





"""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    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


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

		with open(_SCREAMING_SNAKE_CASE	,						"wb"     ) as f:
				with requests.get(_SCREAMING_SNAKE_CASE	,						stream=_SCREAMING_SNAKE_CASE     ) as req:
						for chunk in req.iter_content(chunk_size=2_048     ):
								f.write(_SCREAMING_SNAKE_CASE     )


def    a__       (       ):




		"""simple docstring"""


		UpperCamelCase =						VivitConfig()

		UpperCamelCase =						400
		UpperCamelCase =						"huggingface/label-files"
		UpperCamelCase =						"kinetics400-id2label.json"

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


def    a__       (       ):




		"""simple docstring"""


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


def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


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

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

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


def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		return current.flatten()


def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


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


def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


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


def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		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    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


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


def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE=False     ):




		"""simple docstring"""


		UpperCamelCase =						get_n_layers(_SCREAMING_SNAKE_CASE     )

		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(_SCREAMING_SNAKE_CASE     ):
				new_state.update(transform_state_encoder_block(_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     )     )

		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(_SCREAMING_SNAKE_CASE     ) for k, v in new_state.items()}


def    a__       (       ):




		"""simple docstring"""


		UpperCamelCase =						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    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase =						"checkpoint"

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

		UpperCamelCase =						restore_checkpoint(_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     )
		UpperCamelCase =						transform_state(_SCREAMING_SNAKE_CASE	,						classification_head=_SCREAMING_SNAKE_CASE     )

		UpperCamelCase =						get_vivit_config()

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

		UpperCamelCase =						VivitForVideoClassification(_SCREAMING_SNAKE_CASE     )
		model.load_state_dict(_SCREAMING_SNAKE_CASE     )
		model.eval()

		UpperCamelCase =						get_processor()

		UpperCamelCase =						prepare_video()
		UpperCamelCase =						extractor(_SCREAMING_SNAKE_CASE	,						return_tensors="pt"     )

		UpperCamelCase =						model(**_SCREAMING_SNAKE_CASE     )

		UpperCamelCase =						torch.Size([1, 400]     )
		UpperCamelCase =						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]	,						_SCREAMING_SNAKE_CASE	,						atol=1e-4     ), outputs.logits[0, :5]

		model.save_pretrained(_SCREAMING_SNAKE_CASE     )
		extractor.save_pretrained(_SCREAMING_SNAKE_CASE     )


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

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

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













"""simple docstring"""





import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize





class 							_lowerCamelCase   :


						def __init__(self ,				__a ,				__a=sys.maxsize		)			-> Tuple:
								UpperCamelCase =						"bilinear"
								UpperCamelCase =						max_size
								UpperCamelCase =						short_edge_length




						def __call__(self ,				__a		)			-> Dict:
								UpperCamelCase =						[]
								for img in imgs:
										UpperCamelCase ,					UpperCamelCase =						img.shape[:2]
										# later: provide list and randomly choose index for resize
										UpperCamelCase =						np.random.randint(self.short_edge_length[0] ,				self.short_edge_length[1] + 1		)
										if size == 0:
												return img
										UpperCamelCase =						size * 1.0 / min(__a ,				__a		)
										if h < w:
												UpperCamelCase ,					UpperCamelCase =						size, scale * w
										else:
												UpperCamelCase ,					UpperCamelCase =						scale * h, size
										if max(__a ,				__a		) > self.max_size:
												UpperCamelCase =						self.max_size * 1.0 / max(__a ,				__a		)
												UpperCamelCase =						newh * scale
												UpperCamelCase =						neww * scale
										UpperCamelCase =						int(neww + 0.5		)
										UpperCamelCase =						int(newh + 0.5		)

										if img.dtype == np.uinta:
												UpperCamelCase =						Image.fromarray(__a		)
												UpperCamelCase =						pil_image.resize((neww, newh) ,				PILImageResampling.BILINEAR		)
												UpperCamelCase =						np.asarray(__a		)
										else:
												UpperCamelCase =						img.permute(2 ,				0 ,				1		).unsqueeze(0		)  # 3, 0, 1)  # hw(c) -> nchw
												UpperCamelCase =						nn.functional.interpolate(
												    __a ,				(newh, neww) ,				mode=self.interp_method ,				align_corners=__a		).squeeze(0		)
										img_augs.append(__a		)

								return img_augs





class 							_lowerCamelCase   :


						def __init__(self ,				__a		)			-> Any:
								UpperCamelCase =						ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] ,				cfg.INPUT.MAX_SIZE_TEST		)
								UpperCamelCase =						cfg.INPUT.FORMAT
								UpperCamelCase =						cfg.SIZE_DIVISIBILITY
								UpperCamelCase =						cfg.PAD_VALUE
								UpperCamelCase =						cfg.INPUT.MAX_SIZE_TEST
								UpperCamelCase =						cfg.MODEL.DEVICE
								UpperCamelCase =						torch.tensor(cfg.MODEL.PIXEL_STD		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		) ,				1 ,				1		)
								UpperCamelCase =						torch.tensor(cfg.MODEL.PIXEL_MEAN		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		) ,				1 ,				1		)
								UpperCamelCase =						lambda __a		: (x - self.pixel_mean) / self.pixel_std


						def      snake_case_ (self ,				__a		)			-> List[Any]:
								UpperCamelCase =						tuple(max(__a		) for s in zip(*[img.shape for img in images]		)		)
								UpperCamelCase =						[im.shape[-2:] for im in images]
								UpperCamelCase =						[
								    nn.functional.pad(
								        __a ,				[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] ,				value=self.pad_value ,				)
								    for size, im in zip(__a ,				__a		)
								]

								return torch.stack(__a		), torch.tensor(__a		)




						def __call__(self ,				__a ,				__a=False		)			-> Optional[int]:
								with torch.no_grad():
										if not isinstance(__a ,				__a		):
												UpperCamelCase =						[images]
										if single_image:
												assert len(__a		) == 1
										for i in range(len(__a		)		):
												if isinstance(images[i] ,				torch.Tensor		):
														images.insert(__a ,				images.pop(__a		).to(self.device		).float()		)
												elif not isinstance(images[i] ,				torch.Tensor		):
														images.insert(
														    __a ,				torch.as_tensor(img_tensorize(images.pop(__a		) ,				input_format=self.input_format		)		)
														    .to(self.device		)
														    .float() ,				)
            # resize smallest edge
										UpperCamelCase =						torch.tensor([im.shape[:2] for im in images]		)
										UpperCamelCase =						self.aug(__a		)
										# transpose images and convert to torch tensors
										# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
										# now normalize before pad to avoid useless arithmetic
										UpperCamelCase =						[self.normalizer(__a		) for x in images]
										# now pad them to do the following operations
										UpperCamelCase ,					UpperCamelCase =						self.pad(__a		)
										# Normalize

										if self.size_divisibility > 0:
												raise NotImplementedError()
										# pad
										UpperCamelCase =						torch.true_divide(__a ,				__a		)
										if single_image:
												return images[0], sizes[0], scales_yx[0]
										else:
												return images, sizes, scales_yx



def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		boxes[:, 0::2] *= scale_yx[:, 1]
		boxes[:, 1::2] *= scale_yx[:, 0]
		return boxes






def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		assert torch.isfinite(_SCREAMING_SNAKE_CASE     ).all(), "Box tensor contains infinite or NaN!"
		UpperCamelCase ,					UpperCamelCase =						box_size
		tensor[:, 0].clamp_(min=0	,						max=_SCREAMING_SNAKE_CASE     )
		tensor[:, 1].clamp_(min=0	,						max=_SCREAMING_SNAKE_CASE     )
		tensor[:, 2].clamp_(min=0	,						max=_SCREAMING_SNAKE_CASE     )
		tensor[:, 3].clamp_(min=0	,						max=_SCREAMING_SNAKE_CASE     )






