


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_snake_case					:		List[Any]      =    logging.get_logger(__name__)


_snake_case					:		List[str]      =    {
    "Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json",
    "Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json",
    "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json",
    "Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json",
    "Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json",
    "Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json",
    "Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json",
    "Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json",
    "Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json",
    "Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json",
    "Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json",
    "Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json",
}

class     a		(lowerCamelCase__				):




							"""simple docstring"""


							__UpperCAmelCase   :							Dict					  =  """codegen"""
							__UpperCAmelCase   :							Any					  =  {
							    """max_position_embeddings""": """n_positions""",
							    """hidden_size""": """n_embd""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}

							def __init__(							self	:  Dict   ,     lowerCamelCase	:  Optional[int]=50400   ,     lowerCamelCase	:  Dict=2048   ,     lowerCamelCase	:  List[Any]=2048   ,     lowerCamelCase	:  List[Any]=4096   ,     lowerCamelCase	:  int=28   ,     lowerCamelCase	:  List[Any]=16   ,     lowerCamelCase	:  Any=64   ,     lowerCamelCase	:  List[str]=None   ,     lowerCamelCase	:  str="gelu_new"   ,     lowerCamelCase	:  List[Any]=0.0   ,     lowerCamelCase	:  Tuple=0.0   ,     lowerCamelCase	:  Optional[int]=0.0   ,     lowerCamelCase	:  List[str]=1E-5   ,     lowerCamelCase	:  Union[str, Any]=0.02   ,     lowerCamelCase	:  int=True   ,     lowerCamelCase	:  str=50256   ,     lowerCamelCase	:  Union[str, Any]=50256   ,     lowerCamelCase	:  int=False   ,     **lowerCamelCase	:  Union[str, Any]   ,     )     ->   List[str]:
														__snake_case		:      Dict										=       vocab_size
														__snake_case		:      Any										=       n_ctx
														__snake_case		:      Tuple										=       n_positions
														__snake_case		:      List[Any]										=       n_embd
														__snake_case		:      Tuple										=       n_layer
														__snake_case		:      Tuple										=       n_head
														__snake_case		:      Any										=       n_inner
														__snake_case		:      List[Any]										=       rotary_dim
														__snake_case		:      Union[str, Any]										=       activation_function
														__snake_case		:      Tuple										=       resid_pdrop
														__snake_case		:      Union[str, Any]										=       embd_pdrop
														__snake_case		:      Union[str, Any]										=       attn_pdrop
														__snake_case		:      Union[str, Any]										=       layer_norm_epsilon
														__snake_case		:      Union[str, Any]										=       initializer_range
														__snake_case		:      Tuple										=       use_cache

														__snake_case		:      List[str]										=       bos_token_id
														__snake_case		:      int										=       eos_token_id

														super().__init__(
														    bos_token_id=snake_case__   ,     eos_token_id=snake_case__   ,     tie_word_embeddings=snake_case__   ,     **snake_case__    )

class     a		(lowerCamelCase__				):




							"""simple docstring"""


							def __init__(							self	:  List[str]   ,     lowerCamelCase	:  List[str]   ,     lowerCamelCase	:  List[Any] = "default"   ,     lowerCamelCase	:  List[Any] = None   ,     lowerCamelCase	:  Union[str, Any] = False   ,     )     ->   List[Any]:
														super().__init__(snake_case__   ,     task=snake_case__   ,     patching_specs=snake_case__   ,     use_past=snake_case__    )
														if not getattr(self._config   ,     "pad_token_id"   ,     snake_case__    ):
																					# TODO: how to do that better?
																					__snake_case		:      Optional[Any]										=       0

							@property
							def    __snake_case (							self	:  int    )     ->   Mapping[str, Mapping[int, str]]:
														__snake_case		:      int										=       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}    )
														if self.use_past:
																					self.fill_with_past_key_values_(snake_case__   ,     direction="inputs"    )
																					__snake_case		:      Union[str, Any]										=       {0: '''batch''', 1: '''past_sequence + sequence'''}
														else:
																					__snake_case		:      Optional[Any]										=       {0: '''batch''', 1: '''sequence'''}

														return common_inputs

							@property
							def    __snake_case (							self	:  Union[str, Any]    )     ->   int:
														return self._config.n_layer

							@property
							def    __snake_case (							self	:  Dict    )     ->   int:
														return self._config.n_head

							def    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  Any   ,     lowerCamelCase	:  List[Any] = -1   ,     lowerCamelCase	:  Dict = -1   ,     lowerCamelCase	:  Union[str, Any] = False   ,     lowerCamelCase	:  Optional[Any] = None   ,     )     ->   Mapping[str, Any]:
														__snake_case		:      int										=       super(snake_case__   ,     self    ).generate_dummy_inputs(
														    snake_case__   ,     batch_size=snake_case__   ,     seq_length=snake_case__   ,     is_pair=snake_case__   ,     framework=snake_case__    )

														# We need to order the input in the way they appears in the forward()
														__snake_case		:      Optional[int]										=       OrderedDict({"input_ids": common_inputs["input_ids"]}    )

														# Need to add the past_keys
														if self.use_past:
																					if not is_torch_available():
																												raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."    )
																					else:
																												import torch

																												__snake_case		:      List[str]										=       common_inputs['''input_ids'''].shape
																												# Not using the same length for past_key_values
																												__snake_case		:      Optional[Any]										=       seqlen + 2
																												__snake_case		:      Any										=       (
																												    batch,
																												    self.num_attention_heads,
																												    past_key_values_length,
																												    self._config.hidden_size // self.num_attention_heads,
																												)
																												__snake_case		:      List[str]										=       [
																												    (torch.zeros(snake_case__    ), torch.zeros(snake_case__    )) for _ in range(self.num_layers    )
																												]

														__snake_case		:      List[Any]										=       common_inputs['''attention_mask''']
														if self.use_past:
																					__snake_case		:      Tuple										=       ordered_inputs['''attention_mask'''].dtype
																					__snake_case		:      Optional[Any]										=       torch.cat(
																					    [ordered_inputs["attention_mask"], torch.ones(snake_case__   ,     snake_case__   ,     dtype=snake_case__    )]   ,     dim=1    )

														return ordered_inputs






							@property
							def    __snake_case (							self	:  Union[str, Any]    )     ->   int:
														return 13





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))

