






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      (			UpperCamelCase_				:    Dict						)     ->      Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  'https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'

						with open(UpperCamelCase_			,				'wb'						) as f:
												with requests.get(UpperCamelCase_			,				stream=UpperCamelCase_						) as req:
																		for chunk in req.iter_content(chunk_size=2048						):
																								f.write(UpperCamelCase_						)







def 		_UpperCamelCase      (			)     ->      Any:

						"""simple docstring"""



						lowerCAmelCase__			  =  VivitConfig()

						lowerCAmelCase__			  =  400
						lowerCAmelCase__			  =  'huggingface/label-files'
						lowerCAmelCase__			  =  'kinetics400-id2label.json'

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







def 		_UpperCamelCase      (			)     ->      int:

						"""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(UpperCamelCase_						)
						return list(UpperCamelCase_						)







def 		_UpperCamelCase      (			UpperCamelCase_				:    Dict						)     ->      Union[str, Any]:

						"""simple docstring"""



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

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

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







def 		_UpperCamelCase      (			UpperCamelCase_				:    str						)     ->      Tuple:

						"""simple docstring"""



						return current.flatten()







def 		_UpperCamelCase      (			UpperCamelCase_				:    Optional[Any]						)     ->      Optional[int]:

						"""simple docstring"""



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







def 		_UpperCamelCase      (			UpperCamelCase_				:    Tuple						)     ->      List[str]:

						"""simple docstring"""



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







def 		_UpperCamelCase      (			UpperCamelCase_				:    Optional[Any]			,				UpperCamelCase_				:    List[str]						)     ->      Union[str, Any]:

						"""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      (			UpperCamelCase_				:    Tuple						)     ->      List[Any]:

						"""simple docstring"""



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







def 		_UpperCamelCase      (			UpperCamelCase_				:    int			,				UpperCamelCase_				:    Optional[Any]=False						)     ->      Any:

						"""simple docstring"""



						lowerCAmelCase__			  =  get_n_layers(UpperCamelCase_						)

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

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







def 		_UpperCamelCase      (			)     ->      int:

						"""simple docstring"""



						lowerCAmelCase__			  =  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 		_UpperCamelCase      (			UpperCamelCase_				:    List[str]						)     ->      Any:

						"""simple docstring"""



						lowerCAmelCase__			  =  'checkpoint'

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

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

						lowerCAmelCase__			  =  get_vivit_config()

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

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

						lowerCAmelCase__			  =  get_processor()

						lowerCAmelCase__			  =  prepare_video()
						lowerCAmelCase__			  =  extractor(UpperCamelCase_			,				return_tensors='pt'						)

						lowerCAmelCase__			  =  model(**UpperCamelCase_						)

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

						model.save_pretrained(UpperCamelCase_						)
						extractor.save_pretrained(UpperCamelCase_						)


if __name__ == "__main__":
					__snake_case			:			List[Any]			   =   argparse.ArgumentParser()

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

					__snake_case			:			int			   =   parser.parse_args()
					convert(args.output_model_name)












from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 					lowerCAmelCase							(   lowercase_					):
  __lowerCamelCase		=			42
  __lowerCamelCase		=			42







  def __init__(			self  :List[Any]    ,							_lowercase  :UNetaDModel    ,							_lowercase  :PNDMScheduler					):






     '''simple docstring'''





     super().__init__()

     lowercase__    =  PNDMScheduler.from_config(scheduler.config					)

     self.register_modules(unet=_lowercase    ,							scheduler=_lowercase					)







  @torch.no_grad()
  def __call__(			self  :Union[str, Any]    ,							_lowercase  :int = 1    ,							_lowercase  :int = 50    ,							_lowercase  :Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,							_lowercase  :Optional[str] = "pil"    ,							_lowercase  :bool = True    ,							**_lowercase  :List[Any]    ,							):






     '''simple docstring'''





     lowercase__    =  randn_tensor(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,							generator=_lowercase    ,							device=self.device    ,							)

     self.scheduler.set_timesteps(_lowercase					)
     for t in self.progress_bar(self.scheduler.timesteps					):
        lowercase__    =  self.unet(_lowercase    ,							_lowercase					).sample

        lowercase__    =  self.scheduler.step(_lowercase    ,							_lowercase    ,							_lowercase					).prev_sample

     lowercase__    =  (image / 2 + 0.5).clamp(0    ,							1					)
     lowercase__    =  image.cpu().permute(0    ,							2    ,							3    ,							1					).numpy()
     if output_type == "pil":
        lowercase__    =  self.numpy_to_pil(_lowercase					)

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=_lowercase					)






