



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     __lowerCAmelCase					(    A		):
			UpperCamelCase           =  '''bloom'''
			UpperCamelCase           =  ['''past_key_values''']
			UpperCamelCase           =  {
			    '''num_hidden_layers''': '''n_layer''',
			    '''num_attention_heads''': '''n_head''',
			}



			def __init__(		self     :						Dict				,	A     :						int=25_08_80				,	A     :						Union[str, Any]=64				,	A     :						Any=2				,	A     :						Any=8				,	A     :						List[str]=1E-5				,	A     :						Optional[Any]=0.0_2				,	A     :						List[Any]=True				,	A     :						Any=1				,	A     :						Tuple=2				,	A     :						Optional[int]=False				,	A     :						str=0.0				,	A     :						Optional[int]=0.0				,	A     :						str=1				,	A     :						str=False				,	**A     :						List[Any]				,	)     ->					List[Any]:



						"""simple docstring"""






						_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     __lowerCAmelCase					(    A		):
			UpperCamelCase           =  version.parse('''1.12'''		)



			def __init__(		self     :						int				,	A     :						PretrainedConfig				,	A     :						str = "default"				,	A     :						List[PatchingSpec] = None				,	A     :						bool = False				,	)     ->					Union[str, Any]:



						"""simple docstring"""






						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 					_lowerCamelCase (		self     :						Optional[int])     ->					Mapping[str, Mapping[int, str]]:



						"""simple docstring"""






						_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 					_lowerCamelCase (		self     :						Optional[Any])     ->					int:



						"""simple docstring"""






						return self._config.n_layer



			@property
			def 					_lowerCamelCase (		self     :						List[Any])     ->					int:



						"""simple docstring"""






						return self._config.n_head



			@property
			def 					_lowerCamelCase (		self     :						Optional[Any])     ->					float:



						"""simple docstring"""






						return 1E-3



			def 					_lowerCamelCase (		self     :						List[str]				,	A     :						"PreTrainedTokenizer"				,	A     :						int = -1				,	A     :						int = -1				,	A     :						bool = False				,	A     :						Optional["TensorType"] = None				,	)     ->					Mapping[str, Any]:



						"""simple docstring"""






						_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 					_lowerCamelCase (		self     :						List[str])     ->					int:



						"""simple docstring"""






						return 13





import unittest

from knapsack import knapsack as k







class     __lowerCAmelCase					(    unittest.TestCase		):



			def 					_lowerCamelCase (		self     :						Optional[Any])     ->					Any:



						"""simple docstring"""






						_UpperCAmelCase	 =     0
						_UpperCAmelCase	 =     [0]
						_UpperCAmelCase	 =     [0]
						_UpperCAmelCase	 =     len(A)
						self.assertEqual(k.knapsack(A				,	A				,	A				,	A)				,	0)

						_UpperCAmelCase	 =     [60]
						_UpperCAmelCase	 =     [10]
						_UpperCAmelCase	 =     len(A)
						self.assertEqual(k.knapsack(A				,	A				,	A				,	A)				,	0)



			def 					_lowerCamelCase (		self     :						str)     ->					List[str]:



						"""simple docstring"""






						_UpperCAmelCase	 =     3
						_UpperCAmelCase	 =     [1, 2, 3]
						_UpperCAmelCase	 =     [3, 2, 1]
						_UpperCAmelCase	 =     len(A)
						self.assertEqual(k.knapsack(A				,	A				,	A				,	A)				,	5)



			def 					_lowerCamelCase (		self     :						Tuple)     ->					Tuple:



						"""simple docstring"""






						_UpperCAmelCase	 =     50
						_UpperCAmelCase	 =     [60, 1_00, 1_20]
						_UpperCAmelCase	 =     [10, 20, 30]
						_UpperCAmelCase	 =     len(A)
						self.assertEqual(k.knapsack(A				,	A				,	A				,	A)				,	2_20)


if __name__ == "__main__":
							unittest.main()
