



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  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_			= "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=20_48):
																				f.write(_lowerCAmelCase)



def  _lowerCAmelCase     ():
					UpperCamelCase_			= VivitConfig()

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

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



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



def  _lowerCAmelCase     (_lowerCAmelCase):
					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  _lowerCAmelCase     (_lowerCAmelCase):
					return current.flatten()



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



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



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



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase=False):
					UpperCamelCase_			= get_n_layers(_lowerCAmelCase)

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

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



def  _lowerCAmelCase     ():
					UpperCamelCase_			= 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  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_			= "checkpoint"

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

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

					UpperCamelCase_			= get_vivit_config()

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

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

					UpperCamelCase_			= get_processor()

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

					UpperCamelCase_			= model(**_lowerCAmelCase)

					UpperCamelCase_			= torch.Size([1, 4_00])
					UpperCamelCase_			= 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] ,						_lowerCAmelCase ,						atol=1e-4), outputs.logits[0, :5]

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


if __name__ == "__main__":
	UpperCAmelCase :				Optional[int]							=argparse.ArgumentParser()

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

	UpperCAmelCase :				Dict							=parser.parse_args()
	convert(args.output_model_name)




'''simple docstring'''


import torch
from torch import autograd






class 						lowerCAmelCase__      (      autograd.Function						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  torch.tensor						,	lowerCamelCase__	:  float						,	lowerCamelCase__	:  bool			) ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[int]             =   inputs.numel()
					_UpperCAmelCase		:							Any             =   int(0.0_0_5 * nb_elems			) + 1
					if sigmoid:
							_UpperCAmelCase		:							List[str]             =   (torch.sigmoid(lowerCamelCase__			) > threshold).type(inputs.type()			)
					else:
							_UpperCAmelCase		:							List[Any]             =   (inputs > threshold).type(inputs.type()			)
					if mask.sum() < nb_min:
							# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
							_UpperCAmelCase		:							str             =   inputs.flatten().kthvalue(max(nb_elems - nb_min						,	1			)			).values
							_UpperCAmelCase		:							str             =   (inputs > k_threshold).type(inputs.type()			)
					return mask






			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  List[Any]						,	lowerCamelCase__	:  Optional[int]			) ->Dict:



					'''simple docstring'''







					return gradOutput, None, None






class 						lowerCAmelCase__      (      autograd.Function						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  torch.tensor						,	lowerCamelCase__	:  float			) ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[int]             =   inputs.clone()
					_UpperCAmelCase       ,     _UpperCAmelCase		:							Tuple             =   inputs.flatten().sort(descending=lowerCamelCase__			)
					_UpperCAmelCase		:							Optional[int]             =   int(threshold * inputs.numel()			)

					# flat_out and mask access the same memory.
					_UpperCAmelCase		:							str             =   mask.flatten()
					_UpperCAmelCase		:							Optional[int]             =   0
					_UpperCAmelCase		:							Optional[Any]             =   1
					return mask






			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  Any			) ->Union[str, Any]:



					'''simple docstring'''







					return gradOutput, None






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  torch.tensor						,	lowerCamelCase__	:  float			) ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:							List[Any]             =   inputs.clone()
					_UpperCAmelCase       ,     _UpperCAmelCase		:							Optional[Any]             =   inputs.abs().flatten().sort(descending=lowerCamelCase__			)
					_UpperCAmelCase		:							int             =   int(threshold * inputs.numel()			)

					# flat_out and mask access the same memory.
					_UpperCAmelCase		:							int             =   mask.flatten()
					_UpperCAmelCase		:							List[Any]             =   0
					_UpperCAmelCase		:							int             =   1
					return mask



