
"""simple docstring"""




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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


A__   :					Any							=   logging.get_logger(__name__)


A__   :					Dict							=   {
    'Salesforce/codegen-350M-nl': 'https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json',
    'Salesforce/codegen-350M-multi': 'https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json',
    'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json',
    'Salesforce/codegen-2B-nl': 'https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json',
    'Salesforce/codegen-2B-multi': 'https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json',
    'Salesforce/codegen-2B-mono': 'https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json',
    'Salesforce/codegen-6B-nl': 'https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json',
    'Salesforce/codegen-6B-multi': 'https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json',
    'Salesforce/codegen-6B-mono': 'https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json',
    'Salesforce/codegen-16B-nl': 'https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json',
    'Salesforce/codegen-16B-multi': 'https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json',
    'Salesforce/codegen-16B-mono': 'https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json',
}


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		'''codegen'''
       UpperCamelCase_   =		{
           '''max_position_embeddings''': '''n_positions''',
           '''hidden_size''': '''n_embd''',
           '''num_attention_heads''': '''n_head''',
           '''num_hidden_layers''': '''n_layer''',
       }
       def __init__(     self ,				A_=5_0400 ,				A_=2048 ,				A_=2048 ,				A_=4096 ,				A_=28 ,				A_=16 ,				A_=64 ,				A_=None ,				A_="gelu_new" ,				A_=0.0 ,				A_=0.0 ,				A_=0.0 ,				A_=1E-5 ,				A_=0.02 ,				A_=True ,				A_=5_0256 ,				A_=5_0256 ,				A_=False ,				**A_ ,				)     ->	List[Any]:





         """simple docstring"""




         _lowercase:			Dict       =     vocab_size
         _lowercase:			Optional[int]       =     n_ctx
         _lowercase:			Union[str, Any]       =     n_positions
         _lowercase:			Tuple       =     n_embd
         _lowercase:			Dict       =     n_layer
         _lowercase:			Any       =     n_head
         _lowercase:			int       =     n_inner
         _lowercase:			Optional[int]       =     rotary_dim
         _lowercase:			Any       =     activation_function
         _lowercase:			int       =     resid_pdrop
         _lowercase:			Union[str, Any]       =     embd_pdrop
         _lowercase:			int       =     attn_pdrop
         _lowercase:			Any       =     layer_norm_epsilon
         _lowercase:			Optional[Any]       =     initializer_range
         _lowercase:			Optional[int]       =     use_cache

         _lowercase:			Any       =     bos_token_id
         _lowercase:			Optional[Any]       =     eos_token_id

         super().__init__(
             bos_token_id=A_ ,				eos_token_id=A_ ,				tie_word_embeddings=A_ ,				**A_       )


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       def __init__(     self ,				A_ ,				A_ = "default" ,				A_ = None ,				A_ = False ,				)     ->	Union[str, Any]:





         """simple docstring"""




         super().__init__(A_ ,				task=A_ ,				patching_specs=A_ ,				use_past=A_       )
         if not getattr(self._config ,				'''pad_token_id''' ,				A_       ):
           # TODO: how to do that better?
           _lowercase:			Union[str, Any]       =     0
       @property
       def 							lowercase_							(     self       )     ->	Mapping[str, Mapping[int, str]]:





         """simple docstring"""




         _lowercase:			str       =     OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}       )
         if self.use_past:
           self.fill_with_past_key_values_(A_ ,				direction='''inputs'''       )
           _lowercase:			str       =     {0: '''batch''', 1: '''past_sequence + sequence'''}
         else:
           _lowercase:			Optional[Any]       =     {0: '''batch''', 1: '''sequence'''}

         return common_inputs
       @property
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         return self._config.n_layer
       @property
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         return self._config.n_head
       def 							lowercase_							(     self ,				A_ ,				A_ = -1 ,				A_ = -1 ,				A_ = False ,				A_ = None ,				)     ->	Mapping[str, Any]:





         """simple docstring"""




         _lowercase:			Any       =     super(A_ ,				self       ).generate_dummy_inputs(
             A_ ,				batch_size=A_ ,				seq_length=A_ ,				is_pair=A_ ,				framework=A_       )

         # We need to order the input in the way they appears in the forward()
         _lowercase:			Any       =     OrderedDict({'''input_ids''': common_inputs['''input_ids''']}       )

         # Need to add the past_keys
         if self.use_past:
           if not is_torch_available():
             raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''       )
           else:
             import torch

             _lowercase ,				_lowercase:			List[str]       =     common_inputs['''input_ids'''].shape
             # Not using the same length for past_key_values
             _lowercase:			List[Any]       =     seqlen + 2
             _lowercase:			Tuple       =     (
                 batch,
                 self.num_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // self.num_attention_heads,
             )
             _lowercase:			Tuple       =     [
                 (torch.zeros(A_       ), torch.zeros(A_       )) for _ in range(self.num_layers       )
             ]

         _lowercase:			Optional[int]       =     common_inputs['''attention_mask''']
         if self.use_past:
           _lowercase:			Optional[Any]       =     ordered_inputs['''attention_mask'''].dtype
           _lowercase:			int       =     torch.cat(
               [ordered_inputs['''attention_mask'''], torch.ones(A_ ,				A_ ,				dtype=A_       )] ,				dim=1       )

         return ordered_inputs



       @property
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         return 13


"""simple docstring"""




import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
A__   :					Tuple							=   pd.read_csv(
    'https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'
    'position_salaries.csv'
)
A__   :					Optional[int]							=   dataset.iloc[:, 1:2].values
A__   :					Union[str, Any]							=   dataset.iloc[:, 2].values


A__							, A__							, A__							, A__   :					Dict							=   train_test_split(X, y, test_size=0.2, random_state=0)


A__   :					Union[str, Any]							=   PolynomialFeatures(degree=4)
A__   :					List[Any]							=   poly_reg.fit_transform(X)
A__   :					Dict							=   LinearRegression()
pol_reg.fit(X_poly, y)




def  _lowerCAmelCase		(		):






  """simple docstring"""



  plt.scatter(_UpperCamelCase ,				_UpperCamelCase ,				color='''red'''						)
  plt.plot(_UpperCamelCase ,				pol_reg.predict(poly_reg.fit_transform(_UpperCamelCase						)						) ,				color='''blue'''						)
  plt.title('''Truth or Bluff (Linear Regression)'''						)
  plt.xlabel('''Position level'''						)
  plt.ylabel('''Salary'''						)
  plt.show()


if __name__ == "__main__":
  viz_polymonial()

  # Predicting a new result with Polymonial Regression
  pol_reg.predict(poly_reg.fit_transform([[5.5]]))
  # output should be 132148.43750003
