


'''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     :     Tuple  )  ->      Tuple:
       _a   :			str					 ="""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_ (  )  ->      VivitConfig:
       _a   :			Tuple					 =VivitConfig()

       _a   :			List[str]					 =400
       _a   :			Tuple					 ="""huggingface/label-files"""
       _a   :			Optional[Any]					 ="""kinetics400-id2label.json"""

       _a   :			Union[str, Any]					 =json.load(open(hf_hub_download(_UpperCAmelCase   ,_UpperCAmelCase   ,repo_type="""dataset"""  )   ,"""r"""  )  )
       _a   :			Optional[Any]					 ={int(_UpperCAmelCase  ): v for k, v in idalabel.items()}
       _a   :			int					 =idalabel
       _a   :			List[str]					 ={v: k for k, v in idalabel.items()}
       return config


def      SCREAMING_SNAKE_CASE_ (  )  ->      int:
       _a   :			Tuple					 =hf_hub_download(
           repo_id="""hf-internal-testing/spaghetti-video"""   ,filename="""eating_spaghetti_32_frames.npy"""   ,repo_type="""dataset"""  )
       _a   :			str					 =np.load(_UpperCAmelCase  )
       return list(_UpperCAmelCase  )


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     np.ndarray  )  ->      Optional[Any]:
       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     :     np.ndarray  )  ->      Optional[int]:
       return current.flatten()


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     np.ndarray  )  ->      Optional[Any]:
       return np.reshape(_UpperCAmelCase   ,(current.shape[0], current.shape[1] * current.shape[2])  ).T


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     np.ndarray  )  ->      List[Any]:
       return np.reshape(_UpperCAmelCase   ,(current.shape[0] * current.shape[1], current.shape[2])  ).T


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Optional[int]   ,_UpperCAmelCase     :     str  )  ->      Optional[int]:
       _a   :			int					 =state_dict["""optimizer"""]["""target"""]["""Transformer"""][F"encoderblock_{i}"]

       _a   :			Optional[int]					 =F"encoder.layer.{i}."
       _a   :			Dict					 ={
           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     :     int  )  ->      str:
       return sum([1 if """encoderblock_""" in k else 0 for k in state_dict["""optimizer"""]["""target"""]["""Transformer"""].keys()]  )


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     int   ,_UpperCAmelCase     :     Optional[Any]=False  )  ->      List[Any]:
       _a   :			List[str]					 =get_n_layers(_UpperCAmelCase  )

       _a   :			int					 =OrderedDict()

       _a   :			Optional[int]					 =state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""encoder_norm"""]["""bias"""]
       _a   :			Tuple					 =state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""encoder_norm"""]["""scale"""]

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

       _a   :			Optional[int]					 =state_dict["""optimizer"""]["""target"""]["""cls"""]
       _a   :			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:
              _a   :			Any					 ={"""vivit.""" + k: v for k, v in new_state.items()}
              _a   :			Any					 =np.transpose(state_dict["""optimizer"""]["""target"""]["""output_projection"""]["""kernel"""]  )
              _a   :			Any					 =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_ (  )  ->      VivitImageProcessor:
       _a   :			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     :     str  )  ->      str:
       _a   :			str					 ="""checkpoint"""

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

       _a   :			int					 =restore_checkpoint(_UpperCAmelCase   ,_UpperCAmelCase  )
       _a   :			str					 =transform_state(_UpperCAmelCase   ,classification_head=_UpperCAmelCase  )

       _a   :			str					 =get_vivit_config()

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

       _a   :			List[str]					 =VivitForVideoClassification(_UpperCAmelCase  )
       model.load_state_dict(_UpperCAmelCase  )
       model.eval()

       _a   :			int					 =get_processor()

       _a   :			Union[str, Any]					 =prepare_video()
       _a   :			Union[str, Any]					 =extractor(_UpperCAmelCase   ,return_tensors="""pt"""  )

       _a   :			int					 =model(**_UpperCAmelCase  )

       _a   :			str					 =torch.Size([1, 400]  )
       _a   :			List[Any]					 =torch.tensor([-1.0_5_4_3, 2.0_7_6_4, -0.2_1_0_4, 0.4_4_3_9, -0.9_6_5_8]  )

       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__":
   A__:   Optional[int]    =	argparse.ArgumentParser()

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

   A__:   Tuple    =	parser.parse_args()
   convert(args.output_model_name)






'''simple docstring'''







def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Union[str, Any]  )  ->      int:
       _a   :			List[str]					 =[701, 301, 132, 57, 23, 10, 4, 1]
       for gap in gaps:
              for i in range(_UpperCAmelCase   ,len(_UpperCAmelCase  )  ):
                     _a   :			Tuple					 =collection[i]
                     _a   :			Optional[Any]					 =i
                     while j >= gap and collection[j - gap] > insert_value:
                            _a   :			Union[str, Any]					 =collection[j - gap]
                            j -= gap
                     if j != i:
                            _a   :			Union[str, Any]					 =insert_value
       return collection


if __name__ == "__main__":
   from doctest import testmod

   testmod()
   A__:   int    =	input('''Enter numbers separated by a comma:\n''').strip()
   A__:   Optional[Any]    =	[int(item) for item in user_input.split(''',''')]
   print(shell_sort(unsorted))


