code: 
  
  
  
  """simple docstring"""
  import argparse
  import json
  
  import gdown
  import numpy as np
  import torch
  from huggingface_hub import hf_hub_download
  
  from transformers import TimesformerConfig, TimesformerForVideoClassification, VideoMAEImageProcessor
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	Optional[int]							):
      _UpperCAmelCase							:						Union[str, Any]       							=					TimesformerConfig()
  
      if "large" in model_name:
          _UpperCAmelCase							:						Tuple       							=					96
  
      if "hr" in model_name:
          _UpperCAmelCase							:						Tuple       							=					16
          _UpperCAmelCase							:						Any       							=					448
  
      _UpperCAmelCase							:						int       							=					'''huggingface/label-files'''
      if "k400" in model_name:
          _UpperCAmelCase							:						Optional[Any]       							=					400
          _UpperCAmelCase							:						Optional[Any]       							=					'''kinetics400-id2label.json'''
      elif "k600" in model_name:
          _UpperCAmelCase							:						Union[str, Any]       							=					600
          _UpperCAmelCase							:						Any       							=					'''kinetics600-id2label.json'''
      elif "ssv2" in model_name:
          _UpperCAmelCase							:						Any       							=					174
          _UpperCAmelCase							:						str       							=					'''something-something-v2-id2label.json'''
      else:
          raise ValueError('''Model name should either contain \'k400\', \'k600\' or \'ssv2\'.'''							)
      _UpperCAmelCase							:						Any       							=					json.load(open(hf_hub_download(UpperCamelCase__  , UpperCamelCase__  , repo_type='''dataset'''							)  , '''r'''							)							)
      _UpperCAmelCase							:						List[Any]       							=					{int(UpperCamelCase__							): v for k, v in idalabel.items()}
      _UpperCAmelCase							:						Tuple       							=					idalabel
      _UpperCAmelCase							:						List[str]       							=					{v: k for k, v in idalabel.items()}
  
      return config
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	Optional[Any]							):
      if "encoder." in name:
          _UpperCAmelCase							:						Tuple       							=					name.replace('''encoder.'''  , ''''''							)
      if "cls_token" in name:
          _UpperCAmelCase							:						List[str]       							=					name.replace('''cls_token'''  , '''timesformer.embeddings.cls_token'''							)
      if "pos_embed" in name:
          _UpperCAmelCase							:						Tuple       							=					name.replace('''pos_embed'''  , '''timesformer.embeddings.position_embeddings'''							)
      if "time_embed" in name:
          _UpperCAmelCase							:						List[str]       							=					name.replace('''time_embed'''  , '''timesformer.embeddings.time_embeddings'''							)
      if "patch_embed.proj" in name:
          _UpperCAmelCase							:						Tuple       							=					name.replace('''patch_embed.proj'''  , '''timesformer.embeddings.patch_embeddings.projection'''							)
      if "patch_embed.norm" in name:
          _UpperCAmelCase							:						str       							=					name.replace('''patch_embed.norm'''  , '''timesformer.embeddings.norm'''							)
      if "blocks" in name:
          _UpperCAmelCase							:						Tuple       							=					name.replace('''blocks'''  , '''timesformer.encoder.layer'''							)
      if "attn.proj" in name:
          _UpperCAmelCase							:						Optional[Any]       							=					name.replace('''attn.proj'''  , '''attention.output.dense'''							)
      if "attn" in name and "bias" not in name and "temporal" not in name:
          _UpperCAmelCase							:						Dict       							=					name.replace('''attn'''  , '''attention.self'''							)
      if "attn" in name and "temporal" not in name:
          _UpperCAmelCase							:						Tuple       							=					name.replace('''attn'''  , '''attention.attention'''							)
      if "temporal_norm1" in name:
          _UpperCAmelCase							:						Any       							=					name.replace('''temporal_norm1'''  , '''temporal_layernorm'''							)
      if "temporal_attn.proj" in name:
          _UpperCAmelCase							:						List[str]       							=					name.replace('''temporal_attn'''  , '''temporal_attention.output.dense'''							)
      if "temporal_fc" in name:
          _UpperCAmelCase							:						List[str]       							=					name.replace('''temporal_fc'''  , '''temporal_dense'''							)
      if "norm1" in name and "temporal" not in name:
          _UpperCAmelCase							:						Tuple       							=					name.replace('''norm1'''  , '''layernorm_before'''							)
      if "norm2" in name:
          _UpperCAmelCase							:						List[Any]       							=					name.replace('''norm2'''  , '''layernorm_after'''							)
      if "mlp.fc1" in name:
          _UpperCAmelCase							:						List[str]       							=					name.replace('''mlp.fc1'''  , '''intermediate.dense'''							)
      if "mlp.fc2" in name:
          _UpperCAmelCase							:						Optional[int]       							=					name.replace('''mlp.fc2'''  , '''output.dense'''							)
      if "norm.weight" in name and "fc" not in name and "temporal" not in name:
          _UpperCAmelCase							:						List[str]       							=					name.replace('''norm.weight'''  , '''timesformer.layernorm.weight'''							)
      if "norm.bias" in name and "fc" not in name and "temporal" not in name:
          _UpperCAmelCase							:						str       							=					name.replace('''norm.bias'''  , '''timesformer.layernorm.bias'''							)
      if "head" in name:
          _UpperCAmelCase							:						List[str]       							=					name.replace('''head'''  , '''classifier'''							)
  
      return name
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	Union[str, Any]  , UpperCamelCase__	:	List[str]							):
      for key in orig_state_dict.copy().keys():
          _UpperCAmelCase							:						str       							=					orig_state_dict.pop(UpperCamelCase__							)
  
          if key.startswith('''model.'''							):
              _UpperCAmelCase							:						Optional[Any]       							=					key.replace('''model.'''  , ''''''							)
  
          if "qkv" in key:
              _UpperCAmelCase							:						List[str]       							=					key.split('''.'''							)
              _UpperCAmelCase							:						int       							=					int(key_split[1]							)
              _UpperCAmelCase							:						List[Any]       							=					'''timesformer.encoder.layer.'''
              if "temporal" in key:
                  _UpperCAmelCase							:						int       							=					'''.temporal_attention.attention.qkv.'''
              else:
                  _UpperCAmelCase							:						List[str]       							=					'''.attention.attention.qkv.'''
              if "weight" in key:
                  _UpperCAmelCase							:						List[Any]       							=					val
              else:
                  _UpperCAmelCase							:						Any       							=					val
          else:
              _UpperCAmelCase							:						Union[str, Any]       							=					val
  
      return orig_state_dict
  
  
  
  
  
  def 				lowerCamelCase_		():
      _UpperCAmelCase							:						Dict       							=					hf_hub_download(
          repo_id='''hf-internal-testing/spaghetti-video'''  , filename='''eating_spaghetti.npy'''  , repo_type='''dataset'''							)
      _UpperCAmelCase							:						Tuple       							=					np.load(UpperCamelCase__							)
      return list(UpperCamelCase__							)
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	List[Any]  , UpperCamelCase__	:	Optional[Any]  , UpperCamelCase__	:	Optional[int]  , UpperCamelCase__	:	Tuple							):
      _UpperCAmelCase							:						Any       							=					get_timesformer_config(UpperCamelCase__							)
  
      _UpperCAmelCase							:						int       							=					TimesformerForVideoClassification(UpperCamelCase__							)
  
      # download original checkpoint, hosted on Google Drive
      _UpperCAmelCase							:						Optional[int]       							=					'''pytorch_model.bin'''
      gdown.cached_download(UpperCamelCase__  , UpperCamelCase__  , quiet=UpperCamelCase__							)
      _UpperCAmelCase							:						Optional[Any]       							=					torch.load(UpperCamelCase__  , map_location='''cpu'''							)
      if "model" in files:
          _UpperCAmelCase							:						Tuple       							=					files['''model''']
      elif "module" in files:
          _UpperCAmelCase							:						List[str]       							=					files['''module''']
      else:
          _UpperCAmelCase							:						Union[str, Any]       							=					files['''model_state''']
      _UpperCAmelCase							:						Dict       							=					convert_state_dict(UpperCamelCase__  , UpperCamelCase__							)
  
      model.load_state_dict(UpperCamelCase__							)
      model.eval()
  
      # verify model on basic input
      _UpperCAmelCase							:						List[str]       							=					VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]  , image_std=[0.5, 0.5, 0.5]							)
      _UpperCAmelCase							:						int       							=					prepare_video()
      _UpperCAmelCase							:						List[str]       							=					image_processor(video[:8]  , return_tensors='''pt'''							)
  
      _UpperCAmelCase							:						int       							=					model(**UpperCamelCase__							)
      _UpperCAmelCase							:						Optional[int]       							=					outputs.logits
  
      _UpperCAmelCase							:						Union[str, Any]       							=					[
          # Kinetics-400 checkpoints (hr = high resolution input of 448px instead of 224px)
          '''timesformer-base-finetuned-k400''',
          '''timesformer-large-finetuned-k400''',
          '''timesformer-hr-finetuned-k400''',
          # Kinetics-600 checkpoints (hr = high resolution input of 448px instead of 224px)
          '''timesformer-base-finetuned-k600''',
          '''timesformer-large-finetuned-k600''',
          '''timesformer-hr-finetuned-k600''',
          # Something-Something-v2 checkpoints (hr = high resolution input of 448px instead of 224px)
          '''timesformer-base-finetuned-ssv2''',
          '''timesformer-large-finetuned-ssv2''',
          '''timesformer-hr-finetuned-ssv2''',
      ]
  
      # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
      if model_name == "timesformer-base-finetuned-k400":
          _UpperCAmelCase							:						List[Any]       							=					torch.Size([1, 400]							)
          _UpperCAmelCase							:						Union[str, Any]       							=					torch.tensor([-0.3016, -0.7713, -0.4205]							)
      elif model_name == "timesformer-base-finetuned-k600":
          _UpperCAmelCase							:						Tuple       							=					torch.Size([1, 600]							)
          _UpperCAmelCase							:						Optional[int]       							=					torch.tensor([-0.7267, -0.7466, 3.2404]							)
      elif model_name == "timesformer-base-finetuned-ssv2":
          _UpperCAmelCase							:						int       							=					torch.Size([1, 174]							)
          _UpperCAmelCase							:						Optional[Any]       							=					torch.tensor([-0.9059, 0.6433, -3.1457]							)
      elif model_name == "timesformer-large-finetuned-k400":
          _UpperCAmelCase							:						Any       							=					torch.Size([1, 400]							)
          _UpperCAmelCase							:						str       							=					torch.tensor([0, 0, 0]							)
      elif model_name == "timesformer-large-finetuned-k600":
          _UpperCAmelCase							:						Tuple       							=					torch.Size([1, 600]							)
          _UpperCAmelCase							:						Any       							=					torch.tensor([0, 0, 0]							)
      elif model_name == "timesformer-large-finetuned-ssv2":
          _UpperCAmelCase							:						List[Any]       							=					torch.Size([1, 174]							)
          _UpperCAmelCase							:						Optional[Any]       							=					torch.tensor([0, 0, 0]							)
      elif model_name == "timesformer-hr-finetuned-k400":
          _UpperCAmelCase							:						List[Any]       							=					torch.Size([1, 400]							)
          _UpperCAmelCase							:						Optional[int]       							=					torch.tensor([-0.9617, -3.7311, -3.7708]							)
      elif model_name == "timesformer-hr-finetuned-k600":
          _UpperCAmelCase							:						List[Any]       							=					torch.Size([1, 600]							)
          _UpperCAmelCase							:						Tuple       							=					torch.tensor([2.5273, 0.7127, 1.8848]							)
      elif model_name == "timesformer-hr-finetuned-ssv2":
          _UpperCAmelCase							:						List[str]       							=					torch.Size([1, 174]							)
          _UpperCAmelCase							:						Tuple       							=					torch.tensor([-3.6756, -0.7513, 0.7180]							)
      else:
          raise ValueError(F'Model name not supported. Should be one of {model_names}'							)
  
      # verify logits
      assert logits.shape == expected_shape
      assert torch.allclose(logits[0, :3]  , UpperCamelCase__  , atol=1E-4							)
      print('''Logits ok!'''							)
  
      if pytorch_dump_folder_path is not None:
          print(F'Saving model and image processor to {pytorch_dump_folder_path}'							)
          image_processor.save_pretrained(UpperCamelCase__							)
          model.save_pretrained(UpperCamelCase__							)
  
      if push_to_hub:
          print('''Pushing to the hub...'''							)
          model.push_to_hub(F'fcakyon/{model_name}'							)
  
  
  if __name__ == "__main__":
         _lowerCAmelCase					:int  =      argparse.ArgumentParser()
         # Required parameters
         parser.add_argument(
             '--checkpoint_url',
             default='https://drive.google.com/u/1/uc?id=17yvuYp9L4mn-HpIcK5Zo6K3UoOy1kA5l&export=download',
             type=str,
             help=(
                 'URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'
                 ' download link.'
             ),
         )
         parser.add_argument(
             '--pytorch_dump_folder_path',
             default='',
             type=str,
             help='Path to the output PyTorch model directory.',
         )
         parser.add_argument('--model_name', default='timesformer-base-finetuned-k400', type=str, help='Name of the model.')
         parser.add_argument(
             '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
         )
  
         _lowerCAmelCase					:str  =      parser.parse_args()
         convert_timesformer_checkpoint(
             args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
         )
  
  
  
style_context: 
  
  
  
  """simple docstring"""
  import random
  class 							_UpperCAmelCase							:
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A ,			A ,			A = 0.01 ,			A = 1_0_0_0 ,			A = -1 ,			)							->      None:
           _UpperCAmelCase							:						Dict       							=					sample
           if len(self.sample	) == 0:
               raise ValueError('''Sample data can not be empty'''	)
           _UpperCAmelCase							:						Tuple       							=					target
           if len(self.target	) == 0:
               raise ValueError('''Target data can not be empty'''	)
           if len(self.sample	) != len(self.target	):
               raise ValueError('''Sample data and Target data do not have matching lengths'''	)
           _UpperCAmelCase							:						List[Any]       							=					learning_rate
           _UpperCAmelCase							:						Union[str, Any]       							=					epoch_number
           _UpperCAmelCase							:						Any       							=					bias
           _UpperCAmelCase							:						str       							=					len(A	)
           _UpperCAmelCase							:						Tuple       							=					len(sample[0]	)  # number of columns in dataset
           _UpperCAmelCase							:						list       							=					[]
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      None:
           for sample in self.sample:
               sample.insert(0 ,			self.bias	)
  
           for _ in range(self.col_sample	):
               self.weight.append(random.random()	)
  
           self.weight.insert(0 ,			self.bias	)
  
           _UpperCAmelCase							:						Tuple       							=					0
  
           while True:
               _UpperCAmelCase							:						Tuple       							=					False
               for i in range(self.number_sample	):
                   _UpperCAmelCase							:						Optional[int]       							=					0
                   for j in range(self.col_sample + 1	):
                       _UpperCAmelCase							:						int       							=					u + self.weight[j] * self.sample[i][j]
                   _UpperCAmelCase							:						Tuple       							=					self.sign(A	)
                   if y != self.target[i]:
                       for j in range(self.col_sample + 1	):
                           _UpperCAmelCase							:						int       							=					(
                               self.weight[j]
                               + self.learning_rate
                               * (self.target[i] - y)
                               * self.sample[i][j]
                           )
                       _UpperCAmelCase							:						Union[str, Any]       							=					True
              # print('Epoch: \n',epoch_count)
               _UpperCAmelCase							:						int       							=					epoch_count + 1
               # if you want control the epoch or just by error
               if not has_misclassified:
                   print(('''\nEpoch:\n''', epoch_count)	)
                   print('''------------------------\n'''	)
                   # if epoch_count > self.epoch_number or not error:
                   break
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      None:
           if len(self.sample	) == 0:
               raise ValueError('''Sample data can not be empty'''	)
           sample.insert(0 ,			self.bias	)
           _UpperCAmelCase							:						str       							=					0
           for i in range(self.col_sample + 1	):
               _UpperCAmelCase							:						Optional[Any]       							=					u + self.weight[i] * sample[i]
  
           _UpperCAmelCase							:						Optional[int]       							=					self.sign(A	)
  
           if y == -1:
               print(('''Sample: ''', sample)	)
               print('''classification: P1'''	)
           else:
               print(('''Sample: ''', sample)	)
               print('''classification: P2'''	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      int:
           return 1 if u >= 0 else -1
  
  
  
  _lowerCAmelCase					:Optional[int]  =      [
      [-0.6508, 0.1097, 4.0009],
      [-1.4492, 0.8896, 4.4005],
      [2.0850, 0.6876, 12.0710],
      [0.2626, 1.1476, 7.7985],
      [0.6418, 1.0234, 7.0427],
      [0.2569, 0.6730, 8.3265],
      [1.1155, 0.6043, 7.4446],
      [0.0914, 0.3399, 7.0677],
      [0.0121, 0.5256, 4.6316],
      [-0.0429, 0.4660, 5.4323],
      [0.4340, 0.6870, 8.2287],
      [0.2735, 1.0287, 7.1934],
      [0.4839, 0.4851, 7.4850],
      [0.4089, -0.1267, 5.5019],
      [1.4391, 0.1614, 8.5843],
      [-0.9115, -0.1973, 2.1962],
      [0.3654, 1.0475, 7.4858],
      [0.2144, 0.7515, 7.1699],
      [0.2013, 1.0014, 6.5489],
      [0.6483, 0.2183, 5.8991],
      [-0.1147, 0.2242, 7.2435],
      [-0.7970, 0.8795, 3.8762],
      [-1.0625, 0.6366, 2.4707],
      [0.5307, 0.1285, 5.6883],
      [-1.2200, 0.7777, 1.7252],
      [0.3957, 0.1076, 5.6623],
      [-0.1013, 0.5989, 7.1812],
      [2.4482, 0.9455, 11.2095],
      [2.0149, 0.6192, 10.9263],
      [0.2012, 0.2611, 5.4631],
  ]
  
  _lowerCAmelCase					:Dict  =      [
      -1,
      -1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      -1,
      -1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      -1,
      1,
      -1,
      1,
  ]
  
  
  if __name__ == "__main__":
    import doctest
  
    doctest.testmod()
  
    _lowerCAmelCase					:int  =      Perceptron(
        sample=samples, target=target, learning_rate=0.01, epoch_number=1_000, bias=-1
    )
    network.training()
    print('Finished training perceptron')
    print('Enter values to predict or q to exit')
    while True:
      _lowerCAmelCase					:list  =      []
      for i in range(len(samples[0])):
        _lowerCAmelCase					:List[Any]  =      input('value: ').strip()
        if user_input == "q":
          break
        _lowerCAmelCase					:Any  =      float(user_input)
        sample.insert(i, observation)
      network.sort(sample)
  
  
  