



from __future__ import annotations

lowerCamelCase		       =      1.6021E-19  # units = C






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				)	->						tuple[str, float]:
				if (conductivity, electron_conc, mobility).count(0 ) != 1:
								raise ValueError("You cannot supply more or less than 2 values" )
				elif conductivity < 0:
								raise ValueError("Conductivity cannot be negative" )
				elif electron_conc < 0:
								raise ValueError("Electron concentration cannot be negative" )
				elif mobility < 0:
								raise ValueError("mobility cannot be negative" )
				elif conductivity == 0:
								return (
								    "conductivity",
								    mobility * electron_conc * ELECTRON_CHARGE,
								)
				elif electron_conc == 0:
								return (
								    "electron_conc",
								    conductivity / (mobility * ELECTRON_CHARGE),
								)
				else:
								return (
								    "mobility",
								    conductivity / (electron_conc * ELECTRON_CHARGE),
								)


if __name__ == "__main__":
						import doctest

						doctest.testmod()







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		       =      logging.get_logger(__name__)


lowerCamelCase		       =      {
    """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						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:str =       "codegen"
							A				:Tuple =       {
							    "max_position_embeddings": "n_positions",
							    "hidden_size": "n_embd",
							    "num_attention_heads": "n_head",
							    "num_hidden_layers": "n_layer",
							}



							def __init__(							self ,					__UpperCAmelCase=5_0400 ,					__UpperCAmelCase=2048 ,					__UpperCAmelCase=2048 ,					__UpperCAmelCase=4096 ,					__UpperCAmelCase=28 ,					__UpperCAmelCase=16 ,					__UpperCAmelCase=64 ,					__UpperCAmelCase=None ,					__UpperCAmelCase="gelu_new" ,					__UpperCAmelCase=0.0 ,					__UpperCAmelCase=0.0 ,					__UpperCAmelCase=0.0 ,					__UpperCAmelCase=1E-5 ,					__UpperCAmelCase=0.0_2 ,					__UpperCAmelCase=True ,					__UpperCAmelCase=5_0256 ,					__UpperCAmelCase=5_0256 ,					__UpperCAmelCase=False ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



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

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

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





class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = "default" ,					__UpperCAmelCase = None ,					__UpperCAmelCase = False ,					):







											"""simple docstring"""



											super().__init__(__UpperCAmelCase ,					task=__UpperCAmelCase ,					patching_specs=__UpperCAmelCase ,					use_past=__UpperCAmelCase				)
											if not getattr(self._config ,					"pad_token_id" ,					__UpperCAmelCase				):
															# TODO: how to do that better?
															a__	:							Union[str, Any]      						= 0



							@property
							def 		_A	(							self				):







											"""simple docstring"""



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

											return common_inputs



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return self._config.n_layer



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return self._config.n_head



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = -1 ,					__UpperCAmelCase = -1 ,					__UpperCAmelCase = False ,					__UpperCAmelCase = None ,					):







											"""simple docstring"""



											a__	:							Dict      						= super(__UpperCAmelCase ,					self				).generate_dummy_inputs(
											    __UpperCAmelCase ,					batch_size=__UpperCAmelCase ,					seq_length=__UpperCAmelCase ,					is_pair=__UpperCAmelCase ,					framework=__UpperCAmelCase				)

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

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

											return ordered_inputs




							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return 13


