






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 					_snake_case						(			_A       ,    _A       ,    _A ):
	_A          =   [r'h\.\d+\.attn\.bias', r'h\.\d+\.attn\.masked_bias']






	@register_to_config
	def __init__(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = None					,UpperCamelCase = 50_257					,UpperCamelCase = 1_024					,UpperCamelCase = 768					,UpperCamelCase = 12					,UpperCamelCase = 12					,UpperCamelCase = None					,UpperCamelCase = "gelu_new"					,UpperCamelCase = 0.1					,UpperCamelCase = 0.1					,UpperCamelCase = 0.1					,UpperCamelCase = 1E-5					,UpperCamelCase = 0.02					,UpperCamelCase = True					,UpperCamelCase = True					,UpperCamelCase = False					,UpperCamelCase = False					,)						->    int:
					super().__init__()

					snake_case__      :List[str]         =   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.'       )

					snake_case__      :List[str]         =   prefix_inner_dim
					snake_case__      :List[Any]         =   prefix_hidden_dim

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

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






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = None					,UpperCamelCase = None					,)						->    List[str]:
					snake_case__      :str         =   self.transformer.transformer.wte(UpperCamelCase       )
					snake_case__      :Optional[int]         =   self.encode_prefix(UpperCamelCase       )
					snake_case__      :Optional[Any]         =   self.decode_prefix(UpperCamelCase       )
					snake_case__      :List[Any]         =   torch.cat((prefix_embeds, embedding_text)					,dim=1       )

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






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase       )						->    torch.Tensor:
					return torch.zeros(UpperCamelCase					,self.prefix_length					,dtype=torch.intaa					,device=UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    Union[str, Any]:
					return self.encode_prefix(UpperCamelCase       )






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






	@torch.no_grad()
	def    lowerCAmelCase_							(		self					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase = 5					,UpperCamelCase = 67					,UpperCamelCase = 1.0					,UpperCamelCase = None					,)						->    Any:
					snake_case__      :str         =   eos_token_id
					snake_case__      :Any         =   None
					snake_case__      :Optional[Any]         =   None
					snake_case__      :str         =   torch.ones(UpperCamelCase					,device=UpperCamelCase					,dtype=torch.int       )
					snake_case__      :Dict         =   torch.zeros(UpperCamelCase					,device=UpperCamelCase					,dtype=torch.bool       )

					if input_embeds is not None:
									snake_case__      :List[Any]         =   input_embeds
					else:
									snake_case__      :Union[str, Any]         =   self.transformer.transformer.wte(UpperCamelCase       )

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

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

									snake_case__      :Union[str, Any]         =   self.transformer.transformer.wte(next_tokens.squeeze()       ).view(generated.shape[0]					,1					,-1       )
									snake_case__      :List[Any]         =   torch.cat((generated, next_token_embed)					,dim=1       )
									snake_case__      :Tuple         =   is_stopped + next_tokens.eq(UpperCamelCase       ).squeeze()
									if is_stopped.all():
													break

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







def 					lowercase_ (    __snake_case     :							int      )     -> bool:





				'''simple docstring'''


				if p < 2:
								raise ValueError("p should not be less than 2!"      )
				elif p == 2:
								return True

				snake_case__      :List[str]         =   4
				snake_case__      :Optional[int]         =   (1 << p) - 1
				for _ in range(p - 2      ):
								snake_case__      :List[Any]         =   ((s * s) - 2) % m
				return s == 0


if __name__ == "__main__":
	print(lucas_lehmer_test(7))
	print(lucas_lehmer_test(1_1))