


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     __magic_name__  (      lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							Optional[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     __magic_name__  (      ):
	'''simple docstring'''


	lowerCamelCase_			:							Optional[Any]      =       VivitConfig()

	lowerCamelCase_			:							int      =       400
	lowerCamelCase_			:							str      =       "huggingface/label-files"
	lowerCamelCase_			:							Optional[Any]      =       "kinetics400-id2label.json"

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







def     __magic_name__  (      ):
	'''simple docstring'''


	lowerCamelCase_			:							Optional[Any]      =       hf_hub_download(
	    repo_id="hf-internal-testing/spaghetti-video"      ,					filename="eating_spaghetti_32_frames.npy"      ,					repo_type="dataset")
	lowerCamelCase_			:							Any      =       np.load(lowerCAmelCase_)
	return list(lowerCAmelCase_)







def     __magic_name__  (      lowerCAmelCase_):
	'''simple docstring'''


	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     __magic_name__  (      lowerCAmelCase_):
	'''simple docstring'''


	return current.flatten()







def     __magic_name__  (      lowerCAmelCase_):
	'''simple docstring'''


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







def     __magic_name__  (      lowerCAmelCase_):
	'''simple docstring'''


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







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							str      =       state_dict["optimizer"]["target"]["Transformer"][F"""encoderblock_{i}"""]

	lowerCamelCase_			:							Optional[Any]      =       F"""encoder.layer.{i}."""
	lowerCamelCase_			:							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     __magic_name__  (      lowerCAmelCase_):
	'''simple docstring'''


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







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_=False):
	'''simple docstring'''


	lowerCamelCase_			:							Any      =       get_n_layers(lowerCAmelCase_)

	lowerCamelCase_			:							Any      =       OrderedDict()

	lowerCamelCase_			:							Union[str, Any]      =       state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["bias"]
	lowerCamelCase_			:							int      =       state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["scale"]

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

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

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







def     __magic_name__  (      ):
	'''simple docstring'''


	lowerCamelCase_			:							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     __magic_name__  (      lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							int      =       "checkpoint"

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

	lowerCamelCase_			:							Any      =       restore_checkpoint(lowerCAmelCase_      ,					lowerCAmelCase_)
	lowerCamelCase_			:							List[Any]      =       transform_state(lowerCAmelCase_      ,					classification_head=lowerCAmelCase_)

	lowerCamelCase_			:							List[str]      =       get_vivit_config()

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

	lowerCamelCase_			:							List[Any]      =       VivitForVideoClassification(lowerCAmelCase_)
	model.load_state_dict(lowerCAmelCase_)
	model.eval()

	lowerCamelCase_			:							Any      =       get_processor()

	lowerCamelCase_			:							Dict      =       prepare_video()
	lowerCamelCase_			:							List[Any]      =       extractor(lowerCAmelCase_      ,					return_tensors="pt")

	lowerCamelCase_			:							int      =       model(**lowerCAmelCase_)

	lowerCamelCase_			:							Union[str, Any]      =       torch.Size([1, 400])
	lowerCamelCase_			:							str      =       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__":
							__magic_name__       					=      argparse.ArgumentParser()

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

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





import math
import os

import cva
import numpy as np

__magic_name__       					=      2_55.0







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							List[Any]      =       np.mean((original - contrast) ** 2)
	if mse == 0:
		return 100

	return 20 * math.logaa(PIXEL_MAX / math.sqrt(lowerCAmelCase_))







def     __magic_name__  (      ):
	'''simple docstring'''


	lowerCamelCase_			:							List[str]      =       os.path.dirname(os.path.realpath(lowerCAmelCase_))
	# Loading images (original image and compressed image)
	lowerCamelCase_			:							Optional[int]      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/original_image.png"))
	lowerCamelCase_			:							List[str]      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/compressed_image.png")      ,					1)

	lowerCamelCase_			:							str      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/PSNR-example-base.png"))
	lowerCamelCase_			:							int      =       cva.imread(
	    os.path.join(lowerCAmelCase_      ,					"image_data/PSNR-example-comp-10.jpg")      ,					1)

	# Value expected: 29.73dB
	print("-- First Test --")
	print(F"""PSNR value is {peak_signal_to_noise_ratio(lowerCAmelCase_      ,					lowerCAmelCase_)} dB""")

	# # Value expected: 31.53dB (Wikipedia Example)
	print("\n-- Second Test --")
	print(F"""PSNR value is {peak_signal_to_noise_ratio(lowerCAmelCase_      ,					lowerCAmelCase_)} dB""")


if __name__ == "__main__":
							main()

