"""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   _lowerCamelCase ( UpperCAmelCase__					)							->     Tuple:


   '''simple docstring'''


   a__     				=		'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=20_48					):
            f.write(UpperCAmelCase__					)





def   _lowerCamelCase ( )							->     VivitConfig:


   '''simple docstring'''


   a__     				=		VivitConfig()

   a__     				=		4_00
   a__     				=		'huggingface/label-files'
   a__     				=		'kinetics400-id2label.json'

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





def   _lowerCamelCase ( )							->     int:


   '''simple docstring'''


   a__     				=		hf_hub_download(
       repo_id='hf-internal-testing/spaghetti-video',filename='eating_spaghetti_32_frames.npy',repo_type='dataset'					)
   a__     				=		np.load(UpperCAmelCase__					)
   return list(UpperCAmelCase__					)





def   _lowerCamelCase ( UpperCAmelCase__					)							->     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   _lowerCamelCase ( UpperCAmelCase__					)							->     Union[str, Any]:


   '''simple docstring'''


   return current.flatten()





def   _lowerCamelCase ( UpperCAmelCase__					)							->     Dict:


   '''simple docstring'''


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





def   _lowerCamelCase ( UpperCAmelCase__					)							->     List[Any]:


   '''simple docstring'''


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





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__					)							->     Optional[int]:


   '''simple docstring'''


   a__     				=		state_dict['optimizer']['target']['Transformer'][f'''encoderblock_{i}''']

   a__     				=		f'''encoder.layer.{i}.'''
   a__     				=		{
       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 ( UpperCAmelCase__					)							->     Any:


   '''simple docstring'''


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





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__=False					)							->     Optional[Any]:


   '''simple docstring'''


   a__     				=		get_n_layers(UpperCAmelCase__					)

   a__     				=		OrderedDict()

   a__     				=		state_dict['optimizer']['target']['Transformer']['encoder_norm']['bias']
   a__     				=		state_dict['optimizer']['target']['Transformer']['encoder_norm']['scale']

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

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

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





def   _lowerCamelCase ( )							->     VivitImageProcessor:


   '''simple docstring'''


   a__     				=		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 ( UpperCAmelCase__					)							->     Optional[int]:


   '''simple docstring'''


   a__     				=		'checkpoint'

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

   a__     				=		restore_checkpoint(UpperCAmelCase__,UpperCAmelCase__					)
   a__     				=		transform_state(UpperCAmelCase__,classification_head=UpperCAmelCase__					)

   a__     				=		get_vivit_config()

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

   a__     				=		VivitForVideoClassification(UpperCAmelCase__					)
   model.load_state_dict(UpperCAmelCase__					)
   model.eval()

   a__     				=		get_processor()

   a__     				=		prepare_video()
   a__     				=		extractor(UpperCAmelCase__,return_tensors='pt'					)

   a__     				=		model(**UpperCAmelCase__					)

   a__     				=		torch.Size([1, 4_00]					)
   a__     				=		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__":
     __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)



"""simple docstring"""



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 def __init__(   self		:      Dict					,   _snake_case		:      List[str]					,   _snake_case		:      Any				)				->			Optional[Any]:




    '''simple docstring'''






    super().__init__()
    self.register_modules(unet=_snake_case					,   scheduler=_snake_case				)



 @torch.no_grad()
 def __call__(   self		:      int					,   _snake_case		:      int = 1					,   _snake_case		:      Optional[torch.Generator] = None					,   _snake_case		:      int = 50					,   _snake_case		:      Optional[str] = "pil"					,   _snake_case		:      bool = True					,   **_snake_case		:      str					,   )				->			Union[ImagePipelineOutput, Tuple]:




    '''simple docstring'''






    a__     				=		torch.randn(
        (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,   generator=_snake_case					,   )
    a__     				=		image.to(self.device				)

    # set step values
    self.scheduler.set_timesteps(_snake_case				)

    for t in self.progress_bar(self.scheduler.timesteps				):
       # 1. predict noise model_output
       a__     				=		self.unet(_snake_case					,   _snake_case				).sample

       # 2. predict previous mean of image x_t-1 and add variance depending on eta
       # eta corresponds to η in paper and should be between [0, 1]
       # do x_t -> x_t-1
       a__     				=		self.scheduler.step(_snake_case					,   _snake_case					,   _snake_case				).prev_sample

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

    if not return_dict:
       return (image,), "This is a local test"

    return ImagePipelineOutput(images=_snake_case				), "This is a local test"


