





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_   ):
						__magic_name__:     Tuple	 =    [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 = 50257						,				_A : int = 1024						,				_A : int = 768						,				_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 = 0.0_2						,				_A : bool = True						,				_A : bool = True						,				_A : bool = False						,				_A : bool = False						,				)							->      Union[str, Any]:







							"""simple docstring"""


							super().__init__()

							snake_case_						:     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."""		)

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

							snake_case_						:     Optional[int]     =		(
							    nn.Linear(self.prefix_inner_dim						,				self.prefix_hidden_dim		)
							    if self.prefix_hidden_dim is not None
							    else nn.Identity()
							)
							snake_case_						:     Tuple     =		(
							    nn.Linear(self.prefix_hidden_dim						,				_A		) if self.prefix_hidden_dim is not None else nn.Identity()
							)

							snake_case_						:     int     =		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						,				)
							snake_case_						:     str     =		GPTaLMHeadModel(_A		)






						def UpperCAmelCase_			( self : Dict						,				_A : torch.Tensor						,				_A : torch.Tensor						,				_A : Optional[torch.Tensor] = None						,				_A : Optional[torch.Tensor] = None						,				)							->      Optional[Any]:







							"""simple docstring"""


							snake_case_						:     Union[str, Any]     =		self.transformer.transformer.wte(_A		)
							snake_case_						:     Dict     =		self.encode_prefix(_A		)
							snake_case_						:     int     =		self.decode_prefix(_A		)
							snake_case_						:     Dict     =		torch.cat((prefix_embeds, embedding_text)						,				dim=1		)

							if labels is not None:
								snake_case_						:     int     =		self.get_dummy_token(input_ids.shape[0]						,				input_ids.device		)
								snake_case_						:     Union[str, Any]     =		torch.cat((dummy_token, input_ids)						,				dim=1		)
							snake_case_						:     Tuple     =		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 UpperCAmelCase_			( self : Optional[Any]						,				_A : int						,				_A : torch.device		)							->      torch.Tensor:







							"""simple docstring"""


							return torch.zeros(_A						,				self.prefix_length						,				dtype=torch.intaa						,				device=_A		)






						def UpperCAmelCase_			( self : Tuple						,				_A : List[str]		)							->      int:







							"""simple docstring"""


							return self.encode_prefix(_A		)






						@torch.no_grad()
						def UpperCAmelCase_			( self : Optional[Any]						,				_A : Union[str, Any]						,				_A : Optional[Any]						,				_A : Dict		)							->      Optional[int]:







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		torch.split(_A						,				1						,				dim=0		)
							snake_case_						:     Dict     =		[]
							snake_case_						:     Optional[Any]     =		[]
							for feature in features:
								snake_case_						:     Dict     =		self.decode_prefix(feature.to(_A		)		)  # back to the clip feature
								# Only support beam search for now
								snake_case_  ,snake_case_						:     Dict     =		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]		)
							snake_case_						:     Optional[Any]     =		torch.stack(_A		)
							snake_case_						:     Union[str, Any]     =		torch.stack(_A		)
							return generated_tokens, generated_seq_lengths






						@torch.no_grad()
						def UpperCAmelCase_			( self : Optional[Any]						,				_A : List[Any]=None						,				_A : Union[str, Any]=None						,				_A : Optional[int]=None						,				_A : int = 5						,				_A : int = 67						,				_A : float = 1.0						,				_A : Optional[int] = None						,				)							->      Any:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		eos_token_id
							snake_case_						:     List[Any]     =		None
							snake_case_						:     Optional[Any]     =		None
							snake_case_						:     List[Any]     =		torch.ones(_A						,				device=_A						,				dtype=torch.int		)
							snake_case_						:     str     =		torch.zeros(_A						,				device=_A						,				dtype=torch.bool		)

							if input_embeds is not None:
								snake_case_						:     List[Any]     =		input_embeds
							else:
								snake_case_						:     int     =		self.transformer.transformer.wte(_A		)

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

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

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

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








def 							SCREAMING_SNAKE_CASE__  (      __a = 60_08_51_47_51_43				):

	try:
		snake_case_						:     Optional[Any]     =		int(__a				)
	except (TypeError, ValueError):
		raise TypeError('Parameter n must be int or castable to int.'				)
	if n <= 0:
		raise ValueError('Parameter n must be greater than or equal to one.'				)
	snake_case_						:     Any     =		2
	snake_case_						:     Any     =		0
	if n == 2:
		return 2
	while n > 2:
		while n % i != 0:
			i += 1
		snake_case_						:     Tuple     =		i
		while n % i == 0:
			snake_case_						:     List[str]     =		n // i
		i += 1
	return int(__a				)


if __name__ == "__main__":
						print(F'''{solution() = }''')

