






'''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				( snake_case  ,	snake_case  ,	snake_case					):





							"""simple docstring"""

							A_						  =					[R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]

							@register_to_config
							def __init__(  self:   int		,     __A:   int		,     __A:   int		,     __A:   Optional[int] = None		,     __A:   int = 5_02_57		,     __A:   int = 10_24		,     __A:   int = 7_68		,     __A:   int = 12		,     __A:   int = 12		,     __A:   Optional[int] = None		,     __A:   str = "gelu_new"		,     __A:   float = 0.1		,     __A:   float = 0.1		,     __A:   float = 0.1		,     __A:   float = 1e-5		,     __A:   float = 0.02		,     __A:   bool = True		,     __A:   bool = True		,     __A:   bool = False		,     __A:   bool = False		,     )    ->				Union[str, Any]:
												super().__init__()

												_A								= 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								= prefix_inner_dim
												_A								= prefix_hidden_dim

												_A								= (
												    nn.Linear(self.prefix_inner_dim		,     self.prefix_hidden_dim   )
												    if self.prefix_hidden_dim is not None
												    else nn.Identity()
												)
												_A								= (
												    nn.Linear(self.prefix_hidden_dim		,     __A   ) if self.prefix_hidden_dim is not None else nn.Identity()
												)

												_A								= GPTaConfig(
												    vocab_size=__A		,     n_positions=__A		,     n_embd=__A		,     n_layer=__A		,     n_head=__A		,     n_inner=__A		,     activation_function=__A		,     resid_pdrop=__A		,     embd_pdrop=__A		,     attn_pdrop=__A		,     layer_norm_epsilon=__A		,     initializer_range=__A		,     scale_attn_weights=__A		,     use_cache=__A		,     scale_attn_by_inverse_layer_idx=__A		,     reorder_and_upcast_attn=__A		,     )
												_A								= GPTaLMHeadModel(__A   )

							def       __A (  self:   Any		,     __A:   torch.Tensor		,     __A:   torch.Tensor		,     __A:   Optional[torch.Tensor] = None		,     __A:   Optional[torch.Tensor] = None		,     )    ->				List[Any]:
												_A								= self.transformer.transformer.wte(__A   )
												_A								= self.encode_prefix(__A   )
												_A								= self.decode_prefix(__A   )
												_A								= torch.cat((prefix_embeds, embedding_text)		,     dim=1   )

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

							def       __A (  self:   Union[str, Any]		,     __A:   int		,     __A:   torch.device   )    ->				torch.Tensor:
												return torch.zeros(__A		,     self.prefix_length		,     dtype=torch.intaa		,     device=__A   )

							def       __A (  self:   Optional[int]		,     __A:   Union[str, Any]   )    ->				Any:
												return self.encode_prefix(__A   )

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



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

												if input_embeds is not None:
																	_A								= input_embeds
												else:
																	_A								= self.transformer.transformer.wte(__A   )

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

																	if scores is None:
																						_A							,_A								= logits.topk(__A		,     -1   )
																						_A								= generated.expand(__A		,     *generated.shape[1:]   )
																						_A							,_A								= next_tokens.permute(1		,     0   ), scores.squeeze(0   )
																						if tokens is None:
																											_A								= next_tokens
																						else:
																											_A								= tokens.expand(__A		,     *tokens.shape[1:]   )
																											_A								= torch.cat((tokens, next_tokens)		,     dim=1   )
																	else:
																						_A								= -float(np.inf   )
																						_A								= 0
																						_A								= scores[:, None] + logits
																						seq_lengths[~is_stopped] += 1
																						_A								= scores_sum / seq_lengths[:, None]
																						_A							,_A								= scores_sum_average.view(-1   ).topk(__A		,     -1   )
																						_A								= next_tokens // scores_sum.shape[1]
																						_A								= seq_lengths[next_tokens_source]
																						_A								= next_tokens % scores_sum.shape[1]
																						_A								= next_tokens.unsqueeze(1   )
																						_A								= tokens[next_tokens_source]
																						_A								= torch.cat((tokens, next_tokens)		,     dim=1   )
																						_A								= generated[next_tokens_source]
																						_A								= scores_sum_average * seq_lengths
																						_A								= is_stopped[next_tokens_source]

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

												_A								= scores / seq_lengths
												_A								= scores.argsort(descending=__A   )
												# tokens tensors are already padded to max_seq_length
												_A								= [tokens[i] for i in order]
												_A								= torch.stack(__A		,     dim=0   )
												_A								= torch.tensor([seq_lengths[i] for i in order]		,     dtype=seq_lengths.dtype   )
												return output_texts, seq_lengths











import math




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= []
					_A								= 2
					_A								= int(math.sqrt(_lowercase ) )  # Size of every segment
					_A								= [True] * (end + 1)
					_A								= []

					while start <= end:
										if temp[start] is True:
															in_prime.append(_lowercase )
															for i in range(start * start					,			end + 1					,			_lowercase ):
																				_A								= False
										start += 1
					prime += in_prime

					_A								= end + 1
					_A								= min(2 * end					,			_lowercase )

					while low <= n:
										_A								= [True] * (high - low + 1)
										for each in in_prime:
															_A								= math.floor(low / each ) * each
															if t < low:
																				t += each

															for j in range(_lowercase					,			high + 1					,			_lowercase ):
																				_A								= False

										for j in range(len(_lowercase ) ):
															if temp[j] is True:
																				prime.append(j + low )

										_A								= high + 1
										_A								= min(high + end					,			_lowercase )

					return prime


print(sieve(10**6))



