


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase_		   =				{"""configuration_gptj""": ["""GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTJConfig""", """GPTJOnnxConfig"""]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_		   =				[
       """GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """GPTJForCausalLM""",
       """GPTJForQuestionAnswering""",
       """GPTJForSequenceClassification""",
       """GPTJModel""",
       """GPTJPreTrainedModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_		   =				[
       """TFGPTJForCausalLM""",
       """TFGPTJForQuestionAnswering""",
       """TFGPTJForSequenceClassification""",
       """TFGPTJModel""",
       """TFGPTJPreTrainedModel""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_		   =				[
       """FlaxGPTJForCausalLM""",
       """FlaxGPTJModel""",
       """FlaxGPTJPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_gptj import (
          GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
          GPTJForCausalLM,
          GPTJForQuestionAnswering,
          GPTJForSequenceClassification,
          GPTJModel,
          GPTJPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_gptj import (
          TFGPTJForCausalLM,
          TFGPTJForQuestionAnswering,
          TFGPTJForSequenceClassification,
          TFGPTJModel,
          TFGPTJPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
   import sys

   UpperCAmelCase_		   =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








import numpy as np
import requests

def   SCREAMING_SNAKE_CASE_       (  )     ->    Any:
   _A						     =					requests.get(
       '''https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'''
       '''master/Week1/ADRvsRating.csv'''							)
   _A						     =					response.text.splitlines()
   _A						     =					[]
   for item in lines:
      _A						     =					item.split(''','''							)
      data.append(_snake_case							)
   data.pop(0							)  # This is for removing the labels from the list
   _A						     =					np.matrix(_snake_case							)
   return dataset

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :int  ,      _snake_case  :Dict  ,      _snake_case  :Tuple  ,      _snake_case  :Tuple  ,      _snake_case  :str							)     ->    int:
   _A						     =					len_data

   _A						     =					np.dot(_snake_case  ,      data_x.transpose()							)
   prod -= data_y.transpose()
   _A						     =					np.dot(_snake_case  ,      _snake_case							)
   _A						     =					theta - (alpha / n) * sum_grad
   return theta

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :Union[str, Any]  ,      _snake_case  :Any  ,      _snake_case  :Optional[Any]  ,      _snake_case  :List[Any]							)     ->    Tuple:
   _A						     =					np.dot(_snake_case  ,      data_x.transpose()							)
   prod -= data_y.transpose()
   _A						     =					np.sum(np.square(_snake_case							)							)
   _A						     =					sum_elem / (2 * len_data)
   return error

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :Union[str, Any]  ,      _snake_case  :Union[str, Any]							)     ->    List[str]:
   _A						     =					100_000
   _A						     =					0.000_1550

   _A						     =					data_x.shape[1]
   _A						     =					data_x.shape[0] - 1

   _A						     =					np.zeros((1, no_features)							)

   for i in range(0  ,      _snake_case							):
      _A						     =					run_steep_gradient_descent(_snake_case  ,      _snake_case  ,      _snake_case  ,      _snake_case  ,      _snake_case							)
      _A						     =					sum_of_square_error(_snake_case  ,      _snake_case  ,      _snake_case  ,      _snake_case							)
      print(F'''At Iteration {i + 1} - Error is {error:.5f}'''							)

   return theta

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :List[Any]  ,      _snake_case  :Optional[int]							)     ->    Tuple:
   _A						     =					sum(abs(y - predicted_y[i]							) for i, y in enumerate(_snake_case							)							)
   return total / len(_snake_case							)

def   SCREAMING_SNAKE_CASE_       (  )     ->    Tuple:
   _A						     =					collect_dataset()

   _A						     =					data.shape[0]
   _A						     =					np.c_[np.ones(_snake_case							), data[:, :-1]].astype(_snake_case							)
   _A						     =					data[:, -1].astype(_snake_case							)

   _A						     =					run_linear_regression(_snake_case  ,      _snake_case							)
   _A						     =					theta.shape[1]
   print('''Resultant Feature vector : '''							)
   for i in range(0  ,      _snake_case							):
      print(F'''{theta[0, i]:.5f}'''							)


if __name__ == "__main__":
   main()



