





from typing import Callable, Optional, Union

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


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

lowercase     :			str     						=	{
    """microsoft/prophetnet-large-uncased""": (
        """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
    ),
}

class 			__A(    __UpperCAmelCase			):
     __A							=							"prophetnet"
     __A							=							["past_key_values"]
     __A							=							{
         "num_attention_heads": "num_encoder_attention_heads",
     }

     def __init__(   self,							A = 0.1,							A = "gelu",							A = 3_0522,							A = 1024,							A = 4096,							A = 12,							A = 16,							A = 4096,							A = 12,							A = 16,							A = 0.1,							A = 0.1,							A = 512,							A = 0.02,							A = True,							A = True,							A = 0,							A = 2,							A = 32,							A = 128,							A = False,							A = 0.0,							A = True,							A = 0,							A = 1,							A = 2,							**A,							):





            """simple docstring"""

            _UpperCamelCase														=    vocab_size
            _UpperCamelCase														=    hidden_size
            _UpperCamelCase														=    encoder_ffn_dim
            _UpperCamelCase														=    num_encoder_layers
            _UpperCamelCase														=    num_encoder_attention_heads
            _UpperCamelCase														=    decoder_ffn_dim
            _UpperCamelCase														=    num_decoder_layers
            _UpperCamelCase														=    num_decoder_attention_heads
            _UpperCamelCase														=    max_position_embeddings
            _UpperCamelCase														=    init_std  # Normal(0, this parameter)
            _UpperCamelCase														=    activation_function

            # parameters for prophetnet
            _UpperCamelCase														=    ngram
            _UpperCamelCase														=    num_buckets
            _UpperCamelCase														=    relative_max_distance
            _UpperCamelCase														=    disable_ngram_loss
            _UpperCamelCase														=    eps

            # 3 Types of Dropout
            _UpperCamelCase														=    attention_dropout
            _UpperCamelCase														=    activation_dropout
            _UpperCamelCase														=    dropout

            _UpperCamelCase														=    use_cache

            super().__init__(
                pad_token_id=A,							bos_token_id=A,							eos_token_id=A,							is_encoder_decoder=A,							add_cross_attention=A,							decoder_start_token_id=A,							**A,							)

     @property
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return self.num_encoder_layers + self.num_decoder_layers



     @num_hidden_layers.setter
     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            raise NotImplementedError(
                '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
                ''' `num_decoder_layers`.'''   )













import numpy as np
import requests


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


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    len_data

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


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    np.dot(lowerCAmelCase     ,							data_x.transpose()       )
       prod -= data_y.transpose()
       _UpperCamelCase														=    np.sum(np.square(lowerCAmelCase       )       )
       _UpperCamelCase														=    sum_elem / (2 * len_data)
       return error


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    100_000
       _UpperCamelCase														=    0.0_0_0_1_5_5_0

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

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

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

       return theta


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    sum(abs(y - predicted_y[i]       ) for i, y in enumerate(lowerCAmelCase       )       )
       return total / len(lowerCAmelCase       )


def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    collect_dataset()

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

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


if __name__ == "__main__":
       main()






