





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      __snake_case						(					_lowerCAmelCase : List[Any]	) ->						List[Any]:
   A_					:				List[Any]          =			"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=2048	):
            f.write(_lowerCAmelCase	)

def      __snake_case						(					) ->						VivitConfig:
   A_					:				Dict          =			VivitConfig()

   A_					:				Dict          =			400
   A_					:				Optional[int]          =			"huggingface/label-files"
   A_					:				List[Any]          =			"kinetics400-id2label.json"

   A_					:				Tuple          =			json.load(open(hf_hub_download(_lowerCAmelCase				,				_lowerCAmelCase				,				repo_type="dataset"	)				,				"r"	)	)
   A_					:				int          =			{int(_lowerCAmelCase	): v for k, v in idalabel.items()}
   A_					:				Any          =			idalabel
   A_					:				Optional[Any]          =			{v: k for k, v in idalabel.items()}
   return config

def      __snake_case						(					) ->						Union[str, Any]:
   A_					:				Tuple          =			hf_hub_download(
       repo_id="hf-internal-testing/spaghetti-video"				,				filename="eating_spaghetti_32_frames.npy"				,				repo_type="dataset"	)
   A_					:				int          =			np.load(_lowerCAmelCase	)
   return list(_lowerCAmelCase	)

def      __snake_case						(					_lowerCAmelCase : np.ndarray	) ->						str:
   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      __snake_case						(					_lowerCAmelCase : np.ndarray	) ->						List[str]:
   return current.flatten()

def      __snake_case						(					_lowerCAmelCase : np.ndarray	) ->						Union[str, Any]:
   return np.reshape(_lowerCAmelCase				,				(current.shape[0], current.shape[1] * current.shape[2])	).T

def      __snake_case						(					_lowerCAmelCase : np.ndarray	) ->						Tuple:
   return np.reshape(_lowerCAmelCase				,				(current.shape[0] * current.shape[1], current.shape[2])	).T

def      __snake_case						(					_lowerCAmelCase : Optional[int]				,				_lowerCAmelCase : List[str]	) ->						Optional[int]:
   A_					:				Any          =			state_dict["optimizer"]["target"]["Transformer"][f"encoderblock_{i}"]

   A_					:				Dict          =			f"encoder.layer.{i}."
   A_					:				Optional[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      __snake_case						(					_lowerCAmelCase : List[str]	) ->						List[Any]:
   return sum([1 if "encoderblock_" in k else 0 for k in state_dict["optimizer"]["target"]["Transformer"].keys()]	)

def      __snake_case						(					_lowerCAmelCase : int				,				_lowerCAmelCase : Union[str, Any]=False	) ->						Dict:
   A_					:				str          =			get_n_layers(_lowerCAmelCase	)

   A_					:				Optional[int]          =			OrderedDict()

   A_					:				List[str]          =			state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["bias"]
   A_					:				str          =			state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["scale"]

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

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

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

def      __snake_case						(					) ->						VivitImageProcessor:
   A_					:				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      __snake_case						(					_lowerCAmelCase : str	) ->						Union[str, Any]:
   A_					:				List[str]          =			"checkpoint"

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

   A_					:				Dict          =			restore_checkpoint(_lowerCAmelCase				,				_lowerCAmelCase	)
   A_					:				Tuple          =			transform_state(_lowerCAmelCase				,				classification_head=_lowerCAmelCase	)

   A_					:				Tuple          =			get_vivit_config()

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

   A_					:				Union[str, Any]          =			VivitForVideoClassification(_lowerCAmelCase	)
   model.load_state_dict(_lowerCAmelCase	)
   model.eval()

   A_					:				Any          =			get_processor()

   A_					:				int          =			prepare_video()
   A_					:				Tuple          =			extractor(_lowerCAmelCase				,				return_tensors="pt"	)

   A_					:				Any          =			model(**_lowerCAmelCase	)

   A_					:				List[Any]          =			torch.Size([1, 400]	)
   A_					:				Union[str, Any]          =			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]				,				_lowerCAmelCase				,				atol=1e-4	), outputs.logits[0, :5]

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


if __name__ == "__main__":
 _lowerCAmelCase							:      Any						=	argparse.ArgumentParser()

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

 _lowerCAmelCase							:      int						=	parser.parse_args()
 convert(args.output_model_name)







import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

_lowerCAmelCase							:      Union[str, Any]						=	0.25 + 0.0j
_lowerCAmelCase							:      str						=	-0.4 + 0.6j
_lowerCAmelCase							:      Optional[int]						=	-0.1 + 0.6_5_1j
_lowerCAmelCase							:      Union[str, Any]						=	-2.0
_lowerCAmelCase							:      str						=	56
_lowerCAmelCase							:      Optional[Any]						=	2.0
_lowerCAmelCase							:      Dict						=	666

def      __snake_case						(					_lowerCAmelCase : complex				,				_lowerCAmelCase : numpy.ndarray	) ->						numpy.ndarray:
   return numpy.exp(_lowerCAmelCase	) + c_parameter

def      __snake_case						(					_lowerCAmelCase : complex				,				_lowerCAmelCase : numpy.ndarray	) ->						numpy.ndarray:
   return z_values * z_values + c_parameter

def      __snake_case						(					_lowerCAmelCase : float				,				_lowerCAmelCase : int	) ->						numpy.ndarray:
   A_					:				Tuple          =			numpy.linspace(-window_size				,				_lowerCAmelCase				,				_lowerCAmelCase	)
   A_					:				Optional[int]          =			x.reshape((nb_pixels, 1)	)
   A_					:				str          =			numpy.linspace(-window_size				,				_lowerCAmelCase				,				_lowerCAmelCase	)
   A_					:				Tuple          =			y.reshape((1, nb_pixels)	)
   return x + 1.0j * y

def      __snake_case						(					_lowerCAmelCase : Callable[[Any, numpy.ndarray], numpy.ndarray]				,				_lowerCAmelCase : Any				,				_lowerCAmelCase : int				,				_lowerCAmelCase : numpy.ndarray				,				_lowerCAmelCase : float | None = None				,				) ->						numpy.ndarray:
   A_					:				List[Any]          =			z_a.astype("complex64"	)
   for _ in range(_lowerCAmelCase	):
      A_					:				List[Any]          =			eval_function(_lowerCAmelCase				,				_lowerCAmelCase	)
      if infinity is not None:
         numpy.nan_to_num(_lowerCAmelCase				,				copy=_lowerCAmelCase				,				nan=_lowerCAmelCase	)
         A_					:				List[Any]          =			infinity
   return z_n

def      __snake_case						(					_lowerCAmelCase : str				,				_lowerCAmelCase : Any				,				_lowerCAmelCase : float				,				_lowerCAmelCase : numpy.ndarray				,				) ->						None:
   A_					:				Optional[int]          =			(abs(_lowerCAmelCase	)).transpose()
   A_					:				Optional[int]          =			abs_z_final[::-1, :]
   pyplot.matshow(abs_z_final < escape_radius	)
   pyplot.title(f"Julia set of ${function_label}$, $c={function_params}$"	)
   pyplot.show()

def      __snake_case						(					) ->						None:
   warnings.filterwarnings(
       "ignore"				,				category=_lowerCAmelCase				,				message="overflow encountered in multiply"	)
   warnings.filterwarnings(
       "ignore"				,				category=_lowerCAmelCase				,				message="invalid value encountered in multiply"				,				)
   warnings.filterwarnings(
       "ignore"				,				category=_lowerCAmelCase				,				message="overflow encountered in absolute"	)
   warnings.filterwarnings(
       "ignore"				,				category=_lowerCAmelCase				,				message="overflow encountered in exp"	)


if __name__ == "__main__":
 _lowerCAmelCase							:      List[str]						=	prepare_grid(window_size, nb_pixels)

 ignore_overflow_warnings()  # See file header for explanations

 _lowerCAmelCase							:      str						=	24
 _lowerCAmelCase							:      str						=	2 * abs(c_cauliflower) + 1
 _lowerCAmelCase							:      Optional[Any]						=	iterate_function(
     eval_quadratic_polynomial,
     c_cauliflower,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results('''z^2+c''', c_cauliflower, escape_radius, z_final)

 _lowerCAmelCase							:      Optional[int]						=	64
 _lowerCAmelCase							:      List[Any]						=	2 * abs(c_polynomial_a) + 1
 _lowerCAmelCase							:      List[str]						=	iterate_function(
     eval_quadratic_polynomial,
     c_polynomial_a,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results('''z^2+c''', c_polynomial_a, escape_radius, z_final)

 _lowerCAmelCase							:      Tuple						=	161
 _lowerCAmelCase							:      int						=	2 * abs(c_polynomial_a) + 1
 _lowerCAmelCase							:      List[Any]						=	iterate_function(
     eval_quadratic_polynomial,
     c_polynomial_a,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results('''z^2+c''', c_polynomial_a, escape_radius, z_final)

 _lowerCAmelCase							:      List[str]						=	12
 _lowerCAmelCase							:      str						=	1_0000.0
 _lowerCAmelCase							:      Tuple						=	iterate_function(
     eval_exponential,
     c_exponential,
     nb_iterations,
     z_a + 2,
     infinity=1.0e10,
 )
 show_results('''e^z+c''', c_exponential, escape_radius, z_final)
