





'''simple docstring'''

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

from packaging import version


if TYPE_CHECKING:
    from ... import PreTrainedTokenizer, TensorType

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


a   	=    logging.get_logger(__name__)

a   	=    {
    "bigscience/bloom": "https://huggingface.co/bigscience/bloom/resolve/main/config.json",
    "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/config.json",
    "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json",
    "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json",
    "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/config.json",
    "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json",
}







class __a			( _snake_case      ):
   __UpperCamelCase :			List[str]						   =						'bloom'
   __UpperCamelCase :			Optional[Any]						   =						['past_key_values']
   __UpperCamelCase :			Optional[int]						   =						{
       'num_hidden_layers': 'n_layer',
       'num_attention_heads': 'n_head',
   }





   def __init__(       self					:      Union[str, Any]					,lowerCamelCase					:      str=25_0880					,lowerCamelCase					:      List[Any]=64					,lowerCamelCase					:      Optional[Any]=2					,lowerCamelCase					:      Tuple=8					,lowerCamelCase					:      Union[str, Any]=1E-5					,lowerCamelCase					:      Optional[Any]=0.02					,lowerCamelCase					:      str=True					,lowerCamelCase					:      List[Any]=1					,lowerCamelCase					:      Union[str, Any]=2					,lowerCamelCase					:      Optional[int]=False					,lowerCamelCase					:      Optional[int]=0.0					,lowerCamelCase					:      List[Any]=0.0					,lowerCamelCase					:      Optional[Any]=1					,lowerCamelCase					:      Tuple=False					,**lowerCamelCase					:      str					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							vocab_size
      # Backward compatibility with n_embed kwarg
      __SCREAMING_SNAKE_CASE   				=							kwargs.pop("""n_embed"""					,lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							hidden_size if n_embed is None else n_embed
      __SCREAMING_SNAKE_CASE   				=							n_layer
      __SCREAMING_SNAKE_CASE   				=							n_head
      __SCREAMING_SNAKE_CASE   				=							layer_norm_epsilon
      __SCREAMING_SNAKE_CASE   				=							initializer_range
      __SCREAMING_SNAKE_CASE   				=							use_cache
      __SCREAMING_SNAKE_CASE   				=							pretraining_tp
      __SCREAMING_SNAKE_CASE   				=							apply_residual_connection_post_layernorm
      __SCREAMING_SNAKE_CASE   				=							hidden_dropout
      __SCREAMING_SNAKE_CASE   				=							attention_dropout

      __SCREAMING_SNAKE_CASE   				=							bos_token_id
      __SCREAMING_SNAKE_CASE   				=							eos_token_id
      __SCREAMING_SNAKE_CASE   				=							slow_but_exact

      super().__init__(bos_token_id=lowerCamelCase					,eos_token_id=lowerCamelCase					,**lowerCamelCase     )







class __a			( _snake_case      ):
   __UpperCamelCase :			int						   =						version.parse('1.12'      )





   def __init__(       self					:      Optional[int]					,lowerCamelCase					:      PretrainedConfig					,lowerCamelCase					:      str = "default"					,lowerCamelCase					:      List[PatchingSpec] = None					,lowerCamelCase					:      bool = False					,):

      '''simple docstring'''

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





   @property
   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}     )
      if self.use_past:
         # BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
         self.fill_with_past_key_values_(lowerCamelCase					,direction="""inputs"""					,inverted_values_shape=lowerCamelCase     )
         __SCREAMING_SNAKE_CASE   				=							{0: """batch""", 1: """past_sequence + sequence"""}
      else:
         __SCREAMING_SNAKE_CASE   				=							{0: """batch""", 1: """sequence"""}

      return common_inputs





   @property
   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      return self._config.n_layer





   @property
   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      return self._config.n_head





   @property
   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      return 1E-3





   def   UpperCAmelCase__  (       self					:      str					,lowerCamelCase					:      "PreTrainedTokenizer"					,lowerCamelCase					:      int = -1					,lowerCamelCase					:      int = -1					,lowerCamelCase					:      bool = False					,lowerCamelCase					:      Optional["TensorType"] = None					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							super(lowerCamelCase					,self     ).generate_dummy_inputs(
          lowerCamelCase					,batch_size=lowerCamelCase					,seq_length=lowerCamelCase					,is_pair=lowerCamelCase					,framework=lowerCamelCase     )

      # We need to order the input in the way they appears in the forward()
      __SCREAMING_SNAKE_CASE   				=							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				,  __SCREAMING_SNAKE_CASE   				=							common_inputs["""input_ids"""].shape
            # Not using the same length for past_key_values
            __SCREAMING_SNAKE_CASE   				=							seqlen + 2
            __SCREAMING_SNAKE_CASE   				=							self._config.hidden_size // self.num_attention_heads
            __SCREAMING_SNAKE_CASE   				=							(
                batch * self.num_attention_heads,
                head_dim,
                past_key_values_length,
            )
            __SCREAMING_SNAKE_CASE   				=							(
                batch * self.num_attention_heads,
                past_key_values_length,
                head_dim,
            )
            __SCREAMING_SNAKE_CASE   				=							[
                (torch.zeros(lowerCamelCase     ), torch.zeros(lowerCamelCase     )) for _ in range(self.num_layers     )
            ]

      __SCREAMING_SNAKE_CASE   				=							common_inputs["""attention_mask"""]
      if self.use_past:
         __SCREAMING_SNAKE_CASE   				=							ordered_inputs["""attention_mask"""].dtype
         __SCREAMING_SNAKE_CASE   				=							torch.cat(
             [ordered_inputs["""attention_mask"""], torch.ones(lowerCamelCase					,lowerCamelCase					,dtype=lowerCamelCase     )]					,dim=1     )

      return ordered_inputs





   @property
   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      return 13












def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int = 600_851_475_143     ):

		try:
				SCREAMING_SNAKE_CASE__								=   int(UpperCamelCase__     )
		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."""     )
		SCREAMING_SNAKE_CASE__								=   1
		SCREAMING_SNAKE_CASE__								=   2
		while i * i <= n:
				while n % i == 0:
						SCREAMING_SNAKE_CASE__								=   i
						n //= i
				i += 1
		if n > 1:
				SCREAMING_SNAKE_CASE__								=   n
		return int(UpperCamelCase__     )


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