



"""simple docstring"""






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  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     lowerCAmelCase_	,     lowerCAmelCase_     ):


					'''simple docstring'''





					_a         =							[r'h\.\d+\.attn\.bias', r'h\.\d+\.attn\.masked_bias']



					@register_to_config
					def __init__( self							:						Optional[int],						lowerCamelCase							:						int,						lowerCamelCase							:						int,						lowerCamelCase							:						Optional[int] = None,						lowerCamelCase							:						int = 5_0257,						lowerCamelCase							:						int = 1024,						lowerCamelCase							:						int = 768,						lowerCamelCase							:						int = 12,						lowerCamelCase							:						int = 12,						lowerCamelCase							:						Optional[int] = None,						lowerCamelCase							:						str = "gelu_new",						lowerCamelCase							:						float = 0.1,						lowerCamelCase							:						float = 0.1,						lowerCamelCase							:						float = 0.1,						lowerCamelCase							:						float = 1E-5,						lowerCamelCase							:						float = 0.02,						lowerCamelCase							:						bool = True,						lowerCamelCase							:						bool = True,						lowerCamelCase							:						bool = False,						lowerCamelCase							:						bool = False,						)->      Optional[Any]:
								super().__init__()

								lowerCamelCase__	:	List[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.'''  )

								lowerCamelCase__	:	List[Any]				=prefix_inner_dim
								lowerCamelCase__	:	Tuple				=prefix_hidden_dim

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

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



					def 	snake_case ( self							:						List[str],						lowerCamelCase							:						torch.Tensor,						lowerCamelCase							:						torch.Tensor,						lowerCamelCase							:						Optional[torch.Tensor] = None,						lowerCamelCase							:						Optional[torch.Tensor] = None,						)->      int:
								lowerCamelCase__	:	int				=self.transformer.transformer.wte(lowerCamelCase  )
								lowerCamelCase__	:	Dict				=self.encode_prefix(lowerCamelCase  )
								lowerCamelCase__	:	Tuple				=self.decode_prefix(lowerCamelCase  )
								lowerCamelCase__	:	List[Any]				=torch.cat((prefix_embeds, embedding_text),						dim=1  )

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



					def 	snake_case ( self							:						List[str],						lowerCamelCase							:						int,						lowerCamelCase							:						torch.device  )->      torch.Tensor:
								return torch.zeros(lowerCamelCase,						self.prefix_length,						dtype=torch.intaa,						device=lowerCamelCase  )



					def 	snake_case ( self							:						str,						lowerCamelCase							:						int  )->      int:
								return self.encode_prefix(lowerCamelCase  )



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




					@torch.no_grad()
					def 	snake_case ( self							:						Dict,						lowerCamelCase							:						Dict=None,						lowerCamelCase							:						int=None,						lowerCamelCase							:						Any=None,						lowerCamelCase							:						int = 5,						lowerCamelCase							:						int = 67,						lowerCamelCase							:						float = 1.0,						lowerCamelCase							:						Optional[int] = None,						)->      str:
								lowerCamelCase__	:	str				=eos_token_id
								lowerCamelCase__	:	Any				=None
								lowerCamelCase__	:	int				=None
								lowerCamelCase__	:	List[str]				=torch.ones(lowerCamelCase,						device=lowerCamelCase,						dtype=torch.int  )
								lowerCamelCase__	:	Any				=torch.zeros(lowerCamelCase,						device=lowerCamelCase,						dtype=torch.bool  )

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

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

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

											lowerCamelCase__	:	List[str]				=self.transformer.transformer.wte(next_tokens.squeeze()  ).view(generated.shape[0],						1,						-1  )
											lowerCamelCase__	:	List[str]				=torch.cat((generated, next_token_embed),						dim=1  )
											lowerCamelCase__	:	Dict				=is_stopped + next_tokens.eq(lowerCamelCase  ).squeeze()
											if is_stopped.all():
														break

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









"""simple docstring"""






def   snake_case__     (	__lowerCamelCase       :					list[int]			):
			"""simple docstring"""
			if not numbers:
						return 0

			if not isinstance(__lowerCamelCase							,	(list, tuple)			) or not all(
			    isinstance(__lowerCamelCase							,	__lowerCamelCase			) for number in numbers			):
						raise ValueError('''numbers must be an iterable of integers'''			)

			lowerCamelCase__	:	Any				=numbers[0]

			for i in range(1							,	len(__lowerCamelCase			)			):
						# update the maximum and minimum subarray products
						lowerCamelCase__	:	Dict				=numbers[i]
						if number < 0:
									lowerCamelCase__			,     lowerCamelCase__	:	List[Any]				=min_till_now, max_till_now
						lowerCamelCase__	:	Optional[int]				=max(__lowerCamelCase							,	max_till_now * number			)
						lowerCamelCase__	:	Dict				=min(__lowerCamelCase							,	min_till_now * number			)

						# update the maximum product found till now
						lowerCamelCase__	:	Tuple				=max(__lowerCamelCase							,	__lowerCamelCase			)

			return max_prod




