






import numpy

# List of input, output pairs
snake_case__      :	List[str]       	=      (
    ((5, 2, 3), 1_5),
    ((6, 5, 9), 2_5),
    ((1_1, 1_2, 1_3), 4_1),
    ((1, 1, 1), 8),
    ((1_1, 1_2, 1_3), 4_1),
)
snake_case__      :	Optional[Any]       	=      (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
snake_case__      :	Optional[int]       	=      [2, 4, 1, 5]
snake_case__      :	Tuple       	=      len(train_data)
snake_case__      :	Optional[Any]       	=      0.009






def     _snake_case    (__lowercase    ,      __lowercase="train"):
			return calculate_hypothesis_value(__lowercase    ,      __lowercase) - output(
			    __lowercase    ,      __lowercase)






def     _snake_case    (__lowercase):
			UpperCamelCase_									=     0
			for i in range(len(__lowercase) - 1):
						hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
			hyp_val += parameter_vector[0]
			return hyp_val






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






def     _snake_case    (__lowercase    ,      __lowercase):
			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     _snake_case    (__lowercase    ,      __lowercase=m):
			UpperCamelCase_									=     0
			for i in range(__lowercase):
						if index == -1:
									summation_value += _error(__lowercase)
						else:
									summation_value += _error(__lowercase) * train_data[i][0][index]
			return summation_value






def     _snake_case    (__lowercase):
			UpperCamelCase_									=     summation_of_cost_derivative(__lowercase    ,      __lowercase) / m
			return cost_derivative_value






def     _snake_case    ():
			global parameter_vector
			# Tune these values to set a tolerance value for predicted output
			UpperCamelCase_									=     0.000_002
			UpperCamelCase_									=     0
			UpperCamelCase_									=     0
			while True:
						j += 1
						UpperCamelCase_									=     [0, 0, 0, 0]
						for i in range(0    ,      len(__lowercase)):
									UpperCamelCase_									=     get_cost_derivative(i - 1)
									UpperCamelCase_									=     (
									    parameter_vector[i] - LEARNING_RATE * cost_derivative
									)
						if numpy.allclose(
						    __lowercase    ,      __lowercase    ,      atol=__lowercase    ,      rtol=__lowercase    ,      ):
									break
						UpperCamelCase_									=     temp_parameter_vector
			print(('Number of iterations:', j))






def     _snake_case    ():
			for i in range(len(__lowercase)):
						print(('Actual output value:', output(__lowercase    ,      'test')))
						print(('Hypothesis output:', calculate_hypothesis_value(__lowercase    ,      'test')))


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













import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__      :	Optional[int]       	=      logging.get_logger(__name__)

snake_case__      :	List[Any]       	=      {
    """microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}


class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   """git_vision_model"""




  def __init__(     self		,		_UpperCAmelCase=768		,		_UpperCAmelCase=3072		,		_UpperCAmelCase=12		,		_UpperCAmelCase=12		,		_UpperCAmelCase=3		,		_UpperCAmelCase=224		,		_UpperCAmelCase=16		,		_UpperCAmelCase="quick_gelu"		,		_UpperCAmelCase=1e-5		,		_UpperCAmelCase=0.0		,		_UpperCAmelCase=0.0_2		,		**_UpperCAmelCase		,		)			-> Optional[Any]:
     super().__init__(**_UpperCAmelCase )

     UpperCamelCase_									=     hidden_size
     UpperCamelCase_									=     intermediate_size
     UpperCamelCase_									=     num_hidden_layers
     UpperCamelCase_									=     num_attention_heads
     UpperCamelCase_									=     num_channels
     UpperCamelCase_									=     patch_size
     UpperCamelCase_									=     image_size
     UpperCamelCase_									=     initializer_range
     UpperCamelCase_									=     attention_dropout
     UpperCamelCase_									=     layer_norm_eps
     UpperCamelCase_									=     hidden_act




  @classmethod
  def    _UpperCAmelCase     (     cls		,		_UpperCAmelCase		,		**_UpperCAmelCase )			-> "PretrainedConfig":
     cls._set_token_in_kwargs(_UpperCAmelCase )

     UpperCamelCase_		,      UpperCamelCase_									=     cls.get_config_dict(_UpperCAmelCase		,		**_UpperCAmelCase )

     # get the vision config dict if we are loading from GITConfig
     if config_dict.get('model_type' ) == "git":
        UpperCamelCase_									=     config_dict['vision_config']

     if "model_type" in config_dict and hasattr(cls		,		'model_type' ) and config_dict["model_type"] != cls.model_type:
        logger.warning(
            f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
            f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )

     return cls.from_dict(_UpperCAmelCase		,		**_UpperCAmelCase )




class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   """git"""




  def __init__(     self		,		_UpperCAmelCase=None		,		_UpperCAmelCase=30522		,		_UpperCAmelCase=768		,		_UpperCAmelCase=6		,		_UpperCAmelCase=12		,		_UpperCAmelCase=3072		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=1024		,		_UpperCAmelCase=0.0_2		,		_UpperCAmelCase=1e-12		,		_UpperCAmelCase=0		,		_UpperCAmelCase="absolute"		,		_UpperCAmelCase=True		,		_UpperCAmelCase=False		,		_UpperCAmelCase=101		,		_UpperCAmelCase=102		,		_UpperCAmelCase=None		,		**_UpperCAmelCase		,		)			-> Optional[int]:
     super().__init__(bos_token_id=_UpperCAmelCase		,		eos_token_id=_UpperCAmelCase		,		pad_token_id=_UpperCAmelCase		,		**_UpperCAmelCase )

     if vision_config is None:
        UpperCamelCase_									=     {}
        logger.info('vision_config is None. initializing the GitVisionConfig with default values.' )

     UpperCamelCase_									=     GitVisionConfig(**_UpperCAmelCase )
     UpperCamelCase_									=     vocab_size
     UpperCamelCase_									=     hidden_size
     UpperCamelCase_									=     num_hidden_layers
     UpperCamelCase_									=     num_attention_heads
     UpperCamelCase_									=     hidden_act
     UpperCamelCase_									=     intermediate_size
     UpperCamelCase_									=     hidden_dropout_prob
     UpperCamelCase_									=     attention_probs_dropout_prob
     UpperCamelCase_									=     max_position_embeddings
     UpperCamelCase_									=     initializer_range
     UpperCamelCase_									=     layer_norm_eps
     UpperCamelCase_									=     position_embedding_type
     UpperCamelCase_									=     use_cache
     UpperCamelCase_									=     tie_word_embeddings
     UpperCamelCase_									=     num_image_with_embedding

     UpperCamelCase_									=     bos_token_id
     UpperCamelCase_									=     eos_token_id




  def    _UpperCAmelCase     (     self )			-> str:
     UpperCamelCase_									=     copy.deepcopy(self.__dict__ )
     UpperCamelCase_									=     self.vision_config.to_dict()
     UpperCamelCase_									=     self.__class__.model_type
     return output





