i / sqrt_a	) for i in range(n + 1	)]


if __name__ == "__main__":
 __A     						=      30
 time_func(fib_iterative, num)
 time_func(fib_recursive, num)  # Around 3s runtime
 time_func(fib_recursive_cached, num)  # Around 0ms runtime
 time_func(fib_memoization, num)
 time_func(fib_binet, num)

