

'''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


UpperCAmelCase__ :     Tuple				    =    logging.get_logger(__name__)


UpperCAmelCase__ :     int				    =    {
    '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        a__       (   __lowercase       ):







  """simple docstring"""




  UpperCAmelCase__      : List[Any]			       ="""codegen"""
  UpperCAmelCase__      : List[Any]			       ={
      """max_position_embeddings""": """n_positions""",
      """hidden_size""": """n_embd""",
      """num_attention_heads""": """n_head""",
      """num_hidden_layers""": """n_layer""",
  }


  def __init__(			self    : Union[str, Any]				,				UpperCAmelCase__    : str=5_0_4_0_0				,				UpperCAmelCase__    : Dict=2_0_4_8				,				UpperCAmelCase__    : str=2_0_4_8				,				UpperCAmelCase__    : List[str]=4_0_9_6				,				UpperCAmelCase__    : List[Any]=2_8				,				UpperCAmelCase__    : List[Any]=1_6				,				UpperCAmelCase__    : int=6_4				,				UpperCAmelCase__    : Optional[int]=None				,				UpperCAmelCase__    : List[Any]="gelu_new"				,				UpperCAmelCase__    : Any=0.0				,				UpperCAmelCase__    : str=0.0				,				UpperCAmelCase__    : Optional[int]=0.0				,				UpperCAmelCase__    : Optional[int]=1e-5				,				UpperCAmelCase__    : Tuple=0.02				,				UpperCAmelCase__    : Optional[Any]=True				,				UpperCAmelCase__    : Optional[int]=5_0_2_5_6				,				UpperCAmelCase__    : Any=5_0_2_5_6				,				UpperCAmelCase__    : str=False				,				**UpperCAmelCase__    : Any				,				)					->Optional[Any]:
        """simple docstring"""







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

        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   bos_token_id
        SCREAMING_SNAKE_CASE :      Dict       =   eos_token_id

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







class        a__       (   __lowercase       ):







  """simple docstring"""




  def __init__(			self    : Dict				,				UpperCAmelCase__    : PretrainedConfig				,				UpperCAmelCase__    : str = "default"				,				UpperCAmelCase__    : List[PatchingSpec] = None				,				UpperCAmelCase__    : bool = False				,				)					->Optional[Any]:
        """simple docstring"""







        super().__init__(UpperCAmelCase__				,				task=UpperCAmelCase__				,				patching_specs=UpperCAmelCase__				,				use_past=UpperCAmelCase__		)
        if not getattr(self._config				,				"""pad_token_id"""				,				UpperCAmelCase__		):
              # TODO: how to do that better?
              SCREAMING_SNAKE_CASE :      Tuple       =   0


  @property
  def  _lowercase      (			self    : int		)					->Mapping[str, Mapping[int, str]]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}		)
        if self.use_past:
              self.fill_with_past_key_values_(UpperCAmelCase__				,				direction="""inputs"""		)
              SCREAMING_SNAKE_CASE :      Any       =   {0: "batch", 1: "past_sequence + sequence"}
        else:
              SCREAMING_SNAKE_CASE :      Tuple       =   {0: "batch", 1: "sequence"}

        return common_inputs


  @property
  def  _lowercase      (			self    : Union[str, Any]		)					->int:
        """simple docstring"""







        return self._config.n_layer


  @property
  def  _lowercase      (			self    : Optional[int]		)					->int:
        """simple docstring"""







        return self._config.n_head


  def  _lowercase      (			self    : str				,				UpperCAmelCase__    : PreTrainedTokenizer				,				UpperCAmelCase__    : int = -1				,				UpperCAmelCase__    : int = -1				,				UpperCAmelCase__    : bool = False				,				UpperCAmelCase__    : Optional[TensorType] = None				,				)					->Mapping[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   super(UpperCAmelCase__				,				self		).generate_dummy_inputs(
            UpperCAmelCase__				,				batch_size=UpperCAmelCase__				,				seq_length=UpperCAmelCase__				,				is_pair=UpperCAmelCase__				,				framework=UpperCAmelCase__		)

        # We need to order the input in the way they appears in the forward()
        SCREAMING_SNAKE_CASE :      str       =   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

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

        SCREAMING_SNAKE_CASE :      List[Any]       =   common_inputs["attention_mask"]
        if self.use_past:
              SCREAMING_SNAKE_CASE :      int       =   ordered_inputs["attention_mask"].dtype
              SCREAMING_SNAKE_CASE :      Any       =   torch.cat(
                  [ordered_inputs["""attention_mask"""], torch.ones(UpperCAmelCase__				,				UpperCAmelCase__				,				dtype=UpperCAmelCase__		)]				,				dim=1		)

        return ordered_inputs




  @property
  def  _lowercase      (			self    : Optional[Any]		)					->int:
        """simple docstring"""







        return 1_3







from __future__ import annotations






def 					__lowercase      (     _A						,    _A			)     ->						list[str]:
      if nth_term == "":
            return [""]
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   int(_A			)
      SCREAMING_SNAKE_CASE :      Optional[int]       =   int(_A			)
      SCREAMING_SNAKE_CASE :      list[str]       =   []
      for temp in range(int(_A			)			):
            series.append(F"1 / {pow(temp + 1						,    int(_A			)			)}" if series else """1"""			)
      return series


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  UpperCAmelCase__ :     Dict				    =    int(input("""Enter the last number (nth term) of the P-Series"""))
  UpperCAmelCase__ :     Optional[int]				    =    int(input("""Enter the power for  P-Series"""))
  print("""Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p""")
  print(p_series(nth_term, power))




