

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

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


A_	:			Dict  	=      logging.get_logger(__name__)


A_	:			Tuple  	=      {
    'Salesforce/codegen-350M-nl': 'https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json',
    'Salesforce/codegen-350M-multi': 'https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json',
    'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json',
    'Salesforce/codegen-2B-nl': 'https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json',
    'Salesforce/codegen-2B-multi': 'https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json',
    'Salesforce/codegen-2B-mono': 'https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json',
    'Salesforce/codegen-6B-nl': 'https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json',
    'Salesforce/codegen-6B-multi': 'https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json',
    'Salesforce/codegen-6B-mono': 'https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json',
    'Salesforce/codegen-16B-nl': 'https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json',
    'Salesforce/codegen-16B-multi': 'https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json',
    'Salesforce/codegen-16B-mono': 'https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json',
}



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       Optional[int]  							=	'''codegen'''
					UpperCAmelCase__:       Union[str, Any]  							=	{
					    '''max_position_embeddings''': '''n_positions''',
					    '''hidden_size''': '''n_embd''',
					    '''num_attention_heads''': '''n_head''',
					    '''num_hidden_layers''': '''n_layer''',
					}






					def __init__(			self   ,	A__=5_0400   ,	A__=2048   ,	A__=2048   ,	A__=4096   ,	A__=28   ,	A__=16   ,	A__=64   ,	A__=None   ,	A__="gelu_new"   ,	A__=0.0   ,	A__=0.0   ,	A__=0.0   ,	A__=1e-5   ,	A__=0.0_2   ,	A__=True   ,	A__=5_0256   ,	A__=5_0256   ,	A__=False   ,	**A__   ,	):
						A__							:   str					       =		vocab_size
						A__							:   Optional[int]					       =		n_ctx
						A__							:   Optional[Any]					       =		n_positions
						A__							:   Dict					       =		n_embd
						A__							:   List[str]					       =		n_layer
						A__							:   Dict					       =		n_head
						A__							:   Tuple					       =		n_inner
						A__							:   Union[str, Any]					       =		rotary_dim
						A__							:   Optional[Any]					       =		activation_function
						A__							:   Union[str, Any]					       =		resid_pdrop
						A__							:   str					       =		embd_pdrop
						A__							:   List[str]					       =		attn_pdrop
						A__							:   Tuple					       =		layer_norm_epsilon
						A__							:   Optional[Any]					       =		initializer_range
						A__							:   Optional[int]					       =		use_cache

						A__							:   Tuple					       =		bos_token_id
						A__							:   List[Any]					       =		eos_token_id

						super().__init__(
						    bos_token_id=A__   ,	eos_token_id=A__   ,	tie_word_embeddings=A__   ,	**A__      )



class    _a						(__magic_name__    ):


					'''simple docstring'''






					def __init__(			self   ,	A__   ,	A__ = "default"   ,	A__ = None   ,	A__ = False   ,	):
						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?
							A__							:   Tuple					       =		0






					@property
					def   __A      (			self      ):
						A__							:   Optional[int]					       =		OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}      )
						if self.use_past:
							self.fill_with_past_key_values_(A__   ,	direction="""inputs"""      )
							A__							:   List[str]					       =		{0: """batch""", 1: """past_sequence + sequence"""}
						else:
							A__							:   Optional[int]					       =		{0: """batch""", 1: """sequence"""}

						return common_inputs






					@property
					def   __A      (			self      ):
						return self._config.n_layer






					@property
					def   __A      (			self      ):
						return self._config.n_head






					def   __A      (			self   ,	A__   ,	A__ = -1   ,	A__ = -1   ,	A__ = False   ,	A__ = None   ,	):
						A__							:   Any					       =		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()
						A__							:   List[Any]					       =		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

								A__       ,       A__							:   Optional[int]					       =		common_inputs["""input_ids"""].shape
								# Not using the same length for past_key_values
								A__							:   Union[str, Any]					       =		seqlen + 2
								A__							:   Tuple					       =		(
								    batch,
								    self.num_attention_heads,
								    past_key_values_length,
								    self._config.hidden_size // self.num_attention_heads,
								)
								A__							:   str					       =		[
								    (torch.zeros(A__      ), torch.zeros(A__      )) for _ in range(self.num_layers      )
								]

						A__							:   int					       =		common_inputs["""attention_mask"""]
						if self.use_past:
							A__							:   Any					       =		ordered_inputs["""attention_mask"""].dtype
							A__							:   Optional[int]					       =		torch.cat(
							    [ordered_inputs["""attention_mask"""], torch.ones(A__   ,	A__   ,	dtype=A__      )]   ,	dim=1      )

						return ordered_inputs






					@property
					def   __A      (			self      ):
						return 13






from __future__ import annotations

def 						UpperCamelCase (lowercase_:			list[int]   ,				lowercase_:			list[int]   ,				lowercase_:			int )					->				tuple[float, list[float]]:
	A__							:   Tuple					       =		list(range(len(lowercase_ ) ) )
	A__							:   Union[str, Any]					       =		[v / w for v, w in zip(lowercase_   ,				lowercase_ )]
	index.sort(key=lambda lowercase_ : ratio[i]   ,				reverse=lowercase_ )

	A__							:   float					       =		0
	A__							:   list[float]					       =		[0] * len(lowercase_ )
	for i in index:
		if weight[i] <= capacity:
			A__							:   Optional[int]					       =		1
			max_value += value[i]
			capacity -= weight[i]
		else:
			A__							:   Union[str, Any]					       =		capacity / weight[i]
			max_value += value[i] * capacity / weight[i]
			break

	return max_value, fractions


if __name__ == "__main__":
	import doctest

	doctest.testmod()



