



'''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


lowerCAmelCase:      List[Any]		 =		logging.get_logger(__name__)


lowerCAmelCase:      str		 =		{
    '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__(      lowerCamelCase__			):
    lowercase__				=					"""codegen"""
    lowercase__				=					{
        """max_position_embeddings""": """n_positions""",
        """hidden_size""": """n_embd""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }







    def __init__(   self				:						Any		,    __snake_case				:						List[Any]=5_04_00		,    __snake_case				:						int=20_48		,    __snake_case				:						List[Any]=20_48		,    __snake_case				:						List[Any]=40_96		,    __snake_case				:						Any=28		,    __snake_case				:						Optional[Any]=16		,    __snake_case				:						Union[str, Any]=64		,    __snake_case				:						Optional[int]=None		,    __snake_case				:						Tuple="gelu_new"		,    __snake_case				:						Union[str, Any]=0.0		,    __snake_case				:						Dict=0.0		,    __snake_case				:						int=0.0		,    __snake_case				:						str=1e-5		,    __snake_case				:						str=0.02		,    __snake_case				:						Optional[int]=True		,    __snake_case				:						Dict=5_02_56		,    __snake_case				:						Tuple=5_02_56		,    __snake_case				:						Any=False		,    **__snake_case				:						Optional[int]		,    ):
      a		:      int       =  vocab_size
      a		:      str       =  n_ctx
      a		:      Union[str, Any]       =  n_positions
      a		:      Optional[Any]       =  n_embd
      a		:      int       =  n_layer
      a		:      Any       =  n_head
      a		:      Union[str, Any]       =  n_inner
      a		:      Union[str, Any]       =  rotary_dim
      a		:      int       =  activation_function
      a		:      List[str]       =  resid_pdrop
      a		:      Optional[int]       =  embd_pdrop
      a		:      str       =  attn_pdrop
      a		:      List[str]       =  layer_norm_epsilon
      a		:      str       =  initializer_range
      a		:      Optional[Any]       =  use_cache

      a		:      int       =  bos_token_id
      a		:      Optional[int]       =  eos_token_id

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

class 							a__(      lowerCamelCase__			):







    def __init__(   self				:						List[str]		,    __snake_case				:						PretrainedConfig		,    __snake_case				:						str = "default"		,    __snake_case				:						List[PatchingSpec] = None		,    __snake_case				:						bool = False		,    ):
      super().__init__(__snake_case		,    task=__snake_case		,    patching_specs=__snake_case		,    use_past=__snake_case			)
      if not getattr(self._config		,    'pad_token_id'		,    __snake_case			):
        # TODO: how to do that better?
        a		:      Any       =  0







    @property
    def     lowercase_ (   self				:						List[Any]			):
      a		:      List[str]       =  OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}			)
      if self.use_past:
        self.fill_with_past_key_values_(__snake_case		,    direction='inputs'			)
        a		:      List[str]       =  {0: 'batch', 1: 'past_sequence + sequence'}
      else:
        a		:      int       =  {0: 'batch', 1: 'sequence'}

      return common_inputs







    @property
    def     lowercase_ (   self				:						Dict			):
      return self._config.n_layer







    @property
    def     lowercase_ (   self				:						int			):
      return self._config.n_head







    def     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						PreTrainedTokenizer		,    __snake_case				:						int = -1		,    __snake_case				:						int = -1		,    __snake_case				:						bool = False		,    __snake_case				:						Optional[TensorType] = None		,    ):
      a		:      List[Any]       =  super(__snake_case		,    self			).generate_dummy_inputs(
          __snake_case		,    batch_size=__snake_case		,    seq_length=__snake_case		,    is_pair=__snake_case		,    framework=__snake_case			)

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

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

      a		:      Optional[Any]       =  common_inputs['attention_mask']
      if self.use_past:
        a		:      List[str]       =  ordered_inputs['attention_mask'].dtype
        a		:      str       =  torch.cat(
            [ordered_inputs['attention_mask'], torch.ones(__snake_case		,    __snake_case		,    dtype=__snake_case			)]		,    dim=1			)

      return ordered_inputs







    @property
    def     lowercase_ (   self				:						int			):
      return 13




'''simple docstring'''






def    lowerCamelCase__ (		_A = 6008_5147_5143       ):

  try:
    a		:      Optional[int]       =  int(_A       )
  except (TypeError, ValueError):
    raise TypeError('Parameter n must be int or castable to int.'       )
  if n <= 0:
    raise ValueError('Parameter n must be greater than or equal to one.'       )
  a		:      Any       =  1
  a		:      Union[str, Any]       =  2
  while i * i <= n:
    while n % i == 0:
      a		:      str       =  i
      n //= i
    i += 1
  if n > 1:
    a		:      Any       =  n
  return int(_A       )


if __name__ == "__main__":
     print(F"{solution() = }")