


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Dict					  =  "Wav2Vec2FeatureExtractor"
							__UpperCAmelCase   :							List[str]					  =  "AutoTokenizer"

							def __init__(							self	:  List[str]   ,     lowerCamelCase	:  List[str]   ,     lowerCamelCase	:  Dict    )     ->   List[Any]:
														super().__init__(lowerCamelCase   ,     lowerCamelCase    )
														__snake_case		:      Dict										=       self.feature_extractor
														__snake_case		:      Union[str, Any]										=       False

							@classmethod
							def    __snake_case (							cls	:  Tuple   ,     lowerCamelCase	:  Tuple   ,     **lowerCamelCase	:  Optional[Any]    )     ->   List[Any]:
														try:
																					return super().from_pretrained(lowerCamelCase   ,     **lowerCamelCase    )
														except OSError:
																					warnings.warn(
																					    F'Loading a tokenizer inside {cls.__name__} from a config that does not'
																					    " include a `tokenizer_class` attribute is deprecated and will be "
																					    "removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"
																					    " attribute to either your `config.json` or `tokenizer_config.json` "
																					    "file to suppress this warning: "   ,     lowerCamelCase   ,     )

																					__snake_case		:      Tuple										=       WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase   ,     **lowerCamelCase    )
																					__snake_case		:      Any										=       WavaVecaCTCTokenizer.from_pretrained(lowerCamelCase   ,     **lowerCamelCase    )

																					return cls(feature_extractor=lowerCamelCase   ,     tokenizer=lowerCamelCase    )

							def __call__(							self	:  Dict   ,     *lowerCamelCase	:  Union[str, Any]   ,     **lowerCamelCase	:  Optional[int]    )     ->   Union[str, Any]:
														# For backward compatibility
														if self._in_target_context_manager:
																					return self.current_processor(*lowerCamelCase   ,     **lowerCamelCase    )

														if "raw_speech" in kwargs:
																					warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."    )
																					__snake_case		:      Any										=       kwargs.pop("raw_speech"    )
														else:
																					__snake_case		:      Optional[Any]										=       kwargs.pop("audio"   ,     lowerCamelCase    )
														__snake_case		:      Optional[int]										=       kwargs.pop("sampling_rate"   ,     lowerCamelCase    )
														__snake_case		:      str										=       kwargs.pop("text"   ,     lowerCamelCase    )
														if len(lowerCamelCase    ) > 0:
																					__snake_case		:      List[Any]										=       args[0]
																					__snake_case		:      str										=       args[1:]

														if audio is None and text is None:
																					raise ValueError("You need to specify either an `audio` or `text` input to process."    )

														if audio is not None:
																					__snake_case		:      Union[str, Any]										=       self.feature_extractor(lowerCamelCase   ,     *lowerCamelCase   ,     sampling_rate=lowerCamelCase   ,     **lowerCamelCase    )
														if text is not None:
																					__snake_case		:      Union[str, Any]										=       self.tokenizer(lowerCamelCase   ,     **lowerCamelCase    )

														if text is None:
																					return inputs
														elif audio is None:
																					return encodings
														else:
																					__snake_case		:      List[Any]										=       encodings["input_ids"]
																					return inputs

							def    __snake_case (							self	:  Dict   ,     *lowerCamelCase	:  str   ,     **lowerCamelCase	:  Dict    )     ->   int:
														# For backward compatibility
														if self._in_target_context_manager:
																					return self.current_processor.pad(*lowerCamelCase   ,     **lowerCamelCase    )

														__snake_case		:      str										=       kwargs.pop("input_features"   ,     lowerCamelCase    )
														__snake_case		:      List[str]										=       kwargs.pop("labels"   ,     lowerCamelCase    )
														if len(lowerCamelCase    ) > 0:
																					__snake_case		:      Optional[Any]										=       args[0]
																					__snake_case		:      Optional[int]										=       args[1:]

														if input_features is not None:
																					__snake_case		:      Optional[Any]										=       self.feature_extractor.pad(lowerCamelCase   ,     *lowerCamelCase   ,     **lowerCamelCase    )
														if labels is not None:
																					__snake_case		:      int										=       self.tokenizer.pad(lowerCamelCase   ,     **lowerCamelCase    )

														if labels is None:
																					return input_features
														elif input_features is None:
																					return labels
														else:
																					__snake_case		:      Union[str, Any]										=       labels["input_ids"]
																					return input_features

							def    __snake_case (							self	:  Optional[int]   ,     *lowerCamelCase	:  Any   ,     **lowerCamelCase	:  List[Any]    )     ->   Any:
														return self.tokenizer.batch_decode(*lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Union[str, Any]   ,     *lowerCamelCase	:  List[Any]   ,     **lowerCamelCase	:  Optional[Any]    )     ->   Union[str, Any]:
														return self.tokenizer.decode(*lowerCamelCase   ,     **lowerCamelCase    )






							@contextmanager
							def    __snake_case (							self	:  Union[str, Any]    )     ->   Union[str, Any]:
														warnings.warn(
														    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
														    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
														    "your audio inputs, or in a separate call."    )
														__snake_case		:      Dict										=       True
														__snake_case		:      Dict										=       self.tokenizer
														yield
														__snake_case		:      Dict										=       self.feature_extractor
														__snake_case		:      Union[str, Any]										=       False





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))

