




"""simple docstring"""

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 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	A_	:					Any						 =		'''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=2048						):
				f.write(a_						)

def 		lowercase_   (		):




	"""simple docstring"""






	A_	:					List[Any]						 =		VivitConfig()

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

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

def 		lowercase_   (		):




	"""simple docstring"""






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

def 		lowercase_   (		_UpperCAmelCase						):




	"""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 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	return current.flatten()

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






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

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






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

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	A_	:					int						 =		state_dict['''optimizer''']['''target''']['''Transformer'''][f"""encoderblock_{i}"""]

	A_	:					Dict						 =		f"""encoder.layer.{i}."""
	A_	:					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 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






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

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase=False						):




	"""simple docstring"""






	A_	:					Tuple						 =		get_n_layers(a_						)

	A_	:					Optional[Any]						 =		OrderedDict()

	A_	:					Optional[int]						 =		state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''bias''']
	A_	:					Optional[Any]						 =		state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''scale''']

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

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

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

def 		lowercase_   (		):




	"""simple docstring"""






	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 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	A_	:					List[Any]						 =		'''checkpoint'''

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

	A_	:					Optional[Any]						 =		restore_checkpoint(a_	,   a_						)
	A_	:					Any						 =		transform_state(a_	,   classification_head=a_						)

	A_	:					Optional[int]						 =		get_vivit_config()

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

	A_	:					Dict						 =		VivitForVideoClassification(a_						)
	model.load_state_dict(a_						)
	model.eval()

	A_	:					Optional[int]						 =		get_processor()

	A_	:					Dict						 =		prepare_video()
	A_	:					Optional[int]						 =		extractor(a_	,   return_tensors='''pt'''						)

	A_	:					int						 =		model(**a_						)

	A_	:					List[Any]						 =		torch.Size([1, 400]						)
	A_	:					List[Any]						 =		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]	,   a_	,   atol=1E-4						), outputs.logits[0, :5]

	model.save_pretrained(a_						)
	extractor.save_pretrained(a_						)


if __name__ == "__main__":
		_lowerCamelCase	:      List[str]   =							argparse.ArgumentParser()

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

		_lowerCamelCase	:      Optional[Any]   =							parser.parse_args()
		convert(args.output_model_name)







"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


SCREAMING_SNAKE_CASE_  					= logging.get_logger(__name__)






class      __a      (       lowerCAmelCase_      ):







       def __init__(       self				,*lowerCamelCase_				,**lowerCamelCase_		):



          '''simple docstring'''

          warnings.warn(
              "The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
              " Please use MobileNetV1ImageProcessor instead."				,lowerCamelCase_				,)
          super().__init__(*lowerCamelCase_				,**lowerCamelCase_		)

