"""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


UpperCAmelCase       =  logging.get_logger(__name__)

UpperCAmelCase       =  {
    """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      UpperCAmelCase_       (						__SCREAMING_SNAKE_CASE):
							snake_case__        =      'bloom'
							snake_case__        =      ['past_key_values']
							snake_case__        =      {
							    'num_hidden_layers': 'n_layer',
							    'num_attention_heads': 'n_head',
							}






							def __init__(		self :      Tuple     ,						__UpperCamelCase :      Dict=25_0880     ,						__UpperCamelCase :      List[Any]=64     ,						__UpperCamelCase :      List[str]=2     ,						__UpperCamelCase :      Union[str, Any]=8     ,						__UpperCamelCase :      List[str]=1E-5     ,						__UpperCamelCase :      List[str]=0.0_2     ,						__UpperCamelCase :      Optional[Any]=True     ,						__UpperCamelCase :      Tuple=1     ,						__UpperCamelCase :      Optional[int]=2     ,						__UpperCamelCase :      List[Any]=False     ,						__UpperCamelCase :      List[Any]=0.0     ,						__UpperCamelCase :      Optional[int]=0.0     ,						__UpperCamelCase :      Tuple=1     ,						__UpperCamelCase :      Union[str, Any]=False     ,						**__UpperCamelCase :      Optional[Any]     ,						)       ->       List[str]:
												_UpperCamelCase      						=	vocab_size
												# Backward compatibility with n_embed kwarg
												_UpperCamelCase      						=	kwargs.pop('''n_embed'''     ,						_a  )
												_UpperCamelCase      						=	hidden_size if n_embed is None else n_embed
												_UpperCamelCase      						=	n_layer
												_UpperCamelCase      						=	n_head
												_UpperCamelCase      						=	layer_norm_epsilon
												_UpperCamelCase      						=	initializer_range
												_UpperCamelCase      						=	use_cache
												_UpperCamelCase      						=	pretraining_tp
												_UpperCamelCase      						=	apply_residual_connection_post_layernorm
												_UpperCamelCase      						=	hidden_dropout
												_UpperCamelCase      						=	attention_dropout

												_UpperCamelCase      						=	bos_token_id
												_UpperCamelCase      						=	eos_token_id
												_UpperCamelCase      						=	slow_but_exact

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







class      UpperCAmelCase_       (						__SCREAMING_SNAKE_CASE):
							snake_case__        =      version.parse('''1.12''')






							def __init__(		self :      Dict     ,						__UpperCamelCase :      Dict     ,						__UpperCamelCase :      Optional[Any] = "default"     ,						__UpperCamelCase :      Optional[int] = None     ,						__UpperCamelCase :      Tuple = False     ,						)       ->       Union[str, Any]:
												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?
																	_UpperCamelCase      						=	0






							@property
							def       _UpperCamelCase			(		self :      Optional[Any]  )       ->       List[str]:
												_UpperCamelCase      						=	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_(_a     ,						direction='''inputs'''     ,						inverted_values_shape=_a  )
																	_UpperCamelCase      						=	{0: '''batch''', 1: '''past_sequence + sequence'''}
												else:
																	_UpperCamelCase      						=	{0: '''batch''', 1: '''sequence'''}

												return common_inputs






							@property
							def       _UpperCamelCase			(		self :      Optional[int]  )       ->       Tuple:
												return self._config.n_layer






							@property
							def       _UpperCamelCase			(		self :      int  )       ->       Optional[int]:
												return self._config.n_head






							@property
							def       _UpperCamelCase			(		self :      Optional[int]  )       ->       List[Any]:
												return 1E-3






							def       _UpperCamelCase			(		self :      Dict     ,						__UpperCamelCase :      str     ,						__UpperCamelCase :      Dict = -1     ,						__UpperCamelCase :      List[Any] = -1     ,						__UpperCamelCase :      Dict = False     ,						__UpperCamelCase :      List[Any] = None     ,						)       ->       Any:
												_UpperCamelCase      						=	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()
												_UpperCamelCase      						=	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

																						_UpperCamelCase						,							_UpperCamelCase      						=	common_inputs['''input_ids'''].shape
																						# Not using the same length for past_key_values
																						_UpperCamelCase      						=	seqlen + 2
																						_UpperCamelCase      						=	self._config.hidden_size // self.num_attention_heads
																						_UpperCamelCase      						=	(
																						    batch * self.num_attention_heads,
																						    head_dim,
																						    past_key_values_length,
																						)
																						_UpperCamelCase      						=	(
																						    batch * self.num_attention_heads,
																						    past_key_values_length,
																						    head_dim,
																						)
																						_UpperCamelCase      						=	[
																						    (torch.zeros(_a  ), torch.zeros(_a  )) for _ in range(self.num_layers  )
																						]

												_UpperCamelCase      						=	common_inputs['''attention_mask''']
												if self.use_past:
																	_UpperCamelCase      						=	ordered_inputs['''attention_mask'''].dtype
																	_UpperCamelCase      						=	torch.cat(
																	    [ordered_inputs['''attention_mask'''], torch.ones(_a     ,						_a     ,						dtype=_a  )]     ,						dim=1  )

												return ordered_inputs






							@property
							def       _UpperCamelCase			(		self :      Any  )       ->       Tuple:
												return 13




"""simple docstring"""




import numpy as np

def        lowercase							(		lowerCAmelCase__ :     np.ndarray    ,					lowerCAmelCase__ :     float			) ->      np.ndarray:
		return np.where(vector > 0    ,					lowerCAmelCase__    ,					(alpha * (np.exp(lowerCAmelCase__			) - 1))			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()






