






'''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 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase							):


   """simple docstring"""




   lowercase_		:      Dict          =       "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 						__SCREAMING_SNAKE_CASE   (					):


   """simple docstring"""




   lowercase_		:      Optional[Any]          =       VivitConfig()

   lowercase_		:      Optional[int]          =       400
   lowercase_		:      str          =       "huggingface/label-files"
   lowercase_		:      Optional[Any]          =       "kinetics400-id2label.json"

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




def 						__SCREAMING_SNAKE_CASE   (					):


   """simple docstring"""




   lowercase_		:      Dict          =       hf_hub_download(
       repo_id="hf-internal-testing/spaghetti-video"  ,						filename="eating_spaghetti_32_frames.npy"  ,						repo_type="dataset"							)
   lowercase_		:      Tuple          =       np.load(_UpperCamelCase							)
   return list(_UpperCamelCase							)




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase							):


   """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 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase							):


   """simple docstring"""




   return current.flatten()




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase							):


   """simple docstring"""




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




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase							):


   """simple docstring"""




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




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase							):


   """simple docstring"""




   lowercase_		:      int          =       state_dict["optimizer"]["target"]["Transformer"][F"""encoderblock_{i}"""]

   lowercase_		:      int          =       F"""encoder.layer.{i}."""
   lowercase_		:      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 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase							):


   """simple docstring"""




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




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase=False							):


   """simple docstring"""




   lowercase_		:      Optional[int]          =       get_n_layers(_UpperCamelCase							)

   lowercase_		:      int          =       OrderedDict()

   lowercase_		:      Optional[int]          =       state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["bias"]
   lowercase_		:      Optional[Any]          =       state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["scale"]

   lowercase_		:      Union[str, Any]          =       np.transpose(
       state_dict["optimizer"]["target"]["embedding"]["kernel"]  ,						(4, 3, 0, 1, 2)							)
   lowercase_		:      str          =       state_dict["optimizer"]["target"]["embedding"]["bias"]

   lowercase_		:      Optional[int]          =       state_dict["optimizer"]["target"]["cls"]
   lowercase_		:      Optional[int]          =       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:
      lowercase_		:      List[str]          =       {"vivit." + k: v for k, v in new_state.items()}
      lowercase_		:      Dict          =       np.transpose(state_dict["optimizer"]["target"]["output_projection"]["kernel"]							)
      lowercase_		:      int          =       np.transpose(state_dict["optimizer"]["target"]["output_projection"]["bias"]							)

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




def 						__SCREAMING_SNAKE_CASE   (					):


   """simple docstring"""




   lowercase_		:      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 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase							):


   """simple docstring"""




   lowercase_		:      Optional[Any]          =       "checkpoint"

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

   lowercase_		:      Union[str, Any]          =       restore_checkpoint(_UpperCamelCase  ,						_UpperCamelCase							)
   lowercase_		:      List[str]          =       transform_state(_UpperCamelCase  ,						classification_head=_UpperCamelCase							)

   lowercase_		:      Tuple          =       get_vivit_config()

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

   lowercase_		:      str          =       VivitForVideoClassification(_UpperCamelCase							)
   model.load_state_dict(_UpperCamelCase							)
   model.eval()

   lowercase_		:      Optional[Any]          =       get_processor()

   lowercase_		:      Optional[int]          =       prepare_video()
   lowercase_		:      str          =       extractor(_UpperCamelCase  ,						return_tensors="pt"							)

   lowercase_		:      Dict          =       model(**_UpperCamelCase							)

   lowercase_		:      Tuple          =       torch.Size([1, 400]							)
   lowercase_		:      Dict          =       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__":
 UpperCamelCase__	=     argparse.ArgumentParser()

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

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












'''simple docstring'''
def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase							):


   """simple docstring"""




   sequence.append(_UpperCamelCase							)

   lowercase_		:      Union[str, Any]          =       0
   while sequence[index] != target:
      index += 1

   sequence.pop()

   if index == len(_UpperCamelCase							):
      return None

   return index


if __name__ == "__main__":
 UpperCamelCase__	=     input('Enter numbers separated by comma:\n').strip()
 UpperCamelCase__	=     [int(item) for item in user_input.split(',')]

 UpperCamelCase__	=     input('Enter a single number to be found in the list:\n')
 UpperCamelCase__	=     int(target_input)
 UpperCamelCase__	=     sentinel_linear_search(sequence, target)
 if result is not None:
  print(f"""{target} found at positions: {result}""")
 else:
  print('Not found')




