

'''simple docstring'''

import numpy

# List of input, output pairs
A_				  =							(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
A_				  =							(((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
A_				  =							[2, 4, 1, 5]
A_				  =							len(train_data)
A_				  =							0.009





def       A_     (       snake_case					,				snake_case="train"       ):
  return calculate_hypothesis_value(__snake_case					,				__snake_case       ) - output(
      __snake_case					,				__snake_case       )





def       A_     (       snake_case       ):
  SCREAMING_SNAKE_CASE:Dict  					=    0
  for i in range(len(__snake_case       ) - 1       ):
    hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
  hyp_val += parameter_vector[0]
  return hyp_val





def       A_     (       snake_case					,				snake_case       ):
  if data_set == "train":
    return train_data[example_no][1]
  elif data_set == "test":
    return test_data[example_no][1]
  return None





def       A_     (       snake_case					,				snake_case       ):
  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_     (       snake_case					,				snake_case=m       ):
  SCREAMING_SNAKE_CASE:Union[str, Any]  					=    0
  for i in range(__snake_case       ):
    if index == -1:
      summation_value += _error(__snake_case       )
    else:
      summation_value += _error(__snake_case       ) * train_data[i][0][index]
  return summation_value





def       A_     (       snake_case       ):
  SCREAMING_SNAKE_CASE:Optional[int]  					=    summation_of_cost_derivative(__snake_case					,				__snake_case       ) / m
  return cost_derivative_value





def       A_     (       ):
  global parameter_vector
  # Tune these values to set a tolerance value for predicted output
  SCREAMING_SNAKE_CASE:Union[str, Any]  					=    0.00_0002
  SCREAMING_SNAKE_CASE:Optional[int]  					=    0
  SCREAMING_SNAKE_CASE:Tuple  					=    0
  while True:
    j += 1
    SCREAMING_SNAKE_CASE:Union[str, Any]  					=    [0, 0, 0, 0]
    for i in range(0					,				len(__snake_case       )       ):
      SCREAMING_SNAKE_CASE:Optional[int]  					=    get_cost_derivative(i - 1       )
      SCREAMING_SNAKE_CASE:Any  					=    (
          parameter_vector[i] - LEARNING_RATE * cost_derivative
      )
    if numpy.allclose(
        __snake_case					,				__snake_case					,				atol=__snake_case					,				rtol=__snake_case					,				):
      break
    SCREAMING_SNAKE_CASE:Optional[Any]  					=    temp_parameter_vector
  print(("Number of iterations:", j)       )





def       A_     (       ):
  for i in range(len(__snake_case       )       ):
    print(("Actual output value:", output(__snake_case					,				"test"       ))       )
    print(("Hypothesis output:", calculate_hypothesis_value(__snake_case					,				"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 TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_A			:       Optional[int]   		=       {
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			:       Union[str, Any]   		=       ["""LlamaTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			:       Dict   		=       ["""LlamaTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			:       int   		=       [
       """LlamaForCausalLM""",
       """LlamaModel""",
       """LlamaPreTrainedModel""",
       """LlamaForSequenceClassification""",
   ]


if TYPE_CHECKING:
   from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_llama import LlamaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_llama_fast import LlamaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
   import sys

   _A			:       str   		=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


