




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						:							Union[str, Any]							=  logging.get_logger(__name__)

__A						:							int							=  {
    """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_      (   A       ):




							'''simple docstring'''



							a__						  =     '''bloom'''
							a__						  =     ['''past_key_values''']
							a__						  =     {
							    '''num_hidden_layers''': '''n_layer''',
							    '''num_attention_heads''': '''n_head''',
							}





							def __init__(					self       :	Dict       ,     a       :	Dict=250_880       ,     a       :	List[str]=64       ,     a       :	List[Any]=2       ,     a       :	Union[str, Any]=8       ,     a       :	Tuple=1E-5       ,     a       :	Tuple=0.02       ,     a       :	Union[str, Any]=True       ,     a       :	Optional[Any]=1       ,     a       :	List[Any]=2       ,     a       :	Optional[int]=False       ,     a       :	Dict=0.0       ,     a       :	List[Any]=0.0       ,     a       :	Optional[int]=1       ,     a       :	Union[str, Any]=False       ,     **a       :	str       ,     )			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					vocab_size
										# Backward compatibility with n_embed kwarg
										SCREAMING_SNAKE_CASE           =					kwargs.pop("""n_embed"""       ,     a			)
										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=a       ,     eos_token_id=a       ,     **a			)





class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     version.parse('''1.12'''       )





							def __init__(					self       :	Tuple       ,     a       :	PretrainedConfig       ,     a       :	str = "default"       ,     a       :	List[PatchingSpec] = None       ,     a       :	bool = 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?
													SCREAMING_SNAKE_CASE           =					0





							@property
							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Mapping[str, Mapping[int, str]]:
										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_(a       ,     direction="""inputs"""       ,     inverted_values_shape=a			)
													SCREAMING_SNAKE_CASE           =					{0: """batch""", 1: """past_sequence + sequence"""}
										else:
													SCREAMING_SNAKE_CASE           =					{0: """batch""", 1: """sequence"""}

										return common_inputs





							@property
							def    _UpperCAmelCase     (					self       :	Any			)			->					int:
										return self._config.n_layer





							@property
							def    _UpperCAmelCase     (					self       :	List[str]			)			->					int:
										return self._config.n_head





							@property
							def    _UpperCAmelCase     (					self       :	Tuple			)			->					float:
										return 1E-3





							def    _UpperCAmelCase     (					self       :	List[Any]       ,     a       :	"PreTrainedTokenizer"       ,     a       :	int = -1       ,     a       :	int = -1       ,     a       :	bool = False       ,     a       :	Optional["TensorType"] = None       ,     )			->					Mapping[str, Any]:
										SCREAMING_SNAKE_CASE           =					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()
										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(a			), torch.zeros(a			)) 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(a       ,     a       ,     dtype=a			)]       ,     dim=1			)

										return ordered_inputs







							@property
							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					int:
										return 13












from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

__A						:							Dict							=  typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
__A						:							Tuple							=  typing.Union[np.floataa, int, float]  # noqa: UP007
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return np.sqrt(np.sum((np.asarray(SCREAMING_SNAKE_CASE   ) - np.asarray(SCREAMING_SNAKE_CASE   )) ** 2   )   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return sum((va - va) ** 2 for va, va in zip(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )   ) ** (1 / 2)


if __name__ == "__main__":
		def 					lowerCamelCase_    (    ):
					'''simple docstring'''
					from timeit import timeit

					print("""Without Numpy"""   )
					print(
					    timeit(
					        """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"""					,	number=1_00_00					,	globals=globals()					,	)   )
					print("""With Numpy"""   )
					print(
					    timeit(
					        """euclidean_distance([1, 2, 3], [4, 5, 6])"""					,	number=1_00_00					,	globals=globals()					,	)   )




		benchmark()






