




import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class       a__   ( snake_case__			,    snake_case__			,    snake_case__				):






						@register_to_config
						def __init__(       self		,      _A		,      _A		,      _A		,      _A		,      _A		,      _A		,      _A		,      _A		,      _A		,      _A = False		,      ):




											"""simple docstring"""



											super().__init__()

											__lowerCAmelCase           =	nn.Embedding(_A		,      _A      )

											__lowerCAmelCase           =	nn.Embedding(_A		,      _A      )
											__lowerCAmelCase           =	False

											__lowerCAmelCase           =	nn.Dropout(p=_A      )

											__lowerCAmelCase           =	TaConfig(
											    vocab_size=_A		,      d_model=_A		,      num_heads=_A		,      d_kv=_A		,      d_ff=_A		,      dropout_rate=_A		,      feed_forward_proj=_A		,      is_decoder=_A		,      is_encoder_decoder=_A		,      )

											__lowerCAmelCase           =	nn.ModuleList()
											for lyr_num in range(_A      ):
																__lowerCAmelCase           =	TaBlock(_A      )
																self.encoders.append(_A      )

											__lowerCAmelCase           =	TaLayerNorm(_A      )
											__lowerCAmelCase           =	nn.Dropout(p=_A      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.token_embedder(_A      )

											__lowerCAmelCase           =	encoder_input_tokens.shape[1]
											__lowerCAmelCase           =	torch.arange(_A		,      device=encoder_input_tokens.device      )
											x += self.position_encoding(_A      )

											__lowerCAmelCase           =	self.dropout_pre(_A      )

											# inverted the attention mask
											__lowerCAmelCase           =	encoder_input_tokens.size()
											__lowerCAmelCase           =	self.get_extended_attention_mask(_A		,      _A      )

											for lyr in self.encoders:
																__lowerCAmelCase           =	lyr(_A		,      _A      )[0]
											__lowerCAmelCase           =	self.layer_norm(_A      )

											return self.dropout_post(_A      ), encoder_inputs_mask










from __future__ import annotations







def      _a				(						SCREAMING_SNAKE_CASE_							:    float				,				SCREAMING_SNAKE_CASE_							:    float				,				SCREAMING_SNAKE_CASE_							:    float				,				):
					if (electron_conc, hole_conc, intrinsic_conc).count(0					) != 1:
										raise ValueError("You cannot supply more or less than 2 values"					)
					elif electron_conc < 0:
										raise ValueError("Electron concentration cannot be negative in a semiconductor"					)
					elif hole_conc < 0:
										raise ValueError("Hole concentration cannot be negative in a semiconductor"					)
					elif intrinsic_conc < 0:
										raise ValueError(
										    "Intrinsic concentration cannot be negative in a semiconductor"					)
					elif electron_conc == 0:
										return (
										    "electron_conc",
										    intrinsic_conc**2 / hole_conc,
										)
					elif hole_conc == 0:
										return (
										    "hole_conc",
										    intrinsic_conc**2 / electron_conc,
										)
					elif intrinsic_conc == 0:
										return (
										    "intrinsic_conc",
										    (electron_conc * hole_conc) ** 0.5,
										)
					else:
										return (-1, -1)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




