





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


_lowerCAmelCase							:					Optional[Any]		    =		logging.get_logger(__name__)


_lowerCAmelCase							:					str		    =		{
    "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     __magic_name__     (							a_							):





     """simple docstring"""



     __UpperCamelCase    			=   '''codegen'''
     __UpperCamelCase    			=   {
         '''max_position_embeddings''': '''n_positions''',
         '''hidden_size''': '''n_embd''',
         '''num_attention_heads''': '''n_head''',
         '''num_hidden_layers''': '''n_layer''',
     }



     def __init__(							self				:Union[str, Any]	,					snake_case				:List[str]=50_400	,					snake_case				:Dict=2_048	,					snake_case				:str=2_048	,					snake_case				:List[Any]=4_096	,					snake_case				:Union[str, Any]=28	,					snake_case				:Any=16	,					snake_case				:Tuple=64	,					snake_case				:List[Any]=None	,					snake_case				:List[str]="gelu_new"	,					snake_case				:Tuple=0.0	,					snake_case				:int=0.0	,					snake_case				:Optional[int]=0.0	,					snake_case				:str=1e-5	,					snake_case				:Dict=0.02	,					snake_case				:Union[str, Any]=True	,					snake_case				:Dict=50_256	,					snake_case				:Optional[int]=50_256	,					snake_case				:List[Any]=False	,					**snake_case				:Optional[int]	,					):





        '''simple docstring'''




        A_    : int								=	vocab_size
        A_    : Tuple								=	n_ctx
        A_    : Union[str, Any]								=	n_positions
        A_    : Union[str, Any]								=	n_embd
        A_    : Dict								=	n_layer
        A_    : Union[str, Any]								=	n_head
        A_    : Optional[int]								=	n_inner
        A_    : Tuple								=	rotary_dim
        A_    : List[Any]								=	activation_function
        A_    : Any								=	resid_pdrop
        A_    : Tuple								=	embd_pdrop
        A_    : Tuple								=	attn_pdrop
        A_    : Any								=	layer_norm_epsilon
        A_    : Tuple								=	initializer_range
        A_    : str								=	use_cache

        A_    : List[str]								=	bos_token_id
        A_    : List[str]								=	eos_token_id

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




class     __magic_name__     (							a_							):





     """simple docstring"""



     def __init__(							self				:List[Any]	,					snake_case				:Optional[Any]	,					snake_case				:List[str] = "default"	,					snake_case				:List[str] = None	,					snake_case				:Tuple = False	,					):





        '''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?
           A_    : str								=	0



     @property
     def   SCREAMING_SNAKE_CASE						(							self				:int     ):





        '''simple docstring'''




        A_    : Optional[int]								=	OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}     )
        if self.use_past:
           self.fill_with_past_key_values_(_A	,					direction="inputs"     )
           A_    : Optional[Any]								=	{0: "batch", 1: "past_sequence + sequence"}
        else:
           A_    : Dict								=	{0: "batch", 1: "sequence"}

        return common_inputs



     @property
     def   SCREAMING_SNAKE_CASE						(							self				:Dict     ):





        '''simple docstring'''




        return self._config.n_layer



     @property
     def   SCREAMING_SNAKE_CASE						(							self				:Optional[Any]     ):





        '''simple docstring'''




        return self._config.n_head



     def   SCREAMING_SNAKE_CASE						(							self				:List[Any]	,					snake_case				:Tuple	,					snake_case				:List[str] = -1	,					snake_case				:Dict = -1	,					snake_case				:Dict = False	,					snake_case				:Dict = None	,					):





        '''simple docstring'''




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

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

        return ordered_inputs





     @property
     def   SCREAMING_SNAKE_CASE						(							self				:List[str]     ):





        '''simple docstring'''




        return 13







from __future__ import annotations

from collections import namedtuple





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				tuple:







			'''simple docstring'''

			UpperCAmelCase   		=				namedtuple('''result'''			,			'''name value''' )
			if (voltage, current, power).count(0 ) != 1:
						raise ValueError('''Only one argument must be 0''' )
			elif power < 0:
						raise ValueError(
						    '''Power cannot be negative in any electrical/electronics system''' )
			elif voltage == 0:
						return result('''voltage'''			,			power / current )
			elif current == 0:
						return result('''current'''			,			power / voltage )
			elif power == 0:
						return result('''power'''			,			float(round(abs(voltage * current )			,			2 ) ) )
			else:
						raise ValueError('''Exactly one argument must be 0''' )


if __name__ == "__main__":
				import doctest

				doctest.testmod()



