

from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class    _a						(_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       Tuple  							=	[R'h\.\d+\.attn\.bias', R'h\.\d+\.attn\.masked_bias']






					@register_to_config
					def __init__(			self   ,	A__   ,	A__   ,	A__ = None   ,	A__ = 5_0257   ,	A__ = 1024   ,	A__ = 768   ,	A__ = 12   ,	A__ = 12   ,	A__ = None   ,	A__ = "gelu_new"   ,	A__ = 0.1   ,	A__ = 0.1   ,	A__ = 0.1   ,	A__ = 1e-5   ,	A__ = 0.0_2   ,	A__ = True   ,	A__ = True   ,	A__ = False   ,	A__ = False   ,	):
						super().__init__()

						A__							:   Optional[Any]					       =		prefix_length

						if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
							raise ValueError(
							    F"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
							    F""" `n_embd`: {n_embd} are not equal."""      )

						A__							:   List[Any]					       =		prefix_inner_dim
						A__							:   Optional[int]					       =		prefix_hidden_dim

						A__							:   List[Any]					       =		(
						    nn.Linear(self.prefix_inner_dim   ,	self.prefix_hidden_dim      )
						    if self.prefix_hidden_dim is not None
						    else nn.Identity()
						)
						A__							:   Any					       =		(
						    nn.Linear(self.prefix_hidden_dim   ,	SCREAMING_SNAKE_CASE_      ) if self.prefix_hidden_dim is not None else nn.Identity()
						)

						A__							:   Tuple					       =		GPTaConfig(
						    vocab_size=SCREAMING_SNAKE_CASE_   ,	n_positions=SCREAMING_SNAKE_CASE_   ,	n_embd=SCREAMING_SNAKE_CASE_   ,	n_layer=SCREAMING_SNAKE_CASE_   ,	n_head=SCREAMING_SNAKE_CASE_   ,	n_inner=SCREAMING_SNAKE_CASE_   ,	activation_function=SCREAMING_SNAKE_CASE_   ,	resid_pdrop=SCREAMING_SNAKE_CASE_   ,	embd_pdrop=SCREAMING_SNAKE_CASE_   ,	attn_pdrop=SCREAMING_SNAKE_CASE_   ,	layer_norm_epsilon=SCREAMING_SNAKE_CASE_   ,	initializer_range=SCREAMING_SNAKE_CASE_   ,	scale_attn_weights=SCREAMING_SNAKE_CASE_   ,	use_cache=SCREAMING_SNAKE_CASE_   ,	scale_attn_by_inverse_layer_idx=SCREAMING_SNAKE_CASE_   ,	reorder_and_upcast_attn=SCREAMING_SNAKE_CASE_   ,	)
						A__							:   int					       =		GPTaLMHeadModel(SCREAMING_SNAKE_CASE_      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__ = None   ,	A__ = None   ,	):
						A__							:   Union[str, Any]					       =		self.transformer.transformer.wte(SCREAMING_SNAKE_CASE_      )
						A__							:   Dict					       =		self.encode_prefix(SCREAMING_SNAKE_CASE_      )
						A__							:   Union[str, Any]					       =		self.decode_prefix(SCREAMING_SNAKE_CASE_      )
						A__							:   int					       =		torch.cat((prefix_embeds, embedding_text)   ,	dim=1      )

						if labels is not None:
							A__							:   List[str]					       =		self.get_dummy_token(input_ids.shape[0]   ,	input_ids.device      )
							A__							:   List[str]					       =		torch.cat((dummy_token, input_ids)   ,	dim=1      )
						A__							:   List[Any]					       =		self.transformer(inputs_embeds=SCREAMING_SNAKE_CASE_   ,	labels=SCREAMING_SNAKE_CASE_   ,	attention_mask=SCREAMING_SNAKE_CASE_      )
						if self.prefix_hidden_dim is not None:
							return out, hidden
						else:
							return out






					def   __A      (			self   ,	A__   ,	A__      ):
						return torch.zeros(SCREAMING_SNAKE_CASE_   ,	self.prefix_length   ,	dtype=torch.intaa   ,	device=SCREAMING_SNAKE_CASE_      )






					def   __A      (			self   ,	A__      ):
						return self.encode_prefix(SCREAMING_SNAKE_CASE_      )






					@torch.no_grad()
					def   __A      (			self   ,	A__   ,	A__   ,	A__      ):
						A__							:   Union[str, Any]					       =		torch.split(SCREAMING_SNAKE_CASE_   ,	1   ,	dim=0      )
						A__							:   Tuple					       =		[]
						A__							:   Any					       =		[]
						for feature in features:
							A__							:   Tuple					       =		self.decode_prefix(feature.to(SCREAMING_SNAKE_CASE_      )      )  # back to the clip feature
							# Only support beam search for now
							A__							:   List[Any]					       =		self.generate_beam(
							    input_embeds=SCREAMING_SNAKE_CASE_   ,	device=SCREAMING_SNAKE_CASE_   ,	eos_token_id=SCREAMING_SNAKE_CASE_      )
							generated_tokens.append(output_tokens[0]      )
							generated_seq_lengths.append(seq_lengths[0]      )
						A__							:   List[Any]					       =		torch.stack(SCREAMING_SNAKE_CASE_      )
						A__							:   Optional[Any]					       =		torch.stack(SCREAMING_SNAKE_CASE_      )
						return generated_tokens, generated_seq_lengths






					@torch.no_grad()
					def   __A      (			self   ,	A__=None   ,	A__=None   ,	A__=None   ,	A__ = 5   ,	A__ = 67   ,	A__ = 1.0   ,	A__ = None   ,	):
						A__							:   Union[str, Any]					       =		eos_token_id
						A__							:   str					       =		None
						A__							:   Union[str, Any]					       =		None
						A__							:   Optional[Any]					       =		torch.ones(SCREAMING_SNAKE_CASE_   ,	device=SCREAMING_SNAKE_CASE_   ,	dtype=torch.int      )
						A__							:   str					       =		torch.zeros(SCREAMING_SNAKE_CASE_   ,	device=SCREAMING_SNAKE_CASE_   ,	dtype=torch.bool      )

						if input_embeds is not None:
							A__							:   Optional[int]					       =		input_embeds
						else:
							A__							:   Any					       =		self.transformer.transformer.wte(SCREAMING_SNAKE_CASE_      )

						for i in range(SCREAMING_SNAKE_CASE_      ):
							A__							:   List[str]					       =		self.transformer(inputs_embeds=SCREAMING_SNAKE_CASE_      )
							A__							:   str					       =		outputs.logits
							A__							:   List[str]					       =		logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
							A__							:   List[Any]					       =		logits.softmax(-1      ).log()

							if scores is None:
								A__							:   Any					       =		logits.topk(SCREAMING_SNAKE_CASE_   ,	-1      )
								A__							:   Union[str, Any]					       =		generated.expand(SCREAMING_SNAKE_CASE_   ,	*generated.shape[1:]      )
								A__							:   List[str]					       =		next_tokens.permute(1   ,	0      ), scores.squeeze(0      )
								if tokens is None:
									A__							:   Optional[Any]					       =		next_tokens
								else:
									A__							:   Optional[Any]					       =		tokens.expand(SCREAMING_SNAKE_CASE_   ,	*tokens.shape[1:]      )
									A__							:   Optional[Any]					       =		torch.cat((tokens, next_tokens)   ,	dim=1      )
							else:
								A__							:   Union[str, Any]					       =		-float(np.inf      )
								A__							:   Union[str, Any]					       =		0
								A__							:   str					       =		scores[:, None] + logits
								seq_lengths[~is_stopped] += 1
								A__							:   str					       =		scores_sum / seq_lengths[:, None]
								A__							:   List[Any]					       =		scores_sum_average.view(-1      ).topk(SCREAMING_SNAKE_CASE_   ,	-1      )
								A__							:   str					       =		next_tokens // scores_sum.shape[1]
								A__							:   Union[str, Any]					       =		seq_lengths[next_tokens_source]
								A__							:   Dict					       =		next_tokens % scores_sum.shape[1]
								A__							:   Optional[int]					       =		next_tokens.unsqueeze(1      )
								A__							:   Tuple					       =		tokens[next_tokens_source]
								A__							:   str					       =		torch.cat((tokens, next_tokens)   ,	dim=1      )
								A__							:   Optional[Any]					       =		generated[next_tokens_source]
								A__							:   Optional[int]					       =		scores_sum_average * seq_lengths
								A__							:   Tuple					       =		is_stopped[next_tokens_source]

							A__							:   int					       =		self.transformer.transformer.wte(next_tokens.squeeze()      ).view(generated.shape[0]   ,	1   ,	-1      )
							A__							:   int					       =		torch.cat((generated, next_token_embed)   ,	dim=1      )
							A__							:   Any					       =		is_stopped + next_tokens.eq(SCREAMING_SNAKE_CASE_      ).squeeze()
							if is_stopped.all():
								break

						A__							:   Optional[Any]					       =		scores / seq_lengths
						A__							:   Tuple					       =		scores.argsort(descending=SCREAMING_SNAKE_CASE_      )
						# tokens tensors are already padded to max_seq_length
						A__							:   str					       =		[tokens[i] for i in order]
						A__							:   List[Any]					       =		torch.stack(SCREAMING_SNAKE_CASE_   ,	dim=0      )
						A__							:   Dict					       =		torch.tensor([seq_lengths[i] for i in order]   ,	dtype=seq_lengths.dtype      )
						return output_texts, seq_lengths






from __future__ import annotations

def 						UpperCamelCase (lowercase_:			float   ,				lowercase_:			float   ,				lowercase_:			float )					->				dict[str, float]:
	if (voltage, current, resistance).count(0 ) != 1:
		raise ValueError("""One and only one argument must be 0""" )
	if resistance < 0:
		raise ValueError("""Resistance cannot be negative""" )
	if voltage == 0:
		return {"voltage": float(current * resistance )}
	elif current == 0:
		return {"current": voltage / resistance}
	elif resistance == 0:
		return {"resistance": voltage / current}
	else:
		raise ValueError("""Exactly one argument must be 0""" )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



