


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		(_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Any					  =  [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 = 50257   ,     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[int]:
														super().__init__()

														__snake_case		:      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.'    )

														__snake_case		:      Optional[Any]										=       prefix_inner_dim
														__snake_case		:      int										=       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		:      Optional[Any]										=       (
														    nn.Linear(self.prefix_hidden_dim   ,     lowerCamelCase    ) if self.prefix_hidden_dim is not None else nn.Identity()
														)

														__snake_case		:      str										=       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   ,     )
														__snake_case		:      Tuple										=       GPTaLMHeadModel(lowerCamelCase    )

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

														if labels is not None:
																					__snake_case		:      Tuple										=       self.get_dummy_token(input_ids.shape[0]   ,     input_ids.device    )
																					__snake_case		:      Tuple										=       torch.cat((dummy_token, input_ids)   ,     dim=1    )
														__snake_case		:      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	:  str   ,     lowerCamelCase	:  int   ,     lowerCamelCase	:  torch.device    )     ->   torch.Tensor:
														return torch.zeros(lowerCamelCase   ,     self.prefix_length   ,     dtype=torch.intaa   ,     device=lowerCamelCase    )

							def    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  str    )     ->   Dict:
														return self.encode_prefix(lowerCamelCase    )

							@torch.no_grad()
							def    __snake_case (							self	:  Tuple   ,     lowerCamelCase	:  Optional[int]   ,     lowerCamelCase	:  List[Any]   ,     lowerCamelCase	:  int    )     ->   List[str]:
														__snake_case		:      Optional[int]										=       torch.split(lowerCamelCase   ,     1   ,     dim=0    )
														__snake_case		:      str										=       []
														__snake_case		:      List[Any]										=       []
														for feature in features:
																					__snake_case		:      List[Any]										=       self.decode_prefix(feature.to(lowerCamelCase    )    )  # back to the clip feature
																					# Only support beam search for now
																					__snake_case			,      __snake_case		:      List[str]										=       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]    )
														__snake_case		:      str										=       torch.stack(lowerCamelCase    )
														__snake_case		:      Optional[int]										=       torch.stack(lowerCamelCase    )
														return generated_tokens, generated_seq_lengths






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

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

														for i in range(lowerCamelCase    ):
																					__snake_case		:      Dict										=       self.transformer(inputs_embeds=lowerCamelCase    )
																					__snake_case		:      Union[str, Any]										=       outputs.logits
																					__snake_case		:      str										=       logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
																					__snake_case		:      Optional[int]										=       logits.softmax(-1    ).log()

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

																					__snake_case		:      List[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		:      Dict										=       is_stopped + next_tokens.eq(lowerCamelCase    ).squeeze()
																					if is_stopped.all():
																												break

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





from __future__ import annotations

_snake_case					:		Union[str, Any]      =    []







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							for i in range(len(__lowerCamelCase     )     ):
														if board[row][i] == 1:
																					return False
							for i in range(len(__lowerCamelCase     )     ):
														if board[i][column] == 1:
																					return False
							for i, j in zip(range(__lowerCamelCase							,     -1							,     -1     )							,     range(__lowerCamelCase							,     -1							,     -1     )     ):
														if board[i][j] == 1:
																					return False
							for i, j in zip(range(__lowerCamelCase							,     -1							,     -1     )							,     range(__lowerCamelCase							,     len(__lowerCamelCase     )     )     ):
														if board[i][j] == 1:
																					return False
							return True







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							if row >= len(__lowerCamelCase     ):
														solution.append(__lowerCamelCase     )
														printboard(__lowerCamelCase     )
														print()
														return True
							for i in range(len(__lowerCamelCase     )     ):
														if is_safe(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
																					__snake_case		:      Optional[Any]										=       1
																					solve(__lowerCamelCase							,     row + 1     )
																					__snake_case		:      Union[str, Any]										=       0
							return False







def     lowerCAmelCase_					( __lowerCamelCase     ):
							for i in range(len(__lowerCamelCase     )     ):
														for j in range(len(__lowerCamelCase     )     ):
																					if board[i][j] == 1:
																												print("Q"							,     end=" "     )
																					else:
																												print("."							,     end=" "     )
														print()


# n=int(input("The no. of queens"))
_snake_case					:		List[str]      =    8
_snake_case					:		Optional[int]      =    [[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print("The total no. of solutions are :", len(solution))

