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


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

__lowerCamelCase			: Union[str, Any]							   =		{
    '''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 __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							"bloom"
   lowerCAmelCase_														=							["past_key_values"]
   lowerCAmelCase_														=							{
       "num_hidden_layers": "n_layer",
       "num_attention_heads": "n_head",
   }




   def __init__(    self			:				Dict       ,	_lowercase			:				Dict=25_08_80       ,	_lowercase			:				Tuple=64       ,	_lowercase			:				int=2       ,	_lowercase			:				Optional[Any]=8       ,	_lowercase			:				Optional[Any]=1E-5       ,	_lowercase			:				Tuple=0.02       ,	_lowercase			:				Optional[Any]=True       ,	_lowercase			:				int=1       ,	_lowercase			:				Optional[int]=2       ,	_lowercase			:				str=False       ,	_lowercase			:				List[Any]=0.0       ,	_lowercase			:				str=0.0       ,	_lowercase			:				Union[str, Any]=1       ,	_lowercase			:				str=False       ,	**_lowercase			:				List[Any]       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      vocab_size
     # Backward compatibility with n_embed kwarg
     SCREAMING_SNAKE_CASE__										=      kwargs.pop("""n_embed"""       ,	_lowercase      )
     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=_lowercase       ,	eos_token_id=_lowercase       ,	**_lowercase      )







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							version.parse("1.12"		)




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



     """simple docstring"""






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




   @property
   def 	__a						(    self			:				Dict      ):



     """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_(_lowercase       ,	direction="""inputs"""       ,	inverted_values_shape=_lowercase      )
       SCREAMING_SNAKE_CASE__										=      {0: """batch""", 1: """past_sequence + sequence"""}
     else:
       SCREAMING_SNAKE_CASE__										=      {0: """batch""", 1: """sequence"""}

     return common_inputs




   @property
   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     return self._config.n_layer




   @property
   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     return self._config.n_head




   @property
   def 	__a						(    self			:				Any      ):



     """simple docstring"""






     return 1E-3




   def 	__a						(    self			:				Dict       ,	_lowercase			:				"PreTrainedTokenizer"       ,	_lowercase			:				int = -1       ,	_lowercase			:				int = -1       ,	_lowercase			:				bool = False       ,	_lowercase			:				Optional["TensorType"] = None       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      super(_lowercase       ,	self      ).generate_dummy_inputs(
         _lowercase       ,	batch_size=_lowercase       ,	seq_length=_lowercase       ,	is_pair=_lowercase       ,	framework=_lowercase      )

     # 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(_lowercase      ), torch.zeros(_lowercase      )) 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(_lowercase       ,	_lowercase       ,	dtype=_lowercase      )]       ,	dim=1      )

     return ordered_inputs




   @property
   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     return 13

from numpy import exp, pi, sqrt



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Any ,     __UpperCamelCase    :       float = 0.0 ,     __UpperCamelCase    :       float = 1.0	)							->					int:







  """simple docstring"""





  return 1 / sqrt(2 * pi * sigma**2	) * exp(-((x - mu) ** 2) / (2 * sigma**2)	)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
