



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    (				snake_case      ):





					"""simple docstring"""

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

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


def 		_UpperCAmelCase    (				):





					"""simple docstring"""

					_lowerCAmelCase      =   VivitConfig()

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

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


def 		_UpperCAmelCase    (				):





					"""simple docstring"""

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


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

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

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

					else:
										raise Exception(F'Invalid number of dimesions: {np.ndim(snake_case      )}'      )


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

					return current.flatten()


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

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


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

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


def 		_UpperCAmelCase    (				snake_case     ,						snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   state_dict["""optimizer"""]["""target"""]["""Transformer"""][F'encoderblock_{i}']

					_lowerCAmelCase      =   F'encoder.layer.{i}.'
					_lowerCAmelCase      =   {
					    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    (				snake_case      ):





					"""simple docstring"""

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


def 		_UpperCAmelCase    (				snake_case     ,						snake_case=False      ):





					"""simple docstring"""

					_lowerCAmelCase      =   get_n_layers(snake_case      )

					_lowerCAmelCase      =   OrderedDict()

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

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

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

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

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

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


def 		_UpperCAmelCase    (				):





					"""simple docstring"""

					_lowerCAmelCase      =   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 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   """checkpoint"""

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

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

					_lowerCAmelCase      =   get_vivit_config()

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

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

					_lowerCAmelCase      =   get_processor()

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

					_lowerCAmelCase      =   model(**snake_case      )

					_lowerCAmelCase      =   torch.Size([1, 4_00]      )
					_lowerCAmelCase      =   torch.tensor([-1.0_543, 2.0_764, -0.2_104, 0.4_439, -0.9_658]      )

					assert outputs.logits.shape == expected_shape
					assert torch.allclose(outputs.logits[0, :5]     ,						snake_case     ,						atol=1E-4      ), outputs.logits[0, :5]

					model.save_pretrained(snake_case      )
					extractor.save_pretrained(snake_case      )


if __name__ == "__main__":
	A__     				=	argparse.ArgumentParser()

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

	A__     				=	parser.parse_args()
	convert(args.output_model_name)








from math import exp


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

					return 8 * x - 2 * exp(-x      )


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   lower_bound
					_lowerCAmelCase      =   upper_bound
					for _ in range(0     ,						snake_case      ):
										_lowerCAmelCase     ,     _lowerCAmelCase      =   xa, xa - (f(snake_case      ) * (xa - xa)) / (f(snake_case      ) - f(snake_case      ))
					return xa


if __name__ == "__main__":
	print(f"Example: {secant_method(1, 3, 2)}")



