"""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       A__    (	lowercase_     :	Tuple						)  ->							str:
   __lowerCamelCase    		=					'https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'

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



def       A__    (	)  ->							Tuple:
   __lowerCamelCase    		=					VivitConfig()

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

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



def       A__    (	)  ->							List[Any]:
   __lowerCamelCase    		=					hf_hub_download(
       repo_id='hf-internal-testing/spaghetti-video'      , filename='eating_spaghetti_32_frames.npy'      , repo_type='dataset'						)
   __lowerCamelCase    		=					np.load(__a						)
   return list(__a						)



def       A__    (	lowercase_     :	np.ndarray						)  ->							Any:
   if np.ndim(__a						) == 2:
      return transform_attention_bias(__a						)

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

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



def       A__    (	lowercase_     :	np.ndarray						)  ->							List[Any]:
   return current.flatten()



def       A__    (	lowercase_     :	np.ndarray						)  ->							Union[str, Any]:
   return np.reshape(__a      , (current.shape[0], current.shape[1] * current.shape[2])						).T



def       A__    (	lowercase_     :	np.ndarray						)  ->							Dict:
   return np.reshape(__a      , (current.shape[0] * current.shape[1], current.shape[2])						).T



def       A__    (	lowercase_     :	List[Any]      , lowercase_     :	int						)  ->							Union[str, Any]:
   __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       A__    (	lowercase_     :	str						)  ->							List[str]:
   return sum([1 if 'encoderblock_' in k else 0 for k in state_dict['optimizer']['target']['Transformer'].keys()]						)



def       A__    (	lowercase_     :	Dict      , lowercase_     :	Union[str, Any]=False						)  ->							int:
   __lowerCamelCase    		=					get_n_layers(__a						)

   __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(__a						):
      new_state.update(transform_state_encoder_block(__a      , __a						)						)

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



def       A__    (	)  ->							Any:
   __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       A__    (	lowercase_     :	str						)  ->							Any:
   __lowerCamelCase    		=					'checkpoint'

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

   __lowerCamelCase    		=					restore_checkpoint(__a      , __a						)
   __lowerCamelCase    		=					transform_state(__a      , classification_head=__a						)

   __lowerCamelCase    		=					get_vivit_config()

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

   __lowerCamelCase    		=					VivitForVideoClassification(__a						)
   model.load_state_dict(__a						)
   model.eval()

   __lowerCamelCase    		=					get_processor()

   __lowerCamelCase    		=					prepare_video()
   __lowerCamelCase    		=					extractor(__a      , return_tensors='pt'						)

   __lowerCamelCase    		=					model(**__a						)

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

   model.save_pretrained(__a						)
   extractor.save_pretrained(__a						)


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE	:		Any   =   argparse.ArgumentParser()

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

     _SCREAMING_SNAKE_CASE	:		str   =   parser.parse_args()
     convert(args.output_model_name)











from collections import defaultdict

def       A_   (						__a  :					str		):

				"""simple docstring"""
				a__					   =		defaultdict(__a		)
				# Creating a dictionary containing count of each word
				for word in sentence.split():
								occurrence[word] += 1
				return occurrence


if __name__ == "__main__":
	for word, count in word_occurrence("""INPUT STRING""").items():
		print(f"""{word}: {count}""")

