

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							:  List[str]   	=						logging.get_logger(__name__)


lowerCAmelCase							:  Dict   	=						{
    """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   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							Tuple										=     '''codegen'''
			_UpperCAmelCase   :							List[Any]										=     {
			    '''max_position_embeddings''': '''n_positions''',
			    '''hidden_size''': '''n_embd''',
			    '''num_attention_heads''': '''n_head''',
			    '''num_hidden_layers''': '''n_layer''',
			}



			def __init__( self  :     List[Any]   ,						lowerCAmelCase__  :     int=5_0400   ,						lowerCAmelCase__  :     Dict=2048   ,						lowerCAmelCase__  :     Optional[int]=2048   ,						lowerCAmelCase__  :     Union[str, Any]=4096   ,						lowerCAmelCase__  :     Optional[int]=28   ,						lowerCAmelCase__  :     str=16   ,						lowerCAmelCase__  :     Union[str, Any]=64   ,						lowerCAmelCase__  :     List[Any]=None   ,						lowerCAmelCase__  :     Tuple="gelu_new"   ,						lowerCAmelCase__  :     List[Any]=0.0   ,						lowerCAmelCase__  :     Any=0.0   ,						lowerCAmelCase__  :     Any=0.0   ,						lowerCAmelCase__  :     Any=1E-5   ,						lowerCAmelCase__  :     Optional[int]=0.02   ,						lowerCAmelCase__  :     Any=True   ,						lowerCAmelCase__  :     Any=5_0256   ,						lowerCAmelCase__  :     int=5_0256   ,						lowerCAmelCase__  :     Union[str, Any]=False   ,						**lowerCAmelCase__  :     str   ,						):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       vocab_size
									SCREAMING_SNAKE_CASE_: List[str]  		=       n_ctx
									SCREAMING_SNAKE_CASE_: List[Any]  		=       n_positions
									SCREAMING_SNAKE_CASE_: List[str]  		=       n_embd
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       n_layer
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       n_head
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       n_inner
									SCREAMING_SNAKE_CASE_: List[str]  		=       rotary_dim
									SCREAMING_SNAKE_CASE_: Dict  		=       activation_function
									SCREAMING_SNAKE_CASE_: Dict  		=       resid_pdrop
									SCREAMING_SNAKE_CASE_: List[str]  		=       embd_pdrop
									SCREAMING_SNAKE_CASE_: List[Any]  		=       attn_pdrop
									SCREAMING_SNAKE_CASE_: int  		=       layer_norm_epsilon
									SCREAMING_SNAKE_CASE_: str  		=       initializer_range
									SCREAMING_SNAKE_CASE_: List[Any]  		=       use_cache

									SCREAMING_SNAKE_CASE_: Any  		=       bos_token_id
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       eos_token_id

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







class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			def __init__( self  :     List[Any]   ,						lowerCAmelCase__  :     PretrainedConfig   ,						lowerCAmelCase__  :     str = "default"   ,						lowerCAmelCase__  :     List[PatchingSpec] = None   ,						lowerCAmelCase__  :     bool = False   ,						):
									super().__init__(lowerCAmelCase__   ,						task=lowerCAmelCase__   ,						patching_specs=lowerCAmelCase__   ,						use_past=lowerCAmelCase__)
									if not getattr(self._config   ,						"pad_token_id"   ,						lowerCAmelCase__):
															# TODO: how to do that better?
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       0



			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: Tuple  		=       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
									if self.use_past:
															self.fill_with_past_key_values_(lowerCAmelCase__   ,						direction="inputs")
															SCREAMING_SNAKE_CASE_: Tuple  		=       {0: "batch", 1: "past_sequence + sequence"}
									else:
															SCREAMING_SNAKE_CASE_: int  		=       {0: "batch", 1: "sequence"}

									return common_inputs



			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									return self._config.n_layer



			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									return self._config.n_head



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]   ,						lowerCAmelCase__  :     PreTrainedTokenizer   ,						lowerCAmelCase__  :     int = -1   ,						lowerCAmelCase__  :     int = -1   ,						lowerCAmelCase__  :     bool = False   ,						lowerCAmelCase__  :     Optional[TensorType] = None   ,						):
									SCREAMING_SNAKE_CASE_: str  		=       super(lowerCAmelCase__   ,						self).generate_dummy_inputs(
									    lowerCAmelCase__   ,						batch_size=lowerCAmelCase__   ,						seq_length=lowerCAmelCase__   ,						is_pair=lowerCAmelCase__   ,						framework=lowerCAmelCase__)

									# We need to order the input in the way they appears in the forward()
									SCREAMING_SNAKE_CASE_: str  		=       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_: Any  		=       common_inputs["input_ids"].shape
																					# Not using the same length for past_key_values
																					SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       seqlen + 2
																					SCREAMING_SNAKE_CASE_: Optional[Any]  		=       (
																					    batch,
																					    self.num_attention_heads,
																					    past_key_values_length,
																					    self._config.hidden_size // self.num_attention_heads,
																					)
																					SCREAMING_SNAKE_CASE_: Tuple  		=       [
																					    (torch.zeros(lowerCAmelCase__), torch.zeros(lowerCAmelCase__)) for _ in range(self.num_layers)
																					]

									SCREAMING_SNAKE_CASE_: Dict  		=       common_inputs["attention_mask"]
									if self.use_past:
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       ordered_inputs["attention_mask"].dtype
															SCREAMING_SNAKE_CASE_: List[str]  		=       torch.cat(
															    [ordered_inputs["attention_mask"], torch.ones(lowerCAmelCase__   ,						lowerCAmelCase__   ,						dtype=lowerCAmelCase__)]   ,						dim=1)

									return ordered_inputs




			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									return 13










from __future__ import annotations
def 							_UpperCAmelCase				(UpperCamelCase__     :  list[int]				,							UpperCamelCase__     :  list[int]				,							UpperCamelCase__     :  int						):
       _A      :       Dict				    = list(range(len(UpperCamelCase__						)						)						)
       _A      :       Any				    = [v / w for v, w in zip(UpperCamelCase__				,							UpperCamelCase__						)]
       index.sort(key=lambda UpperCamelCase__						: ratio[i]				,							reverse=UpperCamelCase__						)

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

       return max_value, fractions


if __name__ == "__main__":
    import doctest

    doctest.testmod()
