




'''simple docstring'''

import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin



class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase				,     _UpperCamelCase	):





			@register_to_config
			def __init__(      self						:    Dict      ,		a_						:    int = 128      ,		a_						:    int = 256      ,		a_						:    float = 2000.0      ,		a_						:    int = 768      ,		a_						:    int = 12      ,		a_						:    int = 12      ,		a_						:    int = 64      ,		a_						:    int = 2_048      ,		a_						:    float = 0.1      ,		):



						"""simple docstring"""




						super().__init__()

						__snake_case			=		nn.Sequential(
						    nn.Linear(a_      ,		d_model * 4      ,		bias=a_							)      ,		nn.SiLU()      ,		nn.Linear(d_model * 4      ,		d_model * 4      ,		bias=a_							)      ,		nn.SiLU()      ,		)

						__snake_case			=		nn.Embedding(a_      ,		a_							)
						__snake_case			=		False

						__snake_case			=		nn.Linear(a_      ,		a_      ,		bias=a_							)

						__snake_case			=		nn.Dropout(p=a_							)

						__snake_case			=		nn.ModuleList()
						for lyr_num in range(a_							):
									# FiLM conditional T5 decoder
									__snake_case			=		DecoderLayer(d_model=a_      ,		d_kv=a_      ,		num_heads=a_      ,		d_ff=a_      ,		dropout_rate=a_							)
									self.decoders.append(a_							)

						__snake_case			=		TaLayerNorm(a_							)

						__snake_case			=		nn.Dropout(p=a_							)
						__snake_case			=		nn.Linear(a_      ,		a_      ,		bias=a_							)





			def 							A (      self						:    Any      ,		a_						:    Tuple      ,		a_						:    Optional[Any]							):



						"""simple docstring"""




						__snake_case			=		torch.mul(query_input.unsqueeze(-1							)      ,		key_input.unsqueeze(-2							)							)
						return mask.unsqueeze(-3							)






			def 							A (      self						:    int      ,		a_						:    Any      ,		a_						:    Optional[int]      ,		a_						:    Any							):



						"""simple docstring"""




						__snake_case		,     __snake_case		,     __snake_case			=		decoder_input_tokens.shape
						assert decoder_noise_time.shape == (batch,)

						# decoder_noise_time is in [0, 1), so rescale to expected timing range.
						__snake_case			=		get_timestep_embedding(
						    decoder_noise_time * self.config.max_decoder_noise_time      ,		embedding_dim=self.config.d_model      ,		max_period=self.config.max_decoder_noise_time      ,		).to(dtype=self.dtype							)

						__snake_case			=		self.conditioning_emb(a_							).unsqueeze(1							)

						assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

						__snake_case			=		decoder_input_tokens.shape[1]

						# If we want to use relative positions for audio context, we can just offset
						# this sequence by the length of encodings_and_masks.
						__snake_case			=		torch.broadcast_to(
						    torch.arange(a_      ,		device=decoder_input_tokens.device							)      ,		(batch, seq_length)      ,		)

						__snake_case			=		self.position_encoding(a_							)

						__snake_case			=		self.continuous_inputs_projection(a_							)
						inputs += position_encodings
						__snake_case			=		self.dropout(a_							)

						# decoder: No padding present.
						__snake_case			=		torch.ones(
						    decoder_input_tokens.shape[:2]      ,		device=decoder_input_tokens.device      ,		dtype=inputs.dtype							)

						# Translate encoding masks to encoder-decoder masks.
						__snake_case			=		[(x, self.encoder_decoder_mask(a_      ,		a_							)) for x, y in encodings_and_masks]

						# cross attend style: concat encodings
						__snake_case			=		torch.cat([x[0] for x in encodings_and_encdec_masks]      ,		dim=1							)
						__snake_case			=		torch.cat([x[1] for x in encodings_and_encdec_masks]      ,		dim=-1							)

						for lyr in self.decoders:
									__snake_case			=		lyr(
									    a_      ,		conditioning_emb=a_      ,		encoder_hidden_states=a_      ,		encoder_attention_mask=a_      ,		)[0]

						__snake_case			=		self.decoder_norm(a_							)
						__snake_case			=		self.post_dropout(a_							)

						__snake_case			=		self.spec_out(a_							)
						return spec_out







class 		SCREAMING_SNAKE_CASE__  (			nn.Module	):





			def __init__(      self						:    Dict      ,		a_						:    Optional[int]      ,		a_						:    Dict      ,		a_						:    List[str]      ,		a_						:    Any      ,		a_						:    Tuple      ,		a_						:    Optional[int]=1e-6							):



						"""simple docstring"""




						super().__init__()
						__snake_case			=		nn.ModuleList()

						# cond self attention: layer 0
						self.layer.append(
						    TaLayerSelfAttentionCond(d_model=a_      ,		d_kv=a_      ,		num_heads=a_      ,		dropout_rate=a_							)							)

						# cross attention: layer 1
						self.layer.append(
						    TaLayerCrossAttention(
						        d_model=a_      ,		d_kv=a_      ,		num_heads=a_      ,		dropout_rate=a_      ,		layer_norm_epsilon=a_      ,		)							)

						# Film Cond MLP + dropout: last layer
						self.layer.append(
						    TaLayerFFCond(d_model=a_      ,		d_ff=a_      ,		dropout_rate=a_      ,		layer_norm_epsilon=a_							)							)






			def 							A (      self						:    Any      ,		a_						:    List[str]      ,		a_						:    Tuple=None      ,		a_						:    Any=None      ,		a_						:    Union[str, Any]=None      ,		a_						:    List[str]=None      ,		a_						:    Dict=None      ,		):



						"""simple docstring"""




						__snake_case			=		self.layer[0](
						    a_      ,		conditioning_emb=a_      ,		attention_mask=a_      ,		)

						if encoder_hidden_states is not None:
									__snake_case			=		torch.where(encoder_attention_mask > 0      ,		0      ,		-1e10							).to(
									    encoder_hidden_states.dtype							)

									__snake_case			=		self.layer[1](
									    a_      ,		key_value_states=a_      ,		attention_mask=a_      ,		)

						# Apply Film Conditional Feed Forward layer
						__snake_case			=		self.layer[-1](a_      ,		a_							)

						return (hidden_states,)







class 		SCREAMING_SNAKE_CASE__  (			nn.Module	):





			def __init__(      self						:    Union[str, Any]      ,		a_						:    Optional[Any]      ,		a_						:    Optional[Any]      ,		a_						:    List[Any]      ,		a_						:    Tuple							):



						"""simple docstring"""




						super().__init__()
						__snake_case			=		TaLayerNorm(a_							)
						__snake_case			=		TaFiLMLayer(in_features=d_model * 4      ,		out_features=a_							)
						__snake_case			=		Attention(query_dim=a_      ,		heads=a_      ,		dim_head=a_      ,		out_bias=a_      ,		scale_qk=a_							)
						__snake_case			=		nn.Dropout(a_							)






			def 							A (      self						:    Optional[Any]      ,		a_						:    str      ,		a_						:    Union[str, Any]=None      ,		a_						:    Tuple=None      ,		):



						"""simple docstring"""




						__snake_case			=		self.layer_norm(a_							)

						if conditioning_emb is not None:
									__snake_case			=		self.FiLMLayer(a_      ,		a_							)

						# Self-attention block
						__snake_case			=		self.attention(a_							)

						__snake_case			=		hidden_states + self.dropout(a_							)

						return hidden_states







class 		SCREAMING_SNAKE_CASE__  (			nn.Module	):





			def __init__(      self						:    Any      ,		a_						:    List[str]      ,		a_						:    List[str]      ,		a_						:    List[str]      ,		a_						:    Optional[Any]      ,		a_						:    int							):



						"""simple docstring"""




						super().__init__()
						__snake_case			=		Attention(query_dim=a_      ,		heads=a_      ,		dim_head=a_      ,		out_bias=a_      ,		scale_qk=a_							)
						__snake_case			=		TaLayerNorm(a_      ,		eps=a_							)
						__snake_case			=		nn.Dropout(a_							)






			def 							A (      self						:    int      ,		a_						:    Any      ,		a_						:    Dict=None      ,		a_						:    Optional[Any]=None      ,		):



						"""simple docstring"""




						__snake_case			=		self.layer_norm(a_							)
						__snake_case			=		self.attention(
						    a_      ,		encoder_hidden_states=a_      ,		attention_mask=attention_mask.squeeze(1							)      ,		)
						__snake_case			=		hidden_states + self.dropout(a_							)
						return layer_output







class 		SCREAMING_SNAKE_CASE__  (			nn.Module	):





			def __init__(      self						:    Union[str, Any]      ,		a_						:    Optional[int]      ,		a_						:    Tuple      ,		a_						:    Optional[int]      ,		a_						:    List[str]							):



						"""simple docstring"""




						super().__init__()
						__snake_case			=		TaDenseGatedActDense(d_model=a_      ,		d_ff=a_      ,		dropout_rate=a_							)
						__snake_case			=		TaFiLMLayer(in_features=d_model * 4      ,		out_features=a_							)
						__snake_case			=		TaLayerNorm(a_      ,		eps=a_							)
						__snake_case			=		nn.Dropout(a_							)






			def 							A (      self						:    Optional[int]      ,		a_						:    Union[str, Any]      ,		a_						:    Union[str, Any]=None							):



						"""simple docstring"""




						__snake_case			=		self.layer_norm(a_							)
						if conditioning_emb is not None:
									__snake_case			=		self.film(a_      ,		a_							)

						__snake_case			=		self.DenseReluDense(a_							)
						__snake_case			=		hidden_states + self.dropout(a_							)
						return hidden_states







class 		SCREAMING_SNAKE_CASE__  (			nn.Module	):





			def __init__(      self						:    Optional[int]      ,		a_						:    str      ,		a_						:    Any      ,		a_						:    Dict							):



						"""simple docstring"""




						super().__init__()
						__snake_case			=		nn.Linear(a_      ,		a_      ,		bias=a_							)
						__snake_case			=		nn.Linear(a_      ,		a_      ,		bias=a_							)
						__snake_case			=		nn.Linear(a_      ,		a_      ,		bias=a_							)
						__snake_case			=		nn.Dropout(a_							)
						__snake_case			=		NewGELUActivation()






			def 							A (      self						:    Any      ,		a_						:    str							):



						"""simple docstring"""




						__snake_case			=		self.act(self.wi_a(a_							)							)
						__snake_case			=		self.wi_a(a_							)
						__snake_case			=		hidden_gelu * hidden_linear
						__snake_case			=		self.dropout(a_							)

						__snake_case			=		self.wo(a_							)
						return hidden_states







class 		SCREAMING_SNAKE_CASE__  (			nn.Module	):





			def __init__(      self						:    List[str]      ,		a_						:    Optional[Any]      ,		a_						:    Optional[Any]=1e-6							):



						"""simple docstring"""




						super().__init__()
						__snake_case			=		nn.Parameter(torch.ones(a_							)							)
						__snake_case			=		eps






			def 							A (      self						:    List[str]      ,		a_						:    Any							):



						"""simple docstring"""




						__snake_case			=		hidden_states.to(torch.floataa							).pow(2							).mean(-1      ,		keepdim=a_							)
						__snake_case			=		hidden_states * torch.rsqrt(variance + self.variance_epsilon							)

						# convert into half-precision if necessary
						if self.weight.dtype in [torch.floataa, torch.bfloataa]:
									__snake_case			=		hidden_states.to(self.weight.dtype							)

						return self.weight * hidden_states







class 		SCREAMING_SNAKE_CASE__  (			nn.Module	):





			def 							A (      self						:    List[str]      ,		a_						:    torch.Tensor							):



						"""simple docstring"""




						return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi							) * (input + 0.044715 * torch.pow(a_      ,		3.0							))							))







class 		SCREAMING_SNAKE_CASE__  (			nn.Module	):





			def __init__(      self						:    int      ,		a_						:    Union[str, Any]      ,		a_						:    List[str]							):



						"""simple docstring"""




						super().__init__()
						__snake_case			=		nn.Linear(a_      ,		out_features * 2      ,		bias=a_							)






			def 							A (      self						:    Optional[int]      ,		a_						:    Union[str, Any]      ,		a_						:    Optional[int]							):



						"""simple docstring"""




						__snake_case			=		self.scale_bias(a_							)
						__snake_case		,     __snake_case			=		torch.chunk(a_      ,		2      ,		-1							)
						__snake_case			=		x * (1 + scale) + shift
						return x









from math import log

from scipy.constants import Boltzmann, physical_constants

A			:						Any        =  3_0_0  # TEMPERATURE (unit = K)






def 					UpperCamelCase   (      __magic_name__	:				float       ,       __magic_name__	:				float       ,       __magic_name__	:				float       ,       )  ->    float:
		"""simple docstring"""




		if donor_conc <= 0:
				raise ValueError("""Donor concentration should be positive"""	)
		elif acceptor_conc <= 0:
				raise ValueError("""Acceptor concentration should be positive"""	)
		elif intrinsic_conc <= 0:
				raise ValueError("""Intrinsic concentration should be positive"""	)
		elif donor_conc <= intrinsic_conc:
				raise ValueError(
				    """Donor concentration should be greater than intrinsic concentration"""	)
		elif acceptor_conc <= intrinsic_conc:
				raise ValueError(
				    """Acceptor concentration should be greater than intrinsic concentration"""	)
		else:
				return (
				    Boltzmann
				    * T
				    * log((donor_conc * acceptor_conc) / intrinsic_conc**2	)
				    / physical_constants["electron volt"][0]
				)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

