
'''simple docstring'''






import numpy

# List of input, output pairs
UpperCAmelCase_       :   Tuple   =       (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
UpperCAmelCase_       :   Tuple   =       (((515, 22, 13), 555), ((61, 35, 49), 150))
UpperCAmelCase_       :   List[str]   =       [2, 4, 1, 5]
UpperCAmelCase_       :   Tuple   =       len(train_data)
UpperCAmelCase_       :   Optional[Any]   =       0.009







def 		A_       (	_lowerCAmelCase   :    Any				,	_lowerCAmelCase   :    Any="train"							):



      """simple docstring"""




      return calculate_hypothesis_value(_lowerCamelCase				,	_lowerCamelCase							) - output(
          _lowerCamelCase				,	_lowerCamelCase							)







def 		A_       (	_lowerCAmelCase   :    Optional[Any]							):



      """simple docstring"""




      _lowerCamelCase				:   Dict								=   0
      for i in range(len(_lowerCamelCase							) - 1							):
            hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
      hyp_val += parameter_vector[0]
      return hyp_val







def 		A_       (	_lowerCAmelCase   :    Any				,	_lowerCAmelCase   :    List[Any]							):



      """simple docstring"""




      if data_set == "train":
            return train_data[example_no][1]
      elif data_set == "test":
            return test_data[example_no][1]
      return None







def 		A_       (	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    Tuple							):



      """simple docstring"""




      if data_set == "train":
            return _hypothesis_value(train_data[example_no][0]							)
      elif data_set == "test":
            return _hypothesis_value(test_data[example_no][0]							)
      return None







def 		A_       (	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    Optional[Any]=m							):



      """simple docstring"""




      _lowerCamelCase				:   int								=   0
      for i in range(_lowerCamelCase							):
            if index == -1:
                  summation_value += _error(_lowerCamelCase							)
            else:
                  summation_value += _error(_lowerCamelCase							) * train_data[i][0][index]
      return summation_value







def 		A_       (	_lowerCAmelCase   :    Any							):



      """simple docstring"""




      _lowerCamelCase				:   Tuple								=   summation_of_cost_derivative(_lowerCamelCase				,	_lowerCamelCase							) / m
      return cost_derivative_value







def 		A_       (	):



      """simple docstring"""




      global parameter_vector
      # Tune these values to set a tolerance value for predicted output
      _lowerCamelCase				:   List[str]								=   0.0_0_0_0_0_2
      _lowerCamelCase				:   List[str]								=   0
      _lowerCamelCase				:   Any								=   0
      while True:
            j += 1
            _lowerCamelCase				:   int								=   [0, 0, 0, 0]
            for i in range(0				,	len(_lowerCamelCase							)							):
                  _lowerCamelCase				:   Union[str, Any]								=   get_cost_derivative(i - 1							)
                  _lowerCamelCase				:   Any								=   (
                      parameter_vector[i] - LEARNING_RATE * cost_derivative
                  )
            if numpy.allclose(
                _lowerCamelCase				,	_lowerCamelCase				,	atol=_lowerCamelCase				,	rtol=_lowerCamelCase				,	):
                  break
            _lowerCamelCase				:   Any								=   temp_parameter_vector
      print(("Number of iterations:", j)							)







def 		A_       (	):



      """simple docstring"""




      for i in range(len(_lowerCamelCase							)							):
            print(("Actual output value:", output(_lowerCamelCase				,	"test"							))							)
            print(("Hypothesis output:", calculate_hypothesis_value(_lowerCamelCase				,	"test"							))							)


if __name__ == "__main__":
      run_gradient_descent()
      print('\nTesting gradient descent for a linear hypothesis function.\n')
      test_gradient_descent()

'''simple docstring'''






from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


UpperCAmelCase_       :   Union[str, Any]   =       logging.get_logger(__name__)

UpperCAmelCase_       :   Optional[int]   =       {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

UpperCAmelCase_       :   Union[str, Any]   =       {
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

UpperCAmelCase_       :   Union[str, Any]   =       {
    'facebook/blenderbot_small-90M': 512,
}

class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		VOCAB_FILES_NAMES
    lowerCAmelCase_				    =		PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase_				    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase_				    =		BlenderbotSmallTokenizer



    def __init__(	self      :       Optional[Any],__A      :       str=None,__A      :       str=None,__A      :       int="<|endoftext|>",__A      :       List[str]="<|endoftext|>",__A      :       Optional[Any]="<|endoftext|>",__A      :       Union[str, Any]=False,__A      :       List[Any]=True,**__A      :       str,):
          super().__init__(
              ByteLevelBPETokenizer(
                  vocab=__A,merges=__A,add_prefix_space=__A,trim_offsets=__A,),bos_token=__A,eos_token=__A,unk_token=__A,**__A,)
          _lowerCamelCase				:   List[Any]								=   add_prefix_space



    def   lowerCamelCase_  (	self      :       Any,__A      :       Optional[Any],__A      :       Optional[int]=None     ):
          _lowerCamelCase				:   Any								=   [self.bos_token_id] + token_ids_a + [self.eos_token_id]
          if token_ids_a is None:
                return output

          return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



    def   lowerCamelCase_  (	self      :       List[Any],__A      :       List[int],__A      :       Optional[List[int]] = None     ):
          _lowerCamelCase				:   Any								=   [self.sep_token_id]
          _lowerCamelCase				:   Union[str, Any]								=   [self.cls_token_id]

          if token_ids_a is None:
                return len(cls + token_ids_a + sep     ) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]