

"""simple docstring"""





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
   from ... import FeatureExtractionMixin, TensorType

_a							=							logging.get_logger(__name__)

_a							=							{
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 __UpperCAmelCase     : str       		=     "imagegpt"
 __UpperCAmelCase     : List[Any]       		=     ["past_key_values"]
 __UpperCAmelCase     : Optional[int]       		=     {
     "hidden_size": "n_embd",
     "max_position_embeddings": "n_positions",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }


 def __init__(      self							:      List[str], UpperCAmelCase__							:      Any=5_1_2 + 1, UpperCAmelCase__							:      Dict=3_2 * 3_2, UpperCAmelCase__							:      List[str]=5_1_2, UpperCAmelCase__							:      Optional[Any]=2_4, UpperCAmelCase__							:      Any=8, UpperCAmelCase__							:      List[Any]=None, UpperCAmelCase__							:      Optional[Any]="quick_gelu", UpperCAmelCase__							:      List[str]=0.1, UpperCAmelCase__							:      Any=0.1, UpperCAmelCase__							:      List[str]=0.1, UpperCAmelCase__							:      Optional[int]=1E-5, UpperCAmelCase__							:      List[Any]=0.02, UpperCAmelCase__							:      List[Any]=True, UpperCAmelCase__							:      Optional[Any]=True, UpperCAmelCase__							:      Any=False, UpperCAmelCase__							:      List[str]=False, UpperCAmelCase__							:      int=False, **UpperCAmelCase__							:      Union[str, Any], ):
    __lowercase								=						vocab_size
    __lowercase								=						n_positions
    __lowercase								=						n_embd
    __lowercase								=						n_layer
    __lowercase								=						n_head
    __lowercase								=						n_inner
    __lowercase								=						activation_function
    __lowercase								=						resid_pdrop
    __lowercase								=						embd_pdrop
    __lowercase								=						attn_pdrop
    __lowercase								=						layer_norm_epsilon
    __lowercase								=						initializer_range
    __lowercase								=						scale_attn_weights
    __lowercase								=						use_cache
    __lowercase								=						scale_attn_by_inverse_layer_idx
    __lowercase								=						reorder_and_upcast_attn
    __lowercase								=						tie_word_embeddings

    super().__init__(tie_word_embeddings=UpperCAmelCase__, **UpperCAmelCase__     )







class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 @property
 def  _lowercase			(      self							:      List[Any]     ):
    return OrderedDict(
        [
            ("input_ids", {0: "batch", 1: "sequence"}),
        ]     )



 def  _lowercase			(      self							:      Optional[Any], UpperCAmelCase__							:      "FeatureExtractionMixin", UpperCAmelCase__							:      int = 1, UpperCAmelCase__							:      int = -1, UpperCAmelCase__							:      bool = False, UpperCAmelCase__							:      Optional["TensorType"] = None, UpperCAmelCase__							:      int = 3, UpperCAmelCase__							:      int = 3_2, UpperCAmelCase__							:      int = 3_2, ):
    __lowercase								=						self._generate_dummy_images(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__     )
    __lowercase								=						dict(preprocessor(images=UpperCAmelCase__, return_tensors=UpperCAmelCase__     )     )

    return inputs







"""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  (  UpperCamelCase_					:	str,      UpperCamelCase_					:	List[Any]="train")     ->						Optional[Any]:
   '''simple docstring'''


   return calculate_hypothesis_value(UpperCamelCase_,      UpperCamelCase_) - output(
       UpperCamelCase_,      UpperCamelCase_)




def   _A  (  UpperCamelCase_					:	List[Any])     ->						Union[str, Any]:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_					:	Dict,      UpperCamelCase_					:	Optional[int])     ->						Dict:
   '''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  (  UpperCamelCase_					:	Dict,      UpperCamelCase_					:	List[str])     ->						int:
   '''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  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	Tuple=m)     ->						int:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_					:	str)     ->						str:
   '''simple docstring'''


   __lowercase								=						summation_of_cost_derivative(UpperCamelCase_,      UpperCamelCase_) / m
   return cost_derivative_value




def   _A  (  )     ->						List[str]:
   '''simple docstring'''


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




def   _A  (  )     ->						int:
   '''simple docstring'''


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


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




